What is Data Management? Definition, Examples, Best Practices (2022)

By Michael Feder

January 24, 2022 • 9 minutes

At a glance

  • Management of data refers to the processes that store, secure, process and analyze data and includes the management of servers, databases, networks and computers.
  • Careers in data management include roles such as computer systems analysts and information security analysts.
  • Eight out of 10 of U.S. business leaders surveyed said investing in data quality has resulted in high return on investment for company initiatives.
  • A bachelor’s degree in computer science or information technology is a common prerequisite for work in data management.

In many ways, data is central to the operations of the modern world. From the ones and zeros that make up a tweet to the digital records of a medical patient, data is constantly stored, secured and shared.

It’s no wonder that data management — the collection of processes that ensure data is stored, secured, processed and analyzed — has become so crucial to modern businesses.

In the following, we’ll explain why data management is important, what its main objectives are, and the place management occupies in the current business landscape.

Why is data management important?

The secure and efficient management of data is critical because of its central role in the modern world. Providing directions through a phone app is a great way to demonstrate this. If a user needs to get a specific location in the city, the application will need to know where they are, what direction they’re facing and where they’re going. All that, along with up-to-date transit and weather information, goes into providing a seamless experience.

Data management ensures this complex process happens quickly. Behind the scenes, this phone application connects data sourced from the user’s phone, a GPS satellite and a remote collection of servers.

Without proper management, the whole process can break down, leading to an inoperative application. That leads to unhappy customers, which can spell ruin for a business.

(Video) Data Management Basics and Best Practices

Product development

Even businesses outside the tech sector can benefit from providing digital products to their customers. A healthcare organization, for instance, can use an application to track and manage patients’ health needs and store that information in a database.

These new products can help grow a business by making services more accessible. When developing these products, businesses depend on data governance to ensure the necessary data is delivered, processed and stored securely and efficiently.

Trust

Businesses of all stripes often work with sensitive user data. Credit card information, home addresses and health records are just some of the data that proper management is designed to protect within their database. Knowing that their data is secure, customers will trust a business and want to return.

Conversely, let’s say customers do not trust that their credit card information is secure within a given company's database. Chances are they won’t go through with a purchase. So, in effect, quality data management can help a business’s bottom line as well as its reputation.

Analytics

Data management isn’t just about processing and moving bytes around. It can be used to develop powerful insights that guide businesses on future decisions, too, especially in today's world of big data. This is known as data analysis, and it’s supported by data management.

Processing market data, for instance, can help an investment bank predict the future of a stock. This, in turn, can give the bank an early start on a positive investment, or the insight to sell a stock before it loses value. Data analysis, in other words, is a crucial aspect of why data management is so important for keeping businesses profitable.

According to a survey conducted by Experian, which canvassed 500 executives and managers at U.S. organizations in a variety of industries, eight out of 10 said investing in data quality has resulted in a high return on investment for business initiatives. It’s no wonder why data management has become such a popular and exciting field.

Information technology is more than data management. Learn more about this diverse and exciting field on our blog!

What are the objectives of data management?

Some major objectives of data management are:

  • Secure storage of data across both hardware (physical servers) and software (cloud-based data centers)
  • Working within compliance and regulatory standards to ensure data governance, including security and privacy
  • The creation and accessibility of data from a diverse number of sources
  • Making data available despite high network strain or a catastrophic server failure
  • Seamless incorporation of data into software, such as applications and analytics programs

Of course, these objectives will not apply to the same degree to every business. A company managing patient records, for instance, may have no interest in incorporating its data into a phone application. Nonetheless, these objectives can help guide and inform the management processes of many businesses.

(Video) 5 Data Management Best Practices for Better Data Analytics #dataanalytics #datamanagement

Types of data management systems

There are four main types of data management systems:

Hierarchical DBMS

In this model, data is organized in a hierarchy from top to bottom. At the top, “parent” data is split into smaller “child” data. For instance, a “parent” piece of data may include a user’s entire social media profile on an application. This user’s particular name, past activity and other derivative pieces of data would all constitute “children” data.

