Data and AI Archives

Building Intelligent Applications: A Comprehensive Guide

Alex Thompson Data and AI March 13, 2024

Introduction

Artificial Intelligence (AI) has made significant strides in recent years, transforming the world of software development and how businesses approach customer experience. AI-powered applications, also known as Intelligent Applications, are rapidly becoming the software industry’s future, with projections that the global AI market size will expand at a CAGR of 37.3% from 2023 to 2030. Developing Intelligent Apps requires a unique approach and specific technologies and methodologies, which this article will address.

“According to Oberlo, The global artificial intelligence market is expected to reach $1.59 Trillion by 2030.”

In this blog, we’ll explore the essential components that make up intelligent app development, their impact on your business, and some practical examples of AI-powered applications.

The key components of building Intelligent Applications

1. Data Extraction and Preparation

To make informed decisions, intelligent apps mostly rely on data. The process of collecting and preparing data entails obtaining pertinent information from several sources, removing noise and irregularities through cleaning and preprocessing, and arranging the data into a structure that can be used to train machine learning models.

2. Machine Learning Models

The brains of intelligent apps are machine learning models. Based on the needs of the application and the type of data, the best machine learning algorithms and model architectures should be chosen. Neural networks, support vector machines, decision trees, and other models are common forms of machine learning models.

3. Training and Assessment

Following their selection, the machine learning models must be trained using the prepared data. To minimize mistakes, the model’s parameters must be adjusted during training. Evaluation metrics are then used to validate the model’s performance.

4. AI Component Integration

To provide real-time predictions and personalized services, AI components must be integrated into the app’s architecture. These artificial intelligence (AI) components may include sentiment analysis, image identification, recommendation systems, and natural language processing features.

Building Intelligent Applications — Step by Step

1. Identify the problem

Identify the Problem

Clearly define the problem the app aims to solve, such as improving user engagement, personalizing content, or enhancing security.

Understand the Business Operational Purposes

Concentrate on understanding the main app technology and potential architecture while developing next-generation applications.

2. Data Collection and Processing

Real-time Data Gathering

Intelligent apps collect information from various sources, such as IoT sensors, websites, mobile apps, and beacons, and examine it in real-time to provide accurate results.

Data Pool Support

This involves real-time data gathering, indexing, and management to ensure that the app has access to the necessary data for intelligent decision-making.

3. Select the Right Algorithms and Models

Machine Learning Tools

These tools simplify the process of implementing AI in apps by helping to train AI models, test their performance, and optimize them for the app.

Deep Learning and Neural Networks

These advanced machine learning techniques are utilized to enhance the capabilities of intelligent apps.

4. Develop the AI Components

Cognitive APIs

These APIs enable developers to add features such as natural language processing, computer vision, and speech recognition to their apps, infusing them with intelligence using just a few simple lines of code.

Low-Code Platforms

These platforms can speed up the development process and make implementing AI in apps easier by providing pre-built components and a visual development environment that simplifies development.

5. Train and Optimize Models

Usability Testing

Before integrating AI features into the app, rigorous usability testing is essential to ensure that the AI-driven interactions align with users’ mental models and expectations, reducing friction and enhancing overall user satisfaction.

AI-Driven Design Systems

These systems provide a comprehensive guide outlining the guidelines, components, and interactions necessary to create consistent and user-friendly experiences, incorporating AI foundations into the design system.

6. User Experience and Testing

User-Centric Approach

Always keep the user in mind when developing the app. A user-centric approach can help create an app that meets the needs and expectations of users.

Make Use of the Python Dictionary

The Python dictionary is a powerful tool that can be used in AI development. It allows for the storage and retrieval of data quickly and efficiently.

7. Monitor and Improve

AI and Analytical Technologies

These are integrated into intelligent applications, giving them the capacity to act intelligently.

Best Practices for AI-powered Mobile App Development

Follow best practices to create powerful AI-powered mobile apps that provide value to users and help businesses grow.

Examples of AI-enhanced Applications

Conclusion

Developing Intelligent Apps requires a unique approach, methodologies, and specific technologies. When creating Intelligent Apps, the key components to consider are data acquisition and management, natural language processing, computer vision, human-machine interaction, and security/ethics.

Intelligent Apps provide unparalleled functionality, improved customer experience, and the potential for enhanced revenue generation. However, achieving the benefits requires understanding the technologies and methodologies relevant to Intelligent App development.

The software industry’s future lies in developing Intelligent Apps that help enterprises gain a competitive advantage in their respective industries. This guide provides a starting point for those seeking to create Intelligent Apps while highlighting the value of these technologies when appropriately integrated within an organization.

Take the First Step Towards Building Intelligent Applications

Chatbot vs Conversational AI – Key Differences in 2024

Alex Thompson Data and AI November 6, 2023

What is Conversational AI?

A group of technologies used to power human-like interactions through automated messaging and voice-activated applications is referred to as conversational artificial intelligence (AI), a growing subfield in AI.

Vast volumes of data, including text and speech, are used to train conversational AI systems. The system learns how to comprehend and process human language through the usage of this data. Vast volumes of data, including text and speech, are used to train conversational AI systems. The system learns how to comprehend and process human language through the usage of this data.

