Implementing Data Strategy Across the Data Lifecycle

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Organizations wishing to implement a Data Strategy—a set of decisions that form a pattern, charting a high-level course of action—face significant challenges in unifying their message across the data lifecycle. Only 30% of companies have a clear organizational Data Strategy, leaving their different departments to figure out how to manage company data assets.

Moreover, two out of three Data Management practices originate at a departmental level instead of from the top, leaving offices duplicating efforts and managing disparate data assets between divisions.

Companies want to manage their critical data assets across the entire organization instead, to avoid wasted time and money, increase revenue, and have the flexibility to innovate. To do so, enterprises need to implement a good Data Strategy across their data lifecycles. In getting started, organizations need to know what defines a good Data Strategy, what their data lifecycle looks like across the enterprise, and best practices for Data Strategy implementation.

What is a Good Data Strategy?

A good Data Strategy unites departments across an organization under an approach best suited to meet business goals. Peter Aiken, in his webinar Data Management + Data Strategy = Interoperability, describes this kind of Data Strategy as a clear pattern in a stream of decisions. Ideally, people, organization-wide, understand this framework and align all their data lifecycle decisions and activities accordingly.

But sometimes, people get caught up in technical detail (like SAP or Google), making these the Data Strategy. As a result, critical people and processes that work with the data get left behind.

This tactic turns out to be risky, as people and processes make up four out of five data problems. To mitigate this, Donna Burbank suggests, in her webinar Building a Data Strategy — Practical Steps for Aligning with Business Goals, that organizations link their technical solutions directly to business outcomes.

Also, Aiken and Burbank suggest keeping a Data Strategy memorable and straightforward. An example of such a good Data Strategy comes from the state of Oregon: “A better Oregon through better data.” This motto translates to Oregon’s agencies as “leveraging the value and capacity of data to improve government operations, accountability, and transparency while maintaining a strong focus on equity.”

Having A Wholistic View of the Data Lifecycle

After getting a good Data Strategy, a business needs to understand the seven data lifecycle phases with an organizational perspective. See below for the data lifecycle stages:

  • Plan: The company creates a roadmap based on an organization’s Data Strategy to guide data activities through the other lifecycle stages.
  • Design & Enable: An organization purchases or develops a data solution and deploys it.
  • Create/Obtain: A business inputs data in the system.
  • Store/Maintain: The firm keeps the data in one or more systems to retrieve for usage. In addition, it performs maintenance on the data to keep it business-ready.
  • Use: A company does something with the data to complete business objectives.
  • Enhance: An organization makes its data more findable.
  • Dispose of: A firm purges the data from its data systems.

Image Credit: DAMA-DMBoK2

Many firms apply this lifecycle knowledge at a project or departmental level without input and support from senior management or the entire business. Aiken describes the results as a “hidden data factory.”

In the case of a hidden data factory, a senior VP or manager (“person A”) has a side project that produces data to achieve person A’s objective. Data production goes on for some time without awareness from other managers or employees in the company. Then, many months or a year later, another manager (“person B”) finds out about person A’s data and wants to integrate it into a new initiative.

Unfortunately, person B’s team spends hours, months, and days validating data from person A and cleaning it to make it usable. Why? Person A’s group used a data methodology when processing and managing the data that matches what person B requires to process and manage their data.

Using data across multiple projects and departments requires employees and stakeholders to understand data flow across the enterprise and get the right people involved before a particular project starts. That way, a company minimizes hidden data factories. The Oregon Data Strategy considers both techniques by planning for data at the outset of a technology project or initiative and including the community affected in the data lifecycle planning and feedback.

How to Implement a Good Data Strategy Across the Data Lifecycle

Designing and using an Implementation Roadmap helps companies prioritize activities, timelines, and communications across their data lifecycles, aligned with their Data Strategies. When deciding what should be on the Implementation Roadmap and how to proceed, experts recommend these best practices:

  • Build a Data Culture Through the Data Strategy: Getting everyone on the same page consistently about using a good, enterprise-wide Data Strategy in data operations takes leadership and Data Literacy. To foster this kind of data culture, the MIT Management Sloan School recommends hiring a Chief Data Officer (CDO). Such a role builds out the Implementation Roadmap and the environment needed for employees and stakeholders to apply an enterprise-wide Data Strategy to their data tasks.
  • Measure and Test Data Activities: As part of creating a data culture in the organization, Burbank recommends measuring and testing results from data operations. She advises doing so from key performance indicators (KPIs).

Her examples of KPIs include the percentage of complete or accurate data, the time it takes to work with that data, return on investment, and cost savings. Including KPIs in the Implementation Roadmap keeps data lifecycle processes and procedures on track with the Data Strategy.

  • Support Strong Foundational Data Practices:

Aiken recommends ensuring strong support for foundational data practices. These pillars include Data Governance, formalized sets of data practices and processes; Data Quality, relevant data that is trustworthy to the business; and Data Architecture, a group of rules and models defining data requirements, guiding data integration, and controlling data assets.

Put all three Data Management components, among other supporting processes, into the Implementation Roadmap. A good Data Strategy works only at the same level as Data Governance, Data Quality, and Data Architecture function. This reasoning has encouraged some organizations to do an implementation roadmap for these foundations first.

  • Focus on Interoperability:

An organization’s critical data typically moves and combines across various systems throughout its lifecycle and needs to be interoperable and meet the directives of the Data Strategy. DataOps provides an automated framework to assist these needs.

DataOps consists of people, processes, and technologies that transport and store data efficiently, providing a unified and consistent experience. Count on putting some DataOps steps in the Implementation Roadmap to ensure data flows smoothly between different offices.

  • Align Data Lifecycle Activities Through Business Use Cases: Organizations perform data lifecycle activities to support their business objectives. Business use cases describe these goals and the required data tasks.

To get good use cases for administering an organization’s Data Strategy across its data lifecycle, consider filling out Bernard Marr’s Data Use Case Template. Also, his outline helps organizations clarify specific KPIs and Data Governance plans, per the use case.

Example of Implementing a Good Data Strategy Across the Data Lifecycle

Oregon’s Data Strategy, mentioned previously, includes the best practices mentioned above. Also, it has several Implementation Roadmaps and all actions in its appendix.

  • Oregon’s Data Strategy lists several steps for a data-informed culture. First, “it will develop a Data Literacy Framework for the State of Oregon. Then it will integrate data literacy training and tools into the current Chief Data Office portfolio.”
  • Oregon plans on “evaluating current demographic data collection standards” and investigating the feasibility of adopting REAL-D Standard for evaluation.
  • Oregon states it will launch a statewide Data Governance program and develop a Data Resource Library.
  • Oregon will launch a state-secured geographic data sharing hub, “GEOHub,” which will established a data-sharing protocol for spatial datasets.
  • Oregon will identify high-value use cases to inform decision-making between the Racial Justice Council and the Enterprise Information Services agencies.

Implementing a Data Strategy across the data lifecycle requires a good Data Strategy, understanding the data lifecycle throughout the organization, and following up with best practices to connect the two.

Image used under license from Shutterstock.com

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