What Is Data Management? Strategies & Examples (2022)

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) Evolution of Data Management Strategy

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:

(Video) The Six Pillars of a Data Strategy | Data and Analytics Guide

  • 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

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

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

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) Build an Effective Data Management Strategy in 7 Steps

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

Chameleon

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.

Flipp

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.

Instacart

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.

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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.

References

FAQs

What is data management strategies? ›

Data Management Strategy is the process of creating a plan to handle the data created, stored, managed and processed by an organization.

What is data management and examples? ›

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.

What is data management answer? ›

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

What do you mean by data strategies explain types of data strategies? ›

A data strategy is a long-term plan that defines the technology, processes, people, and rules required to manage an organization's information assets. All types of businesses collect large amounts of raw data today.

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.

Why do we need data management strategy? ›

Put simply, a data management strategy is an organization's roadmap for using data to achieve its goals. This roadmap ensures that all the activities surrounding data management—from collection to collaboration—work together effectively and efficiently to be as useful as possible and easy to govern.

What are data give 5 examples? ›

In our day to day life, we can collect the following data.
  • Number of females per 1000 males in various states of our country.
  • Production of wheat in the last 10 years in our country.
  • Number of plants in our locality.
  • Rainfall in our city in the last 10 years.
  • Marks obtained by students.

What are the 10 examples of data? ›

10 data types
  • Integer. Integer data types often represent whole numbers in programming. ...
  • Character. In coding, alphabet letters denote characters. ...
  • Date. This data type stores a calendar date with other programming information. ...
  • Floating point (real) ...
  • Long. ...
  • Short. ...
  • String. ...
  • Boolean.
21 Jul 2021

What is data management interview questions and answers? ›

Data Manager Interview Questions:
  • Can you describe an effective data management plan? ...
  • What is the key to ensuring a company is compliant with all laws and regulations? ...
  • Can you share an effective approach to operating with a large amount of data? ...
  • What challenges are you looking for in this data manager position?

Which one is an example of data management services? ›

Some examples of data management systems include: Data governance: Tools like Informatica, Azure Data Catalog, and Talend improve a business's ability to track data and associate it with metadata for later retrieval. Metadata helps improve data structure, organizing it in such a way that makes it more useful.

What are example strategies? ›

10 Examples of effective business strategies
  • Technological advantage. ...
  • Improve customer retention. ...
  • Improve customer service. ...
  • Cross-selling products. ...
  • Increase sales from new products. ...
  • Innovation and pushing boundaries. ...
  • Product diversity. ...
  • Price point strategising.
8 Feb 2022

What are the 5 strategies? ›

Mintzberg developed his 5 Ps of Strategy as five different definitions of (or approaches to) developing strategy. He first wrote about the 5 Ps of Strategy in 1987. Each of the 5 Ps is a different approach to strategy. They are Plan, Ploy, Pattern, Position, and Perspective.

What are the components of a data management strategy? ›

What are the components of a data management strategy? The components of a data management strategy typically include an evaluation of the current state of the organization's data, a vision for where the organization wants to be, and a roadmap for how to get there.

What are the 5 data management functions? ›

It is often referred to by its acronym, DBMS. The functions of a DBMS include concurrency, security, backup and recovery, integrity and data descriptions.

What are the 3 main processes of data management? ›

MDM helps ensure businesses don't use multiple, potentially inconsistent versions of data in different parts of business, including processes, operations, and analytics and reporting. The three key pillars to effective MDM include: data consolidation, data governance, and data quality management.

How do you manage data management? ›

Let's dive into these seven best practices.
  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 are the four components of data strategy? ›

Data strategy: 4 components
  • Business strategy. Your data strategy should reinforce and advance your overall business strategy, which refers to the processes you use to operate and improve your business. ...
  • Organizational roles. ...
  • Data architecture. ...
  • Data management.

