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Giving Customers What They Want from Financial Services

Alex Thompson Data and AI July 29, 2022

Our society has experienced a major shift in its activities over the past couple of decades. Instead of doing things manually, we have discovered ways to ease our workloads using technology. We saw major changes in the way we do our taxes, manage our bank accounts, or even purchase the things we need and want daily with the help of technology. Digital banking was introduced into the market, and it revolutionized the way we handle our transactions and finances.

In this technologically advanced society, financial service providers need to find ways to integrate technology into their services. Their systems and services must cater to their customers’ needs without being too much of a hassle to use. Considering this, a great starting point for financial service providers is to think about flexibility— how can you make your services flexible to the varying needs of your customers? In this article, you will learn three principles that you can apply to make your customers’ digital banking experience seamless and flexible.

What does the financial services industry look like today?

Gone are the days when financial service providers used traditional methods of doing business. The digitalization and incorporation of technology into the way we do business has been prompted by many factors. However, they all have a common target — customer satisfaction.

For instance, the COVID-19 pandemic brought many challenges for businesses in serving their customers. With people stuck in their homes, they couldn’t interact with merchants as before. This need for contactless transactions made digital payment systems more widely used among customers. In fact, Chase’s Digital Banking Attitudes Study found that in the past year, 93% of consumers have used at least used one digital payment method. This proves how essential it is to find alternative payment methods when it comes to day-to-day transactions.

Another rising trend in the financial services industry is the use of Buy Now, Pay Later (BNPL). This new entrant in the financial services industry splits purchases, particularly those worth large amounts, into smaller installments. Often, these installments come with no interest or fees. For an individual with an average income, this option seems to be the most beneficial, especially when their budget is a bit tight. Although BNPL service providers and users are still fewer than those who accept payments via other methods, it is no doubt that BNPLs will be among the major players in the financial services industry in the near future.

How Can You Incorporate Flexibility into Financial Services?

Let’s face it, customers love it when service providers are flexible. This is also applicable to the financial services industry. You have learned that financial services providers alter their business to fit their customers ‘ever-changing needs. Now, it’s good business to focus on how you can provide flexibility in yours. Here are three principles to keep in mind so that you can keep up or even get ahead of the financial industry trends.

Form partnerships

When it comes to financial services, you cannot take care of all the transactions alone. Although you can implement strategies to give the customers seamless experiences when it comes to handling finances, it is not always certain that what you provide is all that your customers need. Sometimes, what you need to give utmost customer satisfaction is to partner with other financial service providers – especially those who provide innovative products and services. This way, you can give your customers the flexibility that they crave without the cost of developing your own tools.

In forming partnerships, you have to consider whether you are in a good position for it. You have to assess whether you have existing operating models which enable sharing of research and insight, particularly those involving your competitors. You also need to think about your partnership strategy. After all, a partnership can be a huge factor in deciding your future strategies. For example, a partnership can eventually lead to the acquisition of the partner, or to building the product used in-house.

Aim to create seamless experiences

In any service that we use, we want a smooth and efficient experience. This is the same when it comes to financial services. Consumers want their transactions to be seamless; they do not want to have any problems with their interactions such as their payments not going through. With this in mind, financial services providers must make their systems and products as seamless as possible.

Highlighting seamless experiences in your services and products not only attracts potential consumers but also helps retain existing ones. This is because customer satisfaction is highly influenced by user experience. Even having one unpleasant experience in the services and products you provide can cost you a customer or your potential market. Remember that the first thing people look at when they are trying something new is the reviews about the experience previous customers had with it.

In creating seamless experiences for your consumers, you first have to evaluate your processes and tools. Take a look at your current strategies and action plans and see whether they are geared towards the continuous improvement of your customers’ experience with your products and services. You should also consider the problems customers frequently experience and generate tools that can help you with them. These tools do not have to be expensive. As long as they can efficiently help create seamless experiences for customers, even low-cost ones will be a huge help.

Focus on long-term strategies than short-term ones

A rapidly changing world calls for decision-makers who are focused on the business’s long-term goals and plans. You should focus your strategies and action plans on how the future of the market can be shaped. This way, you can create new opportunities for the market itself, as well as foster an innovative and proactive mindset for your team.

Focusing on the long-term requires that you look at all the possible futures the market may experience. You can do this by looking at the outside factors that have the potential to influence the financial services industry. Once you have identified the factors and all possible futures, you can start by identifying all the actions you may need to become a frontrunner in the possible future that you’ve created.

Thinking long-term can mean you have to go big on your vision. You can create strategies using this vision to define your value and determine what you can bring to the table.

The Final Say

Technology has given a lot of opportunities for different industries to evolve and innovate their services. This includes the financial services industry. However, despite these opportunities, the goal still stays the same— to give customer satisfaction. You can learn from the latest trends in the market as you create ways to solve customer problems and provide them with the flexibility that they crave. If you are in need of ways to incorporate flexibility in your financial products and services, you can use the principles we’ve mentioned here as your guide. However, you must take into account that these principles are not constant. You should still be able to evolve these principles as new trends in the market arise. This way, you can become the frontrunner in providing customers with the satisfaction they want.

Deciphering Data Mesh Principles

Alex Thompson Data and AI July 4, 2022

Before we can decipher the data mesh principle, we must first understand what the concept encompasses. If you’re here, there’s probably a good chance you have a firm grasp of data mesh principles and what they mean, but perhaps you’d like it broken down a bit more.

Data mesh is a paradigm shift regarding big analytical data and the management of that data. Data mesh has addressed many of the limitations of past data management systems, including data lakes and warehouses.

There are four main principles to data mesh, and they include:

In this article, we’ll look closely at each of the four principles of data mesh so you can understand the benefits and challenges you might experience when implementing Data Mesh principles within your company. You can never be too prepared whenever you employ a new way of managing data. More profound knowledge of data mesh and product development will help you better integrate it as a new data management approach.

Investing in Data Mesh

It’s no secret that many organizations today struggle with managing their data, primarily as it grows seemingly beyond their control. Data lakes and data warehouses can help build a solid infrastructure, but monolithic data platforms are becoming a thing of the past. Thousands of companies (of all sizes) are investing in data mesh architecture and operating models to focus more intently on outcomes while increasing data agility.

Though the need for a more efficient solution to data management is evident, business owners aren’t fully grasping how to implement data mesh architecture into their organizations. Data mesh aims to break down the traditional ways of storing larger amounts of data and allow businesses to share data across specific domains. Still, it’s crucial to note that this alone does not make your new (though decentralized) platform data mesh.

To completely integrate the principles of data mesh, companies must employ them to work together. Each principle is interconnected, working together to provide a way for companies to experience growth on different levels. It’s crucial to develop an operating model that combines the four principles into a finalized, workable data management architecture.

Domain-Oriented Ownership

The first principle of data mesh is often referred to as domain-oriented ownership or domain-driven ownership of data. As company data platforms expand, they’re likely to outgrow the methods used to produce and consume data. Collecting data without the right data management ecosystem puts much pressure on data platforms and organizational structures.

The collection of data has to scale out horizontally at some point, and to do that, you can use the first principle of data mesh, or the decentralized, domain-oriented architecture of data ownership. Companies that expect to decentralize their monolithic platforms must change how they currently think about big data and who owns it.

We cannot keep data flowing from domains into centrally-owned lakes and platforms. Instead, finding a way for domains to serve and host their data in a way that’s easy to consume. Each domain should own all the data, including access control and consumption patterns.

This principle includes the ability to develop an end-to-end data solution, which means the inclusion of architectural considerations and the support of new technologies introduced, such as resources from the cloud. Domain-oriented ownership requires multi-disciplinary teams that can operate independently if needed.

