Steps to Develop an Enterprise Data Management Strategy

The Amazon Web Services outage on December 7, 2021, was a wake-up call for anyone dependent on “smart” things. Manually turning on coffeemakers and lights became a necessity again. Alexa couldn’t provide the weather forecast on Echo devices or enable twinkling on the holiday tree, and Roombas stopped dead in their tracks. Amazon deliveries were delayed because packages couldn’t be scanned, but it was barely noticeable because Ring doorbells couldn’t announce arrivals. A third or more of the Information Age came to an immediate halt, from Netflix and Roku to Litter Robots and Venmo, as data became inaccessible.

Transforming Data

Clearly AWS has created dependencies on raw data by converting it into meaningful information that we use to lead our daily lives. Data in and of itself is not information. The value creation is in what an organization chooses to do with collected data, which starts with an assessment of data collection and usage using the 5-Ws:

  1. Who are our most active clients?
  2. What services are they using the most?
  3. When are the services being most consumed?
  4. Where are our service gaps?
  5. Why are my users not using a specific service?

With this information in hand, organizations can make proactive decisions on where to invest and increase focus.

The Need for Formal Data Management

Even if your organization wants to maintain the status quo, you are likely not doing enough with the data you are capturing about your customers, nor with the data you need to have.Data management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles. The information that you gain from the raw data can then be used to develop your organization’s collective knowledge. In the “How” is where value begins to take shape:

  • How are my users using my services?
  • How can I offer more of my services?
  • How can I provide my services with greater efficiency and effectiveness?
  • How can I provide the services that my users need tomorrow?

Leadership must recognize that the information that can be extracted from data is an asset that maintains value. To be useful, it must be managed operationally and leveraged strategically.

Strategies for Data Management

The management of data, the development of information, the creation of knowledge, and the realization of organizational wisdom permeates through all levels of an organization, and as such needs to addressed through the creation of a data management strategy and governance process. This process becomes a roadmap that documents how the organization is going to proactively collect, organize, protect, store, and share data in order to achieve its mission, vision, goals, and objectives. The Data Management Strategy:

  • Affirms the organization’s value for data
  • Recognizes that data as an asset
  • Identifies the need to manage data throughout its lifecycle from collection to storage and archive

The Data Management Strategy also provides direction on how the organization will manage and use data and steers the organization away from common pitfalls such as:

  • Data scope creep: There is so much data out there that the potential to collect everything and anything can become tempting.
  • Data recognition: By communicating what data will be collected and the intended use of it, the organization can help ensure that duplication of efforts and duplication of data does not occur.
  • Data management costs: Through the communication of the strategy and the alignment of roles and responsibilities, associated data costs can be managed.

Developing a Data Management Strategy

A Data Management Strategy should be focused on the organization’s targets over the next 12-24 months. If your organization is just starting out, some of the focuses might be to identify critical aspects that will yield the greatest returns, such as those identified through the 5-Ws.

Use a Continual Improvement Model to stay on track and provide touchpoints along the way:

  • Build guardrails. Reference the current vision, mission, and goals to ensure you remain on a common path.
  • Assess your current practices. Develop a baseline by identifying what data is currently being collected and how, and whether it supports your goals and objectives.
  • Document your goals and objectives. Identify what additional data assets need to be collected to meet your desired endpoint.
  • Construct your team and processes. Define the methods and frequency by which assets will be collected and the roles and responsibilities of those that will support the effort.

Measuring Outcomes

After the data assets have been collected, stored, and utilized to develop the desired information, the organization should have a feedback loop that offers an evaluation as to whether the desired knowledge has been gained. If goals and objectives are not met, the organization should identify new data assets to be collected. Despite the time and effort in developing those goals and objectives, you should not be tempted to modify goals to meet the data that has been collected.

Your organization now has a critical Data Management Strategy as well as the information needed to develop other crucial documents such as a Data Management Charter, Data Management Scope Statement, and an Implementation Roadmap, all of which will help shape, establish, and maintain your organization’s enterprise data management practice.

For further assistance in developing your enterprise Data Management Strategy, including the identification of critical data assets and information gaps, SkyePoint Decisions is ready with the experience and personnel to assist.