In our modern, digital world, data has become an indispensable resource for organizations across all types of industries. From predictive analytics to machine learning, businesses’ ability to compete in future markets depends on the collection and management of high-quality data.
Big data is now used to understand, target, and serve customers. This is one of the most well-known applications of the 2.5 quintillion bytes of data created every day. The focus is to study customers, interpret their preferences and behaviors, and use this information to predict trends and develop products in response to consumers’ needs and wants.
Seeing the importance of data gathering and analysis, your team will need a comprehensive set of rules to keep your data secure and useful. What you need is called “data governance.” It is the process of regulating the flow of data throughout an organization.
If you’re wondering about the concept of data governance, you’re in the right place. This guide will introduce you to the essentials of data governance and provide practical guidance on devising a framework for the system tailored to your team.
Defining data governance: What it is and why it matters
If you collect information from suppliers and customers in any way, you’re already implementing a form of data governance. In a nutshell, it’s all about how you safeguard the data collected as you operate as a business. Data governance details the roles and responsibilities of individuals who have access to specific sets of data. It also defines the measures in place to ensure accountability and ownership of the data.
Interestingly enough, even though the collection and management of data have been going on for decades, only recently have they been treated as assets. This data explosion and the realities of fast-changing technology have made data management and its branch of data governance crucial for business operations.
Not to be confused as one, data management is an umbrella term for all systems developed and implemented to guide how data is collected, classified, used, archived, protected, and discarded.
On the other hand, the purpose of data governance is to provide policies, definitions, and guidelines on how a company can regulate and measure the benefits of data. This includes how and where data is collected, how and where it will be used, and who’s involved in the various stages between collection and disposal.
Essential components of a data governance framework
To support a company’s data management strategy, the data governance framework should be designed as a comprehensive plan covering all aspects of acquiring, classifying, securing, storing, and deleting data.
Your data governance framework should be tied to the following features of data management:
- Data architecture: Data architecture refers to the system of how users collect, save, translate, distribute, and use data. The objective of data architecture is to interpret relevant data and provide it to people who need it.
- Data storage: Data storage is the collection and preservation of digital information. Digital information is the bits and bytes behind apps, social media, network protocols, user preferences, and more.
- Data stewardship: Data stewardship is the management and maintenance of a company’s data assets to consistently provide users with accessible and relevant data.
- Data security: Data security refers to the practice of preventing unauthorized access to sensitive information and data stored in computers, databases, and on the web.
- Metadata management: Metadata management is the acquisition, maintenance, classification, consolidation, and delivery of metadata.
- Data quality management: Data quality management is a system that guides the collection, application, and distribution of data. Its objective is to produce high-quality, relevant information for business users.
- Data integration: Data integration refers to the measures taken to incorporate data from different sources into a single, centralized view. Its main goal is to enable analytics tools to deliver practical, actionable business intelligence.
- Business intelligence and analytics: Business intelligence and analytics are data management solutions that collect past and present data through the use of statistics and software to interpret raw data. These systems are implemented to help businesses make more data-driven decisions.
Benefits of effective data governance
Here are some of the practical benefits of developing a data governance framework:
- A data-driven decision-making process: Decision-makers can make better choices for the future of the business when there’s consistent, consolidated information available.
- Operational efficiency: A data governance framework can increase the efficiency of data management because the processes are streamlined, which can lower the need for maintenance and upgrades.
- Improved data quality: Decision-makers are more likely to trust the quality of information produced by systems guided by a data governance framework.
- Improved data accountability: With a data governance framework, it’s easier for members to have access to the company’s data assets. By defining the roles and responsibilities of each data user, it will also be easier to assign accountability.
- Reduced costs: The implementation of streamlined systems to process data can reduce the cost of data management.
- Compliance: There are regulations and standards for data security and privacy that companies and anyone with a web presence must adhere to. Having a data governance system will make sure you comply with the requirements.
- Increase in revenue: An effective data governance system will help businesses make better decisions. It can also lower the costs of maintenance and mistakes. All benefits stated above will contribute to an increase in revenue.
How to create an effective data governance framework
The main goal of data governance is to secure your organization’s data and to ensure that your process of collecting and storing your customers’ information is safe.
Consumers are becoming more aware of data privacy, and it’s become a factor in whether to buy from a company. 48% of buyers have switched companies over their data policies or data sharing practices.