Advanced chatbots, or AI chatbots, are the most common kind of conversational AI. AI chatbots combine many types of artificial intelligence (AI) for more advanced capabilities, as opposed to conventional chatbots, which are built on simple software and have restricted capabilities. Conventional voice assistants and virtual agents can be improved with the use of the technology employed in AI chatbots. The technology underlying conversational AI are still in their infancy but are evolving and improving quickly.

Differences between Conversational AI and Chatbot

A collection of fundamental technologies for creating chatbots is called conversational AI. In other words, a conversational AI platform is the foundation upon which an application called an intelligent chatbot is constructed.

However, not every chatbot is built using conversational AI technologies. In actuality, a sizable fraction of chatbots is entirely non-conversational and human-scripted, and/or rule-based. Chatbots, virtual personal assistants, automated message systems, agent-assisting bots, and FAQ bots powered by AI are examples of applications based on conversational AI platforms.

Although conversational AI and chatbots are related technologies used to facilitate interactions between humans and computer systems, there are distinct differences between the two:

Challenges of Conversational AI Technologies

Over the past few years, conversational AI has steadily matured to the point where it can now provide businesses with outstanding business value and outcomes. Nevertheless, it poses some difficulties, such as:

It is challenging to train AI algorithms to comprehend the intricacies of human language. It is difficult for artificial intelligence to consistently read user inputs correctly due to ambiguity, slang, colloquialisms, and changes in sentence structure.

Accurately determining the user’s intent before responding to a query is essential. Answers that are unnecessary or incorrect can result from misinterpreting purpose.

Conversational AI must incorporate context awareness to maintain meaningful discussions. However, it can be challenging to comprehend and remember context across several turns and themes, particularly in dynamic real-world situations.

Access to user data is necessary to customize answers to specific users. Conversational AI developers constantly need help to strike a balance between personalization and user privacy concerns.

Creating conversational AI systems that can communicate effectively in multiple languages and comprehend different cultural references is difficult.

Most conversational AI systems perform well within particular areas but struggle when tasked with tasks outside of their purview. It’s still challenging to develop flexible systems that can handle a variety of subjects.

To deliver valuable services, conversational AI must be integrated with various back-end systems, databases, and APIs. This needs a careful design that takes data security and system compatibility into account.

How to Create Conversational AI?

Thinking about your potential customers’ interactions with your product and the most common queries they might have is the first step in developing conversational AI. You can then direct them to the relevant information using conversational AI capabilities.

Begin by understanding your use cases and requirements

The initial phase towards creating conversational AI is recognizing your organization’s specific requirements and use cases.

What do you want your chatbot to accomplish?
What kind of dialogue would you like it to be capable of having?
What information do you need to gather and monitor?

You can choose the best method for building your chatbot by defining these needs.

Data gathering

Collect relevant and varied datasets for your AI model. This data should comprise user inputs and subsequent replies for the AI to learn from instances.

Select the appropriate platform and tools

Numerous platforms and toolkits are available for building conversational AI. Select the platform that best meets your needs, as each offers benefits and drawbacks.

Preprocess the data

Cleaning and preprocessing it will guarantee that it is accurately formatted and error-free.

Model training

Using the preprocessed data, train your AI model using the selected platform and tools. In this procedure, the model’s performance is optimized using machine learning methods.

Integration

Add your conversational AI to the platform or application of your choice, such as a website or mobile app.

Debugging and testing

Test your conversational AI rigorously to find and solve any errors or problems.

Track and update

Track the performance of AI and user interactions, then change as necessary. Regular updates will improve the model’s capabilities and allow it to react to changing user needs.

Conversational AI: The Future of Communication

Alex Thompson Data and AI October 26, 2023

What is Conversational AI?

“Conversational AI” refers to various techniques that allow computers to interact with people in conversation. They mimic human interactions by recognizing audio and text inputs and translating their contents into other languages utilizing large volumes of data, machine learning, and natural language processing.

Chatbots, which employ NLP to decipher user inputs and carry on a conversation, are one of the most common applications of conversational AI. Other applications of conversational AI include virtual assistants, chatbots for customer service, and voice assistants.

Customer service departments frequently deploy conversational AI solutions. They can be found on websites, online stores, and social media platforms. AI technology can efficiently expedite and streamline the process of answering and directing client questions. It fuels interactions close to humans, enhancing customer experience (CX), improving satisfaction, fostering loyalty, and raising customer lifetime value (LTV).

Numerous elements that collectively enable human-like speech make up conversational AI. The main elements are as follows:

Natural Language Processing (NLP)

NLP is the study of how computers comprehend and use human language. This requires being able to manage colloquial expressions and slang. It comprises entity recognition, sentiment analysis, entity tokenization, syntactic and semantic analysis, and language recognition. NLP aids in understanding user inputs and locating pertinent data.

Natural Language Generation (NLG)

Based on the comprehension of user input, NLG entails producing responses that sound human. It converts structured data or system knowledge into user-understandable, cohesive, and contextually suitable language. Simple rule-based templates and more complex machine-learning models are also used in NLG techniques.

Dialogue Management

The goal of dialogue management is to control the flow of conversations between the user and the system while keeping context. The discussion history can be tracked, user intentions can be understood, system actions can be determined, and appropriate replies can be generated. For managing dialogue, rule-based methods and reinforcement learning are frequently employed.

