Digital Transformation

How Self-service Analytics is Powering Insight-led Organizations

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Shiladitya Bagchi

Subject Matter Expert, Analytics

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Key Points

To gain tangible business value from their data, businesses must implement self-service analytics and make high quality data readily available to users across functions
Data literacy and organizational collaboration will be key to successful implementation of self-service analytics
Using a step-by-step approach, companies can create a self-service culture without compromising on security and compliance

As data continues to proliferate as a result of digital technologies, are companies tapping into its potential effectively? According to Forrester,¹ “between 60 and 73 percent of all data within an enterprise goes unused for analytics.” This means while companies have taken measures to implement data capture and storage, many are still struggling to realize its full value with effective analysis and incorporating data-led insights into decision-making. A primary reason for this is the dependence on the IT department or a centralized data team, or in some cases, third parties to provide actionable insights.

This is where self-service analytics is enabling companies to drive a strong data-led culture. So how does one define self-service analytics? In a general sense, it is the system in which employees across various lines of business can access and work with data to generate insights, and spot trends and opportunities without relying too much on data scientists, IT or larger data teams.

The market for self-service analytics is projected to reach USD 7,998.2 Million by 2026.² IDC predicts that by 2024,³ 80 percent of digitally advanced organizations will have a self-service model in place of the IT as-an-enabler model. Executed properly, this transformation allows all functions within an organization to make decisions more quickly based on accurate data. The result is greater agility, and an ability to adapt to market conditions and exploit opportunities while managing risks more effectively.

Case in point: by introducing self-service analytics, a leading US bank was able to identify True Name Fraud (TNF) more quickly and accurately. By cutting down TNF cases by ~40 percent within a year, the bank was able to curb revenue losses by ~USD 1.2 Million.

Hurdles to Implementation

Implementing self-service analytics comes with its own set of challenges. A perceived shift of power from the IT department to individual business units can cause friction. Business leaders should emphasize that rather than being sidelined, this change offers IT teams the opportunity for new ways of collaboration. IT teams can co-create the data glossary and guide users to gain autonomy. They can also conduct rigorous validation of the analytics methodology and output before adding data to the system of record.

Connected to this challenge is data literacy. In order to leverage the full value of data democratization, users must be sufficiently data literate. The IT department has a role to play in training, upskilling and supporting teams that will leverage self-service analytics.

Data complexity is constantly increasing with more unstructured data from disparate sources entering the system. When implementing self-service analytics, many organizations struggle to decide whether to develop a single source of truth by creating a data lake or warehouse, or by using data virtualization software on existing data silos, before ingesting it into the integrated self-service analytics platform. It’s useful here to have an independent assessment of the current data systems by experienced professionals⁴ who can recommend a solution and help in the implementation.

It’s imperative to integrate self-service analytics into existing systems being used by employees. If employees learn to analyze data on one system and then are compelled to use a different application for business requirements, they are likely to shy away from using it. Integrating self-service analytics applications into existing systems will also help in avoiding duplications and ensuring a comprehensive strategy while enabling single customer view.

Road to Implementation

In order to create a culture of self-service without compromising on security and compliance, organizations should take a number of steps. First, they should prepare the self-service glossary. Also referred to as self-service data governance, this involves defining the master / meta-data, that is the data format, the data control and data access, among other elements, to ensure that data handling is standardized across business units and geographies.

Figure 1. Self-service Analytics Framework

Figure 1. Self-service Analytics Framework

Next, they have to finalize the data ingestion method. As mentioned earlier, organizations have options such as data lake / data warehouse or data virtualization software to create a single source of truth. Once a decision is taken on this, access can be provided to each line of business based on the data glossary defined and documented for further data wrangling / imputation activity.

Another key consideration is the way in which different users access the system. Typically, users might perform data discovery according to a particular report or Key Performance Indicator (KPI). They might also incorporate a variety of advanced analytical models such as regression, classification or association depending on how they plan to use the data.

If access options are developed effectively, the drag and drop functionality in self-service analytics tools will deliver the required insights. This makes it quick and easy to build a system that is accessible and appropriate for users of each line of business.

A data marketplace can host all these components to ensure reusability of glossary, connectors, models and reports. This ensures quick turnaround for queries related to insights, making organizations more responsive to market patterns with accurate interventions.

For instance, a leading Media and Entertainment (M&E) company leveraged self-service revenue forecasting models so that its analysts could focus their attention on core work. This helped the M&E company reduce its monthly model refresh time from five days to a few hours while also enabling the tracking of KPIs that had previously gone unnoticed.

Future of Self-service Analytics

A survey by McKinsey⁵ shows that 43 percent of high-performing organizations allow data to be broadly accessible to frontline employees whenever needed. The move towards digital transformation is one of the key drivers of self-service analytics. As COVID-19 exerts a profound influence on all businesses with changes such as remote working and more online interactions with customers, this trend is set to accelerate.

Increasingly, as technology leaders around the globe explore additional interventions to be included in self-service analytics, chatbots are likely to extract reports based on keywords through text as well as audio queries. Similarly, GPS-enabled systems will extract geo-specific data based on the location of the device accessing the self-service analytics platform.

As it becomes more common, self-service analytics will empower users across the organization to take speedier insight-led decisions, thereby helping businesses to derive more value and deliver superior outcomes from their digital investments.