What are 3 data examples? ›

Data can come in the form of text, observations, figures, images, numbers, graphs, or symbols. For example, data might include individual prices, weights, addresses, ages, names, temperatures, dates, or distances.

What are the 7 types of data? ›

And there you have the 7 Data Types.
  • Useless.
  • Nominal.
  • Binary.
  • Ordinal.
  • Count.
  • Time.
  • Interval.
29 Aug 2018

What are the common examples of data? ›

Common Examples of Data Sets

Home working schedule. Student grades on an exam. Transactions on a website. Search terms in Google.

What are the 2 main types of data? ›

There are two general types of data – quantitative and qualitative and both are equally important. You use both types to demonstrate effectiveness, importance or value.

What are the 8 types of data? ›

These include: int, byte, short, long, float, double, boolean, and char.

Are there 3 types of data? ›

4 Types of Data: Nominal, Ordinal, Discrete, Continuous | upGrad blog.

What is the most important type of data? ›

Understanding the most important types of Data
  • Big data. Big Data is a term heard often in the last couple of years. ...
  • Smart data. In contrast to Big Data, Smart data is actionable and does make sense and has a clear purpose. ...
  • Dark Data. ...
  • Machine data. ...
  • Transaction data. ...
  • Master data. ...
  • Reference Data. ...
  • Reporting data.

What are the 5 categories of data? ›

5 data classification types
  • Public data. Public data is important information, though often available material that's freely accessible for people to read, research, review and store. ...
  • Private data. ...
  • Internal data. ...
  • Confidential data. ...
  • Restricted data.
13 Jul 2021

What is the best example of database? ›

Few examples of Database are:

Microsoft SQL Server – SQL Server, developed by Microsoft, is a Relational Database Management System. It is built on SQL, the standard query language for Database Management Systems. Oracle Database – Developed by Oracle Corporation, Oracle Database is based on a multi-model DBMS.

How do I prepare for a data management interview? ›

Data Manager Interview Questions
  1. What were your practices for backing up and storing media for your previous employer? ...
  2. What development and implementation procedures do you follow for new data systems? ...
  3. How did you manage proper data sharing practices for your previous employer?

What is data management and types? ›

What is Data Management? Data Management refers to the practice of collecting, storing, using and archiving data effectively. All stages of the information lifecycle are strictly governed by data management. Data Management ensures that an organization is using the most updated form of data available.

What are data management responsibilities? ›

A data manager is responsible for developing, overseeing, organizing, storing, and analyzing data and data systems. A data manager ensures that all of this is always done with the utmost security and confidentiality, and in a timely manner.

Which tool is used for data management? ›

Google Cloud

Like Amazon and Azure, the Google Cloud Platform also offers a wide array of cloud-based data management tools. It also provides a useful workflow manager that's leveraged to tie-up different components together.

What are the 5 main types of management information systems? ›

What Are The Types Of Information Systems?
  • Knowledge Work System. ...
  • Management Information System. ...
  • Decision Support System. ...
  • Office Automation System. ...
  • Transaction Processing System. ...
  • Executive Support System.
14 Jul 2022

What is database management system with 5 examples? ›

The DBMS manages incoming data, organizes it, and provides ways for the data to be modified or extracted by users or other programs. Some DBMS examples include MySQL, PostgreSQL, Microsoft Access, SQL Server, FileMaker, Oracle, RDBMS, dBASE, Clipper, and FoxPro.

What are the three examples of a database management system? ›

DBMS Examples
  • Microsoft Access. Microsoft's DBMS, known as Access, combines a graphical user interface, software development tools, and the relational Microsoft Jet Database Engine. ...
  • MySQL. ...
  • Oracle Database. ...
  • MongoDB. ...
  • IBM Db2 DBMS. ...
  • Amazon RDS. ...
  • PostgreSQL. ...
  • Apache Cassandra.
18 Aug 2022

What is management services example? ›

Management Services means supervision, inspection, quality control, consultation, accounting, regulating, in-service training and other related services provided on a systematic basis to support and improve the operation of vending facilities operated by department licensees.

