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Why Robotic Process Automation is Not a Silver Bullet to All of Your Automation Problems

cauvery k Data and AI January 25, 2022

As time goes by and new generations move into new workforce positions, and most employees consistently express the desire to work remotely, the need for Robotic Process automation is apparent. The mantra that lives behind most modern companies, even giants like Target and Starbucks, is to work smarter and not harder.

Automation is great for so many things. The main talking points in favor of automation include increased productivity and higher production rates. Automation is fantastic for better use of materials, improved safety, a better quality product, and shorter labor hours for employees, which leads to an uptick in employee satisfaction.

It only makes sense that, as a business owner, you want to cut costs, standardize processes, and minimize errors. However, you might be missing out on all the perks of automation if you’ve invested only in Robotic Process Automation or RPA.

Frustrations that Come with Robotic Process Automation Investment

While many businesses soar utilizing only Robotic Process Automation as their automation technology, plenty don’t. There are many frustrations that business owners have brought to light concerning Robotic Process Automation’s limitations, mainly rigidity.

It is madly irritating to invest in an automation solution that doesn’t solve your pain points, especially when you have to spend more money than you originally intended to see any results at all. Struggling to find a return on investment probably means you’re missing crucial pieces to your automation puzzle.

RPA definitely has its place, but if you cannot seem to make it work, it’s probably time to investigate intelligent automation solutions. Intelligent automation allows for more complex processes, eliminating the amount of unstructured data accumulated with RPA only. Here are a few downfalls to Robotic Process Automation.

Magnification of Errors

Many robots in Robotic Process Automation cannot detect glaring errors that a human can pinpoint. If your current data has issues, RPA will pass it on through the workflow, which means that mistake is making its way down the line without rectification.

Sustainability in the Long-Term

RPA is popular for fast automation and quick fixes, but there lies a possibility for taking too many shortcuts and ignoring efficiency from the start. A ton of work must go into digitizing and automating administrative processes, and when first implemented, RPA serves as a decoy from establishing efficiency the right way.

Maintenance

RPA systems are notorious for requiring detailed maintenance, and many solutions must be custom-made to fit your business. If you plan to change how your business runs in the future, it’s not likely that your Robotic Process Automation robots will take well to the change. For RPA solutions, minor changes equal massive disruptions.

Risks

RPA is an artificial intelligence that doesn’t solve complicated automation problems like sending or handling purchase invoices. You’ll likely need a more complex form of intelligent automation to complete this job, so investing in RPA might be moot from the very beginning.

Endless Resources

Robotic Process Automation makes it easy for businesses to become overburdened with technical debt and maintenance services. Continually implementing more bots is not cheap, but with RPA, it’s necessary to keep your automation accountable and cover all the bases, sucking up a ton of your internal and financial resources.

Each RPA bot requires system tracking, screen, and field maintenance any time process changes go into effect. Since this is the case, many companies find that it’s too expensive with too much time lost to an automation solution that doesn’t work as well as it should or could.

Finding the Right Solution

Highlighting these issues isn’t to say that Robotic Process Automation won’t work for certain companies and business processes. Instead, it’s about highlighting the benefits of intelligent automation and encouraging business owners to move forward with technology that has more answers and isn’t so rigid and rules-based.

Automation is pointless if it’s not impactful or increases your tech debt. Because of this, we must acknowledge the shortcomings of RPA while recognizing that, in some instances, it holds its own.

Where RPA Does Work

Companies on a global scale find it necessary to upgrade their automation solutions and artificial intelligence. However, RPA does work well regarding some workplace processes, such as clicking and dragging, copying and pasting, making if/then decisions, making simple calculations, and opening emails and email attachments.

It is important to note that RPA carries out these automated tasks without recognizing content and works best with structured data only. Intelligent automation proves to be the missing piece for so many teams worldwide, over and over again.

AIML: All Automation Solutions are Not Made Equal

If your company requires a well-rounded solution to automate data extraction and the processing of important documents, then you’ll need something backed by Machine Learning and Artificial Intelligence. Equipped to handle more complex organizational processes, intelligent automation actually involves humans and asks for help to achieve a better outcome for all.

Benefits of Artificial Intelligence

Artificial intelligence is nothing new, but its capabilities continue to mature year after year. Artificial intelligence is necessary for plenty of jobs within almost every workplace, including:

Machine Learning Perks

Machine Learning is the force behind many innovative technologies, from security and anti-virus applications to supplying the shopping algorithm you see on your Amazon homepage. Machine Learning isn’t perfect, but it has many benefits when applied correctly, and it could have a lasting impact on you and your company goals.

Identify trends and patterns easily
Machine Learning algorithms can improve with time
Adapt without human intervention
Automated predictive analytics

Companies want accuracy and reliability from their automated solutions, especially when it comes to handling increasingly challenging everyday processes. With RPA, one bot performs the same task repeatedly, without adapting or reaching out for human help.

With intelligent automation, the playing field changes completely. There’s no doubt about it; intelligent automation drives better outcomes, period.

The Answer is Intelligent Automation

Intelligent automation is the answer to seamlessly automating your complex business functions. Not only does intelligent automation enable your company or organization to work alongside entirely flexible automation, but intelligent automation assists you in reaching your business goals and objectives.

If you want to quickly accomplish tasks that grow increasingly difficult by the day with accuracy and stellar connectivity, then intelligent automation is for you. Here are just a few benefits to executing intelligent automation.

