摘要: In these uncertain times, effective understanding of your customers, your employees, and your market will illuminate paths for managing the short-term so as to be able to continue to grow and even flourish after the COVID-19 outbreak, a period that most economists think will be measured in months and not years. Companies have critical questions to answer about the shape and size of their workforces, including decisions on whether to furlough workers, reduce workforce size, or re-allocate teams to focus on high priorities. Some companies will even choose to grow and invest now, trading a short-term profit hit for long-term market share.

 


Joe DosSantos

Implement Data Governance that Creates Confidence

As you shop online in your personal life, you don’t always have the knowledge to pick a specific product by brand or feature. You might leverage a search to narrow down candidates, and then you might read reviews to gauge people’s satisfaction with the product. You might also conclude that what you really need is a replacement part. And you might conclude that the product that you desire is not available in your location based on local laws or shipment costs. These are all excellent examples of product metadata that drive the shopping experience, each of which requires dedicated resourcing to address. A data catalog must similarly allow you to find data that you might not have known about. It should allow you to see relative quality through labels, profile assessments, and comments. And finally, it should restrict access to data that is not appropriate for an individual based on role or use case. The data catalog enables the data marketplace approach at scale by automating the ability to onboard, profile, describe, secure, and potentially prepare data quickly in anticipation of analytics needs but requires formal governance to make sure that what appears on the shelves is understood.

Stock Data Before You Need It

When you shop for goods, the products that you desire are generally built and in a warehouse waiting for your order. A thoughtful supply chain has created a demand plan well in advance of the purchase that anticipated this very moment. If everything that we ordered was a make-to-order experience, items would take weeks to arrive. Data should be no different. Processes should aim to put data on the shelves in advance of the analytics execution, which requires speed measured in hours rather than weeks. Many organizations start a search for data when the first request is made. This is already too late. Data Governance, in this context, should create a data demand model that anticipates data requirements based on organizational priorities in order to build up a meaningful baseline of useful data that can grow over time.

詳見全文 Full Text: dataversity

 


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