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What is the Role of Data Governance in Business Intelligence?

Without an efficient data governance framework, data inconsistencies across disparate systems might not get resolved. It is hard to find hidden business insights from disparate data repositories. A solid data governance strategy ensures the data is consistent, compatible, repeatable, and reliable. Business models accentuating advanced data governance policy achieve better outcomes in terms of data quality and data usage. Besides, it increases the efficiency and effectiveness of data accessibility, data reliability, and data activation.

The data governance framework is the backbone of most data-driven organizations. Considering the immense amount of data companies send, receive, process, analyze, and store, data governance is required to ensure such data is accurate, consistent, reliable, secured, and accessible. Indeed, formal data governance is crucial for business intelligence and to establish the operational efficiency of a data-driven business.

The data governance specialists from the EPC Group carry high experience to craft a robust enterprise data governance program by aligning business goals and technical precedence. We are specialized to ordinate our intelligent data governance policies with the strategic goals of your business, so the actionable insights provided to the decision-makers and stakeholders are of the highest quality.

What are the Best Practices of Data Governance?

Data governance is a strategic management system that defines governance policies, enforces decisions, and creates the processes for data collection, regulation, storage, security, analysis, integration, sharing, accessing, and using as per compliance requirements without breaching data privacy and protection laws.

A scalable data governance framework guarantees data integrity, security, transparency, and availability. It also reduces data risk and indicates a better way to generate, manage, and consume data. In addition, data governance defines various layers of responsibilities in an organization. Data governance ultimately determines data architecture, data warehousing, and management alignment.

Consider data governance as a strategic roadmap that supports enterprise data management to achieve operational agility and governance goals. Flexibility is one of the significant features of a robust data governance framework. It means you can tailor it to meet the level of governance and regulatory compliance required by an organization. You get this flexibility because data is used for business intelligence which is dynamic and constantly evolving.

A strategic data governance framework encapsulates data management, metadata management, Master data management, risk management, project management, and program coordination. Here are the scope and goals of each of these aspects:

  1. Data Management
    An administrative process involving how data is acquired, validated, stored, protected, and processed for highlighting the way of accessing such data, its level of reliance, and how timely it is for users.
  2. Metadata Management
    Refining the process and setting strategic goals to collect, categorize, maintain, integrate, control, manage and deliver metadata.
  3. Master Data Management
    To establish a non-invasive approach to address data privacy and drafting a policy around different aspects of master data governance such as data quality, privacy and protection, retention and deletion, and risk management.
  4. Risk Management
    To plan and implement an actionable roadmap to integrate risk management components such as integrity, accuracy, completeness, consistency, effectiveness, efficiency, confidentiality, availability, compliance, and reliability in data governance regulations.
  5. Project Management
    A practice that provides a structure for using data and making data-driven decisions based on the actionable insights to safeguard projects within a larger project.
  6. Program Coordination
    It is a structure and framework for tactical planning, setting goals, making decisions for programs, and assigning responsibilities and roles. Program coordination also recognizes the subsequent stakeholders concerned and who is permitted to grant program events and priorities.

Importance of a Solid Data Governance Framework for Business Intelligence

A solid data governance program simplifies and enhances the business intelligence process.

Easier Access to Specific Data

Data governance tools offer effective data management to specify the source of a particular data and clearly defining what data can be accessed by whom. It makes privacy regulation easier and helps in carrying out business intelligence using high-quality data.

For example, for business intelligence analysis, you want accounting data. Then, apart from getting easily accessible accounting data, you get to know its source, where it is stored, and who is allowed to access it. A good data governance policy makes data comfortably available for business intelligence, ensuring it is structured accordingly. The analytics teams then derive actionable insights from the structured data so stakeholders and business owners can make data-driven decisions backed by facts and insights.

Access to Accurate and Consistent Data

Robust data governance seamlessly resolves data inconsistencies and inaccuracies in different silos or systems. For instance, a particular customer name may be erroneously listed differently in customer service, logistics, and sales systems. Such errors cause problems with data integration efforts. It also gives rise to data integrity issues, affecting the precision of business intelligence, enterprise reporting, and analytical services. Efficient governance capabilities ensure accuracy and consistency across various silos, data warehouses, and systems.

It is hard to make sassy strategic business intelligence decisions without the availability of accurate and consistent data. A business-specific data governance policy ensures its business intelligence results are more effective by making available more precise and consistent data that is easier to analyze. A potent analytics roadmap makes business intelligence effortless by allowing it to access the crucial and most necessary information to succeed in multiple areas, including finance, marketing, sales, or any other department.

Effective Enterprise Data Management

One of the most glaring risks of providing advanced analytics tools to organizations is their limited data technology knowledge and limited access to data. It could cause conflicting data interpretations, data definitions, and metrics. Having strategic data governance program within the organization ensures appropriate monitoring and auditing of its in-house business intelligence platform by eradicating all the traces of bad data quality. With effective data management eliminating data inconsistencies by defining and implementing strict governance policies, privacy regulations, and protocols across the entire business intelligence architecture becomes comfortable.

Boost Confidence in Your Analytics with Data Governance Best Practices

Data governance is an essential aspect of quality control. Business decisions made based on the wrong set of data will yield poor results. Accuracy, reliability, and consistency are the foundations for building data governance best practices. It helps business intelligence processes to access only accurate and structured data. Data accuracy translates into trusted analytics governance for making strategic data-driven decisions.

A lot of business intelligence projects fail because they focus solely on technology. For instance, Microsoft Power BI is an efficient, reputed, and highly result-oriented business intelligence tool. However, when you feed the wrong data set to the Power BI tool, you will end up getting the wrong insights. If you use these insights, then you will end up with wrong or compromised business decisions. Don’t you want a robust data governance organization that prioritizes quality management of your data?

A comprehensive data governance structure accelerates your confidence in business analytics so that every organization makes business intelligence decisions based on trusted data. Data governance assures you that your metrics are accurate as well as your KPIs. Besides, it helps in aligning data governance goals and privacy policy accordingly.

Break Down Silos in an Organization

Strategic data governance programs often break down data silos in an organization. Data silos often build up when distinct business units use separate transaction processing systems that lack a centralized source or data enterprise architecture. One of the distinguishing effects of data governance is standardizing the data in disparate systems using an ordinary process involving decorated decision-makers from the various business departments. Establishing a centralized source or a central data repository makes it easier for business intelligence tools to use and analyze data for retrieving actionable insights that are valuable for making data-driven decisions.

Summary

Having a robust data governance framework gives business intelligence the foundation it needs for creating reports that lead to making informative and strategic decisions based on accurate and consistent data that yield meaningful outcomes.

EPC Group is well known for its impeccable business analytics consulting and data governance consulting services. Our data governance strategy helps in effective data management so you can access practical and structured data faster and with a simplified approach. With our experience in data governance and enterprise data management, we ensure an explicit data ownership model that makes reliable data available to you whenever you want. Our analytics consulting services will help you derive actionable insights for increased operational efficiency and better decisi9on making with minimal disruptions.

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