While traditional data warehouses will remain an important tool for addressing business issues and unearthing insights, data democratization is becoming increasingly important.

Data management technology should allow users across the organization to access data and garner insights, no matter where data is ingested in a traditional data warehouse, says Raj Rathi, director of analytics for AdventEdge Analytics, a CUNA Strategic Services alliance provider.

He addressed the co-located CUNA Technology and CUNA Operations & Member Experience Council Conferences recently in Chicago.

“We should keep all the data in its raw format and decide how to use it later,” Rathi says.

He cites four “V’s” driving this shift:

1. Velocity: The pace at which data is consumed.
2. Volume:  The total number of bytes associated with the data.
3. Variety: Structured and unstructured data such as text, sensor data, audio, video, click streams, and log files.
4. Veracity: The degree to which data is accurate, precise, and trusted.

“The only way to accommodate this is with a modern data management solution,” Rathi says.

While data warehouses provide standardization and data governance and security, and allow for complex transformation, data lakes add flexibility, agility, exploration, and fast delivery to that mix, he says.

Regardless of your approach to data management, “your data journey should start with strategy,” Rathi adds.

This “strategy-first” approach involves five steps:

1. Examine current state. Interview key stakeholders and data users. Consider your staff’s experience, responsibilities, strategic drivers, challenges, needs, and requests.
2. Determine findings. Identify the process flow across the organization; your technical systems and architecture; gaps in data, technology, processes, and resources; and your data analytics readiness and needs.
3. Define future state. Align your business objectives and drivers to your data analytics needs. Articulate best-fit use cases, and people, process, and technology initiatives for augmenting your data analytics maturity. Formulate a solutions blueprint.
4. Prioritize evolution. Score and rank your data, technology, and organizational initiatives based on corporate strategy, critical challenges, opportunities, and readiness.
5. Design a roadmap. Create a phased roadmap to future state implementation, including ranked organizational initiatives. Define your immediate and longer-term data analytics projects and steps.

“Start small with business problems, and move into strategy,” Rathi says. “Take a strategy-first approach—don’t boil the ocean.”

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