Copilot for Data Analytics

by Ramesh Panuganty, Founder & CEO

Copilot for Data Analytics

by Ramesh Panuganty, Founder & CEO

A copilot provides unconditional support to the pilot in navigating the plane, following protocols, executing orders, and providing accurate information immediately so that the captain can make confident decisions for the safety of the flight. Think of Aaron Eckhart’s character of First Officer Jeff Skiles in the film “Sully”. When their plane develops problems, he immediately swings into action to support Captain Sullenberger by providing metrics, running checklists, verifying various statuses, and supporting in whatever way he can for the captain to arrive at an important decision.

When it comes to taking quick actions under high stress, real-life boardroom situations aren’t far behind. Business decision makers also need a copilot to navigate high-stake situations, tackle crises instantly, identify potential red flags beforehand, and work efficiently. Copilots for Data Analytics fulfill this need and ensure that business decision makers cruise efficiently through their daily work and decision making.

What is a copilot for data analytics? How do copilots impact data analytics?

A copilot for Data Analytics is an Artificial Intelligence (AI) assistant that helps users automate time-consuming manual processes, converse with enterprise data in simple language, extract and present actionable insights in easily understandable narratives, and personalize the information experience. The AI copilot integrates powerful capabilities of Generative AI, Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLMs) to extract and deliver insights. A copilot for data analytics plays many roles — an efficient data analyst, a personal business assistant, an always-available advisor, a chief enabler, and an interactive aide.

Data analytics has come a long way from being restricted to technical analysts to encouraging self-service analytics by non-technical business users. Copilots further simplify data analytics by making data more accessible, data querying more conversational, and insights more actionable and consumable. For example, users can ask “Why did demand for Product ABC increase in Q1” and get instant answers in the form of text summaries and best-fit visualizations.

In addition to data analytics, the rise of AI copilots is seen in other sectors too. Copilots such as GitHub copilot, Amazon CodeWhisperer, and Microsoft copilot have proven to be valuable in generating and reviewing code, providing code and writing suggestions, and generating texts and content assets to improve user productivity and minimize errors. A GitHub research showed a 30% improvement in developer productivity with the use of GitHub copilot. The use of copilots across sectors have shown faster rate of work completion, conservation of mental energy used in repetitive tasks, and improvement in work satisfaction and focus.

Maximizing data analytics efficiency by leveraging copilot’s dynamic features

From data preparation to insight generation, a copilot for data analytics optimizes the process for uncovering meaningful insights from raw data, thus reducing the workload of data engineers and analysts, and making business users self-reliant for their information needs. Here are some ways copilot’s features help maximize the efficiency of data analytics:

Curate and enrich data automatically

Copilot can help enrich enterprise data in terms of improving its metadata, adding synonyms, generating meaningful descriptions for data rows and columns, identifying relationships between them, and cataloging and curating data for effective and accurate analysis.

Provide suggestions to improve quality

The quality of data directly influences the quality of insights. Copilots can be used for reporting on data’s quality and providing suggestions to improve it in terms of completeness, clarity, interpretability, consistency, and accuracy.

Ask questions in simple language

Users don’t have to struggle with complex syntax and SQL queries to find insights. Using conversational language and intuitive interfaces, copilots enable users to ask questions directly to their enterprise data. Copilots can also generate search suggestions automatically from enterprise data, thereby simplifying the search process. This democratizes data and makes insights accessible to everyone in an organization.

Leverage LLMs to parse human language queries better

Human language often comprises jargons, colloquial terms, unstructured phrases, non-standard grammar, and words with multiple meanings and contexts. Copilots leverage the benefits of LLMs to parse human language queries, identify the correct context to provide relevant insights, and clean unwanted content.

Create narratives and audio-visual data stories

Data analytics copilots can create insight narratives, textual summaries, audio-visual data stories, interactive presentations, and engaging visualizations faster and better. Using these automatically generated content formats, users can interpret business situations easily, present and share summarized findings in a better way, and make decisions confidently.

Provide tailored insights and business headlines

Learning from user searches, usage patterns, past searches, business metrics, and interests, copilots can generate tailored insights and recommendations that are relevant to the user and cut down unnecessary data noise. Business headlines can be automated to generate and convey insights without having users to actively search for them.

Who can benefit from using copilot for data analytics?

Data analytics copilots empower many user roles within an organization to improve productivity and efficiency in their work.

Data Engineers and Stewards

Copilots help data engineers and data stewards to clean, organize, and prepare enterprise data. By automating time-consuming, manual, and recurring tasks, copilots can process data faster, identify and resolve issues faster, and free up the data teams from mundane tasks to focus on innovation and complex tasks.

Data Scientists and Analysts

Copilots enable data scientists and data analysts to identify relationships in data and curate data better to improve context, simplify business vocabulary, and add meaning to data in terms of auto-generated synonyms, user-friendly names, and meaningful descriptions. By identifying data quality issues and providing recommendations to fix them, copilots help analysts ensure that accurate data is always available for analysis.

Business Users

Copilots empower business users such as product managers, sales representatives, finance officers, marketing executives, and customer success agents to query data in a conversational manner, gain insights in real-time, and get personalized recommendations. Copilots also make business users self-reliant in creating dynamic dashboards and reports. By generating summaries of key findings, audio-visual data stories, and bite-sized insights, copilots help users to understand insights faster and better.

