Skip to main content

Alleviate analyst data requests with easy-to-use self-service analytics. When non-technical business users can access the information in the data warehouse or lakehouse, they no longer need to disrupt data teams for information to make accurate in-the-moment decisions.

In this article you’ll find:

Leveraging business data is vital to running a company. Quantifiable metrics should drive critical decisions on a day-to-day basis, while higher-level analysis determines overall company direction. Data analysts are at the core of these data initiatives and are, therefore, integral to the trajectory of a successful company.

That’s why data analysis is one of the fastest-growing sectors in technology today. Everything we do generates and requires more and more data – and more sophisticated ways to use it. With an ever-growing demand for data collection, storage, retrieval, and access, data analysts have their hands full.

A recent article revealed that data teams spend upwards of 20% of their day dealing with data issues.

With everything they need to manage, sometimes it seems like analysts have no time to work on the main part of their job description: analysis. There simply aren’t enough skilled data professionals within today’s enterprises to meet the growing demand for data-driven decisions at scale. This is where modern self-service analytics comes in: helping data analysts and non-technical users access important information directly from their data warehouse to enable better insight-backed business decisions.

Analysts on the Move

It doesn’t take a thorough investigation to see that data analysis experts must accomplish more than ever. Increasing internal demands and resources stretched beyond your original intention can quickly add up to an exasperated data team. A recent article revealed that data teams can spend upwards of 50% of their time working reactively rather than proactively, conducting analyses and driving strategic growth for the business.

The constant need to be responsive to ad hoc demands (like creating custom reports) can frustrate all parties. You can’t utilize your time and hard-earned expertise to offer meaningful insight from the numbers in front of you when all your time is spent finding data, building data sets, and answering questions on-the-fly.

Think of the data in your lakehouse or warehouse in the context of an iceberg. The information users actually ask you to retrieve represents just the tip – what’s above the surface – of that giant data iceberg. But lurking below the surface is so much more! Below the iceberg are all the questions, queries, and subsequent data and insights that users don’t even ask about. Maybe they don’t know that data is even there or available to them. Maybe they don’t know the right questions to ask to uncover those deeper insights. Or maybe they’re simply trying to get things done and don’t feel they have time to wait for holistic analysis or ad-hoc reports.

Data Challenges

Analogy of a data warehouse and the amount of questions and insights that are never surfaced

When business users can’t see the big picture, they don’t know what this information could help them achieve. These non-technical business users may not know what kinds of questions they can ask or may not want to bother you with something that may appear “trivial.” They may not realize how deep the data goes and what insights they could gain by accessing it. Analysts like you know how much data is untapped and needs to be uncovered, but you spend all your time dealing with other requests, leaving you without the resources or opportunity to bring it to the surface.

As a result, other departments, like finance, operations, sales, and marketing, may have to tackle complex problems without relying on data-informed input, simply because you’re busy completing increasingly intricate day-to-day tasks.

This problem is not new. Other tools and resources have been created and adapted to help data teams keep up with exploding data demands. There’s no shortage of business intelligence and analysis solutions in the market to help you track and monitor important business KPIs and keep an eye on high-level metrics. You’ve probably adopted one or more of these tools and made use of them to varying degrees in your day-to-day workflows.

While crafted with the best intentions, these tools and systems are built for high-level business consumption. They’re not designed for detailed data exploration by non-technical business users, and they take significant time to integrate within the organization because you need to create custom data sets or facilitate data mapping between models.

With so many tools unable to deliver results at scale, it may seem like the only solution is to continue to grow your team of internal experts. But continuously adding new members to the data team is costly and ultimately inefficient. Instead of adding new experts to access and analyze more data, more people within the organization need to be empowered to access and analyze their data more easily.

Becoming more data-driven is an organization-wide problem that requires buy-in and investment from employees at every level of the organization. This democratization of data access is critical in enterprises today, but achieving it is no easy feat.

Finding the Answer with AutoQL

The natural ease of AutoQL can solve your data access and efficiency dilemma.

When business users come to you with data needs, they often have a business question (or multiple), not necessarily a specific data request. It’s your job to determine how data can answer that business question and what data is most relevant to the use case at hand.