This team should generally consist of the product owner, data architect, data engineer, data modeler, and data steward. If you’re unsure what professions to include in your new data management process, you might consider data scientists, machine learning engineers, data analysts, and software engineers. Building the team is half the battle.

Data as a Product

The second principle for data mesh is data as a product. The data mesh process can raise questions regarding the usability of datasets, as well as accessibility. Data as a product can enable consumers to understand and securely discover data distributed across various domains.

However, data doesn’t just become a product existing within a new data mesh infrastructure. Instead, domain teams should set a representation of their own data infrastructure, also called a producer, of which the output is a data product.

Any code or data relevant to that product is kept within the producers. Domain ownership is flexible, allowing one domain to own more than one producer and the products associated with that producer. The producer will publish the data product via an integration pattern or interface, which can look like a table share, API endpoint, database, or data marketplace.

Categorizing data products based on the business needs they serve can highlight the expectations of each mesh product. There are two categories appropriate for data products in the mesh, and these include:

There are heavy costs associated with the data product model, which reduces the speed of advancements. Data products require a strong operational model and flawless design processes. Also, your teams have to have the correct capabilities to build the platform.

Self-Serve Data Infrastructure as a Platform

Data as a product gives a cause for concern about the domain team cost of ownership, leading to the data mesh principle of self-serve data infrastructure as a platform. This third principle developed as a solution to accelerate the completion of producers in the data mesh. The self-serve approach also helps standardize the patterns and tools across domains.

Building data infrastructure as a platform means keeping the domain agnostic and ensuring that the platform disguises any possible problems while providing data with a self-service practice. To build this infrastructure, we must decrease lead times to create new data products faster.

Self-serve data infrastructure requires an approach that is not too restrictive or lacks strategy. Nobody wants a solution that’s difficult or impossible for various people to use. How can we do this? By implementing the following.

Federated Computational Governance

The fourth principle of data mesh involves enabling users to gain value from correlating independent data products and establishing a catalog of shared products. A data product catalog is a metadata store that includes non-technical and technical metadata about data products.

Essentially, the catalog is an online store where company members can look through available data products and evaluate their quality, reliability, frequency, confidentiality, terms of use, and attributes. Ideally, the product browser can directly request data access and sign a contract to dictate the terms of use and the data product SLA.

When executed correctly, a data catalog presents a two-way relationship involving sourcing and publishing data products. Of course, correctly implementing this product is much more than purchasing data catalog software and integrating it with your existing technical metadata store.

One of the most significant risks with a data product store is the possibility of the data product development moving too slowly. One team cannot manage every aspect of this solution; if we expect them to, we’re asking too much. Teams must embed data management into domain teams so team members can execute the proper activities without overwhelming.

Developing a shared responsibility model is the way to govern data and clarify expected roles within the data mesh. We must put together the right mix of resources or risk getting nowhere.

Working Through the Data Mesh Principles

If working through the principles seems challenging, that’s because it’s no easy feat. The principles intertwine, and each one is necessary to address any potential problems or risks that could happen in another. The data mesh principles are not easy to operate, and the only real solution to that problem is to make them work together.

When these four principles are mapped out together and executed correctly, you’ll have streamlined, seamless results that address many of the issues that come up in the data mesh architecture solution. The data mesh principles are advanced, but with the right team in place, companies can execute them in a way that makes them work to store and manage data on a whole new level.

Best Coding Habits for Data Scientists

Alex Thompson Data and AI June 22, 2022

If you are into training machine learning and data science, then you know that code can sometimes get jumbled up and messy. While code complexity is inevitable, it’s crucial that you categorize the complex ideas so that you can evolve the code. Essentially, machine learning code is written in Jupyter notebooks, which are chock-full of side effects and glue code.

The side effects include printed data frames, data visualizations, and print statements. Additionally, glue code lacks abstraction, automated tests, and modularization. As much as this is applicable to teaching people about ML learning, it’s pretty messy when it comes to real projects. Poor coding habits make the code difficult to read and understand, which becomes a problem to modify without making mistakes.

How do you know you have good code?

Adopting excellent coding standards will lead to fewer errors, resulting in more work and less time spent correcting and maintaining code. So, what are the best coding habits for data scientists?

Maintain a Clean Codebase

An unclean code is challenging to understand and even harder to modify. This makes the whole code quite complex to work with. One of the dirts in the code is ‘dead code,’ which is executed correctly, but its result is never used again. To keep your code clean, do not reveal the internal data in design. Avoid using print statements, and the variables should always indicate intent. Lastly, the functions should only do one thing, and most importantly, do not repeat yourself.

Use Functions

Functions make your code simpler by removing complex implementation details and replacing them with its name. That way, everything is well organized and concise, removing any chance of clutter in the code. Because the code is short and clear with precise functions, it becomes easier to read, test and reuse. Instead of having several lines of code for the same thing over and over again, using functions gets rid of code entanglement.

Use an Integrated Development Environment (IDE)

While Jupyter notebooks are great for making prototypes, that’s where many data scientists make the most errors. Coding on the platform for ML learning tends to get messy, as many people include stack traces, unused import statements, glue code, and even print and glorified print statements.

The notebooks give us swift feedback when dealing with a new string of code, which is important. Even so, as the code grows longer, it becomes harder to get feedback on whether the changes made are actually working.

The solution to this is to shift to integrated development environments which will help you write better code. Most of them have inbuilt functions that can highlight errors, autoformat your code, highlight syntax and automatically check up functions. IDEs also have several debugging tools, ensuring that your code is free of bugs without filling it with print statements.

Migrating the code to IDEs will also help with unit testing, which gives you instant feedback on applied changes despite the number of functions.

Use Descriptive Function Names

One way to know a clean, well-written code is if someone who has no clue about programming can understand what’s happening. Using descriptive and precise variable names will help anyone with programming language knowledge know and follow through. Create your code in a self-documenting way, making it easy to follow and modify if necessary.

Adapt Unit Tests

Instead of writing unit tests after doing the entire code, what if you do it while in the process of coding? Yes, there’s a misconception that you cannot use test-driven development while working on machine learning projects. Still, that’s untrue since a big part of the codebase is concerned with data transformations, and only a tiny bit of code is actual machine learning. Using test-driven development helps disintegrate huge, complicated chunks of data transformations into smaller, manageable bits.

As you write your code, it’s vital that you also incorporate a set of automated tests that verify the operation of the function. The beauty of most languages is that they have automated frameworks for this purpose. R has a framework called testthat; Python has a module called unittest, and Java has junit.

Writing unit tests will go a long way in predicting how the code will behave, and you will be able to spot any bugs that creep in with the changes you make. Other people involved with the codebase development will also be able to modify it appropriately since they know what to expect, all from the embedded unit tests.

Use A Consistent Coding Style

When coding for machine learning, it’s important to choose a specific coding style and stick with it. This will help you avoid unnecessary errors, making your code clean and easy to understand. Using a consistent style with your team helps with teamwork since every member of the development team knows what is happening with the code and what to change if need be. Mixing the styles will spell disaster, resulting in a complex, messy code.

Practice Logging

After running the first version of your code, it’s vital that you track its progress. Logging helps with knowing exactly what each level holds. If you’re debugging the code, you can only display debug messages, and if you only want info or warning messages, you can also specifically log those.

The advantage of logging is that you can leave the logs embedded within the code, so you know exactly what to fix if problems arise. Compared to print statements which complicate the codebase, logging keeps everything simple and sorted out. You can also reroute your logs to files to keep track of execution times, data quantities, and other metrics.

Keep Proper Documentation

Maintaining updated records of the code is essential, as it helps simplify the complicated segments. This comes in handy when you need to explain the specific code components or their purpose to your team members. The three types of code documentation are:

This keeps the workflow efficient, especially when working with AIML.

