The explosive growth of data shows no signs of stopping. In fact, the accumulated volume of data will reach 163 zettabytes by 2025. Recognizing the importance of data as valuable assets, companies are increasingly investing in advanced data analytics to derive insights and spot business opportunities.
To bring these capabilities to even more end users, organizations employ self-service analytics and business intelligence. Self-service BI tools empower business users with different backgrounds in statistical analysis — from analytical rookies to tech-savvy BI pros — to access and analyze corporate data with no involvement of IT teams. The trend is so prominent that Gartner predicts by 2019 “self-service analytics and BI users will produce more analysis than data scientists will”.
Self-service business intelligence brings more agility and improves time-to-insight — critical metrics in the competitive business landscape — but it is not without its own challenges.
Poor data quality. Data quality is of paramount importance since the garbage in-garbage out principle holds true here. Inaccurate, unreliable, and inconsistent data lead to flawed reporting, which undermines trust in self-service BI altogether, or worse, leads to biased business decisions.
Chaotic business metrics. In a self-service environment, business users rely on their own data marts to create reports and glean insights. This means that different business departments come up with their own business metrics that suit their business needs best. But when trying to consolidate and compare reports across the entire organization, chaos can erupt.
Data security. As all types of users get access to corporate data warehouses, an enterprise runs the risk of losing control over data security. Sensitive information might appear in users’ dashboards and spreadsheets, and keeping track of all copies of the data becomes next to impossible.
Compliance failures. New data protection laws like GDPR put pressure on businesses that use personal customer data for analytics purposes and make them audit their entire business intelligence processes. And since failures to comply with data privacy regulations carry hefty penalties and fines, regulatory compliance becomes a pressing issue for BI teams.
A sound data governance strategy helps organizations deal with these challenges and unlock the true potential of modern self-service analytics. But before we delve into how exactly data governance can be of assistance, let’s start with the definition.
Data governance is a set of data management practices, strategies, policies, and standards that allow for an effective and secure way of using an enterprise’s data. Though data governance relies on rules and controls, it helps organizations better support data democratization process and put analytics in the hands of everyone who needs it.
Improved Data Quality for Trustworthy Reports
The main objective of data governance is to ensure that corporate data is available, correct, complete, consistent, secure, and trustworthy. To that end, a data governance framework assigns data ownership, roles, and responsibilities, establishes processes for data control and monitoring, and defines technologies to underpin the effort.
Among others, these processes include data cleansing, enrichment, standardization, and monitoring. The result is unsullied and credible data — the foundation of any successful analytics initiative. Knowing that the data can be relied on, self-service BI users across an organization can trust delivered reports and confidently base their business decisions on them.
A Single Version of Truth for Aligned Metrics
Much like data of poor quality, inconsistent and contradictory data from disparate data sources can undermine trust and invalidate BI insights. By consolidating various data marts into a central repository (or “data lake”), an organization ensures a single source of truth that self-service users can access their business intelligence activities.
Data governance helps ensure that a single source of truth is also a single version of truth — one central view of data that everyone in a company agrees is true and trusted. Having a unified set of business metrics and definitions, business units can speak the same data language, align their analytics, and collaborate more efficiently.
Governed Protection for Sensitive Data
For companies, data security has always been high on agenda, but a self-service world reveals new data protection gaps as unfettered access may leave your data vulnerable. Sensitive data includes information that must be safeguarded as it carries the risk of harm in case of unauthorized or accidental disclosure.
A comprehensive data governance strategy gives business leaders peace of mind and allows employees to confidently approach analytics tasks without the risk of disclosing any sensitive data. To that end, a data governance initiative establishes guidelines for maintaining an up-to-date inventory of all company records, prescribes rules for classifying data by sensitivity levels for role-based and permission-based access, and schedules regular data security audits.
A Strong Framework for Regulatory Compliance
With fines for breaches as high as €20 million or 4% of total annual turnover, GDPR compliance is non-negotiable. Designed to safeguard privacy rights, the General Data Protection Regulation prescribes the rules for collecting and processing personal data by businesses. Given that analytics actively use customer data to deliver deeper consumer insights, self-service users need to be sure they do not put an entire organization at risk.
In line with GDPR’s principles, data governance supports businesses with a data lineage practice to track a data flow from its source to the point of use. With this level of visibility, self-service BI users stay confident that the data they use for analysis come from secure, authorized sources.
Data lineage is also critical for identifying and locating an individual’s personally identifiable information (PII) at any given time. Being able to track down PII across all sources, business users can be sure to erase personal data from all records in compliance with the right to be forgotten.
Self-service analytics is gaining much traction. Simple-to-use yet powerful self-service tools provide straightforward access to data and encourage a greater number of users to generate relevant reports, derive insights, and capitalize on analytics capabilities.
But to drive adoption and ensure the success of a self-service analytics platform, a strong data governance plan needs to be in place. Data governance provides strategies and techniques to achieve clean, high-quality input data that would result in trustworthy, accurate outcomes.