What Is Data Management? Strategies & Examples (2023)

Data management is the process of gathering, storing, analyzing, and sharing data within a larger organization. Prospects and customers create tons of valuable information every day, but it’s estimated that only 32% of that data is actually used to a company’s benefit.

The sheer amount of available data can make it difficult to know what to look for and how to use it. Pouring it all into a big pile doesn’t magically reveal insights into customer behaviors or improvements to existing business operations. A company must develop a baseline strategy for data management to convert countless data points into measurable, actionable insights and results.

Key takeaways

  • Data management involves the collection, storage, analysis, and sharing of data within an organization.
  • Every business that creates or uses digital data stands to benefit from data management—especially businesses with high volumes of data.
  • The benefits of data management include data unification, verification, democratization, and privacy.
  • Three key steps for developing a data management strategy include:
    • Determining a “data owner.”
    • Building a data map.
    • Creating and executing action items from collected data.

What is data management?

Data management provides companies with a means to easily evaluate important information in meaningful ways. Customers create new data points every time they use your product or like a social media post.

Relevant data is created through a number of sources, including:

  • Products
  • Websites
  • Marketing channels
  • Customer relationship management (CRM) software
  • Accounting and payment platforms

Companies that actively engage with their data better understand their customers than those that don’t. Combining these sources of data allows companies to build more complete customer profiles and make better-informed business decisions. The lack of a defined data management strategy leads to a situation where executives, team leaders, and even base-level employees make important decisions based on limited, dated, or even inaccurate data.

Where is data management used?

Digital data is used by companies across verticals and of various sizes. Even small companies have websites, product analytics, and more that can be collected and sorted. This means data management practices are leveraged by companies as large as Facebook and Amazon or as small as startups.

Companies with a vast volume of information at their disposal will find data management particularly beneficial. As a general rule, the more relevant data a business has at its disposal, the more accurate their assessment of it will be. A chief reason that Netflix’s “You might like…” recommendation system works so well is because it’s informed by the data of millions of consumers. However, before Netflix was able to leverage that data into an executable strategy, they had to collect, sort, and analyze it first.

Why is data management important?

A 2021 survey of Fortune 1000 companies reveals that 48.5% of respondents use data as a basis for innovation. Additionally, data-centric businesses streamline processes and growth plans using more reliable data. This makes data management an essential practice for any business hoping to hold a competitive advantage over rival offerings.

The benefits of data management include:

(Video) What is Data Management?

1. Data unification

Data management software gathers information from multiple sources in a single platform. Data often becomes siloed within internal tools or individual departments as companies grow. For instance, an analysis of accounting data might reveal the most common renewal window. The ability to access this information will allow your sales team to take advantage of valuable opportunities to renew and upsell customers.

2. Data verification

Data management helps ensure your data is accurate. Reliable data empowers companies to optimize processes, experiment with product features, and plan further into the future with confidence. Without verification, companies may be misled by their own invalid data to invest in harmful processes or strategies.

The use of a well-designed data management tool can help keep your information accurate and up-to-date better than a team of people ever could. Data gathered from one source may be outdated compared to that of a different source. Anything manually entered into a database is subject to human error. Even the act of migrating data from platform to platform can result in conversion errors that corrupt otherwise valid data.

3. Data democratization

Access to your company’s data shouldn’t be limited to C-suite executives. Decisions are being made at every level within your company. Granting data visibility to your teams enables them to make data-informed decisions. For example, product data can help marketers build better campaigns by providing a more complete picture of the features existing customers are using most.

The ability to pull information from a single platform also speeds up business operations. Properly managed data allows teams to pull and analyze data quickly versus waiting for the information to come from other departments after days or even weeks. This capability is especially important as work is increasingly done by teams who aren’t gathered in the same physical location.