Adapt Small, Frequent Commits

If you’re not making frequent commits as you code, you’re settingh yourself up for an overload. Once you make the changes in the code, as you work on a specific problem, the previous changes will appear as uncommitted. This creates more confusion which takes you away from solving the issue at hand.

Making small, frequent commits keeps you grounded, and you can concentrate on the particular issue without visual distractions. You also need not worry about unintentionally breaking the codebase since the previous changes will already be committed. Frequent commits also help you revert to the latest commit and check if there’s a problem, giving you a chance to retry. This saves you time that you’d have spent undoing the accidental damage in the code.

Incorporate Docstrings

Sometimes, even you, the author, don’t fully understand it when writing complex code. As such, it’s important that you include docstrings. These are distinctive comments that you embed in the methods, functions, or classes.

They are simply short notes about the code that you can come back to in the future. Including docstrings in the codebase helps generate automated documentation, especially when using IDEs. These will help you when you need to modify the code and cannot remember a specific function.

Maintain a Version Control System

Another excellent coding habit is to have a version control system. With this system, you can roll back to a previous version and incorporate more people into the project, as well as make changes to a code without affecting the older version.

Data science coding involves a lot of experimentation, and having version control helps a big deal with trying out different things. It’s easy to save two versions of a codebase and compare their functionality, giving you more leeway to play around.

Conclusion

These are the habits we have cultivated to make sure we manage our data science tasks seamlessly. You may already be practicing some of them, but we recommend incorporating all of these into your workflow. They make your work easy to manage while at the same time ensuring that the people you work with understand your code in case modifications are required. No one wants to deal with complex, messy ML models, and a clean code guarantees more work being delivered to your clients. At TVS Next, we work to provide high-end intelligence and engineering solutions for your business. We develop our softwares to give you the ultimate, unforgettable experience, set to reimagine and build a better future.

Artificial Intelligence and the Quality and Observability of Data

Alex Thompson Data and AI June 17, 2022

To truly understand the way data quality and data observability integrates with artificial intelligence, you’ve got to realize these blanket terms for what they are. The quality and observability of data are crucial to the integrity of how a business operates, and you can’t move forward and beat the competition without it.

Poor data can affect your business in a very negative manner, particularly from a financial perspective. Data is at the core of business decisions, so the ability to collect and observe it, preferably with the help of artificial intelligence, is essential to avoid missed opportunities.

What is Data Quality?

Data quality measures the condition of company data based on specific factors such as completeness, currency, reliability, consistency, and accuracy. Measuring your data quality levels will help you identify errors in your data that need resolution and assess if the data in your IT systems serves its purpose.

Emphasizing data quality in business continues to increase as it’s linked to business operations. Data quality management is a vital element of the data management process as a whole, ensuring that organizations format and consistently use data correctly within an organization.

The Importance of Data Quality

Insufficient data can absolutely have significant consequences for businesses. It’s common for low-quality data to be the source of operational issues and incorrect analytics that lead to poorly planned and executed business strategies.

For example, poor data quality can add unnecessary expenses to shipping costs or lose sales due to incomplete customer records. Insufficient data is often responsible for fines that come from improper compliance reporting. IBM estimates that the annual cost of poor-quality data issues in the U.S. is in the trillions.

The bottom line here is that insufficient data loses revenue and causes an overall lack of trust in data reporting across company departments.

What is Data Observability?

Data quality differs from data observability, resulting in happier customers and smoother operational workflows. Data observability is the ability of your organization to fully understand the health of the data that exists in your systems.

Data observability eliminates data downtime and utilizes automated monitoring, triaging, and alerting to identify and then evaluate data immediately. Data observability leads to more productive teams, healthier pipelines, and happier consumers.

Overall, data observability should prevent issues from happening in the first place. It exposes rich information about your data assets so changes and modifications can occur proactively and responsibly.

The Role of AI in Data Quality

We live in a digitally advanced era that relies more on information technology and communication every day. While artificial intelligence brings opportunities, it also presents challenges.

AI and Machine Learning (ML) are the future of data. Data observability will not be effective without data strategies to prevent inaccurate data entry or remove already existing inaccurate data from databases. AI and ML help us to develop these strategies.

How AI Can Help

Every business values the importance of collecting data and the potential contribution it can make to success. In the era of cloud computing and AI, the relevance of data goes far beyond its volume or how we use it. For example, if a company has insufficient quality data, its actions based on analytics will not make a difference, and it might even make things worse.

AI and ML can work together to improve accuracy, consistency, and data manageability. AI enhances the quality of data in many ways. Let’s take a closer look.

Automatic Data Capture

Organizations can lose a lot of money due to poor data capture. AI helps to improve data quality by automating the process of data entry and the implementation of intelligent data capture. This automation ensures that companies can capture all necessary information without system gaps.

Artificial intelligence and ML engineering can help businesses grab data without manual input. When critical data details are captured automatically, employees can forget about administrative work and focus on the customer.

Duplicate Record Identification

Duplicate data entries can lead to outdated records and insufficient data quality. Companies can use AI to eliminate duplicate records, which is nearly impossible to do manually or at least takes extensive time and resources. Contacts, leads and business accounts should be free of duplicate entries, and AI makes it happen.

Detect Abnormalities

One small human error can significantly affect the quality of your company data, and AI systems can remove defects and improve data quality.

Third-Party Data Inclusions

AI can maintain the integrity of data and add to the quality. Third-party organizations can add value to management systems by presenting complete data, contributing to the ability to make decisions precisely.

Artificial intelligence will suggest what components to pull from a specific data set and build connections. When companies have clean and detailed data in one place, they can better make decisions.

AI and Data Observability

AI and data observability have become essential to managing modern IT environments. There is no question that intelligent and automated observability can transform how we work. Regardless of your industry or business niche, your success depends on digital transformation and driving new revenue streams.

AI helps to manage customer relationships and keep your employees productive. Organizations that invest in AI, multi-cloud platforms and cloud-native technologies maximize the benefits of AI and ML investments by increasingly looking to automated observability. AI-powered insights paired with human thought can innovate faster and deliver better overall results.

Streamlining Data Quality and Observability

Your team should not waste time doing manual tasks that you can automate. AI assistance is the leading solution to streamlining data quality and observability, which (in the long run) will be critical to the ability your team has to cope with ever-increasing workloads while continuing to deliver value.

Leaping forward means embracing AI operations, adopting cloud-native architecture and consistently searching for better ways to observe, collect, and analyze data. AI can prioritize issues based on the amount of impact any given problem could have on the company, saving developers time and ensuring that your teams can understand and resolve issues before real impact happens.

AI processes have revolutionized the world of data observability and quality, reducing application delivery times and fueling growth. It’s becoming apparent that we will, at some point, depend on the benefits that artificial intelligence has to offer regarding the collection of data for business purposes, especially marketing and the consumer journey.

Leaning into Automation

Companies have to lean into automation to succeed. There is no more denying that implementing AI within data processes, primarily management components like quality and observability will be crucial to the way companies operate. AI gives us the tools to make decisions that positively impact our businesses, decreasing human error and saving money.

Today, most companies are working toward a digital transformation of sorts, albeit at very different levels. Market demand and consumer needs are constantly shifting, causing a strain on businesses that fall behind digitally. Delivering high-value experiences is essential, and automating data observation and quality management is vital.

Manual efforts no longer scale and continue to hold back innovation. Using AI to modernize your data approach allows you to build applications, optimize performance, and provide automatic analysis of your collected data.

Reimagining Digital Transformation with Industry Clouds

Alex Thompson Data and AI, Modernization June 7, 2022

If it weren’t for competition, companies would likely be resistant to transformation. Because we have to keep up with competitors, we’re almost forced into brainstorming new ways to improve our ROI, looking for innovation at every turn. Digital transformation is a massive component of helping us to innovate at an exponential rate, and we’re often looking to the competition to see which step we should take next.

