How can Enterprises Succeed with Self-Service Analytics?
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How can Enterprises Succeed with Self-Service Analytics?

With technology evolving at a rapid pace, the sheer influx of data from business operations is ever-growing. Managing and analyzing these vast reservoirs of data is proving to be a challenge for analytics teams. They just don’t have enough manpower to analyze all the data being ingested, to be able to offer high-quality insights that can influence business decisions in real-time.

A Forrester report states that even though 74% of organizations wish to be data-driven in their operations, only 29% succeed at leveraging analytics for business decisions and the actual execution of operations.

This is why the introduction of self-service analytics is a turning point in business analytics – advocating for data democratization, where end-users without technical qualifications can access and analyze data.

What is Self-Service Analytics?

It is a practice where general employees can be engaged in leveraging BI tools to gather actionable business intelligence.

Self-service analytics best practices promote business growth from the ground up.

Business decisions are being made at every level of an organization, but seldom are they well-informed decisions. If employees have access to data that is relevant to them and are given the tools to analyze the data sets, they can gather actionable insights and make better-informed business decisions.

Improved decision making in turn enhances operations and the return on investment (ROI) for every team. And if every team generates a greater ROI, the business as a whole becomes more profitable.

The self-service analytics industry has been witnessing a transformation, with more and more companies entering the digital space. The demand for self-service analytics tools has skyrocketed, as organizations have started to realize the advantages of self-service BI.

But this beneficial system can pose unique problems without effective governance.

Risks of Self-Service Analytics

Once all the data is being handled by generalized personnel, there are always risks of not just data leakage and misinterpretation of data, but conflicting reports being created from the same data set.

This results in misunderstanding of data, insights becoming undependable and officials not knowing which data to rely on.

Data and business alignment have to be ensured by those responsible for data governance because without it, employees would be empowered but no real value will be gleaned from the initiative.  

Improving ROI with Self-Service Analytics

As talked about before, self-service business intelligence solutions enhance the capabilities of even the generalized employees to not just access but analyze business data and gather actionable insights. They can leverage these insights to influence business decisions in real-time and enhance the profitability of their department, in turn increasing the overall ROI of the organization.

Expenditure on self-service BI solution licenses and related hardware is easily quantifiable, and these remain the primary focus of organizations when the total cost of analytics solutions is considered.

But what is not considered, is the cost of employing IT personnel to execute and manage business intelligence solutions. Self-service models afford organizations the luxury of saving up to 60% on their investment per user, requiring organizations to invest lesser for a higher ROI.

Self-Service BI and Augmented Analytics

It is not just a rise in the amount of data being ingested, but the growing data complexity that is creating new problems for those trying to analyze the data and obtain actionable insights. Even though major advances have been made when it concerns analytics tools and their capabilities, certain tasks such as building business models or interpreting analysis results, demand human intervention.

This is where Augmented Analytics is revolutionizing the self-service industry, gaining momentum in recent years.

The serious dearth of expert Data Scientists has left a void in an ecosystem desperate for help with analytics. Augmented Analytics addresses this challenge by automating tasks that are usually time-consuming and biased.

The technology is a simple augmentation of human intelligence and contextual awareness, based on Natural Language Processing, Machine Learning, and Automated Insights. This enables employees to generate actionable insights through automation and make better-informed business decisions. 

Automation eliminates human error, saves time, and can even filter out insights that are irrelevant, optimizing the information being leveraged. With relevant and actionable data, generalized employees can now make well-informed business decisions that minimize risks and maximize the potential return from a venture.

How organizations can Succeed with Self-Service Analytics

With analytics capabilities evolving continually, there will be the eventual integration of Augmented Analytics and Self-Service Analytics tools. Automation and human intervention will work in tandem to operate a seamless data collection and analysis process, which will yield actionable data relevant to the employee. To function smoothly, there needs to be a collaborative work environment and the right people need to have the right tools at their disposal.

Organizations need to have a two-part approach if they want to succeed at this integration and have a streamlined self-service BI experience. Organizations need to acquire robust BI tools that will deliver results in both the short and the long term. They also need to inculcate an agile analytics culture, where employees have access to data relevant to their operations and not just generalized data for all. 

To get started on this journey towards a seamless self-service BI platform, companies need to sponsor projects and get employees enthusiastic and motivated to take up challenges and deliver high-quality insights. Simply deploying the BI platform is no good, if the employees aren’t trained to operate on the platform and manage vast data banks. Onboarding and training processes have to be in place to ensure employees can do their bit when operating the high-end BI solutions. 

All that is then left, is embracing a data-driven culture and the subsequent adoption of data stories, where employees can delve into analytics and BI tools to deliver truly actionable insights. And if the operation is agile enough, the organization will be successful in leveraging the insights to enhance business value in real-time. 

Course5 Discovery offers you access to AI-powered augmented analytics tools to help your organization become data-driven and create a work culture that values high-quality insights. Request for a demo today and digitally transform your business through automated analytics and insights that can be easily consumed in narrative form.

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Sushant Ajmani

Sushant is a seasoned digital analytics professional who has been working in the industry for over 23 years. He has worked with over 180+ global...

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