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October 16, 2018
By Ben Douglass
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Data Governance,Data Management
The software tools that businesses have at their disposal to access and analyze their data continues to grow. And the trend towards self-service – giving users at the point of analysis the ability not only to consume but to create – also continues.
Self-service BI tools have given users freedom to connect to data and build their own analytics and visualizations. And the advent of self-service data preparation tools has added the ability to not only access data for analysis but to perform ad-hoc data preparation activities such as data cleansing and blending.
But the challenge with any self-service tool is that, by its very nature, it can encourage individual pockets of data and knowledge. And such pockets or silos can lead to inconsistencies across the business as soon as information assets are compared and contrasted.
Self-service data preparation tools are tremendously productive if placed in the right hands and, more importantly, if they’re used to add value rather than fix issues with the underlying data that is being accessed.
But, if they’re not used correctly, these tools – giving individuals the ability and responsibility of cleaning data and saving it in their own workspace – adds to this problem of “pockets of data and knowledge”. A problem heightened when data is blended with other ‘self-cleansed’ data.
While many of these tools have great ‘social’ functionality, to enable the sharing of individual assets, it also means that the responsibility of governance falls squarely on the shoulders of those creating them and can only ever really be enforced through policy, rather than the product itself.
In addition to the challenge of inconsistency, there’s the challenge of efficiency: when data preparation happens solely at the individual level and when sharing and collaboration is enabled but optional. The process of data prep can quickly become wildly inefficient, with duplication of effort and post-preparation confusion over rectification of inconsistencies, not to mention the actual risk of making important business decisions based on potentially inaccurate information.
However, as I said at the top of the article, these tools are fantastic assets and certainly have an important role to play in the ‘liberation’ and commercial exploitation of business data. But there is one vital factor that must underpin their use in order to negate inconsistency and inefficiency, and to ensure that users of these tools remain empowered to employ them for value-added activity. This vital factor is data governance.
But how is governance best achieved? As I have mentioned above, one option is to implement it as corporate policy, with a reliance on individuals to follow it. And, yes, having a data governance policy is important but – due to being prone to human error, misunderstanding, and indeed ignorance – it is rarely enough on its own. To guarantee data governance, policy needs to be accompanied by product.
There are many tools to help businesses manage their data. They range from those requiring build and maintenance to pre-packaged, productized solutions — such as the data management automation software ZAP Data Hub.
When considering tools delivering governance, the consensus is that they must be automated. They must remove human intervention, and they must have data governance built-in.
As my colleague, Bill Tennant wrote in an article on the launch of Tableau Prep, with data management automation software, data from across a business can be automatically unified into one hub of consistent, governed data that is regularly updated and secure, with access rights set at the user level.
Having a unified hub of business data goes a long way to overcoming inefficiencies that may arise from self-service data prep. Data no longer needs to be accessed from individual silos, reducing time spent on data discovery. The majority of business data is already unified, reducing time spent on data blending. As a result, any data blending that then takes place at the point of analysis becomes wholly focused on enrichment and adding value.
It also means that less data preparation is actually required by individual users at the point of analysis. So, as well as saving time and effort, this improves data governance too, because there is less chance of individuals making mistakes, less chance of silos of knowledge and data occurring across the business, and less onus on the individual to uphold written data governance policies.
To read more on how data governance can drive better insight across a business, download our new white paper. You can watch a two-minute video overview of ZAP Data Hub. And for a free, tailored trial of ZAP Data Hub – accessing your own data to produce a governed hub for your self-service tools – submit your details here.
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