One of the most significant challenges businesses face today is the pursuit of digital transformation. It’s a never ending battle, typically uphill, and in this case, speed matters. Just because digital transformation is complex doesn’t mean that it’s impossible. Companies of all shapes and sizes are successfully implementing digital transformation measures by focusing on five imperative components:

When organizations across the board use this type of fundamental framework, it gives everyone a decipherable language to encourage collaboration for strategic transformation.

Enter: Industry Clouds.

The Adoption of Industry Clouds

Industry clouds can help advance the five components listed in the framework above. Cloud-enabled business solutions help businesses standardize the important modernization aspects of their competitors. This comparison allows them to focus on which capabilities their business has that differ from the competition.

Industry clouds can allow businesses to adapt to emerging cloud and digitization conditions continually evolving by establishing scalability, nimbleness, and options. Industry clouds present the possibility of collaboration when companies struggle to find the right solution.

Hyperscale companies like Amazon, Microsoft, and Google embrace the concept of industry clouds. The market is quickly gaining traction, and, at this point, all enterprises must know what industry clouds are and why they’re important to the future of business modernization.

Understanding the Industry Cloud

You know by now that industry clouds are essential for collaboration, competitive advantages, digital modernization, and preferred by hyperscalers, but what are they? Developed by cloud vendors, system integrators, and software providers, industry clouds are the building blocks that speed up the development of digital solutions specific to an industry niche.

When businesses use industry cloud services, they’ll have access to continuously evolving digital capabilities. Industry clouds provide a necessary blueprint for transformations specific to a particular industry. They allow for organizations of all sizes to innovate and modernize slowly, making for a more agile and sustainable modernization. Companies can focus on their digital modernization in increments instead of a risky and expensive replacement of existing legacy systems.

Changes can focus on the user experiences that matter the most, such as what matters most to consumers. In addition, businesses can take advantage of advanced technologies from industry clouds, including AI and machine learning (ML) models.

The industry cloud makes it possible for companies to stay in line with the competition without building a digital revolution from the ground up. Industry cloud solutions continue to emerge and evolve within every industry. They’re making the latest digital capabilities accessible to businesses everywhere, adopting a more flexible way of working.

Defining Your Industry Cloud Strategy

Though it might not seem apparent from here, the industry cloud can help your organization’s ability to pull ahead of the competition. The industry cloud concept indeed provides the exact solution to everyone, and it’s up to businesses to figure out how to differentiate, which is difficult, but by no means impossible.

The best way to help a business to the forefront of its industry is to select the technology in the industry cloud that suits what the company currently needs to move forward regarding technological advances. Once it’s established what will work as far as modernization is involved, it’s up to the company to fine-tune and maintain them. How can these innovative solutions and insights work in the long run, and how will we continue to apply them?

The industry cloud is something that we can upgrade whenever we want. A competitive edge while employing the cloud will come from how we focus the application of the services. Quick learners may gain advantage more quickly, but that doesn’t mean others can’t or won’t catch up.

However, those within an industry that has successfully deployed digital modernization in addition to the industry cloud will likely be more competitive. There’s no doubt that the industry cloud presents an issue for some organizations when adopting outside technology and the attempt to layer industry cloud innovation with a unique take.

The industry cloud is about establishing a balancing act. Strategic implementation of the industry cloud will focus on ROI and opportunities to drive demand, accelerating development far beyond what would be possible without turning to modernization.

Using a well-defined strategy to define top use cases will assist in the acceleration of development. When using the industry cloud, a piece of internal resources should focus on how businesses plan to differentiate from similarly-focused companies using the same technologies. This is the part of industry cloud utilization that companies will have to build themselves, so while some reliance on the cloud is acceptable, additional digital modernization tactics are essential.

Accelerating Change

With the industry cloud, organizations can shift and switch resources to focus on the strategies they plan to use to move ahead of the competition, which is a fantastic perk. However, the correct implementation of the industry cloud can allow an organization to embrace and actively seek out change.

The speed at which change happens will vary by industry and from company to company. The industry cloud can provide building blocks for redesigning business processes and introducing technology capabilities to keep companies ahead of the game and working toward the consistent innovation that the cloud can help them achieve.

The Shift to the Industry Cloud

Shifting to the cloud became inevitable for many businesses during the pandemic. Almost overnight, there was no choice other than embracing technology and updating legacy systems that had worked internally for years. Still, most organizations have barely scratched the surface of cloud adoption, at least publcially.

Though it seems odd to resist technological advances, the war between traditional on-premises data infrastructure and public cloud providers isn’t over. While some organizations stick to Dell, others explore options like Microsoft Azure. Industry clouds are a great place to meet in the middle because they provide direction but push for creativity and innovation.

The foundation to digitally modernize data exists, even for companies that have yet to take the plunge, because industry clouds are essentially collections of tools, cloud services, and applications pre-optimized for use within an industry. It’s almost like having a modernization freebie, but if businesses cannot apply the use cases to their own evolving needs, the industry cloud won’t be of much help. At least not in the long run.

When making the shift to the industry cloud, it’s crucial to understand that the cloud must meet the industry’s requirements. In healthcare, for example, there’s a high priority placed on improving patient experience, but the need for data protection, privacy, and security measures are extensive. If healthcare practices do not have security measures in place, they directly violate HIPPA compliances.

There is a high value placed on analyzing data and utilizing AI for customer insights and brainstorming product development in different sectors, like financial services. Like healthcare, financial services is a highly regulated industry, so the industry cloud must cater to those regulations just as it should the healthcare industry.

On another note, the retail industry cloud should address the need to collect and analyze large data sets to improve inventory management and all-around customer experience. When businesses genuinely grasp what utilization of the industry cloud means for them, the desire to make the switch becomes imminent. However, they cannot complete that switch without a concrete plan to differentiate from the competition and build their basis of digital modernization on top of the tools utilized within the industry cloud.

We cannot stress enough that for some industry requirements, like privacy and security regulations, the industry cloud is a fantastic place to start but might not be enough. The fear of needing more and a general lack of knowledge on how to migrate to the cloud continue to hold businesses back. Companies within competitive industries fall behind in the race to the cloud, mainly because they fail to recognize the value that public clouds can lend to their internal technology infrastructures.

A New Way to Digitize

Industry clouds offer a new way to digitize without dismantling old systems completely. Industry clouds are still in their early days, and some aren’t as valuable as others. In some cases, industry cloud providers can come off as more of a marketing service than a SaaS offering substantial change for industry-specific businesses. However, that will likely change.

In the meantime, companies who are seriously evaluating industry cloud services from public providers must do so with care. It’s crucial to compare the cloud offerings from varying providers and the general-purpose solution. What is the goal of the service, and how can it help the business? How much will we have to layer on top, and can we make our company stand out with the tools available?

Asking the right questions will help you find the right provider for you.

Measuring ROI in AI: Finding Value that Isn’t Financial

Alex Thompson Data and AI May 20, 2022

It’s crucial to consider your return on investment in artificial intelligence endeavors. When you know your potential ROI, you can plan and customize your production plan approach based on what you want to get out of the deployment. The ROI of any AI project will determine where you allocate your resources and invest your time.

You must note that AI systems require plenty of experimentation, and calculating ROI requires more than an all-or-nothing approach. Plenty of estimates come into play, differing according to industry, making a return on investment analysis essential early on.

Business leaders can justify some AI use cases by studying noticeable potential gains, but other cases will need more to determine worth. Intelligent prioritization means putting high-value products first, but you have to decide what that means to your company.

Correctly Measuring the ROI of AI

It’s only natural that business leaders have begun to look to capitalize on AI opportunities. Still, predicting future returns can be challenging, as well as determining which part of your business the investment should target. Business owners have to understand which AI capabilities can enable better business performance overall before they attempt to measure the true ROI of AI systems.