Increasing process efficiency at a high level
Optimizing back-office operations
Reduce costs and risks
Increase workplace productivity while catching automation errors along the way
Service and product innovation technologies
Improve your customer experience
Monitor fraud detection
Reduce business costs
Save your time for essential tasks
Cut way down on human errors
Reconcile your data from various company systems
Trace audits and analytics
Amp up client service times
Skyrocket employee satisfaction
Endless flexibility to create new processes and modify older ones
Formulate predictions based on collected data

Of course, intelligent automation also has its pitfalls, but when compared to the perks, it’s easy to make the decision. To be fair, there are periodic problems that might pop up when implementing intelligent automation:

With Intelligent Automation, your company no longer adheres to legacy software and current methodologies. Software changes often interrupt how an RPA solution works, but intelligent software is not susceptible to crashes and malfunctions because of minor or major modifications.

Intelligent Automation and Long-Term Company Goals

There is no better solution than intelligent automation to help your company achieve long-term goals. Your business needs automation that opens doors instead of restricting processes. While streamlining is essential and possible with RPA, there is no room to evolve.

Intelligent automation provides many businesses with the choice to move forward, successfully automating processes that involve unstructured data and without requiring out-of-reach training data sets. This type of high-maintenance, low-performance automation (RPA) is simply out of reach for many enterprises.

RPA is not entirely unaffordable, to begin with, but keeping up with it and maintaining it every time you update anything within your process or workflow becomes costly very quickly. When you’re spending too much in one area, it limits cash flow to other parts of your business.

By implementing intelligent automation from the very beginning, you’ll cut costs in the long run while setting yourself up to succeed without stalling due to your automation choice. Companies stuck in one spot due to the restrictions placed on them by their current automation solution become one of two things: frustrated with way too much money invested or completely irrelevant.

To build a future-proof foundation, you have to begin with intelligent automation.

FAQ

What is Robotic Process Automation (RPA)?

RPA is a form of business process automation that leverages software robots—or “bots”—to mimic human interactions with applications, automating repetitive, rule‑based tasks by working through graphical user interfaces

What are the three main types of RPA?

The three primary RPA deployment types are: Attended Automation — bots triggered and guided by human operators.

Unattended Automation — bots that run autonomously without human initiation.

Hybrid Automation — a mix of both, where bots and humans collaborate on the process workflows.

Is RPA a form of Artificial Intelligence (AI)?

Not exactly. While RPA may integrate AI components, it's fundamentally based on predefined workflows and robotic execution—not on intelligent learning or adaptive reasoning

What are typical examples of RPA in use?

Examples include bots that extract structured data from invoices and enter it into accounting systems, automate email handling (like extracting attachments), perform repetitive copy‑paste routines, generate reports, or populate CRM entries—all without content recognition or human judgment

How Modern Data Architecture Drives Business Performance

cauvery k Data and AI January 11, 2022

Modern Data Architecture

From helping businesses make educated decisions to reducing operating costs, big data really is a big deal.
But why?

As technology continues to evolve, so does the need for insight into customer behavior and market trends. Even though most businesses are using big data, not everyone knows how to use it effectively.

To make the most out of data and analytics, you need to understand how modern data architecture operates and the reasoning behind it.

In this guide, we’ll dig deeper into big data and discuss the role of a data architect. We’ll also discuss how modern data architecture can bring your business into the future.

What is Big Data?

Big data is large volumes of data, both structured and unstructured, which businesses are inundated with every day.

But it’s not just the type or sheer amount of data that’s important; it’s how it’s used to make strategic business decisions.

What is Data Architecture?

In only a few short years, businesses shifted from traditional data mining and storage to more streamlined methods.

That’s where data architecture comes in.

Businesses that operate on modern data can anticipate future needs of consumers. They use this information to review emerging market trends and optimize their marketing strategies.

Organizations that fail to upgrade to modern data architecture ultimately lose customers and reduce their market share.

In simplest terms, data architecture defines the tools a business utilizes to analyze and manage data.

Modern data architecture takes things a step further.

Modern data architectures are platforms that bridge the gap between decision-makers and IT professionals.

With modern data architecture, the processes used to capture and deliver important data within the business are also an integral part.

More importantly, modern data architecture outlines how the parties will consume the data and how it’s delivered.

What is Data Architect?

Similar to architects who design residential homes, data architects create blueprints for specific data flows and processes.

The blueprints are based on the objectives within a business or organization. More specifically, they’re visionaries who design the framework for enterprise data and its management.

A data architect works closely with internal stakeholders, alongside external vendors, to create a data strategy that helps businesses make data-driven decisions.

Data architects also work to define reference architectures, which serve as guides for team members to expand current data systems.

Pillars of Successful Data Architecture

Well-designed modern data architecture always flows from right to left; specifically, from the data consumer to the data source.

Previously, data architectures were data warehouses. And because of their initial design and technology used, managing them was a time-consuming process.

Unfortunately, the gap between requests for data and final delivery often resulted in revenue loss or missed opportunities.

While modern data architecture still delivers usable data to a warehouse, it’s more agile and adaptable. It can change and evolve in response to the user’s needs.

With that said, there are basic components to modern data architecture that define its framework:

User-centric. As mentioned above, the focus is now on the user and their needs. Users can be either internal or external stakeholders. Their needs may vary depending on their department or role. Data architects can now collect and deliver requested data to meet the user’s objectives.
Adaptive. In modern data architectures, data flows from the source to the user. Successful data architecture always encourages collaboration.

