In today’s business climate, using data to make quick decisions is a common ask across organizations. To fulfill such asks, business users want more, faster, and better access to data and analytic tools. IT wants to balance this need for speed with the responsibility to protect the data assets from security, privacy, and quality risks. A common solution to this scenario is self-service analytics which has been around for at least two decades. Yet, today’s needs cannot be fulfilled by yesterday’s solutions.

Over the last decade, people have become more data savvy, and technology companies are constantly innovating and changing narratives in a highly competitive environment by introducing new tools. But to achieve self-service nirvana, customers must think beyond business intelligence tools. Through our recent webinar and a series of related blogs, we offer our point of view on the “New Self-Service Analytics” based upon our firm’s 15-year history and recent research and experimentation done within our Centers of Excellence.

Demystifying Self-Service Analytics

Self-service means different things to different people. To level set what “self-service” really means in the industry, we need to consider answers to three questions.

The first question is WHAT does self-service do? The goal of self-service is to make it easier for anyone to do any number of things for the end user across the data-to-decision lifecycle. For example, starting from using tools for reporting, to exploration, or to even advanced use cases like mashing-up and wrangling with data sets. Another key element is to CO-OPT users to do more on their part in that cycle.

The second question is WHY are we doing self-service analytics? There are multiple value drivers across the industry such as – reducing the time-to-decision, transferring labor effort from IT to business, or empowering employees to play with the data and generate their own insights. Ultimately, self-service is about increasing the overall productivity in your ecosystem.

Finally, what is the CONTEXT of the self-service? Is it for the consumer of analytics or for its developer? Is it for an individual or for a team? Most practitioners naturally think in terms of “users”. But there is another concept about making business units in large organizations “self-sufficient”. That is where the scale of self-service analytics gets interesting and challenging.

Challenges with Self-Service Analytics

In a typical organization, operational business units deal with today’s pressures. For example, most customers are responding to the public health, financial, and competitive challenges in the market. On the other hand, IT organizations must not only support business units with today’s operational needs, but also balance legacy systems operations with the pace of technology and data growth. The quest for self-service analytic solutions in this situation results in some interesting challenges for both business and IT.

The Business Perspective

The first challenge the desire to standardize around enterprise tools. As business users become more mobile across organizations, they often want to bring their own tools. If a user is comfortable taking data and using Excel to do the analysis, what’s wrong in it? Should IT allow the user to use Excel or not?

Another challenge is with data comprehension. IT understands where data is stored and in what format, but end users often question if all the data exists and when they do see it, sometimes the way data is coded may not make sense to them. Since the data is growing rapidly, it is very difficult for new users to get a handle on the universe of data sets that exist in their organization.

Lastly, more often than not, sufficient time is not given to discovery. So, needs become wants, and soon business users start demanding features rather than expressing what problem they are facing. And IT may or may not persist with them to dig deeper and might just give in with a self-service tool.

The IT Perspective

But provisioning a tool is not enough. IT also needs more time from business for successful user adoption. None of the BI tools in the market, regardless of their claims, is 100% user friendly. Users need some level of support.

Another implication of legacy systems is about organizational inertia. The new folks in an organization want to modernize systems, and old guards want to rationalize the choices made in the past. The reality is we need the best of both worlds for any successful modernization. So, the perpetual modernization cycle tied to inertia creates more challenges in terms of lack of necessary skills for today’s needs.

Moving Beyond Tools

Breaking through these challenges requires more soft skills than technical skills. We feel it is time to revisit how IT and business collaborate and how self-service analytics are designed to deliver sustainable results. Through our next blog, we will offer a new paradigm for achieving better self-service outcomes and provide recommendations for making progress in different situations.


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Modern analytical applications face an increasing number of challenges. A key challenge is enabling end users to make sense of a world flooded in data. Introduction of new information, analyses, and data & analytics streams into the business decision-making requires developers of analyses to expand their roles – they serve as critical intermediaries between decision-makers and the flood of new incoming information.

If done incorrectly, an analytical application can create challenges for business decision-makers and introduce uncertainty. A poorly constructed analytical application can have serious repercussions, unnecessarily extending timelines as decision-makers grapple to make sense of the very information flows that were originally supposed to help them.