There are ways to measure ROI without only limiting the process to financial returns. There are varying ways for business leaders to think about success regarding AI projects, and they’re not as hard to implement as one might think. So yes, while financial gains in utilizing AI are essential, plenty of other factors make AI well worth the investment of time and money.

Assessing the Future Value of AI Systems

Artificial intelligence is all about the future, including assessing the future value of the AI systems we implement today. When business leaders think about what AI can do for business, it’s usually highly well-marketed instances that stem from very well-defined pieces of the sector, such as the world’s best chess player losing to an AI program.

However, it’s important to note that while AI can solve a problem like chess, it’s because the game has a distinct endpoint. Unfortunately, most issues that pop up in business and Fortune 500 companies do not have a definite measurable outcome. So, you can see where the primary circulating examples of AI and what it can do for your company could be very different.

Most businesses face real-life problems, such as successful product launches and improving customer experience. In short, the topics are sketchy and, at the very least, complex, with the potential for various undefined outcomes. The challenge comes in gauging the ROI on an acquisition when the result of that investment in itself is unclear.

If business leaders don’t understand the core of the business problem that they want to solve, then it’s impossible to determine ROI from a perspective that isn’t financial. The framing of your problem is essential, as there are open-ended issues where AI and ML were not previously in use.

To eradicate questions involving your ROI for AI, you have to pinpoint exactly what your business question entails. Knowing if AI can positively add to your solution is the first step in determining if it’s worth your time.

Scaling AI-Related Problem Solving

Companies of all sizes focus on solving problems on a scale that will impact that functional area (such as development or operations) as well as the business overall. To gain deeper insight into what you want AI to solve, you must frame and reframe the issue at hand, and it’s a nonnegotiable prerequisite to determining your ROI in AI.

You’ve got to pinpoint whether your problem is inefficiency or an improved customer journey. What do you hope to solve or gain by employing artificial intelligence in your company systems and applications?

Problem-solving on a scare contains three solutions after you’ve efficiently framed the issue at hand.

When scaling your AI-related problem solving, keep in mind that every decision your business makes will impact a human in one way or another. For these three problem-solving elements to come together, solving problems at scale, you need to establish improved sophistication within your algorithm and engineering and embrace a better overall understanding of human behavior.

Once you’ve made an effort to take these steps, you’ll have a better idea of what AI can do for you. At this point, you’ve probably noticed that artificial intelligence can’t work for you if you don’t put the research and effort in first. Lack of preparation is why so many businesses fail at the correct utilization of AI and never see a return on their investment

Finding AI Success

Finding the success you want for your company with AI depends on several factors. First, you have to understand that there isn’t one way to get everything right. The use of AI comes with testing, learning, experimenting, and failing. However, business leaders must also pick up what they’ve learned from past failures and understand that those lessons will be important in the near future.

For example, it’s not unheard of to execute 30 to 40 different AI initiatives in a six to eight-week time to show progress. When you focus on working through various AI solutions in a relatively short amount of time, your company will quickly define problems within the software and execution and determine progress and potential future success.

From these 30 to 40 choices, you could come away with four or five that you work into your company at scale. It’s a distinct process of elimination.

AI Requires a Thirst for Innovation

In general, AI and digital modernization require a thirst for innovation and a desire to make your company operations better, more manageable, and provide improved outcomes for your business, employees, and consumers. Your ROI on your AI endeavors comes from an initiative for success and the drive for teams from various areas of expertise to work together.

AI projects succeed when the approach comes from a collaborative framework, and an agile work mode typically yields better outcomes. Also, documenting and compiling past results increases the probability of success, and AI helps businesses become less linear.

The approach to business that will probably always prevail over human intelligence and futuristic machine algorithms is the combination of humans and machines. The value of the success of your AI initiative comes from realizing that AI asks for many business aspects to come together to improve customer experience. If you’re achieving this, can you justify that as an overall improvement on your ROI?

Looking at return on investment has to be cognitive in a way that we look at financial gains from implementing modern software and when the moving parts of a company come together to add value. Measuring ROI in any artificial intelligence journey should focus on how the opportunity affects your business, and financial ROI is only one part of a much more intricate story.

Improving Your ROI from AI

Staying dedicated to digitalization and automation is part of the ROI puzzle. Your business depends on it, and it’s crucial never to stop looking for solutions. You can commit yourself to maximize your efficiency and ROI while focusing on the areas of your business where ROI makes a difference. Regardless of your ROI focus, you’ll always want to be able to demonstrate your success areas.

The correct implementation of AI takes plenty of work and a lot of trial and error, and it’s a risk for almost any company, no matter how established. If you concentrate on how AI assists your company in moving forward, you’ll find that those financial gains will also come, and you’ll cast yourself far above your competition. Find the value your AI brings, and place your focus on every area that shows improvement.

How Data Fabric Can Resolve DWH and the Constraints of Data Lakes

Alex Thompson Data and AI, Modernization May 17, 2022

As the world of cloud computing modernizes digitally and finds more efficient, security-driven ways to store data continues to evolve; we see the evolution of data architectures everywhere. If you’re in the technology or business industries, you’ve likely heard of data fabric.

In short, data fabric is a relatively new data architecture pattern that operates by linking different data sources in a compact cloud environment. Data fabric allows business applications, data management tools, and end-users to securely access data that your company stores in various target locations.

Data fabric technology secures access to varying data storage systems in any location, whether on-premises, in the cloud or in a hybrid or multi-cloud environment. Data fabric allows your APIs to enable two-way access to your stored data. In short, data fabric acts as a security layer that stretches across your applications and data assets to ensure smooth and easy entry to different systems.

The Purpose of Data Fabric

There are a few targeted purposes of data fabric architecture. Aside from controlled and widespread system security, data fabric focuses on metadata management, data reusability, cross-application access, data standardization and quality, and data discoverability.

Data fabric looks to eliminate the days of one-way integration, making it possible for companies on an ever-evolving portfolio of products to interlink and exchange data between applications. While data warehouse (DWH) and data lake technologies aim to break application information barriers, they typically offer better connectivity and cloud-based storage than anything. For example, the purpose of a data lake is to store data until it’s retrieved for further examination and analysis.

Big data is everything. To be blunt, data-driven companies have more success than those that are not because the answers to their setbacks and roadblocks are right in front of them. There’s no doubt that data is the future, and the rapid growth of big data is proof of that.

As businesses on a global scale continue to migrate toward new data management approaches, the birth of new architecture designed to help work through the constraints of DWH and data lakes is necessary.

The Adoption of Data Fabric Architecture

Data-driven companies show substantial growth in contrast to those that operate on different approaches. Businesses that focus on analytics can anticipate changes in the market and understand consumer intent, creating the ability to outlast the competition and design a flawless customer journey.

It’s no secret that investing in analytics pays off, so why are companies hesitant to take the plunge? Regardless of the circumstances, we naturally want to see positive results, and it’s not uncommon for business owners to overlook the technical constraints that accompany relying on a mix of outdated legacy systems and cloud-native solutions for data management.

New architectures, such as microservices, tend to catch the eye of many leaders as possible resolutions. Still, when it comes to data management and exchanges, many data solutions do not coincide.

The Three Layers of Data Information

Typical modern businesses have three ways, or layers, to produce data-consuming applications. These include on-premise legacy systems, data warehouses to store and organize some data, and cloud-based platforms or integrations.

Most legacy software likely relies on older connectivity standards, while modern applications use newer architectures. Companies typically extract and transform data and load it into a targeted destination, like a data lake or DWH.

Many businesses exist on a half-migrated way of life regarding cloud-based solutions. The desire to make the complete migration is due to the multi-purpose business systems and functions of cloud computing. Customer relationship management and essential needs like accounting and HR systems are interconnected in the cloud, creating a ton of valuable data that ends up in a connected data lake in its raw state or, again, stored in a DWH.

