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What’s In Your Data Strategy?

Is it pragmatic?

19Oct

Given that data is a corporate business asset that, like other assets, requires investment, innovation and maintenance to enable business capabilities, and resolve complex business problems. It is fundamental for data leaders to position their organization to address critical data related challenges, and optimize the organization to do business through a clear guiding vision, set of priorities, approach, roadmap and clear execution timelines. It is important that data visionaries have a data strategy that engages all employees.

1. A comprehensive formal data strategy should be driven by the issues causing organizational challenges, financial loss, missed revenue opportunities, inability to meet regulatory needs, fractured enterprise risk management, or inadequate knowledge creation and enterprise reporting. It is not practical to solve these challenges in their entirety using a data ecosystem but, the strategy should provide clear details on what extent it would solve these issues vs it how it would act as an enabler. In addition, it is important to reflect on what other core business processes and models need to be changed to gain the maximum benefits. I suggest developing detailed prioritization, phasing and identify a list of quick wins for each priority. However, focus should be on the specific highest priority to build a data foundation that can be extended and matured for other priorities. It is best for the strategy to avoid lists of generic data principles and blanket objectives which may cause uncertainty in the minds of executive leadership, and it may not connect unless the strategy talks about real issues. Also, the strategy should have clear guiding visions and operating models, and should look at the following elements: 

a. Does it discuss data policies, standards, and procedures with an ownership framework?

b. Does it discuss the deployment of commodity managed data solutions, and enterprise data services with innovation?

c. Does this overall transformation strategy provide a systematic framework to address key data challenges in every dimension including (a) , (b), and beyond?

I believe that organizations are still juggling  these topics because it may overlap with an existing function or/ and organizational team , and there are significant political mazes that continue to confuse stakeholders. 

2. It is important for the strategy to show the types of core data domains, and critical data elements that are needed to manage organizational challenges, how to make them highly available, fit for purpose, and ensure quality within ownership frameworks. It should describe how data competencies fit into the overall strategy such as master data management (MDM), big data and data analytics, data governance, and data architecture. Also, it could be beneficial for the strategy to provide basic information around process framework and proposed methodologies for capturing, storing, protecting, and ensuring the integrity of the data.

3. The strategy should also lay out the communication and change management plans which are key to the data program. It is required to outline an organization partnership framework to help internal teams understand their role within the strategy, and the support needed from them. It is important  to show strategic alignment with other large initiatives, and the potential risks that may damage execution focus. Lastly, the establishment of key measurement criteria is critical for the strategy to benchmark the success of agreed priorities.

The data strategy should be  pragmatic, must always align with real issues, positioned to deliver trust and confidence in using  data in every part of the business, corporate and operation functions. The strategy will become a liability if it is an overly complex set of documents that is unable to produce positive results or meet desired benefits.

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