It amazes me -- but probably shouldn't -- how many organizations accept the data they depend on every day at face value, and yet do not understand how it comes together. They seek to be "data-driven" yet they are not willing to invest in understanding how data drives the organization, how it could be dramatically improved, and then they wonder why they are missing that performance edge that smart executives seek.
Enterprise performance depends on having certainty about the derivation of the entire data chain. If you drive a car, you don't worry about how the car works, but you do develop some degree of trust in the quality of engineering that put the car together. Auto engineering has over time much smarter about the entire process, from design through delivery, and engineers have built quailty in to their products in part through ensuring all components work well together.
It is no different with data. You start with raw material at the source and enrich (or destroy) the quality as it goes through various transformation stages. The value of your data asset improves or diminishes by the steps you take along the way. If you don't have a process to scrutinize them, understand their interactions, and actively manage them, you will end up with a product that might not get you to your destination.
I worked with one large organization that relied on some critical algorithms for years that were embedded in their applications. They controlled strategic resource optimization and pricing. Yet no one ever opened the hood to see how they worked, until they started building out the data warehouse to provide more insightful analytics. And in the process of doing that, they discovered some unfortunate oversights that potentially had cost the organization millions of dollars. No one had really tended to these algorithms, nor understood how changing business conditions had affected their use.
Just like automobile engineering, data has become more complex. The interactions, interdependencies, speed of change and volume of data have all overtaken conventional practices for managing data. You need to have practices that expose both opportunities to exploit data more effectively, and risk that could be costing you millions.
So what should you be doing to reorganize, protect or enrich your data assets?
If you don't have a data governance program, start building one.
Data governance is the business-driven practice of owning and managing your data assets. You can't leave the job to IT anymore. It is bigger than that.
If you don't have a full range of data management practices in place, get moving.
Data quality, for example, is an advanced practice that is fundamental to any organization that relies on data. Other practices like architecture, data integration, master data management and metadata management are all essential to growing the value of your data assets.
Make data governance part of your corporate culture.
Your organization probably spends a lot of time talking about what you're doing to build products, or take care of people... You should be spending just as much time in executive meetings talking about how you're caring for data.
Connect with the analysts and the end-consumers of data.
Make sure you follow the entire value chain of data, from how it is created, to how it is utlimately used. Working closely with data analysts, and with data consumers, will reveal much about where data management problems exist, and where opportunities lie.
For more ideas to help enhance the value of your data assets, get in touch. firstname.lastname@example.org.
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