Trusted, high-quality, business-ready data has become an organization’s most valuable asset, and an effective data governance policy is key to securing all of it. Yet for the longest time, traditional governance policies were at loggerheads with business objectives.

Bottom lines were affected by the need to tightly control data and adhere to the myriad compliance requirements faced by the organization. Yet, traditional governance programs themselves weren’t proving to be effective for many reasons:

• Data quality was turning out to be quite low. In fact, even now, only 3% of enterprise data meets quality standards.
• And to compound this, measuring data quality through a tool was very expensive and led to data duplication.
• It was hard to maintain consistency, because data came from different tools with different schematics and metadata practices.
• Access was hard to control because data flows were not being centrally tracked.
• Documenting data lineage proved to be extremely time-consuming.
• Not to mention, security teams were making the critical error of incorporating these strategies post-implementation rather than at the outset.
• And since most of the aforementioned processes were done manually, it led to quite a few inefficiencies and cases of human error.

No wonder Gartner claims that 80% of all data and analytics (D&A) governance initiatives fail – an initiative that should be an integral component of your organizational security strategy. Thankfully, there’s good news. It’s made a real comeback in recent times, as organizations realized that it is possible to ensure secure and governed data control while meeting all the dynamic needs of a modern business.

What has precipitated this change? Automation, of course. In today’s rapidly changing world, it is becoming clearer than ever that to become an industry leader, automated tools like artificial intelligence & machine learning must form a critical part of your future plans. In fact, 65% of organizations say AI & ML will be one of the key forces driving their digital transformation strategy.

To that end, it wouldn’t be wrong to state that automation is already changing the entire domain of data governance. Automating some of its processes eliminates all the labour-intensive manual tasks associated with data governance, saving time & money as well as minimizing human errors. And by freeing up all this time, it has empowered everyone in the organization to adopt self-service and actively strive to keep the data quality high. Moreover, in an era where the total amount of data drastically increases by the day, automation proves vital in handling the added load.

The advent of automation has turned data governance into something more seamless, something more comprehensive, something more intelligent. And thus, the new practice of intelligent data governance for data security and compliance was born – something we highly recommend for your organization.

What is Intelligent Data Governance?

Intelligent data governance is the application of data quality, integrity and security controls that are thoroughly embedded into your data processes by design. Through it, you can achieve the ultimate organizational data goal – to have a comprehensive, meta-centric data catalogue with defined data classifications powered by automation.

That’s a lot to take in, so let’s go over what intelligent data governance does for your organizational data one facet at a time, and how automation plays a key part in making it all work:

Facet #1: In a world where 40% of the data in top companies is flawed, it keeps your data quality high.

How does it do that?  Through ensuring data quality right at the source by adding the requisite controls. This applies both in the capture and storage phases. In the client-facing data capture stage, controls can be incorporated to prevent inaccurate & incomplete data being created. Some simple examples of these controls can be drop downs for standardized fields like country or nationality, and illegal/excessive character limits for fields like phone number or name.

Similarly, at the storage phase, one should incorporate controls that integrate & monitor the data, so that you can be informed once the thresholds you have set are breached. Keeping these thresholds also helps you adhere to some of your compliance requirements.

How does that help? It helps generate more high-quality data, which leads to more accurate insights, risk mitigation, more effective data protection and easier compliance.

Where does automation come in? For data capture, AI can be used to check the efficacy of the data by comparing it with public or third-party data. For data storage, the possibilities are a lot more limitless. Feeding the data thresholds to an ML tool can lead to instantaneous alerts when they are breached, and rich, insightful metrics from that data can be generated using AI. Moreover, if controls are breached, tasks like rapid remediation and information collection for audits are also within the scope of automation.

Facet #2: In a world where 40% of business-critical data is trapped in silos, it helps consolidate and centralize all your data.

How does it do that? One of the central tenets of intelligent data governance is the creation of a ‘data lake’ – a central repository that unites all these disparate silos into a single virtual entity. This is where all your key data will be – your data sources, your business intelligence reports, your ML models, your compliance assessments, etc. Additionally, this is where all your redundant data is identified and moved away from primary servers into cold storage.

How does that help? The most insight is gained by having a complete picture of all the available data sets. By intelligently and effectively connecting these previously siloed data sets, your data analysis and modelling drastically improves.

Where does automation come in? It provides efficiency across the board, whether it is drastically reducing the consolidation times, or creating quick, relevant analysis reports to further business growth.

Facet #3: In a world where 70% of time is spent finding data and only 30% analysing it, intelligent data governance helps catalogue all your data and fully establish its lineage.

How does it do that?  To create and implement data flows, there must be an understanding of where it comes from, what is done to it in transit and where it is sent. To fully establish its lineage and classify it effectively, ‘metadata’ is the answer. One must define the template for metadata scripts or files – essential information like source, destination, PII indicator and particular data classification should be present in the metadata of each file. Additionally, safeguards should be placed across the data cycle to detect inaccurate or missing data, so that problems can be identified immediately rather than later downstream.

How does that help? This is an essential facet – you can’t apply governance if there is no organized data with proper metadata tags and lineage. If you are spending most of your time trying to locate the right data, that can also diminish your ability to maintain compliance. Having an effective, well-classified catalogue eradicates all this and helps you improve your speed of insight, which leads to increased business agility and data monetization.

Where does automation come in? Once you have fully established your classification measures and subsequent workflows, sit back, and let automation do its magic. All subsequent data will immediately be classified and embedded with controls to accommodate data collection and analysis.

Facet #4: In a world where 74% of all breaches involve a human element, it helps streamline and fortify your data access.

How does it do that? There are two facets at play here. Firstly, it is all about ensuring that only the people & processes with appropriate rights to that data can access it. This requires nuance – not only should roles be properly segregated, but you should also accommodate easy collaboration across roles & teams. Here are examples of a few roles with respect to your data governance:

• Technical users (IT & software engineers) are those heavily involved in the data pipeline from an operational standpoint.
• Business users (executives & data scientists) are those who take the data and build actionable insights out of it.
• Governance roles (data stewards) are for those who manage your entire governance program, from monitoring metadata to ensuring quality & compliance.

Secondly, once these roles are properly defined, monitoring is essential. This means having immutable, instantaneously generated access & audit logs whenever any piece of data is accessed.

How does that help? This delicately balances the double act of opening your data access while also remaining risk-free & compliant. It is this facet that brings the ‘intelligent’ in intelligent data governance. Moreover, by facilitating communication between teams, you are helping foster enterprise-wide collaboration.

Where does automation come in? Automated logging mechanisms help give you the best overall picture of your organizational data. Moreover, machine learning could be essential in ascertaining all the access privileges given to new users.

Ultimately, all these facets help in establishing intelligent data governance as the first step in making your organization being data driven. If you think this entails taking away human jobs and giving them to the machines, this couldn’t be further from the truth. The key to good governance is an insightful human element – data analysts, database admins and the rest of your IT staff will be essential in providing critical insights and making sure everything runs like clockwork.

Naturally, when choosing to incorporate relatively newer technologies like AI & ML into your governance programs, a trusted MSP with experience in the domain is essential. Thankfully, we have all the expertise required to make your data governance program truly ‘intelligent’!