Machine Learning

Conversational AI heavily relies on machine learning. Machine learning models are trained on big datasets to enhance language understanding, answer generation, and dialogue management capabilities. Conversational AI models are typically trained using deep learning, neural networks, and reinforcement learning. Machine learning algorithms can automatically become more effective as they encounter more data.

Text Analysis

Text analysis is the technique of extracting information from text data. This requires identifying the various components of a sentence, such as the subject, verb, and object. It is also necessary to recognize the different word categories in a phrase, such as verbs, nouns, and adjectives. Text analysis is used to decipher a sentence’s meaning and the links between its words.

Computer Vision

Computer vision refers to a computer’s ability to comprehend and interpret digital images. This entails recognizing the many items in an image and their positions and angles. Computer vision is used for identifying both the contents of an image and the associations between its various pieces. It is also utilized to comprehend a photograph’s context and decipher the emotions of the individuals in it.

Speech Recognition

By converting spoken words into written text, speech recognition enables computer systems to process and comprehend user voice inputs. It involves using algorithms that listen to audio signals, recognize words and phonemes, and then translate those words into text. For voice-based conversational AI applications, speech recognition is crucial.

These elements combine to form conversational AI systems that can comprehend human input, produce appropriate responses, maintain context, and participate in engaging and dynamic discussions.

What is Conversation Design, and why is it Necessary for Conversational AI?

Conversation design is the process of establishing meaningful dialogues between humans and conversational AI systems, such as chatbots, virtual assistants, and voice interfaces, in a natural, entertaining, and interactive way. Designing the conversation’s flow, coming up with suitable responses, and keeping the user’s experience in mind while interacting with the system are all part of this process.

Effective conversation design ensures a seamless and positive user experience. Users should feel comfortable and understood while interacting with the AI system. Well-designed conversations lead to higher user satisfaction and adoption. Since conversational AI engaged in customer service must handle requests quickly and satisfactorily, the ability to identify and manage purpose is essential for a successful resolution. Actual conversations train machine learning (ML) models to grasp intent better.

Conversational AI aims to simulate human-like interactions. Conversation design helps create more natural, empathetic, and less robotic responses, enhancing user engagement. The architecture of conversations takes into account possible mistakes and edge circumstances that could occur during interactions. This enables the AI to react appropriately when it receives an unexpected input or query it doesn’t comprehend.

The delivery of solutions for specific use cases, such as customer care, IT service desk, marketing, and sales assistance, depends on conversational AI technology. Additionally, conversational AI allows for interaction with SMS, web-based chat, and other messaging systems’ chat interfaces.

How does Conversational AI work?

A standard conversational AI flow, which is supported by deep neural networks (DNN) and underpinning machine learning, consists of the following:

Automatic Speech Recognition (ASR) is a user interface that turns speech into text or an interface that enables the user to input text into the system.

Natural language processing (NLP) may convert unstructured text into structured data by understanding the user’s intent from text or voice input.

Natural Language Understanding (NLU) is used to extract the user’s purpose, as well as any relevant entities or parameters, from the text. Tokenization, part-of-speech tagging, named entity identification, and purpose categorization are a few of the processes involved.

Artificial Intelligence Model (ALM) predicts the user’s optimum course of action using the user’s intent and the model’s training data. All these processes are inferred by Natural Language Generation (NLG), which then creates a suitable response to communicate with people. It’s important to remember that different conversational AI models combine these strategies in different ways, and the area is constantly changing due to improvements in AI and NLP technology.

Transforming Customer Service with Conversational AI

Conversational AI can transform customer service by giving customers more effective, individualized, and exciting experiences. The following are a few ways that conversational AI might improve customer service:

It is critical to strike the right balance between automation and human connection. However, some questions call for human empathy and comprehension, which AI may not be entirely capable of providing. Therefore, the most successful way to transform customer service is by using a hybrid strategy that incorporates the advantages of both conversational AI and human agents.

Stay tuned for part 2 of the blog as we delve deeper into the world of Conversational AI.

Machine Learning Trends for Financial and Healthcare Industries

Alex Thompson Data and AI June 5, 2023

Machine learning (ML) has surfaced as a game-changing influence in multiple industries, dramatically reshaping the banking, financial services, and healthcare landscape. With its proficiency in processing large quantities of data and generating predictions, machine learning is progressively becoming more valuable.

This blog will examine some of the most significant machine learning trends currently shaping the banking and financial services industry, including churn management, customer segmentation, underwriting, marketing analytics, regulatory reporting, and debt collection. We will also delve into the machine learning trends in healthcare sector, highlighting disease risk prediction, patient personalization, and automating de-identification.

These trends are supported by insights from leading market research firms like Gartner and Forrester and consulting firms like McKinsey, BCG, Accenture, and Deloitte.

ML Trends in Banking and Financial Service Industry

Churn management is a critical concern for banking and financial service providers. Machine learning algorithms can analyze customer behavior, transaction history, and interaction patterns to identify potential churn indicators. By detecting early signs of customer dissatisfaction, businesses can proactively engage customers and offer tailored solutions to retain them.

Example: Citibank implemented a machine learning system to predict customer churn by analyzing transactional data and customer interactions. This approach helped Citibank reduce customer churn by 20% and increase customer retention.

