/ Why data governance is critical for BI success

Why data governance is critical for BI success

Data has become a critical asset for organizations in all industries. From retail companies tracking customer purchase data to healthcare organizations managing patient health records, data is used to make better decisions, improve operations, and drive growth.

But as organizations amass more and more data, they also face greater challenges in terms of managing and governing that data. If data is not properly managed, it can quickly become a liability rather than an asset. This is where data governance comes in.

Data governance offers a framework for organizations to manage their data more effectively. It helps to ensure that data is accurate, consistent, and compliant with regulations. It can also help organizations better understand and use their data.

Data governance is critical for businesses that want to make the most of their data assets. Without proper data governance, organizations risk making bad decisions based on inaccurate data, violating regulations, and losing customer trust.

 

 

What is data governance?

First, let’s define data governance. Data governance is the process of creating and enforcing policies and procedures for managing data. This includes everything from setting standards for how data should be collected and stored to defining roles and responsibilities for those who work with data.

The goal of data governance is to ensure that data is managed in a way that meets the organization’s needs. This includes ensuring that data is accurate, consistent, and compliant with regulations.

When organizations don’t have data governance policies in place, they run the risk of making wrong decisions based on inaccurate data, violating regulations, and losing customer trust.

 

Why is data governance important?

Data governance is important because it helps organizations get the most out of their data assets. Without proper data governance, data is at risk, it often can’t be found in the correct place, and there’s no guarantee that data sets are up to date.

And if you think about it, data is becoming increasingly important to organizations in all industries. In the past, data was primarily used for reporting and analysis. But now, data is being used for much more. It’s being used to make better decisions, improve operations, and drive growth.

This is why data governance is so important. It helps organizations to ensure that their data is managed in a way that meets their needs.

 

What are the benefits of data governance?

There are many benefits to data governance, including:

Ensures data is accurate, consistent, and compliant with regulations

The first and most important benefit of data governance is that it helps to ensure that data is accurate, consistent, and compliant with regulations.

Data that is accurate and consistent can be used to make better decisions. And data compliant with regulations can help avoid costly fines and penalties — saving your company money.

Helps organizations to better understand and use their data

Data governance can also help organizations better understand and use their data. When data is properly governed, it is easier to find, access, and use. This makes it possible for organizations to get more value from their data assets.

For example, data that is properly governed can be used for things like predictive analytics and machine learning, which can help organizations improve their operations and drive growth.

Increases customer trust

With data privacy becoming an increasingly important concern, data governance can also help to increase customer trust. When customers know that their data is being properly managed, they are more likely to do business with your company.

If your company is going to remain above board with privacy laws such as GDPR and CCPA, customers must be able to trust that you are handling their data properly. A robust data governance program can help to build this trust.

In many industries, like financial services or health care, there are regulations that control how businesses can govern their users’ data. If a business violates these regulations, they could face fines or lawsuits.

Drives growth

Finally, data governance can help to drive growth. When data is properly managed, it can be used to improve decision-making, operations, and customer relationships. This can lead to increased revenue and market share for your company.

Consider a company that uses data to improve its customer service. This company will be able to resolve customer issues more quickly and efficiently — leading to happier customers and increased retention. As a result, this company will see increased revenue and market share.

 

 

What are the challenges of data governance?

While there are many benefits to data governance, there are also some challenges that organizations face when trying to establish data governance. These challenges include:

Getting buy-in from stakeholders

In order for a data governance program to be successful, it is important to get buy-in from all stakeholders. This can be a challenge because data governance can require changes to the way that things are done — and some people may be resistant to change.

One way to overcome this challenge is to ensure everyone understands the benefits of data governance. When stakeholders see how data governance can benefit them, they are more likely to get on board.

Establishing clear roles and responsibilities

Another challenge of data governance is establishing clear roles and responsibilities. Data governance is a team effort, and it is crucial to make sure that everyone knows their role. Otherwise, things can quickly become confusing and chaotic.

If you are going to establish data governance, it is important to take the time to establish clear roles and responsibilities. This will help to ensure that everyone is on the same page and that things run smoothly.

Defining processes and procedures

Not only are roles and responsibilities important, but so are processes and procedures. Data governance can be a complex undertaking, and it is important to have well-defined processes and procedures in place.

What processes are required for data governance? This will vary from organization to organization. However, some common processes include:

  • Data collection
  • Data quality management
  • Data security
  • Data privacy

As you can imagine, establishing these processes and procedures can be a challenge. However, it is crucial to take the time to do it right.

Managing data governance tools

Data governance tools can be very helpful but can also add another layer of complexity. These tools need to be managed in order to be effective — and this can be a challenge.

These tools include things like access control systems like Okta and third-party data architecture tools like Snowflake. These are common solutions among enterprise-scale companies, but smaller businesses often don’t need their complexity.

If you are going to use data governance tools, it is important to have someone who is responsible for managing them. This person will need to keep track of the various tools and make sure that they are being used properly.

Ensuring data quality

Ensuring data quality is another challenge of data governance. Data quality is important for many reasons, but it can be difficult to maintain. There are a number of factors that can impact data quality, such as:

  • Inaccurate data: If data is inaccurate, it can lead to bad decision-making
  • Inconsistent data: If data is inconsistent, it can be difficult to use
  • Incomplete data: If data is incomplete, it can make it difficult to get a complete picture of what is going on
  • Outdated data: If data is outdated, it can make it difficult to make decisions in a timely manner

 

How do I implement data governance with my business intelligence solution?

Business intelligence tools have helped organizations overcome many of the challenges associated with data governance. BI tools make it possible to collect, store, and analyze data — which is critical for data governance.

There are a number of different BI tools on the market today, and each has its own strengths and weaknesses. When choosing a BI tool, it is important to consider your specific needs.

Once you have chosen a BI tool, you need to implement it. This can be a challenge, but there are a few tips that can help:

Ensure that you have a data governance policy in place

First, you need to make sure that you have a data governance policy in place. This policy should be clear and concise, and it should outline the roles and responsibilities of everyone involved in data governance.

Define your processes and procedures

As we mentioned earlier, it is important to have well-defined processes and procedures in place. These processes and procedures should be designed to meet your specific needs.

Train your employees and teams

Once you have a data governance policy in place, you need to make sure that your employees are properly trained. They need to understand the policy and they need to know how to follow it.

Monitor and enforce your data governance policy

Finally, you need to monitor and enforce your data governance policy. This is important to ensure that your policy is being followed and that it is effective. This will require some effort, but it is worth it.

 

Increase your data governance ROI

Data governance can be a challenge, but by taking the time to establish data governance processes and procedures, you can improve the quality of your data — and you can make better decisions.

When you can successfully implement data governance, you will see a return on your investment across all of your BI projects. If you want to see a return on your data governance investment, you need to ensure that you are doing it right. And one of the best ways to do it right is to use a BI tool.

Get started with data governance today — and see the difference it can make in your organization.

Check out some related resources:

Manage, Transform, and Visualize Your Data

Building Data Integrations on a Modern BI Platform

Modern BI for All Field Guide: Data Agility

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