It combines data from all parts of the business, in addition to external sources, into one specific place. Within this structure, data is seen as a shared asset.

Scalability. One of the most important pillars of modern data architecture is its scalability. Unlike traditional limitations seen in data lakes or databases, newer data architectures are faster and are easily accessible on the cloud.
Automation. Automating processes using cloud-based tools slashes production time. Processes that once took months to create are now built in just a few hours.
Intelligence. In conjunction with automation, machine learning and AI are the foundation of modern data architecture.

AI can swiftly identify and correct errors in data reporting, create structures for new data and advise on incoming data with in-depth analytics.

Elasticity. Scalability is only part of the puzzle. When it comes to modern data architecture, being able to roll back or scale on-demand is even more important. Elasticity gives administrators the power to shrink or scale without limitation.
Security. Security features are built into the data architecture to limit who has access to it. Well-designed architectures are aware of both current and emerging security threats. They’re also GDPR and HIPAA compliant.

How Data Architecture and the Cloud Work Together

Even though it seems like cloud computing is relatively new, theoretically, it’s been around for 40 years.

But with such an accelerated shift to a more cloud-based model, it’s important to understand how data architecture and the cloud work together.

With rapidly changing demands of data, businesses need scalable or elastic architectures at their disposable.

Thankfully, the cloud allows for rapid scalability, which is a cost-effective solution. It’s especially useful in cases of on-demand development as well as prototyping.

The cloud is also resilient. It can process large amounts of big data quickly in real-time. According to Gartner Research, enterprise data spending will account for approximately 15% of global spending on IT by 2024.

It’s also estimated that over 70% of businesses and organizations currently using the cloud are also planning on increasing cloud spend when they expand their modern data architecture.

Modern Data Architecture and Your Business

The main goal of data architecture is to streamline the way businesses gather, store, distribute and ultimately use data.

Another important goal is to provide valuable data to the right people within the business or organization.

Not so long ago, when specific data was needed, a request was made, and IT would find a way to deliver it in a timely manner.

This typically involved hours of manpower prior to delivery. In turn, this type of architecture made it difficult to access the right information when necessary.

Modern data architecture breaks the barrier between strategists and IT.

It enhances collaboration, communication, workflow, and productivity. Businesses can determine which data is most useful, the best way to source it and how to distribute it to the right internal stakeholders.

Data Fabric and Architecture

In today’s digital landscape, change is inevitable. Businesses that consider data a strategic asset will be better equipped to navigate that change.

But to stay one step ahead, businesses and organizations alike need to use data fabrics to guarantee unlimited access to all their data sources.

Let’s break it down.

Every business needs to use data analytics to its fullest potential. To streamline the process, you also need data agility.

Data agility connects and then combines the data from various sources for review.

Data fabrics house the connections between all the data, regardless of type or where it comes from.

They can also tell you what the data does, and how it relates to other relevant data. Without data fabrics, your ability to use data analytics to its fullest potential might be limited.

Data Architecture Strategies

Similar to marketing, you need to have a solid strategy when developing your data architectures.

Here are a few things to keep in mind:

Information and Data Architectures

It’s also important to understand the difference between information and data architectures. Data architecture is about sourcing, collecting, and using available data.

Informational architectures are more about using the data that’s collected to make strategic business decisions.

The Takeaway

For businesses that embrace modern data architecture, the future has never been brighter. But as traditional methods of data collection, storage, and disbursement fade into the background, the need for meeting customer demands will only continue to grow.

At TVS Next, we help clients harness the power of AI and data assets. And as technology and data analytics continue to expand, businesses that use the power of modern data architecture will have an edge over those that don’t.

The Key Components for Data Modernization

cauvery k Data and AI November 12, 2021

Data Modernization is at the forefront of every organization’s digital transformation initiative. While the transformation itself could be of the business model and processes or the organization’s culture, no real change can occur until data is modernized and made functional.

Imagine trying to create a flexible and open work culture, but your employees are still burdened by having to work with complex and outdated systems and data. Or you want to try and explore other business models and revamp your processes, but you have little to no insight into what your organization is currently doing. Therefore, it is inevitable that change, or digital transformation, in this case, must happen from all fronts for it to yield positive results.

The following are the key components required for the success of a data modernization project:

Strategy
Data

Engineering 
Intelligence

Strategize Your Vision

To flawlessly execute a task, one requires a failproof way of doing it. Strategy is the first key component in every organization’s Data Modernization Project.

First, analyze your current applications and their architecture. Understand your current data processes and the existing bottlenecks in your system. When you do this, you will arrive at a problem statement encompassing all the issues with your current system. Then identify your immediate and future business goals. Once you’ve set your goals, it is time to create a plan to solve your problems and provide a solution to meet all your requirements. Finally, ensure you invest in the right technology and people who can perfectly execute your plan. 

Focus on Data and Data Platform

What is an essential component of data modernization? Hint: It’s there in the name. 

Data is undoubtedly the focal point of data modernization.

When you have succeeded at data modernization, your data will be completely integrated, be immediately available to anyone who needs it, adhere to security protocols, retain high quality, and provide valuable insights. 

