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August 5, 2018
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Data Governance,Data Management,ZAP Data Hub
Thanks to Mark Zuckerberg, a seemingly endless number of security breaches and GDPR taking effect, data governance is rarely out of the news. A sound data governance process is at the heart of corporate best practice, customer trust and compliance with local and international law. As Garth Laird, CEO at ZAP, says,
“Data governance has become a philosophy, a process and, increasingly, a software-based standard that underpins all aspects of data management.”
Without it, mistakes can be made, personal data can be lost and legal compliance can be failed. To ensure your data governance is up to scratch and capable of scaling with your business, an automated data governance system is a must.
This article provides an introduction to data governance and the benefits, and considerations, of automating your organization’s data governance.
As data is collected, it goes through different stages of data automation. Starting with data collection, through to storage, analysis, transformation and visualization. Whenever you hear people talking about big data, personalization, algorithms, machine learning, AI etc. it all relies on data.
Historically, most of the processing logic created was based on a manual calculation of data. So as the amount of data increased, it became harder to manage it manually. This is where data automation comes in. As more and more stages become automated, it allows organizations to scale and make insights more easily.
With organizations accumulating more data than ever, and using it to drive more of their internal decision making, there’s an increasing need for business managers to have access to all relevant data, regardless of where it resides in the organization. They also need to ensure that all decision-makers are using the same data to draw conclusions.
In order to achieve this, data governance is required.
Nicola Askham, a Data Governance Coach, defines data governance as “Proactively managing your data to support your business.” It is the process and management layer for utilizing data effectively. It enables organizations to understand where their data is stored, what it means, and how it flows inside the organization. Data Governance allows organizations to assign clear responsibilities, automate review and approval processes, specify policy and quality rules and ensure regulatory and legal compliance.
Business managers need access to all relevant data in order to make the best decisions for the business, regardless of where the data resides in the organization. Consistency of this data is also important. If decision makers are looking at different data, they’re going to draw different conclusions.
To avoid this kind of confusion, enterprises need an automated way of confirming that all departments are using data consistently.
Automated data governance ensures that all data sources are located and catalogued, as well as helping to identify suspect data sources that might be out-of-date or otherwise inappropriate.
Through error detection, data governance automation can scan spreadsheets and other data sources for errors and omissions, faulty formulas and other issues that compromise the integrity of the data. Automating error detection enables enterprises to find and fix errors that elude manual identification and do so quickly, reducing the chances of errors occurring.
Without a strong data governance program, the consequences are potentially dire. The worst case scenario is a fine of up to €20 million or 4% of global turnover, depending on which is higher, compensation for any damages suffered and a loss of consumer trust in your company.
Automating your data governance won’t remove the risks of this entirely, but it will put you in a great position to control your governance.
Enforcing access controls for all of your data sources is critical for ensuring the ongoing integrity and security of your data. Access controls provide:
For users who do have access, an automated data governance system provides audit trails that will record all changes made to your data sources. By tracking these changes, your management team will be able to observe suspicious changes to the data, shifts in key metrics and provide regulatory compliance to governing bodies.
Automated data governance provides controls for discovering, managing and monitoring the data that drives strategic decision-making and daily operations.
However, don’t be fooled into thinking that these tools are the ultimate answer to implementing good data governance. You will still need to get stakeholders engaged in the process because without their buy-in, the whole initiative is likely to fail. The tool won’t work unless the whole business is signed up to data governance in the first place.
Automation should be positioned as a tool for making it easier for people to executive their data governance responsibilities, rather than removing them of all responsibility for it. If too much attention is focused on the tool, and too little attention is focused on getting stakeholder buy-in, the initiative will fail.
To avoid this, take a structured approach when implementing data governance. Before you start thinking about automation tools, make sure you fully understand what you are doing and why you are doing it. Draft your data governance framework first and consider whether your organisation is mature enough in terms of understanding data governance.
Automated data governance is an incredibly important tool for ensuring your enterprise keeps control over its ever growing data sources. But it needs to be implemented properly with buy-in across the organization to truly have the impact these powerful tools are capable of.
A version of this article was first published on the Flevy Blog website.
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