2018 Enterprise Data World Conference Report: Why a Data Strategy?
It seems such an obvious thing that organizations need a data strategy. In this data-driven age you might ask how you could survive without one. From traditional analytical applications such as the Data Warehouse and BI you now also have other hot information areas such as Master Data Management (MDM), Data Governance, Big Data, Data Science, Cloud Computing and AI. How do you determine the most effective use of these areas within your organization? You will struggle effectively integrating these technologies without an overarching data strategy.
So what is a data strategy? At the 2018 Enterprise Data World Conference, the definition used in the session led by Donna Burbank was “a medium to long term plan for the improvement, management, and exploitation of data across a business, and how it is to be achieved.” You want to be able to transform your business through data. This could be geared towards innovation as well as efficiency. If you want to become more efficient you might look at better marketing campaigns, products, or customer support. If you want to transform the business, data becomes the product. You would monetize information such as click-stream data, purchasing patterns, and sensor data. The data strategy ultimately should define the best way to manage the data assets to provide high value to the business.
There are a couple of things to note here. First, the strategy will point toward the areas and associated technologies to prioritize your information systems assets within an organization’s investment constraints – these are rarely unlimited. This applies not only to the data infrastructure (e.g., Data Warehouse, Master Data Management, BI) but to the surrounding processes (e.g., data governance, business collaboration). Second, the data strategy must be actionable. It is insufficient to have a data strategy without a path that includes resources to achievement of goals.
Burbank also stated that the data strategy must be connected to the business strategy to be successful. It would not be possible to measure the value to the business without tying the data strategy to the business and its associated goals. For example, a restaurant can increase its revenue by better managing its menu data, creating a single view that is visible throughout the organization. A managed care organization would exploit data (providers, location, staff) to be centered around better serving its members. This tie-in to business goals focuses the data strategy to provide the best value.
Over the years I have seen varying levels of success with data strategies. Many do not put forth more than a cursory thought into the strategy and then rarely (or never) revisit it. Others take a more in-depth look at their information resources but then do not tie it to the business strategy. Others define a data strategy that is too inflexible for the business and cannot adapt to changing needs. The companies that are successful treat the data strategy as a living, breathing asset – just as they do the business strategy. There is a shelf life to any static data strategy that is tied to the dynamics of a business. It must be revisited periodically to keep the strategy effective. As we say at McKnight Consulting Group, your business depends on your data strategy.