Machine learning enables accurate customer segmentation, allowing banks and financial institutions to understand their customer base better. ML algorithms can analyze customer data, including demographics, transaction history, and online behavior, to identify distinct customer segments with specific needs and preferences. This information empowers businesses to create targeted marketing campaigns, personalized offerings, and tailored customer experiences.

Example: A leading financial institution employed machine learning to segment its customers based on their financial goals, spending patterns, and risk appetite. By tailoring their product offerings to each segment, the institution achieved a 15% increase in cross-selling and improved customer satisfaction.

In the banking and financial services industry, underwriting is a critical process for assessing loan applications and managing risk. Machine learning algorithms can analyze large amounts of data, including credit scores, financial statements, and historical loan data, to automate and enhance the underwriting process. ML-powered underwriting systems can provide faster and more accurate risk assessments, leading to efficient decision-making and improved loan portfolio quality.

Example: LendingClub, an online lending platform, utilizes machine learning to assess borrower creditworthiness. By analyzing various data points, such as income, credit history, and loan purpose, LendingClub’s machine learning models have improved loan approval accuracy and reduced default rates.

Machine learning empowers banks and financial institutions to better understand customer behavior and preferences, enhancing marketing effectiveness. ML algorithms can analyze customer data, social media interactions, and campaign responses to identify trends, patterns, and customer preferences. This enables businesses to create targeted marketing strategies, optimize campaign performance, and improve customer acquisition and retention rates.

Example: Capital One employs machine learning to personalize marketing offers for credit card customers. By analyzing customer data, spending patterns, and demographic information, Capital One delivers tailored offers, resulting in increased response rates and improved customer engagement.

Regulatory compliance is a significant concern for banks and financial institutions. Machine learning can automate and streamline the regulatory reporting process by analyzing and extracting relevant information from vast amounts of data. ML algorithms can ensure accuracy, identify anomalies, and provide real-time insights, enabling timely compliance with regulatory requirements.

Example: JPMorgan Chase leverages machine learning for regulatory reporting by automating data extraction and verification. This approach has improved accuracy, reduced reporting errors, and increased operational efficiency.

Machine learning can improve debt collection processes by identifying the most effective strategies and predicting the likelihood of repayment. ML algorithms can analyze customer payment history, communication patterns, and external data sources to prioritize collection efforts, tailor communication channels, and optimize resource allocation.

Example: American Express implemented machine learning algorithms to predict the likelihood of customers falling behind on payments. By proactively engaging at-risk customers and offering tailored payment plans, American Express reduced delinquency rates and improved collections efficiency.

ML Trends in Healthcare Industry

Machine learning algorithms can analyze large amounts of patient data, including medical records, genetics, and lifestyle factors, to accurately predict disease risks. By leveraging these predictions, healthcare providers can proactively intervene, develop personalized prevention plans, and improve patient outcomes.

Example: Google’s DeepMind developed a machine learning model to predict the risk of developing acute kidney injury (AKI). The model enabled healthcare professionals to identify at-risk patients earlier by analyzing patient data, allowing for timely intervention and reduced AKI incidence.

Machine learning enables personalized healthcare by analyzing patient data to tailor treatment plans, medication dosages, and therapies to individual characteristics and needs. This approach, known as precision medicine, improves patient outcomes and minimizes adverse effects.

Example: Memorial Sloan Kettering Cancer Center employed machine learning to personalize cancer treatment recommendations. The algorithm helped oncologists determine the most effective and personalized treatment plans by analyzing patient data, including genetic information and treatment history.

To comply with privacy regulations, healthcare providers must de-identify patient data before sharing it for research or analysis. Machine learning can automate de-identification by accurately removing or encrypting personally identifiable information (PII) while preserving data utility.

Example: The National Institutes of Health (NIH) developed machine learning models to automate the de-identification of medical records. This approach increased efficiency, reduced human error, and ensured compliance with privacy regulations.

Conclusion

In conclusion, the financial services and healthcare industries are undergoing a significant transformation driven by machine learning technologies. As these machine learning trends evolve, we expect to see more sophisticated applications that enhance decision-making, improve operational efficiency, and deliver personalized customer experiences. By embracing machine learning, organizations in these sectors can unlock valuable insights from their data, streamline processes, and stay ahead of the competition.

However, it is essential for businesses to not only adopt these technologies but also invest in the necessary infrastructure, skilled workforce, and data management practices. This will ensure that they can fully harness the power of machine learning and capitalize on its potential to drive innovation and growth. As we move forward, the financial services and healthcare industries will undoubtedly continue to be at the forefront of machine learning advancements, setting new benchmarks for other sectors.

Evolution of Generative Artificial Intelligence for Text (ChatGPT)

Alex Thompson Data and AI March 14, 2023

Many companies and research organizations that are pioneers in AI have been actively contributing to the growth of generative AI content by bringing in & applying different AI models to fine-tune the precision of the output.

Before discussing the applications of generative AI in text and large language models, let’s see how the concept has evolved over the decades.

RNN sequencing

After researchers proposed the seq2seq algorithm, which is a class of Recurrent Neural Networks (RNN), it was later adopted & developed by companies like Google, Facebook, Microsoft, etc., to solve Large Language Problems.