Another objective of a data governance strategy is to improve business performance through reliable, relevant information available to data users. For this reason, an effective data governance framework needs to plan for the who, what, how, when, where, and why of data.
Define your strategy
As the first step of your data governance framework, you’ll need to define your strategy according to the long- and short-term goals you want for your company. As you do that, it’s also important to ensure the system you develop evolves as your business grows. It should also name the members of your company who will be involved in the project and what their roles are going to be.
This stage of planning is also the best time for you to lay out how you’ll measure the success of your data governance system against your long- and short-term goals. Having all of these written down and distributed to your team will keep everyone in the loop. You can be assured that you are all working toward one goal.
Assess your current data governance strategy
As a business, if you collect and store information from clients and suppliers, you are already operating with a data governance system. Your next step is to find out what data governance strategies are being practiced in your company. This will help your team determine where the current system produces positive results and which ones need improvement.
If you need skilled professionals to consult or provide an assessment of your data governance strategy, Upwork has the largest pool of independent data analysts on the market.
Establish your policies and procedures
This stage requires you to create the policies and procedures that will guide your data governance strategy. Examples of these are rules and regulations for procuring, the channels you’ll use to collect, and your methods of protecting the data. It should also include a proposed schedule for data quality audits.
Another important component to specify is how these policies will be implemented. To introduce the new system, the data governance team may need to conduct a companywide education campaign.
It’s also highly recommended to provide an accessible and comprehensible copy of the policy to team members. In this document, you can motivate them to comply by describing the tangible effects that a data governance system can provide for your business.
Assign roles and responsibilities
Identifying the right positions and people you need on your data governance team is crucial to the success of this initiative. Your data governance team will need senior executives who have the authority to make high-level decisions and can set the overall direction of the program.
Your data governance team will also need to be filled by a team leader, IT representatives, and coordinators. They’ll work with all departments to make sure that the strategies are implemented the same way across the company. They’re responsible for enforcing policies, proposing projects, and monitoring and reporting on the outcomes of the strategies used, as well.
The roles in this team may include a data quality lead, data steward, metadata lead, and data architect. To ensure you have the right talent on your data governance team without the hassle of onboarding, utilize Upwork to engage skilled architects, data managers, and software developers. Independent and highly qualified professionals are available for long-term contracts or one-off jobs.
Identify and utilize tools
Choosing and utilizing effective data governance tools will not only secure your company’s data, but it will also make sure you meet the requirements of ever-changing compliance standards.
To understand the tools you’ll need for your business, you should first learn the types of data you’ll want to analyze and your data integration requirements. These tools will fall into the category of data analytics, metadata management, data lineage, and data protection, to name a few.
Measure outcomes
Your data governance system is not a “set it and forget it” kind of system. Instead, it’s an ongoing practice to make sure it’s evolving as your company grows. The process of measuring your strategy’s effectiveness can get complicated because of the number of available data formats. But that doesn’t mean you shouldn’t try. It only means you need to provide valuable metrics to assess your program’s success.
Your data governance performance measures can include:
- Data quality measures: Monitor the quality of data that your strategy produces according to completeness, accuracy, and timeliness. Also, set up controls to measure how much of the data you collect is used and if the stored data volumes are increasing or decreasing.
- Security measures: Don’t forget to include policies and measures to guide how paper documents are handled. Checking how your digital and paper metrics are doing over time can tell you if your strategy is functioning as it should.
- Compliance: Create a certification process (either self-certification or other means) for each department to confirm compliance with the data governance policies and procedures.
- Stakeholder criteria: These measures may include the number of workers trained in the new system, improvements in productivity, and stakeholder satisfaction, among others.
- Measured by objective criteria: Provide metrics to measure if outcomes are decreasing, such as an inaccurate decision due to bad data and penalties incurred from regulatory bodies.
A great way to present your metrics to your team and stakeholders is through data visualization. Infographics require minimal explanation and are helpful in communicating information. Engage with top-rated data visualization specialists on Upwork to create an impactful visual for your data governance measures.
Create a data governance framework with independent professionals
An effective data governance strategy is crucial in making sure that your company has access to secure, readily available, high-quality data. Aside from being a requirement for compliance, good governance also boosts productivity and cost-efficiency.
To ensure that data remains a competitive business advantage for your company, invest in an effective data governance strategy, and select the right team members to help you manage it.