4. Data privacy

Collecting great quantities of information comes with great responsibility. Everyone within your company benefits from data visibility. However, certain data, like customer payment information, is sensitive and shouldn’t be available to the company at large. Despite the obvious security risks, more than 70% of employees have access to data they shouldn’t. Companies with proper data management structures in place avoid this problem by limiting permissions to certain types of data only to those with the authority to see it.

The data management process

Without a universal data management plan, every department within a company collects and manages data in isolation and by their own processes. Creating and implementing an organization-wide data management process is necessary for companies to take full advantage of their own data. Luckily, a proper data management plan can be built by following the simple steps outlined below.

1. Designate a data owner

First, you must determine who is going to be in charge of your data—the data owner. Naming a data owner places the responsibility of data management squarely in the hands of a specific individual or team. The data owner has authority over how data is collected and used and who within the organization can see it.

Typical responsibilities of data owners include:

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  • Facilitating the integration of new data streams as a company grows
  • Fielding data-centric questions posed by various departments within a company
  • Determining how data is used by both internal parties (employees) and external parties (partners and vendors)

Data owners are essentially the designated data managers. Because of the important responsibilities involved, data ownership is usually assigned to:

  • CEOs
  • Product managers
  • Analytics teams
  • Senior managers or executives

2. Map your data flow

Once appointed, a company’s designated data owner needs to become acquainted with how digital information flows through your business. They must identify:

  • Sources
  • Existing internal databases (and how to unify their data)
  • Consumers
  • The kinds of data relevant to business objectives

The point of mapping data flow is to identify issues with data migration, storage, and access. It’s important for data owners to keep in mind this data is meant to be consumed by teams within their business. Data owners should interview these internal data consumers to identify potential accessibility issues and gain a better understanding of how data is used by each department.

3. Create and address data management goals

Data flow mapping will reveal opportunities for improvement to existing processes. Data owners should align these data-focused improvements with broader organizational objectives. By doing so, they can produce a list of ways to improve business efficiency.

Data management goals differ from company to company. A startup usually lacks the data volume of a more established business. Their focus often lies on eliminating information gaps and increasing the number of data sources. Larger companies with more complex systems may instead focus on eliminating redundant sets of data or grapple with the technical limitations of data migration.

Data management challenges

Converting information into actionable insights is especially difficult for companies with vast amounts of existing data but no discernible system for managing it. Luckily, data management tools exist that grant companies greater control over how their data is used. When evaluating whether a management tool is right for their company, data owners should keep the following challenges in mind:

Data transformation

Data sets from different sources may not use the same digital format. These format differences hinder a company’s ability to compare its own data against itself. Companies should adopt a data management system capable of converting data in different formats so it can be collected and analyzed in a single repository.

Identity resolution

Identity resolution reconciles the behavior of a single user across multiple devices or settings. For example, some digital products may allow users to engage without signing in. Someone who browses your product, signs in, and then continues isn’t two separate users, but your data may suggest they are.

Registering one user as several is just one of many ways data accuracy can become compromised, especially if it happens with great frequency or volume. Trying to manually track thousands or even millions of users and their journeys manually is impossible. However, a management system with identity resolution capabilities identifies unique users to maintain data integrity, providing one less thing for data owners to worry about.

(Video) Developing an Effective Data Management Strategy and Roadmap

Data accessibility and agility

Data owners safeguard information, but they shouldn’t be the only ones with access to it. Other teams and departments need easy access to different types of data to inform their business decisions. Limited access creates bottlenecks in requests and limits a team’s agility and efficiency.

Agility issues aren’t limited to companies lacking data management infrastructure. 76% of companies with existing data management systems admit that it takes days or even weeks to glean meaningful insights from their own data. Businesses looking to make rapid decisions from their data must adopt data management tools capable of producing requested data within minutes.

Data management tools

Getting the most out of your data requires a management system. There are dozens of data management tools available to companies looking to improve their current solution. Determining which option is best for your company is a matter of where your data comes from and what you plan to do with it once it is collected.