Here’s how you can ensure all the above: 

Choose the right platform to drive data transformation
Modernize your data landscape
Setup a data lake as a central repository for all data
Establish data governance and ensure data security
Build intelligent systems to harness the power of data

Create Value Through Intelligence

I have migrated and modernized my data; What next? 

To produce business value, you should convert your data into an intelligent business aid. 

Therefore, ‘Intelligence’ is the final but critical component of data modernization, generating real-time business insights that drive intelligent decision-making.

Once your data is transformed into a viable product, build intelligent dashboards that show real-time information. Customize the analytics to deliver insights that align with your business requirements and help improve how your organization operates. Ensure you democratize data and create visibility into data for every stakeholder. Leverage existing AI tools in the market or build one on your own to accelerate processing and obtain advanced insights. Finally, incorporate automation wherever possible. 

Summary

Here’s a quick look into the key components of data modernization and the focus points for each component:

Strategy: Understand your problems, define your goals and identify the right solution

Data: Establish data governance and security, and modernize your data landscape

Engineering: Implement your strategy through data lakes and data pipelines. 

Intelligence: Get visibility into business data through advanced data analytics

A Leap into the Cloud

cauvery k Data and AI September 8, 2021

A Guide to Persuade Your Entire Organization to Embrace the Cloud

We don’t need to educate people about cloud anymore. The technological aspects of cloud migration, such as choosing a hybrid cloud or omni cloud model, and running serverless applications that are cloud-native, are oft-talked about.

And yet, Gartner predicts that lack of cloud skills will delay organizations’ cloud adoption process by two or more years.

When it comes to convincing apprehensive members of your organization that cloud adoption is a necessary step for digital transformation, you have to deal with two different groups:

Skeptical board members &
Reluctant employees

Here’s a guide on how you can do it:

1) Audit Existing Applications

Before you embark on the cloud migration journey, the first step is to audit your existing IT infrastructure. Find out the bottlenecks of your existing system, and causes for operational issues and delays. Assessing your applications will help you identify what needs to be changed immediately, and what can be continued to be put to good use. A complete re-engineering of the entire organization’s technology infrastructure might not always be required. Sometimes, retaining some applications as-is might save a lot of time and resource.

2) Present Business Needs

When discovery and assessment of your existing infrastructure is complete, it will give you a better understanding of technological gaps and issues that require addressing. This will help you formulate a business case that specifically meets your organization’s needs. There are multiple business benefits to migration such as decreased IT spending for infrastructure, software and maintenance, and improved security, accessibility and process streamlining.

3) Explain Risks

The necessity for future proofing businesses has never been more realised than since *you-know-what* happened. Explain to your stakeholders about how failing to innovate could potentially bring your business to a standstill during unforeseen circumstances. Also explain the risks of being the business that’s left behind without modernizing while all your competitors advance with the aid of technology.

4) Identify Pain Points

When people are reluctant to trust new technology, there’s often a valid reason behind it. Members of the older generation may feel that they do not have the skills and abilities to quickly embrace the technology change. It’s important to deliver a solution that is not too complex for use, and when the new tools or applications directly meet their needs, many members of your organizations will readily embrace the change.

Not providing adequate training to all your members could also be another issue that might slow down the process. Include orientation and training programs as a part of your migration strategy, and also provide simple guides for people to refer to until they get used to the new system.

5) Throw Light on Individual Benefits

For the business stakeholders, decrease in spending, increase in profits, better security, higher productivity & operational efficiency and enterprise mobility are very appealing benefits.

For other users, the ability for collaborations anywhere, anytime and from any device, empowering them to truly work from anywhere might be a solid benefit that draws them in.

6) Build a Shared Vision

Cloud adoption might mean different things to people in varying levels across the organization. While helping your team members understand what the benefits of cloud adoption for them may be, it’s also important to build a big-picture of how the modernization project will impact the entire organization as a whole.

Building a shared vision not only means creating a roadmap with your success outlined, it also means creating a project where every team and individual is included. That can be achieved by assigning ownerships to everyone, and including every user from inception till the end. When each person bears a little bit of the weight of the huge initiative of cloud migration, the entire process will seem easy and effortless.

How to leverage automation for business success

cauvery k Data and AI September 7, 2021

Organizations very well know the importance of taking the right action at the right time. Be it recruiting a right candidate, or converting a lead into a customer, multiple right actions performed by multiple people combine together to drive the organization’s success. So, what drives these actions? A number of factors such as the available data, experiences and skills of the people involved, their cognitive bias, and also the time required versus the time available to perform the action.

What if the negatives of individual decision making could be removed altogether, and only the positive aspects are harnessed to perform the right actions at the right time? That’s what automation is all about.

For different parts of your organizational engine

Everything happening in an organization can be automated to a certain level. How do you know if something can be automated? Any function that requires a certain process to be followed can be automated. Here are some automation examples:

Human Resources

The HR department is a very valuable asset to every company, for they bring in every employee and make sure everyone is paid on time. Some of the automation possibilities in the HR department include:

Sourcing the right talent meeting the company’s needs
Onboarding and offboarding automation
Automating time logging, leave requests and payroll processing
Defining the KPIs and letting bots find out the top performing employees

Finance

Accounts payable, accounts receivable, cash-flow management, maintaining balance sheets, invoice generation are some of the many automation possibilities within the finance department. Automating finance operations has the added benefit of ensuring accuracy and preventing human errors.