How can businesses stop existing halfway on various data storage and operational platforms? There has to be a way to establish a secure and practical connection between the three layers of data information and fully transform into an organized, data-driven business.

Enter: Data Fabric

This connectivity issue is the exact challenge that data fabric intends to solve. Data fabric is unlike DWHs and data lakes because it doesn’t require businesses to move their data. Instead, data fabric architecture aims for better data monitoring between these connected systems, including on-premise legacy systems, cloud hybrids, or data lakes and warehouses.

How Data Fabric Initiates Change

Today, there’s no shortage of data anywhere, especially in business. Most companies have an extreme amount of data coming in from various locations. It can be incredibly challenging to figure out where to put that data and how to approach the analytics.

Data fabric architecture can help reduce the burden that many companies face regarding the complexities of data and analytics. There is quite a bit that falls under this umbrella.

Data Access

Company data has to be interoperable and, at the same time, remain compliant with data usage regulations and exhibit strict permissions. It can be hard to accomplish this level of regulated data access without overseeing many users.

Data fabric can help by enforcing the correct data governance practices automatically. The data fabric technology helps to standardize data formats and create codes for user access permissions and all usage rights. Data fabric is the perfect way to build siloed data infrastructures that offer insight into how different services and users consume company data.

Management and Distribution

Perfectly-timed access to data is essential for training AI models and predictive analytics solutions. Corporate insights are crucial for business leaders, but it’s challenging to deliver.

Even in major corporations, very few have analytics fully integrated into daily operations, which borderlines on absurd. Analytics is one of the essential components of making consumer predictions. The fact that giant, global companies don’t embrace them as they should proves that making the digital modernization leap isn’t something that happens overnight.

Data fabric can assist by centralizing data management, backed by data regulations and policies. Development teams can configure data fabric architecture to prevent unbalanced load allocation and optimize data workload assignments within your internal tech structure.

In this situation, data fabric allows users in any location to access the data they need at high speed. Data fabric architecture can provide the predictive analytics solution many companies need to thrive.

Security

Few things are more important than data security, both from a consumer and business owner perspective. Dealing with leaked customer and sensitive business data is never desirable, but the rising rate of cyberattacks would suggest it’s never out of the question.

Security factors have made business owners incredibly reserved regarding which third parties they grant access to their data. Integrating additional partners into an already-sensitive business ecosystem is stressful and overwhelming, no matter how much experience you have in the business world.

As we move into a new way of doing, it will become impossible for companies to remain competitive while embracing a platform-based way of collaborating and exchanging data with differing organizations. It’s expansion at its finest.

Data fabric helps in the way of security by establishing standard security regulations for every connected API. As a result, this architecture can ensure consistent protection across all business data points, managing those security regulations from one platform. Data fabric has the potential to spark an ongoing evaluation of user access credentials and usage patterns. You will have the peace of mind of always knowing what is happening with your data.

Compliance

With the big data boom came an influx of regulatory compliance rules that companies must follow. Almost every industry faces high regulation, especially healthcare and finance, as consumer data within these fields are undeniably sensitive. Specific constraints have come into play, and as a result, businesses tend to ditch their analytics projects due to the cost of isolating sensitive data.

Data fabric can help with compliance by allowing unified standards when transforming and utilizing collected data. Also, you can configure data fabric architecture to trace data, which is a factor required by compliance provisions. It helps you comply with changing regulations while using your data to increase revenue. You’ll always know where your data rests, stores, and who has access to it.

Data Fabric vs. Data Lakes and DWH

Data fabric architecture does not intend to replace data lakes and DWH. Instead, it complements the issues within these data storage methods while focusing on compliance, access, and implementing analytics.

Data lakes and warehouses each hold their own space in business data storage. Still, they’re full of restrictions, including swamping, a lack of data strategy and management, low tech maturity, limited scalability, and higher operational costs.

Data fabric can fill in the gaps presented by data lakes and DWHs and better connect any application that draws data from them. Data fabric forces a reassessment of management approaches while creating a consistent approach to managing data safely and securely to make sense for big data and big business. In short, there is more than one way to store your data.

The Data Modernization Challenge

Alex Thompson Data and AI, Modernization April 29, 2022

Data modernization and artificial intelligence are taking over the business world. These days, you can’t turn around without hearing phrases like “machine learning” or “digital modernization.”

Every business owner everywhere has at least a small stake in wanting to digitize their business. After all, it’s near impossible to remain relevant without modernizing legacy technology platforms. Data challenges are no stranger to every company on Earth since modernization is the driving factor behind those data challenges.

While many major corporations, big businesses, and modern start-ups have gotten a handle on modernizing their digital processes and embracing cloud computing, smaller but established companies are struggling to make the change. For the most part, these struggles relate to time and capacity.

Data Management in the Modern World

As data management continues to revolutionize, enterprises of all shapes and sizes are experiencing issues with data quality and integrating cloud-based technology platforms. While many businesses are right in the middle of an attempt at modernizing their current data, the way companies keep their data is evolving from an on-premise-centered approach to hybrid architecture.

Shaping Modern Data Architecture

As companies target legacy technology modernization across the globe, leaders in the tech industry have identified some significant players regarding how businesses choose to manage their data. Though the companies may be radically different, the data management elements remain the same.

Open-Source Frameworks

These templates for software development, typically designed by a social network of software developers, are extremely common among businesses shifting how they manage their data. Open-source frameworks are free to use, and they allow all companies to access the big data infrastructures necessary to implement modernization.

Cloud-Computing

Overall, cloud computing is relatively simple regarding user-friendliness and data storage. Many providers boast cloud storage and other cloud-related perks for relatively low prices. The availability of cloud-hosting companies is encouraging businesses to invest by integrating or moving their legacy systems to the cloud. Migration to the cloud is one of the leading players in data modernization, without question.

Analytics Tools

The evolution of analytics tools is playing its part in the desire that many companies have to modernize their data. Overall, analytics and end-user reporting are better (and more sophisticated) than they have ever been before.

The addition of the Citizen Analyst role is prevalent in many modernizing companies that focus heavily on analytics. A Citizen Analyst is a person who is knowledgeable in analytics and machine learning (ML) systems and algorithms. Your CA, should you choose to have one on staff, will assist the modernization process by identifying profitable business opportunities.

Data Challenges and Modernization Barriers

As the world races toward an even newer and more modern digital era, it’s clear that there are companies left behind. It was once possible to forego a presence on the internet as a business, but those days are long gone. To remain relevant and in line with, or above, your competitors, you have to focus on modernization and your customer journey.

Data challenges and modernization barriers are prevalent, but they don’t have to stop a business from being profitable digitally. However, it’s almost impossible to maintain profits while ignoring modernization.

Data Quality

We touched on this very briefly at the beginning of this article, but data quality is a massive hindrance regarding the mechanical aspects of modernization. Data issues, such as inconsistency and incompleteness, impact company migration to the cloud. Most of them stem from the inability to keep high-quality data both during and after the transition.

Data Sprawl

It can be incredibly challenging to integrate cloud data and on-premise data. The amount of various digital information created, collected, shared, stored, and analyzed by businesses make up their data sprawl. Depending on the size of the enterprise, the sheer size of this data may be overwhelming to move, primarily if you’re dealing with the data showing up as incomplete.

The Role of “Big Data”

Modernization through data strategy is a fantastic concept if properly embraced. Thousands of companies are not using a “big data” platform or data stored in a greater variety, with increasing volumes and more velocity.

This lack of use has nothing to do with the effectiveness of storing data on a “big data” platform. Instead, it suggests that companies have trouble finding the role that “big data” should play within their existing data. They know they have to modernize, but they don’t know where to start, and this state of overwhelm is one of the most significant data modernization challenges in existence.