The element-by-element sequencing model revolutionized how machines conversed with humans, yet, it had limitations and drawbacks like grammar mistakes and bad semantic sense.

LSTM

RNN suffered from a problem called Vanishing Gradient. LSTM (Long Short Term Memory) and GRU (Gated Recurring Unit) were introduced to address this issue.

Though in structure, they remain the same, LSTM preserves the context/information present in the initial part of the statement by preventing the issue of Vanishing Gradient. To retain the part of the statement, it introduced cell state and cell gates with layers such as forget gate, input gate, and output gate.

Transformer model

While LSTM was a rock star during its time in the NLP evolution, it had issues such as slow training and lack of contextual awareness due to a one-directional process. Bi-directional LSTM learned the context in forward & backward directions and concatenated them. Still, it was not ahead and back together, and it struggled to perform tasks such as text summarization and Q&A that deal with long sequences. Enter, Transformers. This popular model was introduced with improved training efficiency. Also, the model could parallelly process the sequences, based on which many text training algorithms were developed.

UNILM

Unified language model was developed from a transformer model, BERT – Bi-directional Encoder Representations. In this model, every output element is connected to every input element, and the language co-relation between the words was dynamically calculated. AI content improved with the tuning of algorithms and extensive training.

Text to Text Transfer Transformer, with text as input, generates target text. This is an enhanced language translation model. It had a bi-directional encoder and a left-right decoder pre-trained on a mix of unsupervised and supervised tasks.

BART

Bi-directional & auto regressive transformers, a model structure proposed in 2020 by Facebook. Consider it as a generalization of BERT and GPT. It combines ideas from both the encoder and decoder. It had a bi-directional encoder and a left-to-right decoder.

GPT: Generative Pre-trained Transformer

GPT is the first autoregressive model based on Transformer architecture. Evolved as GPT, GPT2, GPT3, GPT 3.5 (aka GPT 3 Davinci-003) pre-trained model, which was fine-tuned & released to the public as ChatGPT (based on InstructGPT) by OpenAI.

The backbone of the model is Reinforcement Learning from Human Feedback (RLHF). It’s continuously human-trained for text, audio, and video.

This version of GPT converses like a human to a great extent, which is why this bot has a lot of hype. With all the tremendous efforts that went into scaling AI content, companies are striving to make it more human-like.

More Large Language and Generative AI models were built and released by Google (BARD based on Language Model for Dialogue Applications (LaMDA), HuggingFace (BLOOM), and the latest from Meta Research LLaMA, which was open-sourced.

Application of ChatGPT and Generative AI models

With companies expanding their investment in data analytics to use the power of data to derive critical insights, we must discuss AI bots’ role in data analytics.

The applications of Generative AI and ChatGPT are vast. From generating a new text, answering questions conversing like a human, assisting developers with generating code, explaining code, writing newsletters, blogs, social media posts, and articles (This post was not written by ChatGPT ) to sales reports and generating new images and audio, ChatGPT can do it all.

As we read in the earlier paragraph on various applications of Generative AI, different models come into play for the same. We continue to see ChatGPT experiences from people of various backgrounds and industries. How and where can enterprises use ChatGPT?

As you know, ChatGPT is Language Model. Its application is predominantly in “Text” and tasks that require human-like conversation, taking notes in a meeting, composing an email, writing content, and increasing developers’ productivity.

Key challenges in using open-source AI bots for data analysis

Most data analysis projects deal with sensitive data. Large organizations sign agreements on data privacy protection with customers and the government that prevents them from disclosing sensitive information to open-source tools.

That’s why organizations must understand what kind of support the data engineering team looks for from AI bots and ensure no sensitive information is disclosed.

A known risk: AI models have continuously evolved to ensure improved accuracy. This implies that there is definitely room for errors. The open-source conversational bots, even if well-trained to perform certain activities, hold no responsibility for the output it provides. You need the right eye to ensure the AI gets the correct data, understands it, and does what it should.

Responsible governance & corporate policies

Technology is fast evolving and has the entire world working on it such that innovations, new tools, and upgrades are happening in the flash of an eye. It’s so compelling to try new tools for critical tasks. But, every organization must ensure the right policies are in place to responsibly handle booms or sensations like ChatGPT.

Get buy-in for your data modernization initiative

Alex Thompson Data and AI, Modernization March 6, 2023

If you want to convince your top management to get buy-in for your data modernization initiatives, you are in the right place.

Let’s brainstorm on how we can achieve this. When you want to convince someone of your idea to modernize, you must be prepared to answer all the questions the decision-makers may have. Here are some:

What is the scope of data modernization you are talking about?

When you say ‘modernization,’ you could be talking about:

Whatever your goal, it is essential to explain it clearly — the receiving end should know WHAT CHANGE you are trying to make for the betterment of your organization.

What are the problems your business faces with legacy systems?

Frame the problem statement. Since the Stone Age, necessity has been the mother of all inventions. So, build a strong case for why you need to modernize your organization’s data platform.

Data security issues or compliance risks your company faces since your legacy platform is not designed to tackle modern-day problems.
Your database/source might be incompatible with modern applications.
Inability to address customer demands and ever-changing business requirements, which necessitates a modernized data environment.
Troubleshooting legacy systems is not easy as the type of issues that old data setups have are difficult to tackle. Moreover, you may not have readymade solutions from the service provider handling your data.
Another critical reason is finding the right talent pool — getting new-generation developers to work on legacy systems is challenging. Most developers look for a modernized tech stack to work.