Sales & Operations

Salespersons can leverage automation to schedule appointments, send emails, resources and reminders. AI based chatbots on websites are available 24/7, expanding a company’s horizons across countries and beyond time-zones.

Tech Support

Anybody in IT would know that most of the support tickets are pretty repetitive: Access request, license request, password reset request, or asset request. By automating such service desk tickets, the workforce engaged to provide tech support on shift-basis could be deployed into more valuable projects.

Marketing

Marketing automation entails automated campaigns, dynamic content that changes based customer persona, contact segmentation for better targeting, and research and development. In all, marketing automation saves money by knowing where to spend it, without the hassle of hundreds of hours of research into targeting.

When every business function is automated, not only does it save time and money for the organization, it also frees up skilled workforce to engage in more valuable work that requires human intelligence. Automating repetitive tasks also ensures process compliance. Futuristic businesses have begun delegating work to bots and automation.

Interested in how automation will fit into your business case ?

5 Best Practices to Drive Seamless Cloud Migration

cauvery k Data and AI August 26, 2021

With most organizations unexpectedly thrust into the remote work scene all of a sudden, the popularity of cloud environments has been at an all-time high. While cloud enables an anywhere and anytime kind of access, allowing businesses to function efficiently even in the post-pandemic era, it has also helped businesses see other upsides to it: cost and process streamlining to name a few.

Migrating an entire organization’s data to the cloud is not an easy task. Despite careful considerations and strategic planning initiatives, leaders often are blindsided by unexpected pitfalls that affect their overall business.

Here are 5 best practices to ensure your cloud migration happens without any unforeseen complications.

Plan, plan and plan some more

Of course, no organization would embark on a cloud migration journey without planning its budget. But what companies often fail to account for is their future needs, the maintenance costs for the infrastructure, application modernization requirements, and the price variations between different cloud environments. When they’re hit with sudden costs that they previously thought didn’t exist, organizations hit a roadblock.

It is therefore very important to figure out which cloud solution—public, private, hybrid and multi-cloud—suits your organizational needs and meets your budget.

The road not taken might be your right approach

Your organization is unique and therefore your migration strategy must also be unique. Simply “lifting and shifting” data is not what cloud migration is about. Many on-premise applications might not function efficiently after rehosting. This might become a huge bottleneck, especially when major departments are dependent on malfunctioning applications. No organization wants to deal with IT downtimes, especially after they’ve migrated to the cloud. This will also strain the budget when application modernization requirements arise.

Run a discovery and assessment of all the applications and plan your cloud migration approach: rehosting, re-platforming, refactoring or a mix of all these.

Teamwork makes the dreamwork

It’s common for even the technology workforce to experience digital disdain. Involve your workforce, listen to their apprehensions, provide thorough training and give them enough time to get acquainted with the new system. Involving them after the migration is entirely complete could become a very costly mistake.

This also applies if you choose to seek help from an external team for the migration. Form a team with members from both the migration partner and your workforce. Create a checklist, assign ownerships and streamline the process. Choose the right partner with proven experience in migrating for a company of your scale.

Take one safe step at a time

Another big mistake organizations tend to make is not taking it slow. Create a checklist to not miss anything and follow it strictly. Set goals, prepare for worst-case scenarios, break the process down into stages, and start with smaller departments such as HR or marketing that have the least business impact. This will help you identify potential issues that could occur later when the important datasets are migrated.

Measure your success

Ideally, measuring your success should be your first and your last step in your migration journey. In the first step, you will create a vision of how this migration will positively impact your business and outline your expectations. Once the migration is complete, measure the performance of your applications and business units and see where you stand.

On a side note, documenting this success could also come in handy when you have to convince your stakeholders when you embark on more technology adoption projects.

Cloud is here, and it is here to stay

And that’s a good thing because it helps organizations of all sizes to leverage the best of technologies and make their business thrive better. Now it’s up to us to take the big step.

Why It’s Vital for Companies to Focus on Data Engineering?

cauvery k Data and AI July 12, 2021

Digitalization is multiplying, making data the most prized asset in the world. Organizations are strategically moving towards insight-driven models where business decisions, process enhancement, and technology investments are handled with the knowledge gained from data. Big budgets are planned to make use of abundant data available, and this spending will only increase over the years.

According to a recent IDC report, it is estimated that by 2025 the Global Datasphere will grow to 175 zettabytes (175 trillion gigabytes). It also states that 60% of this data will be created and managed by businesses, driven by Artificial Intelligence (AI), Internet of Things (IoT), and Machine Learning (ML). AI and ML are gaining mainstream focus among many industries, and global spending is expected to grow to $57.6B by 2021.

How Data Engineering is helping businesses succeed?

Organizations often consider Data Science to be the only method to gain meaningful insights necessary to drive their business goals. However, the real potential lies within Data Engineering, which allows companies to build large maintainable data reservoirs. These design data processes are scalable and ensure relevant data is available for Data Science and Data Analytics to process complex statistical programs and algorithms to provide useful results. Only with reliable and accurate insights created from diverse sources can help data analytics harness the full power of data.

Today, AI and ML have become integral parts of organizations, helping them achieve higher operational efficiency, become agile, taper new market opportunities, launch new products with faster go-to-market, and provide higher customer satisfaction. But according to a survey done by MIT Tech Review, 48% of companies said that getting access to high quality and accurate data was the biggest obstacle in successfully implementing an AI program. To overcome this hurdle, businesses must focus on effective Data Engineering, which forms the basic building blocks for AI and ML.