Several of the best data management systems include:


Amplitude is a powerhouse analytics platform trusted by the likes of Ford, Walmart, and IBM. Amplitude contains a wealth of features that provide customers with reliable and secure data that’s easy to collect and view, including:

  • Identity resolution
  • Data transformation
  • Best-in-class data security certifications
  • Data privacy permissions for different levels of users
  • Real-time integrations with products, including Salesforce, Google Ads, and Intercom

Amplitude is a terrific data management system for companies wanting to use their data for innovation and strategy. With Amplitude, companies can easily construct experiments from their data or even build predictions to guide marketing strategies or anticipate customer behavior.


Avo is a tool that helps streamline data management within compatible analytics stacks. It’s an add-on to an existing system that benefits companies unwilling or unable to abandon their current infrastructure. Avo helps companies:

  • Create analytics schemas
  • Audit event tracking plans
  • Publish tracking plans to analytics solutions like Amplitude, Adobe Analytics, and Mixpanel


Protocols is a management tool by customer data platform Segment. It’s built to unite teams around a single resource. It’s especially adept at verifying data quality so companies can build strategies based on reliable information. Protocols can be used to:

  • Build a working tracking plan
  • Identify issues with data integrity with testing code tracking and in-app reporting
  • Document data flow to confirm information accuracy
  • Change incompatible name and event fields without the use of code

Data management best practices

No two companies are likely to find the exact same recipe for data management success. Each company has unique tools, challenges, and needs to factor into their approach to organizing their information infrastructure. However, there are several strategic points that apply in most cases, including:

(Video) Evolution of Data Management Strategy

1. Set goals

Before a data owner can craft a purposeful management strategy, they need to outline what they’re trying to achieve. Goals focus individual efforts and define desired, measurable results. They also set expectations for teams and departments within a company as to what the organization is trying to achieve as a whole.

Business goals should guide everything, including:

  • Determining which data is relevant
  • Evaluating which management tool will work best for a company
  • Prioritizing objectives

2. Build a system that scales

A worthwhile data management system is no small investment. You may be tempted to purchase management software that fits your current needs to minimize cost. However, the point of upgrading your management tools is to create growth. Successful businesses will find themselves outgrowing their “minimal cost” solution as data volume increases, eventually requiring further investment in more robust software.

3. Embrace data visualization

Not everyone within a company that benefits from data will be comfortable analyzing it. Staring at endless spreadsheets and attempting to find value within can prove daunting without a method of displaying the content meaningfully. Illustrating pertinent information in a visual format like a graph increases the accessibility of data for the average employee and encourages the further use of data tools. Visualization capabilities often come standard in well-designed data management systems like Amplitude.

Real-world examples of data management


Product success platform Chameleon used to manage their event tracking manually through Google Sheets. This created a situation in which the resource was constantly out-of-date and inaccurate. There was no way to verify whether the information in the resource reflected the current state of the product. They could no longer trust their data, making it of limited strategic value.

Chameleon adopted the Amplitude-acquired tool Iteratively to assist in data verification. Integrating Iteratively with their existing analytics stack allowed them to build schemas and adopt naming conventions to help confirm and validate events within their product. This greatly improved the trustworthiness of their data. Chameleon was also able to create defined processes for data handling, resulting in increased collaboration between teams.


Planning and shopping app Flipp initially adopted Amplitude to enhance the level of personalization in their marketing campaigns. They achieved their goal, but the Flipp team discovered an additional benefit of using the data management solution: data democratization. Their growth marketing team was able to access reliable data faster than ever before. This allowed them to react to campaigns more quickly than if they had waited for another team to find and send over the data.


Grocery delivery service Instacart struggled for a time with data efficiency. Their data management infrastructure consisted of self-built tools and an internal database. Getting the tools to speak to each other and respond to requests was a frustrating process that required a great deal of time and effort. Additionally, Instacart’s data volume had grown beyond the capacity of their existing management system.

