I published an article a few months back that was titled Where Does Data Governance Fit in a Data Strategy (and other important questions). In the article, I quickly outlined seven primary elements of a data strategy as an answer to one of the “other important questions.”
The list of elements I used in that article were drawn from a list that was available through a data and analytics company called Analytics8. I received several emails in response to that article stating that the list was a great start, but they wanted more information about the primary elements.
Recently, I have been engaged with several organizations focusing on either constructing a new data strategy or reviewing and evaluating their existing data strategy. These exercises are allowing me to dig into the essence of what organizations are attempting to achieve by delivering an official and formal Data Strategy artifact. Upon further review, I have whittled the seven elements that I shared in that article down to an even half-dozen that I describe below.
My question for you, the reader, is how do these elements of a data strategy compare to your data strategy? I would love to hear from you as to whether or not you have a data strategy, who is (or should be) responsible for the strategy, and how your strategy is being executed and operationalized?
I hope this list of six primary elements will be a great start or something that you can use to determine if all of the appropriate pieces of the strategy are addressed. The idea here is to keep this list simple and memorable. Please comment if I have hit that target.
Defining Data Strategy Elements
A data strategy is a thorough plan and policy for moving an organization towards a more data-driven culture. A data strategy is often viewed as a technical exercise, but a modern and comprehensive data strategy addresses more than just the data; it is a roadmap that defines people, process, and technology.
The exercise of creating a data strategy is one in which organization leaders take a deliberate look at what employees need so they are empowered to use the data.
In addition, processes are examined to ensure that data is accessible and of high quality, and that technology is leveraged to enable storage, sharing and analysis of data.
Organizations should consider including the following elements in their data strategy:
- Set Clear Goals and Objectives for Data Management and Use
Viewing the organization’s business requirements and strategic goals of leveraging data as a valuable corporate asset; including an understanding of the questions the business need to answer with data and metadata.
- Establish Clear Roles and Processes for Data Management
A clear definition of the people and processes necessary to deliver on the strategy including organizational structure, skill sets, and how these things will work together and be supported.
- Define Technology as an Enabler to Strategic Success
The technology requirements, including a flexible and scalable design of systems and data resources.
- Deliver Data Governance Based on Formalized Accountability
Application of formal data governance focused on employee behavior that allows confident enterprise-level sharing of effective data.
- Establish Guidelines for Data Analysis and Application
A focus on the ability to turn data into insights and visualization including the improvement of inventory and cataloging of primary data assets focused on improved decision-making, storytelling, efficiency and effectiveness.
- Deliver an Actionable Plan to Complete the Strategy
An action plan and roadmap of the steps that will be taken to move from the current state to the future state.
The work efforts (mentioned earlier) are with clients that are …
1) debating the need for a data strategy
2) evaluating their present data strategy
3) initially planning and authoring their data strategy.
These efforts have provided me the opportunity to build a reusable qualitative assessment, by element, that can be used to evaluate an organization’s data strategy. I will be more than happy to share the details of the assessment with any organization that is looking for assistance developing or enhancing their data strategy.
Business Case for Data Strategy
Not all organizations have, or find that there is a need for, a data strategy. Answering the question as to whether or not a data strategy is necessary is a question that needs to be answered by Senior Leadership in each organization. However, data practitioners and data leaders within an organization can influence Senior Leadership’s decision by making a strong business case for why a strategy is needed and the risks associated with not having a strategy.
A clear understanding of your organization’s vision and goals and the priorities of the organization’s Senior Leadership sets the context for a data strategy business case. Leadership determines the level of success that must be demonstrated through the business case. Explaining how a comprehensive data strategy can deliver business outcomes is the key to making a business case applicable and convincing.
A business case is a case for transformation. When making the case for change, the change must be defensible. Identify costs that plague the organization and opportunities that are being lost in your present situation. While your ability to quantify financial return may be a strong consideration, there is likely room for improvement through elevated data governance, data management and data-driven capabilities.
The volume and variety of data that your organization manages is growing exponentially. This includes both structured and unstructured data. Organizations that are able to harness this explosive growth and make it operational create significant business differentiators with respect to their competition.
Organizations can differentiate themselves from their competition in many ways, including through internal and external growth. Several examples of using data and a data strategy to distinguish the organization include:
- Leveraging data to power the requisition lifecycle from generating interest to motivating demand behavior, from request processing and contentment to completing downstream processes like logistics, finance, and service.
- Reducing the up-and-down effect your supply chain has on inventory by providing real-time, data-driven visibility of your entire demand and supply chain with predictive insights.
- Improving employee productivity, advancement and retention by assisting them in achieving goals through cultivating data-related learning experiences based on their existing talent, your organization’s data strategy, and their work experience.
When an organization decides that a data strategy is not necessary at the enterprise level, it is not uncommon for individual parts of an organization to contemplate more locally and deliver a strategy for the data that is under their management. As the breadth of a data strategy increases from being local to covering the enterprise, the overall influence of the strategy increases and has the potential to incorporate a more encompassing set of data people, processes and technology.
A data strategy is needed because, without a vision and foundation, parts of the company will view data-related capabilities differently. This inevitably leads to duplication of both data and data systems across the organization and makes it quite difficult to determine the ‘truth’ from one’s data while driving up costs associated with operational efficiency and effectiveness.
A data strategy provides the basis for enterprise planning efforts connected to data-related capabilities. The more detailed and comprehensive the elements of the data strategy are, the better the chance that the business and technical parts of the organization will fully understand each other.