A complete data management guide

In this guide to data management, you will learn how to establish a solid foundation for your data governance and metadata management program and more.
Last updated
May 2, 2024

Data management is a complex, multi-faceted process. It's not enough to just collect data; you need to be able to store and manage it effectively too. That means having the right tools in place and knowing how to use them effectively - including metadata collection, which can play an important role in your overall strategy for managing data.

What is data management?

Data management is the process of collecting, organizing, storing and retrieving data. Data management includes all processes related to data collection, storage and retrieval as well as processes related to data quality and security.

Why does it matter?

  • Data management is important because it is the foundation for making data-driven decisions. If you don't have a system in place to ensure that your data is reliable and can be used effectively, then even the most sophisticated analytical tools won't do much good.
  • Data management helps ensure the quality and consistency of your data. Data quality issues such as errors, omissions or incompleteness can render even the best analytics useless if they're based on bad information.
  • Data management helps organizations comply with regulations like GDPR. By creating strong governance processes around information access, storage and usage, organizations can demonstrate compliance with privacy requirements while still keeping their sensitive information secure from cyberthreats (including accidental leaks).
  • Data management helps to ensure that your data is usable for analytics purposes by providing tools like master records (unifying unique identifiers), reference tables (defining relationships between different entities) and metadata libraries (describing what each piece means). This makes sure there's no ambiguity about what's being represented in any given dataset—and ensures there's no room for errors in calculations based on these relationships either!

Understanding the relationship with data governance

Data governance is a set of rules, policies, and processes that control access to data and the use of data. Data governance helps you to manage your data effectively by ensuring compliance with regulations, ensuring data quality, preventing conflicts of interest between business units, and providing accountability for actions taken on the basis of information derived from the system.

Data governance is important because it helps you to comply with regulations like GDPR (General Data Protection Regulation) or HIPAA (Health Insurance Portability Accountability Act). These laws require that companies take steps to protect personal information and maintain safeguards against unauthorized access to it.

How does data integration work?

An enterprise data model is a high-level representation of an organization's business and its data. It defines the relationships between business entities, processes, and information that are stored in databases and other applications.

An enterprise data model can be used in combination with a data governance strategy to help you:

  • Identify what data is needed to support your business operations;
  • Design how you'll structure this information;

What is data lifecycle management?

Data Lifecycle Management (DLM) is the process of managing data throughout its life. It includes data collection, storage, distribution, and destruction. To support DLM you need to manage quality and governance as well as security and access control.

How does data integration work with data management?

Data integration is the process of combining data from multiple sources into a single data store. It can also be used to clean and standardize data, and it’s often used to merge data from multiple sources into a single system. Let’s take a look at each of these three tasks in more detail.

Cleaning: The first step in any data integration project is cleaning the source files so that they’re ready for further processing. This includes removing duplicates or inconsistent values, merging similar records together if needed, making sure all columns are present, etc.

Standardization: If you have multiple systems producing similar but not identical results for certain types of queries (for example, running an aggregation query across two different databases), standardizing those results will ensure that they match up once they're combined into one database. For example, suppose we want to combine two tables containing sales figures from two different stores—one with dollar amounts and one with percentages—into a single table containing both measures (dollar amounts). In order to do this successfully without losing any information when joining them together (i.e., without throwing away missing values) we'll need to convert both columns' values into decimals before performing our join operation

How to set up a data governance and metadata management strategy

Data is the lifeblood of your business, but it can also be a messy, unmanaged mess. With so much data being generated and shared across the enterprise, you need a data governance strategy to help ensure that it's protected and accessible when you need it. In this blog post we'll walk through how to set up a data governance program in your organization using best practices from experts at BMC and other industry leaders.

Dive into a data governance with a team supporting you, a clear vision and set goals you can track over time.

It's best to dive into a data governance strategy with a team supporting you, a clear vision and set goals you can track over time. Make sure you've identified your key stakeholders and are working with them to support their needs before you start.

Once you've made sure that everyone's on board, it's time to get down to business. Being realistic about what can be achieved in the short term is important—it’s tempting to think that once all this work is done, we can be as ambitious as we like with our long-term objectives—but it’s important not to fall at the first hurdle by setting yourself up for failure by being too ambitious in the short term.

A good way of thinking about achieving your goals is using something called SMART: Specific – Measurable – Achievable – Relevant – Timely (or S.MART). For example: "I will lose 10 pounds" isn't specific enough; "I will lose 10 pounds by exercising three times per week" makes it more measurable and achievable; just losing weight might not help me achieve my goal because there may be other factors contributing towards my progress; if I am going through personal issues at work or home that affect my well-being then losing weight won't help me achieve my goal anyway; setting myself unrealistic deadlines means missing out on crucial opportunities while also setting myself up for disappointment when things go wrong (and they will).