Compliance Concerns

Data challenges are prevalent in the form of compliance concerns. With the consistent modernization and movement of primarily sensitive data, plenty of regulations and data protection mandates are rising to the surface.

Obviously, we need rules and regulations in place to protect sensitive data for businesses and consumers. However, many companies worry about the inability to meet ever-changing compliance regulations, potentially facing fines.

The need for regulated data safety isn’t going anywhere anytime soon, so companies must find a way to comply if they’re going to focus on digital modernization and the up-leveling of their business. Regardless of your feelings on the topic, there’s no question that it’s definitely a challenge for data modernization.

Successfully Modernizing Data

Harnessing the power of your current (and ever-growing) database is essential to achieving growth and excellence in your business operations. Successfully modernizing legacy systems means complying with mandates, enabling priceless analytics for your company, and providing a fantastic consumer experience.

Modernization barriers tend to come in the same form for every business, but this doesn’t mean you can’t succeed at launching a digital revolution. However, you’ll have to clear a few roadblocks (other than data quality) along the way.

Misaligned Employee Skills

More often than not, the current skill set of your employees does not align with your data management needs. Everyone struggles (to a certain degree) to find talent for their workforce. When it comes to data modernization, the amount of knowledge your employees have or don’t have can directly impact data management and the implementation of new solutions.

For example, you’ve hit a wall if you’re attempting to employ an advanced analytics platform that your employees do not have the skill set to use. Data Science professionals are essential to data modernization, so this is a problem for many companies.

Open-Source Hurdles

Even though open-source tools open the world of data modernization to almost everyone, many businesses are too wrapped up in security concerns to consider using them. When utilizing an open-source platform, the speed of change is significant, affecting the entire organization if everyone is operating on different pages.

Digital modernization requires company-wide support and effort. Maintenance is also a challenge for open-source, as is the implementation of the applications. As you can see, workplace talent is crucial to pulling off successful data modernization.

Early Stages of Basic Solutions

The most basic data storage solutions are in the (very) early development stages for many businesses, which is quite troublesome. Data lakes and data governance tools are foundational for data-driven companies. Still, because so many of these businesses are in the early stages of fundamental data storage, problems are sure to arise regarding the ability to move forward to a more modernized approach. They’re simply not ready.

Unsatisfied with Implementation

If there’s one thing that many companies have learned throughout attempting to modernize their data, purchasing or downloading the framework to upgrade the way you keep your digital information doesn’t automatically mean you have a complete solution.

Many organizations remain unhappy with the way their data tools are governed or implemented and their analytics platforms and data lakes. It’s not to say that this dissatisfaction comes from the tool itself, but instead that it lacks the ability to meet the needs of the business.

Our Recommendations

With so many companies stuck in the middle of a digital modernization mess, we understand that the bottom line for businesses is to have access to systems that show results immediately. However, technology cannot solve your problems on its own.

To get the best out of data modernization, we suggest:

Test emerging technology as it evolves at a rapid pace. The technology you implement today could become obsolete within the next five years, so select a provider that stays in tune with these changes.
Put the cloud at the center of your modernization strategy, as it’s designed to deal with operational workloads and analytics with high levels of security.

Choose to work with a provider that focuses on the priorities and initiatives of your business. Tangible results come from providers that understand outcomes.

Overcoming Data Modernization Challenges

It’s frustrating to sit in the middle of operating on old legacy systems and attempting to modernize your data with neither end of the spectrum working in your favor. Your best bet is to partner with a service provider that can focus on results while building hybrid strategies.

There are too many benefits to organizing and digitizing your data to work for your business, contributing to growth instead of simply existing for reference. Data modernization can’t be ignored, so ensure that you’re taking the right path.

Building Your Modern Data Platform with Data Lakehouse

Alex Thompson Data and AI, Modernization April 22, 2022

Modern data platforms require a separate storage and processing layer to work efficiently. A data lakehouse is a solution that combines a data warehouse structure (typical in most original legacy tech systems) with the more advanced and convenient features of the data lake.

Data lakehouses enable the same schema and structure as those in your data warehouse, and they apply that structure to unstructured data, like what you’d find in a data lake. Data lakehouses allow users to find and access information more quickly, so your team can begin putting that stored data to work.

Building a Data Platform

Once done out of convenience, building a data platform within your business is now a necessity. Improving your customer experience based on data-given actionable insights will increase revenue and define your brand. However, it can be difficult for companies to pinpoint the right ways to define their data platform.

The technology industry hasn’t exactly developed a blueprint for IT teams to follow, and data layers will look different for every company, typically based on the industry and type of company in question. In this article, we’ll talk about how you can lay the foundation for a modern data platform and utilize that data lakehouse.

Understanding a Data Platform

Think of your data platform as the central nervous system of your company data. Your platform should handle the collection, cleansing, transformation, and application of all data in storage and use it to generate insights. Many companies are data-first and have embraced housing data as an incredibly effective way to scale data.

Gone are the days when companies treated data as a means to an end, final product, or outcome. Instead, data has become more like a type of software. Most companies dedicate entire teams and plenty of time to maintaining and optimizing their data and, in doing so, can achieve accurate data-driven results.

ETL/ELT data pipelines should be layered, which can bring in a certain level of confusion for teams that might be unfamiliar with the data lakehouse or a modern data platform.

How to Build a Modern Data Platform

You cannot build your data platform without a foundation, and each of the platform layers mentioned will assist you in establishing your data lakehouse from the hypothetical ground up. It can be challenging to know where to start, but every business has the same core layers regarding a modern data platform, and they are as follows.

Storage and Processing

You cannot physically have data if you don’t have a place to store and process that data. Not many companies transform and analyze their data when it becomes available, so storage is an absolute necessity. As your company grows, you’ll likely begin to deal with large amounts of data that will become overwhelming if it doesn’t have anywhere to reside in the meantime.

Businesses of all sizes are moving their data to the cloud. The emergence of data storage native to the cloud is everywhere. From data lakes to lakehouses, it’s challenging to come by a company that doesn’t store at least a partial amount of their company data in the cloud.

The cloud offers affordable and accessible storage options for on-premise solutions. The type of storage you’ll choose is entirely related to your business needs, but we’re laying the basis for an effective data lakehouse. Regardless of your direction, you cannot build modern data without the cloud.

Data Delivery

Every modern data platform needs an efficient way to deliver data from one system to another, known as data ingestion. As the amount of data builds, infrastructures tend to become incredibly complex, and many teams are left dealing with mass amounts of structured and unstructured data from various sources.

There are plenty of tools available today to assist internal tech teams in ingesting data. However, there’s no shortage of data teams that build custom tools with code to deliver data from internal and external sources. Artificial intelligence workflow automation is an essential component of the data delivery layer.

Data Transformation

Original data must be cleaned up and readied for analysis and reporting. This cleaning process is called data transformation, and you have to do it to build a modern data platform such as a data lakehouse.

Once you’ve transformed your data, you can move to the modeling stage, which creates a visual representation of your data within the lakehouse. Changing the data makes it understandable, while modeling makes it comprehensive visually. When the graphic layer is complete, you can ready your data for the ever-important analytics phase.

Analytics

There is no point in collecting data if your business can’t effectively use it, which is where analytics come into play. Your data doesn’t have meaning without analytics, and internal statistics are crucial to the data puzzle.

There are plenty of effective analytics software choices available today, and your data or development teams can help you choose the right one for you. The proper analytics layer for your data stack is vital to how you interpret your data, so select your software with care.

Observable Data

Because modern data is so complex, there has to be a certain level of observability for your data team to determine whether the information presented is trustworthy. Your organization does not have the time to deal with partial or incorrect data.

Through effective data observability, your teams can fully comprehend the health of your data. You’ll apply what you’ve learned from your experience with Development Operations to your data pipeline, focusing on usable and actionable data.