Your business may have more biting problems by following traditional data systems & approaches — bring those challenges out on the table!

What benefits would your organization or business reap from data modernization?

Of course, the benefits depend on the nature of your business and what you’re trying to achieve through data modernization.

But, the expected benefits that any organization can count on receiving through data modernization would be:

The enhanced support you get from the modernized platform
Modernized databases and solutions are designed with data privacy, security & performance in mind and ensure higher efficiency
Get on-demand services at reduced costs, as you pay only for what you need. The data market is highly competitive, so solution providers ensure you get cost-effective solutions. You don’t have to pay for additional solutions that your business may not need. Find out which data solution your business needs and whether your solution provider can cater to the needs within your budget. Mention these factors as critical information during your meeting.
You can even leverage more resources or cancel resources for specific data solutions. This kind of elasticity definitely will kindle the interest of your management .
Organizational transformation

Are you talking the language of successful people?

Now, we’re not talking about using a new language. It’s about the universally known language — numbers and statistics to impact how others understand your business proposals.

Thats right! Numbers are critical in decision-making. It will be much better if you have data about how your competitor benefited from data modernization or even how your industry is benefiting. Many research organizations like Gartner and PWC publish free articles that you can use to substantiate your goals.

Also, when you explain your proposal to modernize your data setup, you must explain what the project will mean for your organization regarding numbers.

The best thing to do is to talk about how much you will save costs through data modernization. Any decision maker would need to know about your company’s return on investments (ROI) before they nod to anything new or consider the proposal brought to them.

How do you plan to achieve data modernization?

It’s time to talk about the crux of it.

What data solution are you proposing?
Delivery method – Is data modernization going to be achieved by your own company, or does it have to be outsourced?
Get a full-length program plan if your company will do the data modernization.

For instance, if you are doing a database migration.

Done! Now that you have everything you need to get your top management interested in data modernization, you are all set to go!

Leverage data analytics to maximize marketing ROI

Alex Thompson Data and AI February 1, 2023

Why is data analytics needed in marketing?

Data analytics is an essential component of marketing. Insights derived from marketing data help organizations understand what drives their customers to behave a certain way and refine their marketing strategy to get maximum return on investment (ROI). Marketing analytics also plays a significant role in understanding the performance of a product or service.

For instance, a specific product might do well only in certain regions and among particular demography. This can be understood and dealt with only if we have the performance reports we can get from analyzing the product’s data. This data can be collected via surveys, feedback, and polls filled in by the product users.

Data connected through various channels can then be put together using analytics to help the product team understand where the product’s benefits and drawbacks lie. Identifying such information helps to know the tastes and requirement of specific audiences and use it to create better products or services in the future.

Benefits of data analytics in marketing

Various marketing channels like email marketing, web marketing, and content marketing can be better understood and refined with the responses received for that method of marketing, which in turn improves the strategy or ideas to maximize the return on investment at each stage.

Here is a guide for marketing teams to leverage analytics:

Leverage data analytics to improve marketing ROI

Overall, data analytics is a powerful tool to help businesses better understand their target audience, identify and capitalize on trends, test and optimize marketing campaigns and track ROI effectively. It also helps identify any bottlenecks in the current marketing strategy.

Using data analytics in marketing wisely and effectively can increase brand awareness among consumers, bring in more revenue and effectively increase marketing ROI.

How To Reengineer Data To Make Better Decisions

Alex Thompson Data and AI January 5, 2023

What is Data Reengineering?

In any organization, data’s primary purpose is extracting insights from it. Insights drive the decisions of management for progressing the company. Since businesses started digitalizing rapidly, data generated from business applications has also snowballed.

With these changes happening to the way of doing business and data coming in various forms and volumes, many data applications have become outdated and hinder decision-making.

So, the process of changing existing data applications to accommodate the vast volume and variety of data at a rapid velocity is called Data Reengineering.

Why Data Reengineering?

There can be several scenarios where we need to reengineer the existing application. Here are some:

How to Reengineer Data Projects

Here’s how you can reengineer data:

Choose the Right Infrastructure Setup

This is an important decision that the engineering team has to make. Choosing the right infrastructure will make the newly reengineered application capable of storing and processing data more effectively than the legacy application.

AWS, Azure, and GCP provide Infrastructure-as-a-Service (IaaS) so that companies can dynamically scale up or down the configuration to meet the changing requirements automatically and are billed only for the services used.

For example, we have an Azure Data Factory pipeline that populates about 200 million records into Azure SQL DB configured to the standard service tier. We observed that inserts took a long time, and the pipeline ran for almost a day. The solution for this was to scale up the Azure SQL DB to the premium service tier and scale down when the load completes.

So, we configured the rest API in the pipelines to dynamically scale up to the premium tier before the load starts and scale down to the standard service tier once the load is completed.

Select the Right Technology

Technical software stack needs to be chosen based on the reengineering your company is doing. You can choose from various technologies based on the type and volume of data your organization processes. Below are some examples:

If the change is from mainframe to other technology, you can choose Oracle on-premise or cloud. Here Informatica or similar tools can enable ingestion and orchestration, and Oracle’s in-house language PL/SQL can be used for the business logic.
If the change is from on-premise to cloud, AWS, Azure, or GCP provide Software-as-a-Service (SaaS).

