5 Pillars of Mature Data Governance Framework

A report from Harvard Business Review suggests that on average, leaders actively use less than half of an organization’s structured data to make effective decisions. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data.

If data is so important! As a data-driven organization, the most daunting question is “How do I govern the structured data.” Well, put your gears up to learn how to start climbing a data governance mountain. And if you’re not at the program basecamp but stumbling along in the wooded foothills of the business intelligence Alps, don’t worry, you can begin climbing your mountain there, too.

In our recent episode from “Cut the Clutter Webinar Series”, our speaker answered “What is Data Governance?” and identified why an organization needs data governance and much more. This blog outlines five key pointers in the data governance framework for building a robust data strategy that can be applied across industries and levels of data maturity.

Discovery and Assessment of Data

If you don’t know where your data is, you can’t govern it. Hence, the first step to starting the data governance process is to know the source of your data. It takes time to align multidisciplinary teams with source data as it is a trust exercise. So it’s important to document. Moreover, storing the metrics used in databases, BI dashboards and reports help team members understand the meaning and context.

Additionally, understanding the organization’s data landscape helps in understanding the interdependencies and impact of the changes that you make within your organization. The changes could be system processes or to the infrastructure or when to implement new technologies. Rolling the processes out on the enterprise level sounds very daunting. Hence, it’s important to not boil the ocean. Start with a subset of critical data. Identify the big pain point of your organization. Adopt technology specific to your organization and based on the business problem.

You can also read The Data Confidentiality Law in Healthcare

Data Subject Rights (DSR)

A data subject or the consumer has the right to request and receive confirmation of whether your organization holds its data. Non-compliance is a risk that comes with penalties like hefty fines and penalties, reputation damage, or even legal action. Data governance enables and accelerates compliance. Once your organization is compliant, consumers can submit requests at any time. Besides, your organization is obligated to respond with a copy of any relevant information you have on the subject. There are many data privacy laws passed on annually. Currently, there are almost 30 privacy laws, some of the important ones are listed below:

  • GDPR
  • CCPA
  • CPRA
  • VCDPA
  • CPA
  • Data Security

Data Governance ensures that the right executives have the right access. Protecting our data in our databases and different tools. To secure your data, you first need to get rid of external data for that you need a protection process. An effective data governance plan starts with configuring the systems and solutions best suited for your organization’s business requirements and goals. Moreover, data owners must select the right software and solution partners capable of accommodating your organization’s data, without introducing unnecessary vulnerabilities and risks.

Webinar on Data Governance

Data Architecture

Most of the processes we talked about were centered around discovery, processes, and protection. Data architecture is a framework for how IT infrastructure supports your data strategy. Data architecture’s goal is to align the company’s infrastructure with how data is acquired, transported, stored, queried, and secured. Data architecture is the foundation of any data strategy and as a consequence helps in doing better business.

Most organizations start with data cataloging tools. It helps analysts and other data users to find the data that they need, serves as a repository of available data, and provides information to evaluate fitness data for business cases.

Governance and Stewardship

Sometimes, data owners use Data Governance and Data Stewardship interchangeably, however, there is a big difference between the two. Data Governance brings together multi-disciplined teams to make mutually dependent rules or resolve issues or provide services to data stakeholders. These multi-disciplined teams include data stewards and/or data governors — generally come from the business side of operations. They set guidelines that IT and data groups will follow. Besides, they establish data architectures, implement their own best practices, and address requirements. Data Governance can be considered the overall process of making this work.

In conclusion, the challenge for data-driven enterprises is their ability to manage siloed data. As a consequence, data companies could compromise their customer data or be in violation of the latest regulations and laws, leading to a loss in revenue in a lot of ways.

As a solution, the implementation of a mature data governance framework helps senior managers as they need accurate and timely data to make strategic business decisions. Sales and marketing executives need trustworthy data to understand what customers want. Procurement and supply-chain-management personnel need accurate data to keep inventories stocked and minimize manufacturing costs. Compliance officers check if data is according to both internal and external mandates.

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Factspan Analytics Inc.

Factspan Analytics Inc.

Factspan is a pure play analytics company. We partner with you to build an analytics center of excellence, uncovering insights and solutions from your data.