“That data’s bad.” “That data won’t load.” “That’s tons of duplicates in that data.” Rob was tired of hearing about the same problems that keep recurring every time his team touched his company’s client data. Working for a large logistics company, they needed reliable, regular access to one of their company’s most valuable resources- its data.
To perform almost any business function, a company’s data needs to be stable and clean. This is even more important when the company has directly client-facing operations. What Rob needs to consider is another, more inventive approach to managing his company’s data.
One strategy companies have been successfully deploying involves thinking about data unusually. The concept many have been using is to manage their data just like a consumer product. By thinking about data as a product, companies can earn increased usefulness from their data, among other benefits. These data products can also be reusable and help piece together technologies that will enable companies to derive much sought-after long-term value.
Why current data strategies aren’t working
One of Rob’s most significant struggles with his company’s data was due to his company’s grassroots approach to data management. Individual teams within the company would piece together the data solutions and technology they needed to achieve whatever project they were working on at the time. This led to lots of duplication of effort (and data!) and a hodge-podge, patchwork of tech architecture that was expensive and extremely difficult to maintain and manage.
A second approach that companies use to manage unwieldy data is called the big-bang strategy. With this method, a central team responsible for a company’s data extracts, cleans, and aggregates data at once. This removes some of the duplications of effort that occur, but typically, it’s not specific enough to meet the needs of each unique team within a large company. Users then struggle to confirm the necessary level of governance and quality of the data which doesn’t allow for much time savings.
Unfortunately, while both of these data strategies are common, they’re not very productive. They fail to lay the foundation for current and future data management and don’t produce serious, long-term value.