Design the Right Data Model

During this reengineering phase, you must determine how best the existing data model can accommodate the new types and volume of data flowing in.

Identify the functional and technical gaps and requirements. When you analyze and understand your data, it can result in one of two scenarios.

You will identify new columns to be added to existing tables to provide additional value to the business.
Identify new tables and relate them to the existing tables in the data model. Leverage these new tables to build reports that will help your business leaders to make more effective decisions.

Design the Right ETL/ELT Process

This process involves reconstructing the legacy code to be compatible with the chosen infrastructure, technology stack, and the redesigned data model.

To populate data to the changed data model, your development team needs to incorporate appropriate extract and load strategies so that data can flow schemas at a high velocity and users can access reports with less latency.

Designing the ETL/ELT is not just a code and complete job; You must track the development progress and versions of code properly. Create some information sources to track these, like the ones shown below:

Milestone Tracker: The reengineering project needs to be split into development tasks, and these tasks can be tracked using any project management tool.
Deployment Tracker: This can be used to track the physical changes of schema and code changes.

Once the development efforts are complete, plan a pilot phase to integrate all code changes and new code objects. Run end-to-end loads for both history and incremental loads to confirm that your code is not breaking in the load process.

Validation and Verification

This phase ensures data origination from existing and new sources is populated according to the business logic.

Every micro-frontend application must have a Continuous Delivery Pipeline (CDP), so it can be built and tested separately. It should also be able to get into production independently without any dependencies. Multiple smaller micro-frontend applications in the production can then be composed together into one large working application.

Conclusion

Once all the above steps are completed, you will get to Day Zero. Day Zero is when you take the reengineered solution live to production and do your sanity checks. If everything is working as expected, sunset the legacy solution. Now you can rest assured knowing that your data infrastructure empowers your leaders to make the right decisions on time and accelerate the growth of your business.

DevSecOps and Data Science

Alex Thompson Data and AI August 19, 2022

Before understanding DevSecOps and how it pertains to data science, it’s crucial to grasp the concept of DevSecOps and how it differs from DevOps. DevSecOps is a revolutionary approach to automation, culture, and platform design while integrating security throughout the entire IT lifecycle.

Data has become a significant part of all business operations, and it’s become nearly impossible to operate a successful business without analyzing and using that data to make critical business decisions. Today, the combination of information technology and software development is the future of DevSecOps.

DevOps vs. DevSecOps

DevOps doesn’t focus solely on development and operations departments. DevOps is well-known for agility and responsiveness, but if you want to take full advantage of the DevOps approach, you must integrate IT security.

In the past, security remained isolated to a specific team, present only in the final stages of development. Development cycles used to last months (sometimes years) but now that efficient DevOps practices ensure frequent and rapid development cycles, security throughout the process has become imperative.

If your security practices are outdated, your DevOps will not move along as smoothly as you’d like. When collaborating with DevOps and security, you can create a strategy that encourages shared responsibility integrated throughout the entire IT lifecycle. Security in every step is a crucial mindset, and DevSecOps emphasizes the need to build security into the foundation of all aspects of your business processes and initiatives.

DevSecOps means employing security in your application infrastructure from the beginning. Automating certain security gates will keep the DevOps workflow from slowing or stopping creating agile practices and IT operations. By selecting the right tools to integrate security consistently, your company can build on the cultural changes that DevOps brings, integrating security as soon as possible.

DevSecOps and Automated Built-In Security

Regardless of what you call it, DevSecOps (or DevOps) has always been an integral part of the entire life cycle of an application. DevSecOps focuses on built-in security, not security that functions around data and applications. If you save your security features for the end of the development pipeline, you’ll find your business stuck in the long development cycle you were trying to avoid in the first place. It takes a substantial amount of time to go back and apply security once development is complete.

DevSecOps emphasizes the need to bring in security teams and set a plan for security automation. It highlights that developers should write code with security in mind, sharing visibility, feedback, and insights into known threats like malware.

A great DevSecOps strategy determines a business’s risk tolerance to fully comprehend which security controls are necessary within a given application. Automating repeated tasks is essential to a successful DevSecOps plan because running manual security checks can be incredibly time-consuming.

Data Science and DevSecOps

Overall, the concept of DevSecOps is not new for a data scientist. Many data scientists adopt DevOps into their daily work lives, such as testing algorithms for validity, and the presence of DevOps practices provides more reliable results. Data scientists can save time by honing a consistent process that continuously increases accuracy.

It’s undeniable that DevSecOps is forever increasing in need and popularity. Many companies offer foundational knowledge programs to assist other businesses in developing a solid sense of DevSecOps throughout the IT lifecycle, encouraging them to begin utilizing these “security throughout” ideas in their careers.

Security can exist alongside a DevOps culture. Still, it takes a bit of work and company-wide communication to get everyone on the same page. For example, suppose a data scientist is already familiar with the concept and processes of DevOps. In that case, it’s not challenging to employ the idea of DevSecOps as it applies to data science, but business leaders must clearly communicate the ideas behind the concept.

