How to build a data-centric organization

Data centric strategy is witnessing two extremes. On one hand everyone agrees that enterprises need to leverage the data more effectively to drive innovation, optimize existing processes and set up new revenue channel. On the other hand, around 68% of enterprise is not discoverable, unusable and of poor quality. To resolve this dichotomy, enterprises need to adopt a holistic data strategy which is led by CDO but have equal partnership from other stakeholders including data leaders, AI scientists, data stewards and business leaders. The strategy should focus on establishing the right business metrics to optimize, the correct data architecture and improving data literacy in the organization. Let us take a more granular look at these focus areas.

Understand your business objectives 

The data strategy needs to align with the organization’s larger goals and aspirations. The C-suite and business stakeholders need to be on the same page to support, execute and align data strategy. The priorities need to be clearly articulated along with expected improvements as a collaborative, data-driven environment begins to take shape. Finally, staying realistic and resilient are keys to success. As with any transformation, the changes will take time. So, stay put! 

Assess your current state

Once the C-suite is onboard, the data team should start to introspect to identify what are current gaps, what is working well, what are the shortfalls and what are the key roadblocks to become a data-first organization. A design thinking workshop with the diverse set of stakeholders can help answer these questions very effectively and efficiently.

Map out data strategy framework for the future 

This is a crucial stage where the team should define the target blueprint describing the data architecture, key components, and their interactions. Next, create a plan to describe how the blueprint will be operationalized including management, and governance. Finally, an implementation plan to put the blueprint into practice is vital. The team also needs to make sure that the progress is monitored via measurable metrics. The metrics along with data strategy highlights should be regularly shared with everyone. 

Establish Controls 

One of the essential components of data strategy is robust governance, privacy and meta data management capabilities which will help business in ever growing regulatory guidelines. The metadata and governance layer helps in improved data visibility, compliance, and collaboration across the organization. With the increasing adoption of multi cloud, the data integration layer is unavoidable. This layer allows the users to consume data irrespective of its physical location. 

Create Integrated Solutions 

Managing and accessing silo-ed data is one of the most wasteful tasks. There needs to be a single central catalogue to manage the data which will allow simplified access and usage. Often, the end-to-end implementation of data strategy can be daunting for any organization. Therefore, it is important to go for smaller wins through well-defined MVPs. The successful MVPs should then be scale and adopted widely. 

Scale your team and processes 

The final step in successfully implementing a data-driven strategy is to scale your processes and talk widely about the success the strategy has delivered. The needs to be constant focus on hiring and re skilling the teams to be more data savvy. This is further evident as per recent report, by 2026, 60% of G2000 enterprises will have data literacy programs, including training to help employees spot misinformation and communicate or influence with data, to elevate their data culture.

Sameep Mehta

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Sameep Mehta is an IBM Distinguished Engineer and Lead, Data and AI Platforms at IBM Research India.