Network model

In contrast with a hierarchical model, the network model allows for each “child” piece of data to have multiple “parents.” This does not have a top-down structure, and data can instead be accessed through different pathways.

Relational model

Popular for its simplicity, the relational model of data management places data within rows and columns in a table. Through structured query language (SQL), this data can be accessed and manipulated for a wide number of applications.

In this model, data is stored in “classes,” which are made up of smaller units of data called “objects.” Within objects, units of data are stored along with their operations (e.g., the functions that that data will go on to perform). In effect, this model combines both a database and an application.

Best practices for data management

Here are some practical tips that can help optimize the performance of data managers:

Long-term and short-term storage

Some data may require constant access and manipulation, while other data sees less use. Understanding the distinction can allow data managers to place data in the right location. Long-term preservation systems can keep less-used data safe but accessible when needed. In turn, this can free up resources needed to facilitate more active data transfers.

Always back up

UC San Diego suggests a “Rule of 3”: Keep two copies of everything on-site (local servers) and one copy off-site (in the cloud). This will ensure that, even in the event of catastrophic on-site failure, there will always be a copy of your data in a safe, remote location.

Use file naming to get more organized

Every business will require a different convention but settling on a file-naming system can help keep everyone in an organization on the same page. When looking at a long list of documents, for instance, it’s much easier for employees to find a specific document if it includes its creation data, author name and other important info in the file name.

(Video) Dive Into Data: Data Management Best Practices

Keep good notes

Creating a document that includes any and all comments, thoughts and suggestions can give context to data. This document can help introduce a bulk of otherwise raw data to someone with less context and provide important information for understanding what they’re looking at.

Jobs in data management

Positions within the field of data management include:

Computer systems analysts

Overview: By analyzing businesses’ established computer systems, these analysts propose changes that can make the systems more efficient, secure and cost-effective. They can work under a computer and information systems manager to implement these new solutions for a business.

Salary range: In May 2020, computer systems analysts earned between $56,510 and $152,060, according to the U.S. Bureau of Labor Statistics (BLS).

Educational requirement: A bachelor’s degree in information technology is a common requirement for computer systems analysts.

Job outlook: Employment of computer systems analysts is projected to grow 7% from 2020 to 2030, according to BLS.

Information security analysts

Overview: These analysts are tasked with implementing protocols to keep a business’s data, network and computer systems safe from security threats. They are responsible for keeping data systems up to date with the latest regulatory standards, as well as keeping a watchful eye in case of security breaches.

Salary range: In May 2020, information security analysts made between $60,060 and $163,300, according to BLS.

Educational requirement: A bachelor’s degree in computer and information technology or a related field is typical. Given the importance of keeping business data safe, a master’s in cybersecurity can help a candidate stand out as especially qualified.

Job outlook: This role is projected to grow 33% between 2020 and 2030, according to BLS.

Performance hiccups

Whether due to large amounts of traffic or faulty hardware, the efficient transfer and management of data can present a big challenge in data governance. Investing in up-to-date hardware, as well as running stress tests and diagnostics, can help prepare a data network for strain.

Keeping up with regulations

Ever-changing compliance and regulatory standards, especially related to data security, are enough to make one’s head spin. Using automated tools and maintaining robust security can help data managers keep everything in line with regulations.

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Maintenance

There are always things to fix and change when it comes to managing data. Taking down an entire network to implement these changes, however, can cause unnecessary disruptions for users. Incorporating mirrored databases and failover systems can allow data managers to perform the work they need without interrupting the flow of data.

Privacy laws and regulations

Both domestically and internationally, the laws surrounding data privacy and protection play a central role in how data management operates. On the one hand, data presents a powerful opportunity for businesses to better understand potential customers and provide more quality products. On the other hand, individual privacy is broadly recognized as a human right that needs to be protected.

Understanding the network of laws that govern the collection, processing, management and use of data is a prerequisite to proper data governance. These laws include:

Much of what we take for granted today, whether it be booking a rideshare on your phone or making a purchase online, depends on data management. Those specializing in data management, therefore, are a necessary part of how this modern world works, both today and tomorrow.