Step #1: Get buy-in at the highest level of your organization.

Get buy-in at the highest level of your organization.

The Buy-in stage is all about getting the right people on board and ensuring they're in place at the right time, so you need to start with a clear idea of what kind of buy-in you're looking for. In other words: who are these people? And how do they fit into your overall plan?

Let's look at some examples.

Step #2: Decide on the scope of your program.

Decide on the scope of your data governance program.

If you're new to data governance, it's easy to get confused about the difference between data governance and data management. Data governance refers to policies, processes and procedures for managing an organization's assets—including its information assets. Data management refers to how an organization actually manages its assets once they've been identified as needing a specific level of protection or control.

Data governance is important because it helps companies establish clear ownership over their information assets, create consistency throughout different areas or departments within an organization, prevent inappropriate access or sharing of sensitive personal information (SPI), ensure compliance with regulatory requirements such as HIPAA/HITECH and GDPR, increase productivity by eliminating duplicate efforts across teams within an organization that may not yet be aware of one another's efforts in this area—and more!

Step #3: Build a business case for data governance.

The third step of building your data governance strategy is to build a business case for it. Data governance is important because it helps to ensure that the right data is available at the right time and in the correct format. This helps you best serve your customers, community members, and others who depend on your organization for its services. You’ll need to show how this benefits both the organization itself as well as those who interact with it by providing:

  • Transparency: The ability to understand what data has been collected and why it was collected
  • Accessibility: Ensuring that all stakeholder groups have access to appropriate information in an appropriate format (with full-text search capabilities)
  • Integrity: Providing process guidelines so that only compliant metadata are created

Step #4: Establish your team.

Step 4: Establish your data governance team.

A data governance team is an important element of your organizational strategy, since it provides structure and accountability around the creation, maintenance and sharing of information assets. At a minimum, you should have representation from internal departments that are involved with data creation or use (such as IT, finance and marketing).

The size of this group will vary depending on the number of people who need access to critical business intelligence or reports generated by analytics software. A good rule of thumb is one person per functional area that regularly uses analytics tools such as Tableau or Power BI. For example: If there are three people in sales using Tableau reporting daily then there should be at least three representatives from sales on the governance board (ideally including one senior manager).

This group should also include representatives from legal/compliance and risk management so they can help determine what types of information may require additional protection due to regulatory requirements or cybersecurity concerns. The role these stakeholders play may evolve over time as new regulations come into effect or existing ones change due to technological developments like GDPR compliance becoming mandatory for all EU companies beginning May 25th next year."

Step #5: Determine which stakeholders will be part of the data governance program and how they'll engage.

You should determine which stakeholders will be part of the data governance program and how they'll engage. There are many different types of stakeholders, so you need to consider who they are and what their interests are when deciding how to engage them. Some examples include:

  • Data owners (people who collect, manage, and use data)
  • Data stewards (people who own the policies that govern data)
  • Data custodians (people in charge of ensuring compliance with policies)

Step #6: Set up a metadata management strategy.

This stage is about setting up your metadata management strategy. Metadata management is the process of creating, storing, and retrieving metadata. It’s a key part of the data governance process that should go hand-in-hand with other steps in your strategy, including data quality management and master data management (MDM).

The main objective of this step is to ensure that you have an effective system for documenting all aspects of your organization's information assets so you can easily track what data exists, where it came from and how it's used by different departments or business units within your company.

Step #7: Define KPIs and success metrics to track progress and results.

Before you can measure the success of your data governance strategy, it's important to define what success looks like.

For example, if your goal is to improve the quality of customer service by providing accurate and up-to-date information with fewer errors, then a KPI could be: "percentage of requests fulfilled with an error rate below 5%."

You'll also want to determine what metrics will be used to track progress towards this goal. For example, an LTV metric could be "average order value divided by average number of orders."


With these steps, you can establish a solid foundation for your data governance and metadata management program. It's important to remember that it will take time to develop this strategy and get buy-in from all stakeholders, but the benefits of doing so will be worth it in the long run.

Try Secoda for Free

With Secoda's version control feature, modifications made to datasets are meticulously recorded, documented, and subject to audit, providing data governance teams with a clear understanding of data lineage and historical changes. This functionality empowers teams to swiftly identify and address discrepancies or inaccuracies, ensuring data integrity throughout the entire data lifecycle. Moreover, Secoda's version control fosters seamless collaboration between data governance teams, data analysts, and other stakeholders, establishing a transparent and accountable data governance process. By leveraging the power of Secoda's version control, data governance teams can confidently oversee and manage data assets, mitigate the risk of data-related issues, and maintain consistent data quality standards organization-wide.

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