The laws of physics and the software programs are governed together by the same guidelines. Imagine three atoms, two hydrogens and one oxygen, if we focus our “digital” lens on to them and watch as the three atoms bind together to form a water molecule, they seem to be calculating the optimal distance & angle to attach itself to others. When such stringent rules apply for “elements data”, similar can be applied for “data elements”. Physics as we know, is hard-bound to laws, with close to zero exceptions whereas laws in data- management, usage, integrity, were non-existent until recently. After deep diving into it, one can witness the Achilles heel in the data ecosystem that needs a fix - “Data Governance”.
Now, a question might arise that what is Data Governance (DG)? The most direct answer would be that it is the set of laws consisting of strategies, policies, procedures, technology, and communication designed to support, improve, and optimise data assets. With this, a new question might arise - why should people pay heed to data governance?
While most people would agree that the ability to manage torrents of data is critical to a company’s success. However, with the emergence of data-management, many companies are far behind the curve. Various studies across industries show that on an average, only less than half of an organization’s structured data is used in decision making and less than 1% of its unstructured data is analysed or used at all. More than 80% of employees have access to data they should not, and 85% of analysts’ time is spent in simply realising and organising the data. There are a widespread of data breaches and these reprobate datasets exist in silos, and companies’ data technology often aren’t up to the demands put on it.
Having a data-management function is a good but delayed start, and can never be fully competent in the absence of a logical strategy for organising, governing, analysing, and deploying an organization’s information assets. The “plumbing” aspects of data management may not be as appealing as the predictive models and colourful dashboards they produce, but they’re vital to high performance.
It is imperative for the organizations to comprehend that the I.T. department is not responsible for data management. In fact, it should lie with the parties that have the most to gain or lose. While most of the organizations don’t contemplate deeply enough about where the onus of data. Business departments like marketing, analytics and sales are gaining with the creation of the new value from data. In contrast, I.T. reaps reward only when data is used to improve a product, service, or a decision.
Data Governance Functions
Data Governance sets all on-ground rules for people, processes and tools for active role players such as the mission, the organization, policies & procedures, monitoring & measurement, communication, and lastly the technology enablers. Additionally, another essential function of Data Governance is to have consistent Data Lineage. With that said, let’s take a look at the functions under its umbrella.
In traditional data analytical systems, it is often seen that the most difficult process is to implement & to identify. It focuses at how the schema of data is changing with the shift in landscape right from acquisition, transformation, loading, integration, reporting, etc. Another prime function of DG is to understand who changed what in the data lifecycle, thereby generating a supplemental need of data security functions.
Data Governance – A Newer Outlook
Data Governance is not just about restricting the data usage, but by shifting the focus towards sharing and reusing. It is essential for the firms to realise the positive value that a good governance delivers. By embracing the right data governance strategy, firms can cater to both better value proposition and its security implications; giving users the confidence to use and share data freely, while still keeping the firms and its data appropriately protected.
The General Data Protection Regulation came into force this year, which regulated the management of personal data with utmost sensitivity and to keep data governance in place.
With this, various organizations will have to establish their data governance approach, as some of the GDPR requirements are directly related to data governance. For instance, one needs to ensure in data accuracy and data integrity what information is stored and where on their systems. There is no better way to keep a track of it other than with a data repository. On improving the Data Governance strategy, it will allow organizations to consolidate the information and meet these GDPR requirements.
Therefore, organizations must institute governance to address multiple data privacy regulations with varying intricacies and impact. This integrated enterprise data will architect and assimilate data modelling, data lineage, process modelling and metadata collaboration from a global perspective across the businesses. In addtion, will help establish an ethos of data deterrence and awareness with which everyone within the business can conduct themselves, every day.
It is now the time to Raise, Rise, Redefine and Embrace our Data Governance strategy to ensure that we meet the new regulatory requirements and achive results we expect. This latest amendment initiative will be built on what we have delivered till date and will extend to other data spheres in the organizations as well. This data governance capability will not only make us capable of compling with regulatory requirements. But, will also put us in a great position to support the organization on embracing this digital transformation.
Similar in nature to physical elements and compounds, data governance will exist in multiple phases/ states, sometimes within the same company. For data governance to be effective, one must gauge the observable states of governance within their enterprise, identify the desired state, and determine the energy required to reach the dimension of change given the environmental conditions.
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Ashish is a part of the AIM Writers Programme. He is a Digital Marketing Analyst and former BI Architect at a noted IT firm. With over 10 years of extensive experience in delivering data engineering & cloud-based BI solutions, he has helped clients across domains to enable their digital platforms, extract insights from their business intelligence suites, thereby triggering & tapping newer business opportunities.