Three advantages of effective Data Engineering:

1) Accelerates Data Science

2) Removes bottlenecks from Data Infrastructure

3) Democratizes data for Data Scientists and Data Analytics

Once organizations understand and internalize this, it is easy to see how the potential of Data Engineering is limitless.

How data engineering is helping businesses across industries

Industry influencers and other prominent stakeholders certainly agree that Data Engineering has become a big game-changer in most, if not all, types of modern industries over the last few years. As Data Engineering continues to permeate our day-to-day lives, there has been a significant shift from the hype surrounding it to finding real value in its use.

Manufacturing

Industry 4.0 is here, and the sooner organizations start their digital transformation, the better equipped they become to handle the evolving market conditions. What Industry 4.0 has brought is a significant shift in how manufacturing businesses are changing from being purely process-driven, to becoming data-driven. This essentially means that companies are either adding new digital components or updating their existing components with digital features. However, this creates a complex technology landscape where legacy systems have to interact with modern systems.

An effective Data Engineering solution can communicate and retrieve data from different systems, sort out critical data from a pool of data, and process them to be analyzed further. Data Engineering bridges the gap between Production, Research Development, Maintenance, and Data Science. Data Engineering can help in enhancing the critical aspects of manufacturing industry—production optimization, quality assurance, preventive maintenance, effective utilization of resources, and, ultimately, cost reduction.

Entertainment

Data has the power to make or break a business, and no one understands this better than Netflix. The incredibly successful data-driven company uses insights across its business functions to decide what new content to invest in and launch, enhance operational efficiency, and, most importantly, provide predictive recommendations for its global audience.

Netflix has also used its robust Data Engineering system to convert over 700 billion raw events into business insights, which is one primary reason why the company continues to be the market leader.

Retail

The retail industry is continuously trying to tap into new business opportunities by gaining insights from data sources across the physical and virtual ecosystems. To gain these business insights, data must be gathered from a large network (comprising of POS systems, e-commerce platforms, social media, mobile apps, supply chain systems, vendor management systems, inventory management systems, in-store sensors, cameras, and a growing list of new sources).

An effective Data Engineering solution can bring together massive sets of structured and unstructured data from entire value chain to provide trends, patterns, customer insights, and more. A retailer with stores across the globe and an omnichannel presence can harness data sources in innovative ways with Data Engineering to gain a detailed understanding of the market, the competition, and every step of the customer journey.

Healthcare

Leading healthcare giants are progressively investing in integrating ML into their core functions. However, they are focusing on setting up their data infrastructure by building Data Engineering platforms. The healthcare industry is looking to unlock value from data to gain knowledge into the patient, healthcare worker, and the healthcare system on a large scale.

Data Engineering brings together insights from electronic patient records and hospital data, as well as new advanced data sources like gene sequencing, sensors, and wearables. It offers them to Data Analytics to provide better medical treatment.

How Data Engineering is fueling the businesses of the future

To manage data at large scale and segregate business-critical data from the rest, organizations need a long-term data strategy plan to be future-ready with Data Engineering as a critical approach.

Data Engineering creates scalable data pipelines

Distributed data processing systems can help create reliable data pipelines with low level of network management to meet huge volumes and tap into increasing data sources in a growing ecosystem of touchpoints.

Data Engineering ensures that data is consistent, reliable, and reproducible

For data processing to be successful through the stages of ingestion, analytics, and insights, it is important that the data be compatible by ensuring it complies with the required formats and specifications. Data science can derive better insights from data by providing reliable and reproducible data.

Data Engineering helps ensure that processing latency is low

Most essential business insights are required to be in real-time to have an effective impact, be it with customer experience in the retail industry or predictive analysis in the financial sector. If the data being analyzed has a significant time delay, the insights can be less effective or completely ineffective.

Data Engineering optimizes infrastructure usage and computing resources

Using the right algorithm for data engineering can save a considerable amount of money spent on resources. This can provide significant savings to organizations and help them optimally utilize their technology landscape.

Businesses must design Data Engineering solutions that are unique to their needs and create customized frameworks rather than follow trends. At the same time, many new start-ups begin their data journeys with clearly defined data sets. In contrast, traditional organizations may have larger ones from legacy systems and data sets from new sources. It is important to understand that while the Data Engineering tools for a particular organization are zeroed, no general rule can be used. Only a comprehensive study of a company’s unique technology ecosystem and business needs can determine the type of Data Engineering systems that should be used.

Data Engineering solutions must also be flexible. How data is produced and consumed is constantly evolving, so Data Engineering solutions or frameworks must be flexible to accommodate future requirements. Guiding the movement in this direction is the shift from traditional Extract Transform and Load (ETL) methods of the data pipeline to more pliable patterns like ingesting, model, enhance, transform, and deliver. The latter provides more flexibility by decoupling Data Pipeline services.

Many experts focus on Data Engineering one step further by encouraging companies to adopt a ‘Data Engineering Culture’. This permanently recognizes the need for Data Engineering at all levels of an organization across functions and warns that business predictions will fail without effective Data Engineering and an appropriate ratio of Data Engineers to Data Scientists.

The sooner organizations push for Data Engineering Culture and create organizational alignment, the more equipped they will be for the future, to which data holds the key.