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FAQs

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 data management simple definition? ›

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

What are 5 data examples? ›

Five examples of data includes:
  • weights.
  • prices and costs.
  • numbers of items sold.
  • employee names.
  • product names.
30 Sept 2019

What is a data management strategy? ›

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 the most important data management principle? ›

One of the most important data management principles is developing a data management plan. To be effective, organizational initiatives require a strategic approach to data management.

What is the 4 elements of management in a dataset? ›

Volume, velocity, variety, and veracity. Volume is how much data you are actually managing.

What is data definition with example? ›

Data is defined as facts or figures, or information that's stored in or used by a computer. An example of data is information collected for a research paper. An example of data is an email. noun.

What is data management called? ›

Data management involves a variety of interrelated functions. The primary technology used to deploy and administer databases is a database management system (DBMS), which is software that acts as an interface between the databases it controls and the database administrators, end users and applications that access them.

What is data management and why is it important? ›

Data management is the practice of collecting, organizing, protecting, and storing an organization's data so it can be analyzed for business decisions. As organizations create and consume data at unprecedented rates, data management solutions become essential for making sense of the vast quantities of data.

What are the elements of data management? ›

Elements of a Data Management Plan
  • Roles and Responsibilities. ...
  • Types of Data. ...
  • Data Formats and Metadata. ...
  • Access, Sharing, and Privacy. ...
  • Policies and Provisions for Re-use & Re-distribution. ...
  • Data Storage and Preservation. ...
  • Costs.

What are the three general categories of data management? ›

The three general categories of data to which retention requirements apply are: historical, financial, and research data.

What are the 4 steps in organizing data? ›

Contents
  1. 1.1 Data organization. 1.1.1 Step 1: Data identification. 1.1.2 Step 2: Data storage. 1.1.3 Step 3: Sourcing and collection. 1.1.4 Step 4: Data preparation.
  2. 1.2 Assessing Module 2.
12 Dec 2019

What are the 7 types of data? ›

7 Primary Data Types for machine learning
  • Useless.
  • Nominal.
  • Binary.
  • Ordinal.
  • Count.
  • Time.
  • Interval.
29 Aug 2018

What are some 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. Data is a raw form of knowledge and, on its own, doesn't carry any significance or purpose.

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.

What are 10 types 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 the main function of data management? ›

The goal of data management is to help people, organizations, and connected things optimize the use of data within the bounds of policy and regulation so that they can make decisions and take actions that maximize the benefit to the organization.

What is KPI in data management? ›

Key Performance Indicators or KPIs are quantitative, high-level characteristics that measure success. In the context of policy monitoring, the KPIs quantify the overall progress of data governance and data stewardship activities in the area of data quality.

What are the five 5 basic elements of management? ›

At the most fundamental level, management is a discipline that consists of a set of five general functions: planning, organizing, staffing, leading and controlling.

What are the six 6 key components of management structure? ›

Management experts use the six basic elements of organizational structure to devise the right plan for a specific company. These elements are: departmentalization, chain of command, span of control, centralization or decentralization, work specialization and the degree of formalization.

What are the five core functions of master data management? ›

Master Data Management Essentials

Cleansing and Correction of Erroneous Data. Data Quality Monitoring and Reporting. Business Taxonomy and Hierarchy Management. Concept Standardization (e.g. Address)

What is the best definition data? ›

In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject.

What is a good definition for data? ›

1 : facts about something that can be used in calculating, reasoning, or planning. 2 : information expressed as numbers for use especially in a computer. Hint: Data can be used as a singular or a plural in writing and speaking.

How do you write a good data definition? ›

Writing a data definition: The definition should explicitly describe and explain the meaning of the data element, data entity or concept represented and careful to describe context in which the term is used. A well-formed definition should be: Concise: Described as succinctly as possible.

What is data management and types? ›

Data Management, Defined

Managing digital data in an organization involves a broad range of tasks, policies, procedures, and practices. The work of data management has a wide scope, covering factors such as how to: Create, access, and update data across a diverse data tier.