Data Science and Automation

It should go without saying that data science is a particular field. Though most modern data scientists feel comfortable using automation, that wasn’t always the case. For a while, the fear that automated processes would cause inaccuracies in data was prevalent, but as artificial intelligence and machine learning continues to improve, their use is growing substantially.

Today, automation is a significant component of DevSecOps, and data scientists that choose to use DevSecOps must be comfortable with automated processes, as it’s the best practice for methodology. Data scientists often run automated scripts when attempting to understand what a large influx of data contains and when dealing with quality assurance.

Not all data scientists deal with the same type of data. For example, data scientists that work with terrorism and fraud require automation to avoid falling behind in studying an influx of crucial data. Generally, data scientists always place plenty of focus on security, regardless of the type of data they’re responsible for, even when not an official member of the company DevSecOps team.

Due to a high level of security concerns and knowledge, data scientists tend to fall easily into DevSecOps roles. Many employees and team members will need constant reminders when implementing a DevSecOps business model, but data scientists rarely forget to include the security component.

DevSecOps and the Inevitable Emphasis on Data

Business operations today, regardless of industry, emphasize data. Data has become an integral part of how businesses run, from providing essential consumer demographics to pointing toward potential security breaches or weaknesses.

Global internet users understand that they cannot use a website or social channels without sharing information. It’s become entirely acceptable, as long as the companies that receive that information store it and share it responsibly.

However, data breaches are not uncommon, and when associated with massive social sites like Facebook and major retailers such as Target, people tend to become wary. The application of DevSecOps principles can assist data scientists in helping to promote privacy and security for companies (like social media giants) to keep up with the constant evolution of technology while keeping data safe.

Data Science and the Benefits of DevSecOps

Knowing the benefits of DevSecOps is crucial to understanding how it ties into data science practices. While data scientists often embrace DevSecOps practices without being a part of the internal “team,” there are still many advantages to learning and applying DevSecOps to the daily workflow, including:

Data scientists can benefit greatly from integrating a DevSecOps mindset. Not only does it place security at the forefront, but it keeps it present at all times, regardless if the task is automated or manual.

An Awareness of DevSecOps

All data scientists should be aware of the concept that is DevSecOps. There’s an undeniable influx of data consistently coming into every company worldwide, ranging from consumer statistics to potential data risks. Data scientists need to understand the notion and gain full awareness of what it means to apply it.

Most data scientists already work under strict security measures, but regardless of how they work with data, the principles of DevSecOps can apply and enhance their current techniques.

Getting Started with Feature Transformation for Machine Learning

Alex Thompson Data and AI August 16, 2022

Machine learning is a modern yet essential piece of the digital transformation and data analytics processes. On the other hand, feature transformation is the process of modifying data but keeping the information that data provides. Data modifications like these will make understanding machine learning (ML) algorithms easier, delivering better results.

This article will discuss the importance of feature transformation, a crucial step in the preprocessing stage. Feature transformation allows for the maximum benefit of the dataset’s features and the long-term success of the application or model.

Applying various mathematical techniques to existing application features can result in new features or feature reduction. Modifying existing data and increasing the available information and background experience can increase the model’s success by keeping the information constant.

The Need for Feature Transformation

You might find yourself asking why feature transformation is necessary in the first place. The need becomes more apparent when you understand that if you have too few features, your model will not have much to learn from, while too many features can feed a plethora of unnecessary information. The goal is to be somewhere in the middle.

Data scientists often work with datasets that contain various columns and different units within each column. For example, one column might be centimeters while the other is kilograms. So you can see the range, we’ll use another example of income, with columns ranging from $20,000 to $100,000 or more. Age is another factor with many variables, ranging from 0 to upward of 100.

So, how can we be sure that we’re treating these variables equally when dealing with machine learning models? When feeding features to a model, there is a chance that the income will affect the result because it has a more significant value. However, it doesn’t mean that it’s a more important predictor. To give importance to all variables, feature transformation is necessary.

How to Identify Variable Types

We’ve touched on how feature transformation can affect variables’ effect on an outcome, but how can we determine variable types? We can typically characterize numerical variables into four different types.

When you begin a project based on machine learning, it’s essential to determine the type of data in each feature because it could severely impact how your machine learning models perform. Here are four variable types in feature transformation for machine learning.

Data Preparation

Feature transformation is a mathematical transformation, and the goal is to apply a mathematical equation and then transform the values for our further analysis. Before we do this, however, it’s crucial to prepare the data you’ll be changing.

Analyzing data without preparation is impossible, and you can’t apply genuine feature transformation without examining. So, here are the steps you should take to prepare your data for feature transformations.

The Goal of Feature Transformation

The goal of feature transformation is to create a dataset where each format helps improve your AIML models’ performance. Developing new features and transforming existing features will significantly impact the success of your ML models, and it’s important to think logically about how to treat your prepared, collected data and the current list of variables you have.

When you enter the model-building phase, you should go back and alter your data by utilizing various methods to boost model accuracy. Collecting and taking the time to ensure your data is ready for transformation will reduce the time you spend returning to the transformation stages.

Feature transformation will always be beneficial for further analysis of collected data and changing how our machine learning models operate. Still, knowing how to prep your data and categorize it is crucial, so your transformations provide accurate, helpful, eye-opening results.

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