How TVS Next created a Data Engineering solution for one of India’s top utility companies

In the energy sector, large enterprises are turning real-time data to drive effective energy management. Energy corporations rely on data for efficient resource management, operational optimization, reduced costs, and increased customer satisfaction with better insights into supply and demand in real-time.

TVS Next helped one of India’s leading utility companies build a distributed computing engine for processing and querying data at scale. The solution provided the company with tools to visualize key performance indicators using real-time data. With effective Data Engineering, the client improved the customer experience rather than relying on complex algorithms to predict outcomes.

What are some of the achievements and challenges you have faced while planning a Data Engineering system for your organization? Share your story and get in touch with us here

Improve Retail Business With Machine Learning

cauvery k Data and AI October 22, 2020

Technology has transformed how customers and brands communicate with each other. Shoppers were once dependent on face-to-face, in-store interactions to make purchases and receive support. Now, shoppers do their research before entering a store (81 percent of shoppers conduct online research before buying) and hardly rely on salespersons to help them make decisions. Retailers, however, have understood that by embracing technology, they can extend their storefronts to their customers’ fingertips.

Shoppers can make purchases from within social media apps and compare prices without leaving a store. While these technologies have propelled the retail industry further into the digital age, the technology that is still evolving will have the largest impact on the future of the customer service and retail industries.

Embracing Big Data

More retailers are tracking customer shopping habits through data sources such as social media, purchase history, consumer demand, and market trends. By relying on big data technology to gain a deep understanding of shoppers and their buying trends, retailers can maximize customers’ spending and encourage customer loyalty.

According to research by Accenture report, 70 percent said that big data is necessary to maintain competitiveness, and 82 percent agreed that big data is changing how they interact with and relate to customers.

Matching Products with People

Machine learning technology boosts the reach of big data analytics and can help create an exceptional shopping experience. Innovative retailers can tap into the power of machine learning algorithms to do things like determine available products from outside vendors or recommend the quantity, price, shelf placement, and marketing channel that would reach the right customer in a particular area.

Further, the capability to automate everything through advanced analytics and machine learning soon will mean that basic customer service will be performed by bots that can predict our needs and provide service in the fastest, most immediate way possible: by offering us items we didn’t know we needed. As retailers gain more insight into their customers and products, machine learning will be able to match buyers and sellers based on buyers’ needs and product availability.

Digital Assistants

Shopping is becoming increasingly programmatic. In the future, services like digital assistants (Siri, Cortana, etc.,) will learn more about us and offer us relevant and personalized product offers. Say, for example, you use a particular brand of perfume. Your digital assistant will learn your shopping and usage habits and offer you the best deal on the product at the right time. It might even place the order for you.

Improving the backend

Machine learning and advanced analytics will not only change how we shop and provide customer service, but also simplify how retailers perform basic operations. Data science and machine learning give us the ability to automate so much of the heavy lifting required to find insight within a pile of data. With these tools, retailers can find useable and useful data to change the shopping experience for consumers.

Technology enables us to create an index of every product in the world, enabling retailers to offer customers the best prices, keep products adequately stocked, and track competitors’ minimum-advertised-price violations. A central database of the world’s product information enables retailers to offer the best shopping experience for buyers.

An innovative-technology approach to customer service and commerce will combine data about our behaviors and choices with data about products and product attributes to create the best shopping experience. This approach takes the guesswork out of purchasing and makes the shopping experience more cherishable for everyone.

Top 5 Big Data Trends In 2020

cauvery k Data and AI October 21, 2020

When the world big data rapidly expanded a decade ago, there were no signs that they would slow down. It is primarily aggregated across the internet, such as social networking, web search requests, text, and media files. IoT devices and sensors produce another gigantic share of data. These are the main reasons for the global big data market growth of 49 billion dollars.

Spark will Widespread

Apache Spark is a platform for data processing that can easily perform tasks on very large data sets and also spread the functions of data processing over many devices, either on its own or in combination with other distributed computing resources. These two qualities are important to the worlds of big data and machine learning that require vast data stores to sharpen the masses of computer power. Spark removes some of the programming burdens from developers with an easy-to-use API which sums up many of the grunt tasks of distributed computing and big data processing.

Apache Spark has been one of the main computing frameworks that spread throughout the world. Spark offers native binding for Java, Scala, Python, and R languages, and supports SQLs, data sharing, machine learning, and graphic processing. The Spark software can be used in several ways.

The convergence of IoT, Cloud, and Big Data

In order to facilitate interaction between machines and humans (M2H) and machines (M2 M), the Internet of Things is an opportunity for simplifying operations in many areas. Until now it has been greatly improved. In most cases, sensor-generated data is transmitted for analysis to the Big Data System and final reports are made. This is also the main interconnecting point of the two technologies.

For the next ten years, IoT is expecting a future of $19 trillion in the web industry, which will give room for more IoT and Big Data research and development.

Cloud computing plays a significant part in the storage and management of the data by generating an immense amount of data. It is not only about big data growth but also the development of platforms such as Hadoop for data analytics. As a consequence, it provides new cloud computing opportunities. Therefore, service providers like AWS, Google, and Microsoft have cost-effectively their own Big Data Solutions for businesses of all sizes.

Mixed Reality will improve Data Visualization

AR and VR have gained a lot of traction among customers in the past few years. With the launch of Pokémon Go, Augmented Reality had garnered around 100 million users within just a few weeks of launch. Though AR or VR might not be very useful for large corporations, the concept of Mixed Reality might very well be. Mixed reality combines the virtual world with our real-world and devices like Microsoft Hololens are already gaining traction. Mixed Reality will offer huge opportunities for organizations to better perform tasks and also to better understand the big data.

