Data-centric culture and the democratisation of BI

Published on the 01/02/2018 | Written by Jonathan Cotton


Data centric organisations

Nebulous though the term may be, the benefits of a ‘data-centric culture’ are manifold. But just how do businesses empower their data-savvy workers?...

There’s a lot to think about in the life of a CIO. Increasingly complex compliance requirements, the ever-present spectre of a data-breach, the endless call for ‘digital transformation’ – and let’s not forget about a little thing called growth – it’s little wonder CIOs are obsessed with finding better ways to leverage data.

After all, in 2018 data is power, but data alone isn’t enough. The key is how that data is used, a fact apparent given that most CIOs place BI development among their biggest areas of focus for strategy and investment in 2018.

Providers understand this and are increasingly focused on improving self-service analytic functionality in their products – and gaining funding for the same. Big-data startup Trifacta recently secured US$48 million in funding (of a total of US$124 million) to grow its smart BI offering, and last month self-service data analytics startup Dremio closed a $25 million funding round to develop it’s ‘democratic’ data platform.

It’s an issue Gartner touched on in last year’s CIO survey, predicting that by 2019, the analytics output of business users with self-service capabilities will surpass that of professional data scientists.

“The trend of digitalisation is driving demand for analytics across all areas of modern business and government,” Carlie Idoine, Gartner’s research director said at the time.

“Rapid advancements in artificial intelligence, Internet of Things and SaaS analytics and BI platforms are making it easier and more cost-effective than ever before for nonspecialists to perform effective analysis and better inform their decision making.”

Welcome to the ‘data-driven culture’, a concept whereby companies can expand their data and analytics capabilities, making it easier for non-technical users to access and act on collected data, leveraging strength in numbers, and helping employees make better business decisions faster.

There’s certainly a lot ot be gained and the promise of frictionless evidence-based decision making, the easy identification of new opportunities and a universally digitally-savvy workforce is driving many to put the development of their own democratic BI solution front and centre.

But while simple in concept, such a task can be challenging in the execution. So what are the considerations?

“If data and analytics leaders simply provide access to data and tools alone, self-service initiatives often don’t work out well,” says Idoine.

“This is because the experience and skills of business users vary widely within individual organisations. Therefore, training, support and onboarding processes are needed to help most self-service users produce meaningful output.”

Making data more accessible to core business users also means greater attention to data governance to ensure basic functionality (i.e. that users aren’t overwhelmed with irrelevant data) and to make sure that regulations around data privacy and use aren’t breached.

This can involve a formal process of establishing benefits and potential risks, and establishing a code of conduct for the use of data and the resulting analytics.

“IT leaders must find the right balance of governance to making self-service successful and scalable,” says Idoine. “The success of a self-service initiative will depend hugely on whether the data and analytics governance model is flexible enough to enable and support the free-form analytics explorations of self-service users.”

The next step is being crystal clear on exactly what outcomes are expected and desirable from such a project.

“Start gathering KPI data,” says Sean Higgins, co-founder and business development director at ilos.

“You manage what you measure. If there are metrics that are important to your business that you don’t know off the top of your head then they must not be that important. To create a data-driven culture, make sure the leaders on your team know what they’re being evaluated on and how to measure it. Then at your all-hands meeting, present the numbers from last week and this week.”

One thing seems certain: Self-service BI will continue to evolve as machine learning and AI comes to the table, improving data preparation, discovery, analysis and prediction.

“The latest self-service capabilities are certainly a step in the right direction, but executives, line-of-business leaders and data and analytics professionals know that there’s still progress to be made,” says Doug Henschen, researcher for VP and principal analyst for advisory/analyst firm Constellation Research.

“The next breakthroughs in business intelligence and analytics will see ML and AI used to improve data access and data quality, uncover previously hidden insights, suggest analyses, deliver predictions and suggest actions,” “What’s more, natural language (NL) interfaces will make it easier for business users without knowledge of data science or query languages to gain insights and make better decisions based on data.”

For more: How to Enable Self-Service Analytics and Business Intelligence: Lessons From Gartner Award Finalists

Research: How Machine Learning and AI will Change BI and Analytics

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