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August 15, 2018
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Data Governance,Data Management
A version of this article was first published on the MS Dynamics World website.
In the past, there has been a view that data governance is a barrier to insight, a ‘nay-sayer in the room,’ or a set of policies that adds time and money to data projects. That view has to change, and is changing. And in the new self-service BI environment, with the associated explosion of data – in which almost anyone can access BI tools, run reports and generate their own data visualization projects – sound data governance is not just a requirement, it’s an enabler for greater insight. A business-wide data governance implementation can not only carry out the base expectation of keeping the business out of jail, it can also speed up time to insight, improve data quality, and save time and money.
The self-service model has transformed approaches to data within organizations, although ‘insight silos’ can form as a result. We’ll look at what data governance needs to do in this environment, and how collaboration improves as a result of better governance.
Core to this automation is ensuring that data is audited, activity is logged, access is controlled, and data definitions are consistent. The alternative, happening across many businesses, is a level of manual data governance that is hard to maintain and potentially costly. Ultimately – and most importantly – data governance should empower the end user, the ‘data consumer.’ Automation can take away a lot of the dirty work but it can also ensure that data quality is maintained and that the right person has access to the right information.
A data governance program will set out who is responsible for different aspects of information, and the procedures that govern them. Those responsible for ensuring that these procedures are followed are ‘data stewards,’ with oversight of data management. Underlying this, in a modern data governance context, is technology. In other words, the software enables the management of these procedures.
In many organizations, the technology is limited to Microsoft Excel spreadsheets, SharePoint, or wikis. When data is governed manually, these systems act as a reference point for users of data within an organization. Add to this a variety of Microsoft Word files held on servers which document governance policies and the data landscape quickly becomes unmanageable.
For instance, metadata – the data that labels other data – can guide data scientists to the data they need quickly and efficiently. Manual management of metadata can be an extremely lengthy process and can easily result in user error. Further down the line, such manual processes slow down the time to insight because data scientists take longer to find what they need due to poor metadata standards.
Often, a number of manual updates are required before data goes into a data warehouse – each point creating the risk of error or delay. With an automated data governance program in place, these manual updates are eliminated. And the ability to connect to multiple data sources and deliver insight has helped many business cope with the data explosion. However, without putting automated data governance procedures in place, these organizations run a number of risks.
In an ungoverned scenario, business data could be flowing into BI tools, and could therefore be accessible to everyone. This creates the risk that someone publishes data that is intended to be secure, or that a user may access sensitive data that they shouldn’t have access to. Data governance is in place most importantly to establish the who. Who has access to what level of data, what privileges should be in place, and most importantly, what has been done to that data, and by whom?
Huge repositories of data waste time and reduce the adoption of Power BI. One of the principal causes behind a lack of adoption of Power BI is that the level of data being held is overwhelming. A finance team may find it interesting, for example, that the conversion rate of a marketing campaign is above average, but they may find it easier if that data is withheld from them – and only the most relevant data is provided. A large repository of data will cause attrition as users become overwhelmed with the sheer amount of data available. They will either spend longer searching for what they need, or worse – start creating their own silos of data by way of a ‘shortcut.’
Insight itself becomes siloed. Fragmented use of Power BI can result in insight being held in silos – equivalent to having the data held in silos in the first place – except that the insight gained is not available business-wide. This tends to happen when multiple BI tools are implemented across different departments, each using their own data. This effectively leads to an underground data economy, with Excel sheets and Power BI operating in different pockets.
Power BI has brought enormous benefits, especially to organizations who have prepared their business data. The role of governance within the self-service sphere could be considered a limitation on that agility but, moreover, it should be viewed as a vital support to an organization’s ability to deliver insight.
If you envisage data as books in a library, then metadata is the catalogue system that helps you find a particular book. As our ability to access data sets increases, and businesses develop greater ways of structuring data, we face an explosion of possible data sources. Naturally, the more data you have, the more time you need to interpret that data and deliver insight. Governance should start with the tagging of that data. Metadata is the data relating to your data, and provides a means of identifying, defining and classifying the data within your business. Without the automated tagging of your data, the regular ‘in-flow’ of data into your business can quickly degrade your data set, making data hard to find. When automated, metadata should be created at the precise moment new data is created, saving the time it takes to tag – but also ensuring that data is automatically catalogued, and therefore more easily found.
As mentioned previously, one of the reasons BI tools sometimes suffer a lack of adoption is the sheer weight of data available to users. Indeed, the option to upload data can further add to that burden. Governance involves ensuring that data is restricted according to job role and function. This ensures not only that people are presented with a less overwhelming data set, but that they are equally presented with the most appropriate data for their role. A data set is only any benefit when it is used to make decisions, so a governance program that ensures the right data gets to the right person is performing more than just a traditional role.
With improved data quality, and the right data flowing to the right people, data becomes a valuable, accessible resource for insight. Governance in this instance supports a business-wide effort to use data to improve decision-making. If governance best practices are regularly – and automatically – applied, uptake will improve while errors and compliance violations will decrease.
Governance has hit the news for obvious reasons lately, and data breaches can have a hugely negative impact on the bottom line. But the opportunity is there for organizations to look at governance through the prism of data quality. If a governance program can ensure that the right data is more quickly available to the right person, at the right time, then the business benefits as a whole. With the explosion of data, and the associated uptake in usage of business intelligence tools such as Power BI, there is a pressing need for more sophisticated governance. This governance is not thrown together, and nor will a software package simply glide down from the sky and implement a bespoke program of its own volition. The delivery of better quality data, to the right data consumer, at the ideal moment saves time, saves money and improves decision-making across the business.
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