Deep Learning

Deep learning is an advanced form of machine learning which is based on neural networking. Deep learning help recognize specific items of interest from massive volumes of unstructured data. It is mostly useful for learning from huge volumes of structured and unstructured data. Thus businesses and organizations should pay more attention to deep learning algorithms to deal with the heavy influx of big data.

Data Virtualization

Data virtualization will see strong momentum this year. Data virtualization has the ability to unlock the hidden concepts and conclusions from a large set of data. It also allows enterprises and organizations to retrieve and manipulate data on the go.

To address big data problems, the management and use of computer and data-intensive systems require huge amounts of highly distributed datagrams. Virtualization offers the additional flexibility needed to realize large data platforms. Although virtualization is theoretically no prerequisite for big data analysis, in a virtualized environment software frameworks are more effective.

Conclusion

As mentioned earlier, this year will be an exciting year for big data, and analytics systems will become the top priority for organizations. These systems are expected to perform well operationally, and fulfill promises of business value to the organization.

Extracting Acronyms through Natural Language Processing

cauvery k Data and AI October 21, 2020

Introduction

An acronym is a pronounceable word created from the first letter of each word in a phrase or title. An acronym is a kind of abbreviation consisting of a first letter or initial letters in a word. It’s also called short descriptors of phrase.

Interesting Fact: Acronym was introduced as a modern linguistic element of English during the 1950s. Because acronym is called a term, its meaning is called expansion.

Usage & Challenges

An acronym is primarily used in language processing, web search, ontology mapping, question answering, text messaging, and social media sharing. Acronyms evolve each day dynamically, and finding their definition/expansion becomes a daunting task due to its diverse characteristics. Several researchers experimented with plain text and network expansion pairs for mining acronyms over the past two decades. Manually edited online archives have pairs of acronyms, but regularly reviewing all possible meanings is intimidating.

Solution

To handle this issue, TVS Next has built a specialized product to extract acronyms from a document in a few seconds. This product is built on Python for Natural Language Processing.

Below are some pointers that describe how our research works that help us solve the problem mentioned above.

Heuristics Approach

NLP (Natural Language Processing) and pattern-based methods include heuristics.

The NLP-based approach uses a fuzzy-matching Statistical Model based on the principles of Levenshtein’s Distance algorithm.
The pattern-based approach uses custom rules that work with data from multiple domains, combined with Statistical Modelling to extract the Acronyms and their Expansions. These methods are written after considering features in the text as characteristics of acronyms – ambiguity, nesting, uppercase letters, length, and para-linguistic markers.

An Acronym Finding Program (AFP) is a simple, free-text expansion recognition method. This program applies an inexact matching algorithm for mining AE pairs. A tool known as Three Letter Acronym (TLA) uses para-linguistic markers such as parenthesis, commas, and periods to derive acronym meaning from technical and government documents.

Developing the Product

A Statistical model has created to provide the user with a Solution that gives ease of access to acronyms that appear throughout the document. The designed solution can be integrated into various tools and technologies that deal with text-based information. The solution proves to be useful while combining it with tools that parse PDF documents. It deals with – tables, free-flowing text.

A document consists of multiple tables that are very similar in structure; hence our solution uses a Table Classification method to differentiate the acronym table from the rest. Various types of Statistical Methods were incorporated to quantify features/patterns that help define what an acronym will look like. This solution was used to classify an acronym table from the rest and then extract acronyms from the table.

For free-flowing text, a similar technique has been used where the patterns/features of an acronym are incorporated to differentiate it from the rest of the free-flowing text. There are words extracted that can turn out to be acronyms. These words appear along with their expansion in the text. After extracting suspected acronyms, we quantify the words that consist of acronyms using statistical models and compare them to their expansions.

By enforcing the following statistical models, 80% of acronyms are obtained that are present in a document. It is essential to accommodate variations in how text is written. Simple human punctuation errors can affect the entire acronym, not falling under rules of how acronyms are generally written. A dynamic method where custom rules that works with data from multiple domains are combined along with specific Statistical Models has been implemented that will uncommonly parse texts.

On executing this dynamic method and testing various documents, we could conclude that the Statistical Model-based acronym extraction method has been performing with over 95% accuracy, even surpassing open source solutions provided by Spacy called Blackstone available in the market at the moment. Blackstone works on the techniques mentioned in a research paper written by Ariel S Schwartz et al. [2]., Multiple comparisons were made, between Blackstone and the Statistical-method based Acronym Extraction.

Result

The Statistical Model-based acronym extraction method scanned an entire document of 100+ pages in milliseconds and displayed 98% accuracy. The average time taken to scan a document is a few seconds, and the accuracy of this product has been achieved between 94-98%. The product was tested on documents belonging to various domains, and it still yielded similar results. The product is developed on an experimental basis, and we are set to improve its efficiency and performance each day. There is plenty of room for improvement with subject to market changes. The product experiments with a set of Statistical models and custom rules, and the team is working on dynamic changes using AI that scans documents based on results. This product proves to be useful for lengthy and complicated engineering and medical documents. This product is one of its kind, and we are proud of our development.

At TVS Next, we re-imagine, design, and develop software to enable our clients to build a better world.

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