Intended end users don’t use nearly 55% of Analytical application due to lack of actionable insights. In most organizations, BI developers or business analysts choose the visualizations in dashboards/reports. Although they understand the data, occasionally they lack the skills to represent it visually.

Following are the common challenges observed in analytical applications:

To overcome these challenges BI developers need to adopt additional skills. The skill to understand the business use of data, an ability to tell insightful stories visually in a manner so that users can absorb data easily and quickly, and understand the capabilities and constraints of the BI tools used for creating the analytical application – The art of Data Storytelling.

What is Data Storytelling?

Data Storytelling skills are becoming an essential aspect of gaining and communicating quick insights from the data. It encompasses the entirety of the art and science of turning data and information into knowledge. Data storytellers are trained to develop a deep understanding of how to communicate stories using both visual and narrative aspects. Their skills are an essential part of modern-day business communication.

Storytelling consists of 3 key components – Data, Narrative, and Visualization. Data is the foundation of your story; narrative connects insights and adds emotion (which in turn helps humanize your insights); effective visuals simplify and explain any situation, regardless of complexity. Together these 3 components, make information easy-to-consume, memorable, and actionable.

A compelling visual story enables decision-makers to analyze complex data sets faster, allowing them to see patterns that are otherwise hidden behind a wall of numbers, grid lines, and boxes. It gives them the ability to see relationships among events that, on the surface, may appear completely disconnected.

Data Storytelling is More than UI/UX

To get effective data storytelling results, you need to use a wide array of tactics and methods across the three components mentioned above. One of the more obvious methods is the use of charts and graphs, colors, and layout choices required for presenting data visually. On the surface, this may seem like a straightforward process, but a lot of intelligence gathering and best practices go into these decisions such as understanding the needs of every decision maker and forecasting the desired outcome of the project. This requires people with a specialized skillset – those who not only understand the User Interface and User Experience design (UI/UX) aspects of the project, but also the science of visually representing stories from data.

There are four types of stories for derivation from data namely Descriptive, Diagnostic, Predictive and Prescriptive. We know these four types together answer everything a business needs to know – from what is going on in the business to what solutions to adopt. With the right choice of analytical techniques data experts can exact these insights from the data but may often struggle in representing these stories visually to influence the decision makers.

Storytelling ≠ Story writing

The below example depicts how the same data is represented as a “visual” to aid in discovery of insights and then to narrate a story for comprehension with the help of right visuals, narratives, and highlights.


Businesses are concerned about the value they extract from the data they collect. In order to do that data experts need to use the art of Data Storytelling to communicate the findings meaningfully with decision makers.

Know Your Audience

To be effective, data storyteller need to understand the business context of the analytical application. This allows them to combine the right elements in the right order for the right audience. For example, data storyteller should understand the context of the analysis (strategic, tactical, operational), the intended audience, the insights sought by the user, and more. A 360-degree view of the business dashboard for a CEO will be fundamentally different from an operational KPI-based dashboard for a sales manager and the data storyteller is expected to understand this thoroughly.

Combine Soft and Hard Skills

Data storytellers also apply their understanding of how people absorb information to the design process. The goal is to avoid information overload by presenting the right data elements using the most appropriate visuals. The goal of is to enlighten users, not to leave them in a state of confusion.

More specifically, data storyteller is a design professional with a combination of creative talents and analytics skills. They have an eye for detail. They possess extensive hands-on experience in user interface (UI) design tools like Adobe Illustrator and Adobe Photoshop—skills necessary for designing analytical application mockups. Data storytellers know how Business Intelligence tools work, and how they can complement visualization capabilities with the tools so that they can work with end-users and data & analytics developers alike and deliver visualizations through a collaborative, iterative process.

Data Storytelling is Here to Stay

Data storytelling expertise is an increasingly important skill to master in a world bursting to the seams with data, facts, and statistics. Without it, the ability to construct visual narratives and deliver crucial insights stands at risk of being diminished or even missed entirely.

Watch out this space for more on Data Storytelling!

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