When it comes to achieving sustainable organisational success, enabling data-driven decision-making stands as a strategic imperative.
Businesses are morphing into more “data-fueled” entities that depend on verifiable and quantifiable data and less on assumption.
However, as data – both structured and unstructured – continues to grow in sheer volume – ensuring they are handled properly for insight extraction has become a challenging task. This is where governed business intelligence comes into play.
What is Governed BI?
Governed Business Intelligence refers to the process of implementing thought-out strategies, and tools aiming at augmenting data quality, compliance and reliability in business intelligence initiatives.
While traditional BI is about facilitating data analysis for augmented decision-making, governed BI focuses on implementing a governed framework in your BI processes so that you can maximise the value you generate from traditional BI processes. It is a holistic approach of enabling efficient data handling to uphold data integrity, consistency and quality for improved decision-making.
It transforms BI by integrating the principles of data governance into BI practices. Thus, a highly structured BI governance framework can be established that ensures effective management of data assets.
The governed BI framework comprises data governing standards, controls and processes, that upholds data quality and data compliance within the BI ecosystem.
The key components of governed BI are:
Data Governance Framework: As we have already stated, at the core of governed BI is a structured data governance framework that establishes rules for effective implementation of policies, standards and processes when data assets are managed within a BI ecosystem.
Data Quality Management: Another key element of governed BI is data quality management that comprises rules, policies, standards and best practices that ensure the data fed into the BI system is clean, accurate and consistent.
Metadata Management: It defines the set of technologies and strategies implemented to facilitate the management of data about data. It empowers data users to easily search for and get data they need by adding context to it.
Security and Access Control: Governed BI framework defines security protocols, policies, and access control methods that should be implemented by organisations for safeguarding their BI data.
Importance of Governance in Business Intelligence
Organisations solely depending on traditional BI processes often grapple with data silos, manual data processing, inconsistent and inaccurate data, etc., that can affect their business efforts. Fragmented data insight and low-quality data leads to poor decision making.
These issues with traditional BI are doubled down by poor data governance practices. Companies that don’t capitalise on a standard framework with policies and regulations of data asset management can hardly protect user mission -critical data, and comply with regulations. All these adversely impact their decision-making and ROI.
Governed BI, on the other hand, can help organisations address these issues effectively. The highly controlled data governance, with its structured framework for data governance, helps break data silos and enhance data accuracy. Thus, transparency, and accountability can be ensured.
Again, extracting insights from data is no longer a nice-to-have thing in your business operations, it’s a must-have that ensures optimal revenue flows into your business.
Data is no less than currency in the present evolving business intelligence landscape, and to ensure a highly governed BI process, the importance of ensuring data integrity comes second to none. This is because only accurate and clean data input can beget insights that are meaningful, actionable and precise.
Let’s dive deeper into why governance is critical in BI:
Data Quality and Accuracy: Inaccurate predictions led by errorsome and inconsistent data is believed to be highly hazardous to a brands reputation, according to 70% data managers who participated in a survey. And to get accurate results, you need to ensure you have accurate and clean data fed into your BI system. Implementing governance practices, such as data cleansing, mapping, verification and validation, ensures you only use consistent, accurate and clean data for your BI processes. As a result, data integrity can be upheld and decision-making can be strengthened.
Compliance and Risk Management: Effective risk management and compliance with privacy laws is critical to driving substantial growth in your business. That said, any non-compliance or being hit by a security breach can cause a business to face severe and long-term repercussions – penalties, hefty fines, reputational damage or even imprisonment. Enforcing governance within your BI processes can help fight these issues. The security measures employed with governed BI helps track down and mitigate potential security risks, thus enabling businesses to avoid costly data breaches and operational disruptions.
Consistency and Standardization – When you have an established process of governing framework, it becomes effortless to enable highly effective data modelling, data integration, data synchronisation and reporting. It fosters consistency and standardisation that enables businesses to utilise raw data for effective comparison and correlation.
Data Ethics and Accountability – With governed BI, you can establish a framework with rules about data collection, and processing. Thus, you can rest assured that your staff members are handling user data ethically and transparently. Having established rules and responsibilities means you also foster accountability across your data processing activities within BI.
Optimisation of Resources – When you have a governance policy aligned with your business priorities and objectives, you can effortlessly maximise the value you can get from BI initiatives. This is because a set framework and rules for decision-making and investment prioritisation can help you allocate resources based on priorities. THus, your BI initiatives can render resource-intensive results.
Steps to Implement Governed Business Intelligence
To ensure your governed BI initiative is a success, make sure you follow the steps below:
Assess Your Current BI Processes: First off, take a stock of where you are with your current BI processes, data governance standards, policies, practices and security measures. Thus, you can efficiently identify your strengths as well as the security holes and areas that need improvement. A company’s data governance readiness and current BI state can be better understood by conducting a maturity assessment.
Define data governance policies: next up comes the need for setting clear data governance policies, a thought-out framework for data stewardship, establishing data quality and compliance standards. Rules for data accuracy and quality management, metadata management and access control and data lifecycle should be defined in the governance policies for consistent and secured governance practices.
Select the Right BI Tool: You will find a ton of BI tools with bells and whistles jamming the market. Before you splurge your hard=-earned money on a product make sure it aligns with your requirements for Bi governance, scalability, integration capabilities. Make sure it includes all key elements of governed BI mentioned above.
Build a Team of Experts: Any BI initiative is doomed to failure without a team expert at handling advanced BI tools as well as at data analytics, data science and data engineering. Clearly define the roles and responsibilities and reporting process to foster transparency, lawfulness and accountability within your BI process.
Data usage monitoring: For an effective BI governance, the importance of monitoring data usage is beyond words. Proper monitoring enables your BI team to pinpoint and classify data based on usage. Besides, make sure regular tracking of key BI metrics, security loopholes and incidents, and areas of improvements. Constant measurement of the success of your governed BI strategy against KPIs would help determine if it is increasing conversion and engagement rates, or making tasks effortless for employees, etc. Additionally, periodically review your governance policy as your business evolves and amend it if required.
Best Practice of BI Governance
Stakeholder Involvement: Bring all stakeholders involved in one table to gather support for your BI governance initiative and secure buoy-in for the governed BI policies.
Leverage AI and Automation: Cash in on advanced artificial intelligence and machine learning algorithms and analytics tools for augmented data quality management, improved efficacy while also streamlining the BI governance processes.
Regular Audits: Conducting regular audits and reviewing governance practices help easily comply with evolving regulator standards. In addition, you can track down security loopholes, weaknesses and areas of improvements – effectively and faster.
That said, governing business intelligence is a complex task that needs an effort in coordination. However, businesses often grapple with insufficient stakeholder support, financial constraints, inadequate resources and organisational resistance while implementing governed BI.
Cross-departmental collaboration, training staff of the best practices, etc. can help organisations to overcome these challenges. In addition, it’s vital to go for the best compromise between flexibility for consumers and governance control to drive agility, compliance and responsiveness within your BI governance process.
Data Science and Business Intelligence Strategist at Symbolic Data
George Wilson is the Lead Editor at Symbolic Data, where he spearheads the editorial direction and content strategy. With over a decade of experience in business intelligence and data management, George has established himself as a thought leader in the field. His expertise lies in translating complex data concepts into actionable insights for business executives and CEOs.