Instacart adopted Amplitude as a way to unite the data from these tools through a single solution. Amplitude was also able to handle their growing data load with ease. This vast infrastructure improvement allowed the Instacart team to focus on product improvements instead of getting bogged down in the development and maintenance of their own tools.

(Video) Data management strategies in 5 minutes!

Unlock the power of data management

Data management is critical to organize and make sense of your company’s vast amounts of data. Once you have a data management process in place, you can use your data to understand key customer insights and turn them into actions that drive conversion and retention. Download the Mastering Retention playbook or take a tour of Amplitude to learn how to make the most out of your data.



What is data management strategies? ›

The Data Management Strategy (DMS) is the process of creating strategies/plans for handling the data created, stored, managed and processed by an organization.

What is data management and examples? ›

Using a data management platform provides you with control over your data for multiple use cases. For example, a data management platform could collect customer data from multiple sources, then analyze and organize it to segment your customers by purchase history. Data management platforms can be housed onsite.

What is an example of data strategy? ›

For example, your data strategy could include a goal of keeping data storage costs below a certain threshold. To achieve this goal, the strategy might define storage tools or services that meet your cost requirements, as well as best practices that can help users optimize storage costs.

What is data management answer? ›

Data Management, Defined

Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively.

Why is a data management strategy important? ›

Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.

What is data management and types? ›

Data management refers to the professional practice of constructing and maintaining a framework for ingesting, storing, mining, and archiving the data integral to a modern business. Data management is the spine that connects all segments of the information lifecycle.

What is data analysis example? ›

A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it.

How do you manage data management? ›

7 Best Practices for Effective Data Management in 2019
  1. Outline your business goals. ...
  2. Prioritize data protection and security. ...
  3. Focus on data quality. ...
  4. Reduce duplicate data. ...
  5. Ensure your data is readily accessible to your team. ...
  6. Create a data recovery strategy. ...
  7. Use a quality data management software.

What makes a good data strategy? ›

As you develop your data strategy, remember the seven key elements defined in this blog—business requirements, sourcing and gathering data, technology infrastructure requirements, turning data into insights, people and processes, data governance, and a roadmap—are all critical pieces to the puzzle as you look to ...

What is a strong data strategy? ›

A Well-Developed Data Strategy Has:

A strong Data Management vision. A strong business case/reason. Guiding principles, values, and management perspectives. Well-considered goals for the data assets under management. Metrics and measurements of success.

How do you create a strategy using data? ›

What To Consider When Building an Enterprise Data Strategy
  1. Your Current Data Maturity Level. ...
  2. Your Industry and Company Size. ...
  3. Your Data Management Team. ...
  4. Outline your data architecture. ...
  5. Define the relationship between BI and your teams. ...
  6. Assign ownership. ...
  7. Establish data governance. ...
  8. Reassess regularly.
Jul 1, 2021

What is the function of data management? ›

The data management function is a core set of business processes, such as Finance, Human Resources, or Facilities Management, that provides resources to facilitate the establishment and adoption of best practices across data management disciplines, and which will always be needed across the full lifecycle of patient ...

What is a data and analytics strategy? ›

What is an Analytics Strategy? An analytics strategy is part of a comprehensive strategic vision to specify how data is collected and used to inform business decisions. It is meant to provide clarity on key reporting metrics by: Specifying the sources and types of data that are collected and used for reporting.

What is data strategy in data governance? ›

A data governance strategy provides a framework that connects people to processes and technology. It assigns responsibilities, and makes specific folks accountable for specific data domains. . It creates the standards, processes, and documentation structures for how the organization will collect and manage data.

What is a data strategy roadmap? ›

The data strategy roadmap shows how and when to implement your data vision and mission. It is a valuable change management tool, but equally important, it ensures alignment to your organization's vision, mission, and strategic goals.

What is a data strategist? ›

Data strategists manage the whole process of sourcing alternative datasets, often working closely with quants, data scientists and portfolio managers. They help to screen datasets to select those which are likely to show promise and fit the requirements of the business.


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