Bridging the Data Democracy Gap with Self Service Analytics

by July 21, 2020 0 comments

Data Science

Every analytics professional is a data scientist, with their own data playbook, even those who don’t have any hard coding, modelling expertise.

The complexities surrounding big data, processes and tools have led to more efficient utilizing and management of large data sets. Enterprises have recognised the value of data as a critical business asset to identify trends, patterns and preferences to drive improved customer experiences and drive competitive advantage. The problem is, users too often can’t find the data they need to perform desired analytics. Data trends to be buried in different systems or siloed in departments across the organization.

Real-time data, KPIs and performance metrics matter to businesses now more than ever. Users can’t wait for a custom report or dashboard. They don’t want to see yesterday’s data or to go to another application to perform analytics. They expect to perform analytics and visually explore data inside your application.

 

Driving Business Decisions

In data-driven organizations, self-service analytics or business intelligence (BI) systems enable nontechnical users — such as executives or marketing staff — to access data, perform queries, and generate reports so they can make effective business decisions.

In organizations with traditional analytics and BI systems, when a nontechnical user wants to run an analysis or generate a report, they submit a request to a data professional, who examines the request, locates relevant datasets, constructs and runs a query, validates the results, and creates a visual report. The process might take as little as a couple of days or as long as several weeks.

Self-service analysis tools, on the other hand, automate much of this process and allow nontechnical users to explore data and share visualizations, while maintaining security protocols to protect sensitive information.

 

Self-Service Analytics (SSA) tools for the Enterprise

The industry has seen a surge of self-service analytics (SSA) tools such as Tableau and Qlik that enable analysts and non-technical business users to gain insights and drive data-focused initiatives. SSA and BI empowers knowledge workers and business users to gather desired insights without reliance on IT to run reports.

However, investing in analytics tools alone can’t deliver business value. For an SSA tool to do its job, companies need to ensure that the people using the tool can easily access the data they need across the organization – including siloed data living in various systems – and have full confidence in this data to apply it for greater business insight and results. Effectively integrating disparate data from different systems and devices requires a complete understanding of the organization’s “data map” and the data’s journey and relationship to other similar – or sometimes contradictory – data throughout the organization.

 

Building a Data Catalogue

If the data for which users are searching is not properly catalogued, the self-service tools will not yield valuable results. A data catalogue incorporated with machine learning delivers even greater insight and makes recommendations based on past user behaviours and “data purchases,” much like Amazon does for frequent shoppers. A catalogue makes it easier and quicker for users to find the data for decision-making, but also enables users to define models earlier in the process. This is particularly helpful for making changes on the fly, as in the case with last-minute requests in definitions or KPIs.

As organizations adopt more self-service tools for BI and expand their analytical capabilities, leveraging a data catalogue with these capabilities tied to data governance will give them confidence in knowing their business insights are based on trusted data. This is when we’ll start to see the true value of SSA tools in helping to drive business forward.

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