I’m often asked, “When is a good time to look at my data management strategy?” While generally speaking anytime can be the right time, below are 5 events that may trigger a hastened need for a forward-looking data management strategy.
There is no doubt that the best time to start looking at your data management strategy is at inception. Even though the early days are often wrought with constraints, including financial, any resources dedicated to planning the lifecycle of the organization’s data will drastically improve the go-to-market strategy. An early stage focus on ensuring correct and timely data capture, while designing and implementing core transactional systems, will pay dividends on the ability to produce accurate operational reporting. It will also lay a solid foundation that allows for more advanced analytics as the company matures.
Shifting Competitive Landscape
There comes a time in the history of every business where trends change and the need to alter strategy is signaled. Perhaps consumer preferences shift, innovation disrupts the industry, commoditization occurs, or more convenient alternatives come to market. Whatever the situation, there is a good chance that an organization which is not using data as a strategic differentiator identifies a need to aggregate and analyze volumes of data in search of market signals. Depending on the organization, this may mean exploring a traditional enterprise data warehouse or perhaps a more flexible data lake scenario to provide a base for advanced analytic concepts such as machine learning and artificial intelligence.
Core Platform Transition
When it is time to replace or upgrade an existing core technology platform, such as an ERP or EHR, there is often a requisite project to define data policies and architecture. System transitions come with many decision points, such as how long data must be retained, what data will be converted to the new platform, and how is data mapped from one system concept to another. All of these scenarios require critical thinking, planning, and delicately orchestrated analysis and development work alongside making sure “business as usual” continues. Often times, this scenario requires additional staff who are experts in data and have the bandwidth to work through the transition while other staff support ongoing operations.
One of the most complex and daunting tasks a company can come up against is a merger or acquisition. In addition to the cultural, process, and sale logistics, there is generally a large project undertaken to transition or consolidate the IT applications portfolio. With this consolidation comes the need to archive, transition, or potentially re-map data concepts as the application platform evolves. This scenario tends to hasten conversations about enterprise data warehouse concepts as the need to provide a landing ground for historical data accelerates alongside a growing need to report on blended data concepts.
A sale of a business unit triggers a series of events, one of which can be the transition of technology platforms and the underlying data assets to the buyer. Segmenting records, extracting, and transitioning them to the new owner can be complex and requires a robust data management strategy. A typical divestiture strategy includes decisions on data segmentation, extraction, and transition of ownership in a way that respects data security, ownership, and integrity. All of these require examination of the grain of the data to be transitioned, development of complex extraction logic, and detailed testing, alongside clear data management policies to support a seamless transition. This exercise in “cherry picking” data can be daunting and often requires experts in data management to seamlessly transition data from seller to buyer.
If your company finds itself approaching one of these transitions, it may be time to take stock of the current data environment and associated staffing levels to ensure the challenges can be seen as an opportunity versus a potential threat.