› resources › what-is-data-manag... ›

Too often, businesses are making key decisions based on data they can't really see or understand.That can compromise business intelligence — which is the ke...
Fishery data must be stored securely, but made easily available for analysis. The design of a data management system should follow the basic data processing pri...
Data management systems make the process of data management more manageable, automating some of the most arduous aspects of unifying and reviewing key data. The...

What are the 3 main processes of data management? ›

MDM helps ensure businesses don't use multiple, potentially inconsistent versions of data in different parts of business, including processes, operations, and analytics and reporting. The three key pillars to effective MDM include: data consolidation, data governance, and data quality management.

What are the components of a data management strategy? ›

What are the components of a data management strategy? The components of a data management strategy typically include an evaluation of the current state of the organization's data, a vision for where the organization wants to be, and a roadmap for how to get there.

What are the 3 data management approaches? ›

Three approaches to implement data governance include 1) the Command and Control approach, 2) the Traditional approach, and 3) the Non-Invasive approach. This article compares and contrasts the approaches and quickly summarizes each approach.

What are 4 examples of data types? ›

Some of these types include:
  • Integer. Integer data types often represent whole numbers in programming. ...
  • Character. In coding, alphabet letters denote characters. ...
  • Date. This data type stores a calendar date with other programming information. ...
  • Floating point (real) ...
  • Long. ...
  • Short. ...
  • String. ...
  • Boolean.
21 Jul 2021

What are the 5 data management functions? ›

It is often referred to by its acronym, DBMS. The functions of a DBMS include concurrency, security, backup and recovery, integrity and data descriptions.

What are the 5 main data types? ›

Most modern computer languages recognize five basic categories of data types: Integral, Floating Point, Character, Character String, and composite types, with various specific subtypes defined within each broad category.

What is data management and types? ›

What is Data Management? Data Management refers to the practice of collecting, storing, using and archiving data effectively. All stages of the information lifecycle are strictly governed by data management. Data Management ensures that an organization is using the most updated form of data available.

What is the first step in data management? ›

Data collection

Collecting data is the first step in data processing. Data is pulled from available sources, including data lakes and data warehouses. It is important that the data sources available are trustworthy and well-built so the data collected (and later used as information) is of the highest possible quality.

What are the 4 stages of data? ›

It's a process that can be broken down into four distinct stages of data transformation: (1) Realising; (2) Transforming; (3) Leading and (4) Innovating.

What is an example of data strategy? ›

Data Strategy Examples

A sales department in a business may have raised concerns about the amount of fragmented information they have about users. Unifying data profiles and providing information like previous product sales may enable the sales team to increase profits by being better positioned to close sales.

What are the 5 components of strategy? ›

These five elements of strategy include Arenas, Differentiators, Vehicles, Staging, and Economic Logic. This model was developed by strategy researchers, Donald Hambrick and James Fredrickson. To achieve key objectives, every business must assemble a series of strategies.

How do you develop a data strategy? ›

Organizations should follow these four steps to create a big data strategy.
  1. Define the business goals and objectives. ...
  2. Identify data sources and evaluate processes. ...
  3. Identify and prioritize big data use cases. ...
  4. Create a roadmap for big data projects.
24 Feb 2022

Which tool is used for data management? ›

Google Cloud

Like Amazon and Azure, the Google Cloud Platform also offers a wide array of cloud-based data management tools. It also provides a useful workflow manager that's leveraged to tie-up different components together.

What are the 5 steps of the data approach? ›

  • Step One: Ask The Right Questions. So you're ready to get started. ...
  • Step Two: Data Collection. This brings us to the next step: data collection. ...
  • Step Three: Data Cleaning. You've collected and combined data from multiple sources. ...
  • Step Four: Analyzing The Data. ...
  • Step Five: Interpreting The Results.
16 Mar 2020

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