Potential use cases for copilot in data analytics

Here are some use cases where copilots can enhance business functions across organizations, with AI-Powered Analytics providing deeper insights and driving better decision-making:


Using copilots, sales managers can quickly compare sales across multiple dimensions such as time periods, regions, products and customer categories, know the most preferred products by customers, identify the products with highest profit margins, spot anomalous increase or decrease in sales, and so on simply by asking questions in natural language. Getting answers in real time can help them monitor sales, identify low and high growth areas, and take actions immediately.


Instead of wading through transaction reports, financial statements, and customer details, bankers can ask simple questions like “loans disbursed in Q1”, “what is the percentage of high net-worth customers”, “compare the deposits from 2018 to 2023”, “by how much did the savings deposits increase in the North West region”, “most preferred banking products in 2022” and so on using a copilot for data analytics. Copilots can also provide insights on creditworthiness, investment suggestions, and suspicious transactions.


Retailers are constantly finding answers to what customers want, which factors influence their buying habits, and how and why their preferences change. Copilots for data analytics make it easy for retailers to find answers to all these questions. By asking copilots questions like “what are the top performing stores in 2023”, “which discount coupons were used the most in December 2022”, “compare age segment of customers shopping in stores vs. online”, and so on, retailers can understand trends and service their customers better.

Customer Support

Copilots can give visibility into types of customer requests, frequently asked questions, and common issues faced by customers. These insights can help customer support executives resolve issues faster, provide better guidance, and ensure higher customer satisfaction. Insights from customer support data can also provide valuable inputs and ideas to product development teams for improving products, customer journey, and overall experience.


It is important for marketing teams to target their communication effectively to their customers. Using copilot for data analytics, marketing executives can find out channels receiving the highest traffic, compare leads generated by different social media platforms, evaluate performance of campaigns, and track marketing budget and spends. By reading summaries of key findings, they can understand not only what happened, but also why and how it happened, thus getting the comprehensive picture.

Human Resource Management

Copilots can help human resource managers analyze employee needs and expectations, identify skill sets, evaluate training needs and outcomes, and measure performance. Using simple questions like “top performing sales reps”, “what is the ratio of developers to testers in a project”, “what was the attrition rate in 2021”, “how many employees are proficient in Python”, and so on, HR managers can get useful insights in managing and enhancing the workforce.

Benefits of using copilot for data analytics

AI-powered copilots for data analytics complement human capabilities of processing and analyzing data for deriving insights. The 2023 Forbes Advisor survey of business owners found that AI is perceived as an asset for improving decision-making (44%), decreasing response times (53%), and avoiding mistakes (48%).

Faster insights

Copilots simplify data analytics and help users uncover root causes quickly, receive actionable insights in real-time, gain complete visibility of business situations, and perform complex analysis easily.

Improved data-driven decision-making

Easily understandable, concise, and personalized insight summaries help decision makers identify trends, recognize patterns, and spot deviations better. This inculcates data-driven decision-making among users.

Ease of use

With their conversational and intuitive interface, copilots make it easy for business users, even without technical expertise, to interact effectively with enterprise data.

Accelerated data processing

Using copilot, users can automate data processing and cleaning, improve data preparation workflows, increase efficiency, and reduce time to deliver good quality data for analysis.

Improved productivity

Copilots equip users with self-service analytics capabilities to take quick actions and work efficiently, thus saving significant time and resources in the insight discovery process.

Improved data literacy

By removing technical barriers and simplifying the insight querying process, copilots for data analytics empower users to perform their own analysis confidently, increase the rate of analytics adoption, improve data literacy, and encourage a data-driven culture within organizations.

Get started with MachEye copilot for Data Analytics

MachEye’s AI-powered copilot for data analytics is your personal business companion on your journey to data discovery and insight generation.

  • MachEye’s copilot starts its work as soon as you connect your data source by identifying, cleaning, and curating your enterprise data with its automated data catalog.
  • Based on your data, the copilot generates a list of search suggestions that could be useful for your business users to get started with their analysis.
  • MachEye’s copilot understands natural language and parses the search queries you enter in simple English using SearchAI, MachEye’s intelligent search solution.
  • Going beyond the answer to your queries, MachEye’s copilot automatically generates text summaries of insights, displays best-fit visualizations, and creates audio-visual data stories containing useful related information, actionable insights such as anomalies, trends, clusters, root cause or why analysis, business metrics, and influencing drivers.
  • By learning from your past searches and interest areas, MachEye’s copilot automatically generates tailored business headlines that are sent to you as soon as they are discovered in data, without having to wait for you to search. This way, you always stay abreast with the happenings in your business data.
  • The discoveries made by the copilot such as answers, insights, audio-visuals, business headlines, and so on are easily shareable with your teams, fostering a collaborative work environment.
  • MachEye’s copilot can also be embedded within existing business applications to provide a seamless work experience and increase users’ engagement with analytics.

MachEye’s copilot for Data Analytics simplifies analytics, gives more control to the users, reduces time-to-insights, promotes a data-driven decision making culture, and embeds seamlessly in existing applications.