Check your data for freshness, proper formats, completeness, schema, and lineage. The right observability software will connect seamlessly to your data platform. This level concerns security, compliance, and scaling mass amounts of observable data.

The Discovery Level

Finally, you need real-time access to your data, and data catalogs and warehouses no longer cut it. Consistent access to reliable data is necessary to running a successful business in any industry, period. Data discovery picks up the slack where lack of support for unstructured data falls short.

The presence of data discovery offers a real-time glance into the health of your data and supports data warehouse and lake optimization. Data discovery will authorize your team to trust that their assumptions regarding your data match the reality of what that data presents.

Utilizing the Data Lakehouse

Each of the steps mentioned above will lead you toward a data lakehouse architecture. The data warehouse paradigm enables data storage in an organized hierarchical structure. The data lakehouse is a piece of that structure that can transform unstructured data into something you can use to establish your business and better your brand.

The level of business intelligence that runs the data portion of your company is an imperative component of your success. It would help to leverage your data software and services into actionable insights every moment your data team is on the job.

There has never been a more crucial time to put the best (and most modern) practices into place to ensure that you’re making reliable data-driven decisions every day. Instant and organized access to your data is crucial for you and those on your team who benefit from that access. Consider building that modern data platform, beginning today.

Evolution of the Modern Data Center: Embrace a Hybrid Cloud Environment

Alex Thompson Data and AI April 1, 2022

There’s no question that the public cloud is gathering momentum and attention from a mass number of enterprises and corporations. Businesses of all sizes are dabbling in digital transformation, and the cloud is their final destination.

Updating legacy systems and embracing a complete digital upgrade is not for the faint of heart. However as IaaS systems and SaaS systems become imperative to enhancing customer experience, it’s inevitable. Regardless of the fact that moving business systems to the public cloud has sparked great interest, many companies refuse to take the leap.

Hesitancy to Embrace the Cloud

If operating on the cloud follows through on every promise, such as improved scalability and reduced IT costs, then what is keeping companies from marking the move? There are a few perceived issues that hold various businesses back regarding digital transformation and moving systems to the cloud.

First of all, it’s a huge job. Embracing a cloud environment, though necessary, is a whole lot easier said than done. The reluctance to move while continuing to operate via internal-infrastructure teams could come down to a better total cost of ownership. Operating costs over the lifespan of a business are extremely individual.

It would be ignorant to advise every business that moving to a cloud environment would be financially beneficial. At best, it can only be assumed based on what we’ve seen in the past. With change comes a certain level of fear, primarily when that change might be impossible to avoid.

Safeguarding Sensitive Information

Many business owners and their development teams fear the inability to safeguard sensitive information in an online-only cloud environment. There is an assumed lack of control concerning security features and regulatory needs.

In reality, there is no online security system that is completely foolproof, period. Yes, the cloud is extremely secure. It depends on which operating system and the company you choose to utilize to host your cloud, but security features are typically extensive and state of the art. Again, this hesitancy is understood, because nothing is completely hacker-proof. To set minds at ease, business owners should speak, in detail, with the cloud service providers they think they’d like to work with. Information is the key to making a decision.

Established Skill-Set Enterprises

Companies that hesitate to move to a hybrid cloud environment worry quite a bit about the established skill-set they already have that pertains to their legacy systems. Years have been put into the way your company currently operates, and change is virtually terrifying.

Business owners that are satisfied with the way their business is currently run should think hard about embracing a cloud hybrid environment, mostly because it’s beneficial, and partly because it’s completely inevitable.

Navigating the Inevitable Multi Cloud Infrastructure

If you aren’t familiar, the multi-cloud concept is the way businesses operate on more than one cloud service. It could be two or more public cloud services, or one public and one private. The combinations are endless, and so are the corporate benefits.

Utilizing the multi-cloud is a fantastic way to scale business operations and put a SaaS application into effect while running on old legacy systems. The biggest benefit of the multi-cloud is the fact that businesses can take advantage of specific services from different cloud vendors to put together a system that works for them.

While it seems simple to operate on a multi-cloud infrastructure, it is not. Companies attempting to gather the best of both worlds are struggling to evolve their services because they lack a strategy that makes sense.

The bottom line here is that various cloud providers offer shiny services and attractive features that encourage businesses to use more than one. While this approach to the cloud works well when executed correctly, the service gaps are becoming more apparent. If your multi-cloud services do not mesh well, it’s your customers that will face the largest amount of discomfort, and that will show in your numbers.

Addressing Multi Cloud Issues

To fully address the issues that come with the multi-cloud, including the pressure to build faster systems that jump-start growth and encourage speed and fast delivery times, it’s crucial to fully grasp a firm knowledge of the necessary technology.

The time has come for internal and infrastructure teams to seriously alter the approach they are taking to utilizing cloud platforms and putting them into action. Proper planning is beyond essential. It is completely inappropriate for us to register for cloud services because they’re offering a feature that will work for our business without assessing how it will affect other business operations.

Companies, and every employee within, must fully embrace planning, service operations, capacity delivery, and strategic sourcing. Without encompassing every piece of this puzzle, and negating to inform your teams in regard to what changes to expect every step of the way, it is impossible to see transformative change on a digital scale.

Using the hybrid multi-cloud to its full extent means experiencing extreme savings in labor and expenditures while fueling your capacity to deliver. The whole point of this venture is to improve the customer experience, and when you plan strategically, you will see massive improvement.

A Focus on Internal Infrastructure

There is an obvious gap between companies that can financially support an almost overnight switch from legacy systems to a hybrid multi-cloud. Amazon Web Services and Microsoft Azure have made it undeniably apparent when internal-infrastructure teams are not what they should be.

Plain and simple, consumers appreciate the pricing transparency, delivery capacity, and overall journey taken with the public cloud and the perks it has to offer. Customers have become comfortable with relying on “hyperscale” companies (like Amazon) to deliver the latest technology and absolute best in customer attentiveness.

Because of the massive success seen from operating on cloud technology, there is a substantial amount of attention drawn to those with internal-infrastructures. The cycle is far too long and capacity remains fixed, often with teams predicting business needs too many business quarters in advance. All of this increases the possibility of error.

It’s not to say that those companies that run on an internal infrastructure don’t have some advantages. For example, they have a much more intricate knowledge of the company itself and the customer base. Because of these factors, it is easier for them to deliver an excellent total cost of operations in most cases.

In short, those companies with internal infrastructure can find both hardware and software customer solutions. Internal infrastructure is not bad, but it shouldn’t inhibit growth into external infrastructure where it’s necessary.

The Answer: A Hybrid Data Center

A hybrid, world-class data infrastructure is the answer to finding the balance between companies that are hyperscale and those that rely on internal operations. There is no wrong or right way, but there is a way that comes highly recommended by tech and business experts around the globe, and that is finding a balance between legacy systems and the multi-cloud.

It’s difficult for companies to harness operational agility by using the cloud only. Instead, they should be assessing the way their infrastructure is stacked and evaluating how it works. If they want to increase speed, reduce costs, and ramp up services, complete integration is required.

Moving to the Cloud Means Teamwork

While it might sound tacky, moving successfully to cloud services while keeping the necessary legacy systems intact takes teamwork. Every person on every team has to know their role and move forward with the company as a partner.

When you work on the same level as companies that place their focus on hyperscaling, you have to seriously upgrade your operations. Design and engineering talent will become an in-house necessity, and that’s just fine, because having the talent on hand makes it possible to continue to succeed in a hyperscale multi-cloud environment.

The bottom line means embracing digital service, planning for capacity and taking a more strategic approach to sourcing. When your internal IT teams can manage all of the above, it means your company is well on its way to embracing a hybrid cloud environment and meeting customer expectations.

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