What is the difference between data and data management? ›

Data Governance: The Difference Explained. In the simplest terms, data governance establishes policies and procedures around data, while data management enacts those policies and procedures to compile and use that data for decision-making.

Who is responsible for data management? ›

Who is Responsible for Implementing a Data Management System? The IT department is typically responsible for implementing a data management system. This is usually overseen by a CDO or the lead on the project. However, a company may also choose to outsource the data management implementation process.

What are the 4 major important roles in data and information management? ›

Depending on the size of the organization, its culture, needs, goals, as well as the operating model, the role could also fall onto the following: Data governance program manager. Information security and compliance lead. Data governance council.

What are 3 useful tools for organizing data? ›

Top 10 Powerful Organizational Tools for Work
  • Microsoft OneNote. This tool has a cloud system of data sharing that allows you to synchronize notes across all your devices as you type. ...
  • Trello. Trello is arguably one of the best organizational tools for work. ...
  • Google Docs. ...
  • Canva. ...
  • Evernote. ...
  • Airtable. ...
  • Asana. ...
  • Penzu.
11 Jan 2019

What are the five stages of data? ›

Here, we'll walk you through the five steps of analyzing data.
  • 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. ...
  • Step Four: Analyzing The Data. ...
  • Step Five: Interpreting The Results.
16 Mar 2020

What are the 3 examples of data? ›

The main examples of data are weights, prices, costs, numbers of items sold, employee names, product names, addresses, tax codes, registration marks etc. Images, sounds, multimedia and animated data as shown.

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 examples of data systems? ›

Examples of data systems include Student Information Systems (SISs), assessment systems, Instructional Management Systems (IMSs), and data-warehousing systems, but distinctions between different types of data systems are blurring as these separate systems begin to serve more of the same functions.

What are examples of data services? ›

A few other examples of DaaS providers include: Urban Mapping, a geography data service, provides data for customers to embed into their own websites and applications. Xignite is a company that makes financial data available to customers. D&B Hoovers provides customers with business data on various organizations.

What is data definition with example? ›

Data is defined as facts or figures, or information that's stored in or used by a computer. An example of data is information collected for a research paper. An example of data is an email. noun.

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 are the 5 examples of services? ›

According to the U.S. Census Bureau, the following industries are part of the service sector:
  • Recreation.
  • Arts and entertainment.
  • Social assistance.
  • Health care.
  • Waste management.
  • Professional and technical services.
  • Scientific services.
  • Transportation.

What is the role of data management? ›

Data management is the practice of collecting, organizing, protecting, and storing an organization's data so it can be analyzed for business decisions. As organizations create and consume data at unprecedented rates, data management solutions become essential for making sense of the vast quantities of data.

What are the components of data management? ›

Elements of a Data Management Plan
  • Roles and Responsibilities. ...
  • Types of Data. ...
  • Data Formats and Metadata. ...
  • Access, Sharing, and Privacy. ...
  • Policies and Provisions for Re-use & Re-distribution. ...
  • Data Storage and Preservation. ...
  • Costs.

What are the 8 types of data? ›

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

What is data definition in the system? ›

In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject.

What are 9 types of data? ›

9 Different Data Types to Better Understand Your Business
  • Internal data.
  • External data.
  • Time-stamped data.
  • Structured data.
  • Unstructured data.
  • Open data.
  • Big data.
  • Genomic data.

What are services give 3 examples? ›

A service is an "(intangible) act or use for which a consumer, firm, or government is willing to pay." Examples include work done by barbers, doctors, lawyers, mechanics, banks, insurance companies, and so on.

What are 3 examples of services you use? ›

Examples of personal services include:
  • doctor's visits.
  • haircuts.
  • pedicures.
  • legal advice.
  • surgery.
  • house cleaning.
  • babysitting.
  • therapy sessions.

What are the 7 types of services? ›

Types of Services
  • Business Services. The services used by business organizations to conduct activities are known as business services. ...
  • Social Services. ...
  • Personal Services. ...
  • Banking. ...
  • Insurance. ...
  • Transportation. ...
  • Warehousing. ...
  • Communication.
9 Jun 2022

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