In these cases, you use database query language such as SQL to pull the relevant data and provide it to the querent. If you’re using a BI tool, you’re likely querying against pre-configured data sets that aren’t usually comprehensive or fully representative of the data in your data warehouse or another source. This process works some of the time, but it takes time to learn, and because you’re querying against datasets, you may not be able to easily access all of the information you need to fulfill the request.

AutoQL removes frustrating guesswork and back-and-forths by taking advantage of a skill everyone already has – a user’s natural language, or more simply put, the words they use to communicate every day – and enabling them to query their data warehouse directly.

AutoQL for Modern Data Analyst Teams - Chata

Example of how a business user would converse with AutoQL

With AutoQL, users can ask questions in natural language. AutoQL automatically translates their words into a database query language statement applicable to their data source and executes that query against the database in real time. AutoQL then returns a response that answers the query while providing helpful visuals, all within seconds.

A Win for Analysts

For your data team, implementing yet another digital solution and having to train employees on its capabilities and functions can become a burden on a group that’s already overworked.

That’s why designed a system that allows an organization to break free of the implementation and training roadblocks. In fact, it takes only 5 hours of a data team’s internal support and implementation to get an initial model running for testing purposes – less than a full workday. You and your team will quickly understand how AutoQL functions and where it can fit best in your workflow. Better still, you won’t need to spend endless hours helping or training other departments on how to use yet another complicated BI tool.

AutoQL can be the perfect digital solution for time-strapped analysts and the data-hungry business users they work to support. It recognizes and responds to human language, allowing every decision-maker – regardless of technical aptitude – to explore and analyze the data with unprecedented ease and speed.

Achieve Scalability

As with many things in life, the less complex something is, the easier it is to adopt. The same can be said of digital solutions.

The true beauty of AutoQL lies in its simplicity. Users don’t need to write query language or take part in extensive BI onboarding and data literacy sessions. All anyone needs is natural language and a question, two things everyone has at their disposal, no matter the size and scale of the company.

The vast potential of AutoQL offers more scalability as data needs change and evolve. Hiring another analyst every time business booms isn’t always feasible, and more work often ends up on the desk of existing analysts.

Instead of trying to scale the adoption of complicated tools that require hours of employee training and data analyst implementation, Chata offers a different approach: one that’s built to alleviate the pressure on data teams, while empowering business users with reliable access to their data.

AutoQL provides a simple interface that enables data access using a conversational language approach that is familiar and intuitive for every user. This efficient, easy-to-use, and accessible tool allows data teams to invest less time dealing with data issues and responding to ad-hoc requests and spend more time on focused in-depth analysis that supports higher-level business objectives. With the time and money saved by using effective solutions like AutoQL, the business can invest more resources into new data-driven ideas and innovations – without putting more on your plate.

Keep Your Focus Where You Want

As an analyst, you likely take on more tasks than your job description implies. Sometimes, your technological and logistic abilities may turn you into a pseudo-IT department. If the company you analyze for isn’t intuitively data-driven, you may find yourself answering questions that take time away from the data questions you need to answer for the company as a whole.

AutoQL can clear this up, too. When more people in a company know how to find and view the data they need, they’re more likely to ask the more complex data questions they’ve been wanting to know themselves. This takes the weight off overburdened data teams by democratizing data access across business units.

How a Data Analyst spends their time- AutoQL by Chata.aiImage source:

This self-service shift frees you to do what you do best — analyze. With more bandwidth and fewer menial tasks on your checklists, you can focus your energy on bigger-ticket items, like improving data governance or providing strategic guidance regarding more complicated data-related problems facing the business at large.

Discover the Power of AutoQL with

As a data analyst, you are a critical player in creating and growing a data-driven tomorrow. Equipping your team with tools like AutoQL is a valuable part of enabling greater scalability of resources, higher adoption of self-service tools by non-technical users, and a better data-driven culture of decision-making across the company.

AutoQL has the power to make data accessible to all areas of a business, clearing the way for more meaningful and well-informed decisions and plans. In addition, it gives precious time and resources back to the data analysts that a business relies on for large goals and big-picture planning.

Chata is on the front lines of data access using a unique form of democratization. Our team of experts is ready to show you how AutoQL can optimize the way you perform data analysis for the better. Visit our website to learn more about how Chata can create the ideal win-win data scenario.

Erica Lister

Author Erica Lister

More posts by Erica Lister