Managing the Data Chaos of Self-Service Analytics

December 17, 2015

Contributor: Rob van der Meulen

Business intelligence programs are at a tipping point. How can IT leaders balance the need to support self-service analytics while ensuring data integrity?

The proliferation of data, and the shift toward data-driven businesses, has had such a profound impact that every business is an analytics business, and every employee an analytics user.

“This poses a problem for business intelligence (BI) and analytics leaders,” said Cindi Howson, research vice president at Gartner. “Organizations need improved agility to respond to new data sources and new business requirements. A workforce with access to self-service analytics is a step toward this agility, but traditional BI programs are not ready for self-service.”

Gartner predicts that by 2018 most business users will have access to self-service tools, but that only one in 10 initiatives will be sufficiently well-governed to avoid data inconsistencies that negatively impact the business.

Learn More: Implement a solid data infrastructure that answers complex questions

To achieve BI success, the ability to access and combine data from new sources can now be more important than data quality. So how can BI leaders create enough order — from the chaos of high volumes and velocities of data — to empower employees without compromising data integrity?

  1. BI leaders need to expand their portfolio of BI tools. In many organisations, traditional BI platforms are already being augmented with more agile solutions, often purchased by individual business units. BI leaders should also introduce modern BI platforms into their organizations as they offer powerful new functionality including free form analytical exploration, the development of story boards to support insight and understanding and the sharing of insight in portals. Once introduced they will need to determine how they work alongside existing BI technologies.
  2. Expand adoption and deliver business value, and take a “line of business” driven approach to new tools. Traditionally, the collection of data, as well as content delivery, analysis and insight, was performed by the IT department. This centralized approach needs to work alongside the decentralized processes driven by a user-led approach to analytics. Modern BI tools should be rooted in individual business units, both to encourage user adoption and demonstrate business value.
  3. Ensure critical business information is consistent, by providing the right amount of governance in the right area. Modern BI platforms also present drawbacks, such as the challenge of maintaining data quality when it's being manipulated outside of established platforms by inexperienced users. One size does not fit all. BI leaders need to adopt differing approaches per data source and use case. For example, regulatory and mandatory reporting needs more governance whereas a fast turnaround is more important than data integrity for marketing applications.
  4. Create a clear roles and responsibilities framework. BI leaders must both plan changes to your team and introduce stakeholders outside the IT department to manage change. They should think holistically about analytics capabilities throughout their organization. For example they could use business analysts to explore the value and quality of a new data source and define data transformations before establishing broader governance rules.

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