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Data – the baseball cards of the 21st century
April 3, 2018 Blog Data set


by Johannes Sundén, Regional Presales Manager, Qlik

When I was a kid in Sweden, trading ice-hockey collector cards was all the rage. There were several different brands, cards of varying quality, and we would trade with each other during recess in school.

Running an effective analytics outfit at a modern organization is similar in some respects. Data sets are the equivalent of enterprise trading cards and you’ll want some good quality ones to improve your data collection. Enhancing your collection often means having something to trade with someone else, as these combinations of data are beneficial for both organizations and can have different business impact for the two.

A bank, providing payment terminals to merchants, gets a better overview of how their customers spend. The bank can advise the merchant on the demographic makeup of their customer base and other traits. They also know for example which card holders live in the vicinity and spend in similar establishments, but choose not to spend with another particular merchant, this could prove a powerful opportunity.

There are increasing challenges around managing traded customer data. As consumers, we’re generally prepared to have our data used for business purposes if the value returned to us is great enough. With recent legislation like GDPR and the high-profile customer data use from the likes of the Cambridge Analytica & Facebook issue, organizations need to take an active stand in how their data and APIs are leveraged.

Organizations who trade away their data need to take steps to ensure that this data will be handled in a responsible way, follows policies and relevant laws while being something that they can be honest with their customers about and don’t feel a need to hide.

Having a well labelled album is another way to keep your trading cards safe while improving your collection. One tool that will help users in a data trading world to keep tabs of sensitive data sources and who has access is a well-defined data catalogue. There was a lot of buzz on data catalogues in 2017 when we saw organizations building custom solutions and vendors bringing relevant products to market. This momentum is set to continue and we’ll soon see data catalogues going mainstream. This will make all information assets searchable, not just those in the data warehouse or lake and these catalogues will describe data quality while adding social aspects such as liking, sharing and following. For organizations this will mean moving beyond a rigid data dictionary to a dynamic resource for information workers to accelerate analysis and build on each other’s’ work in a self-service manner.

What’s next?

With wide data access within an organization and a high degree of self-service analysis, this puts a lot of emphasis on a strong governance model to ensure alignment with data security directives and infrastructure integrity. Another area is how to best use concepts like machine learning and artificial intelligence to provide support for knowledge workers. These are areas that will be explored further in upcoming posts.