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.
- 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:
- 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:
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:
- 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:
- 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:
- Existing internal databases (and how to unify their data)
- 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 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 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.
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:
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.
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.
- FiveTran, The State of Data Management Report, 2021
- Harvard Business Review, What’s Your Data Strategy?, 2017
- NewVantage Partners, Big Data and AI Executive Survey 2021
- Seagate, Rethink Data, 2020
- Striim, An Introduction to Database Migration Strategy and Best Practices, 2021
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 with example? ›
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 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.
Whatever business objective you hope to achieve with your data business strategy it is likely to fall into one of the two types of data strategies: defense or offense.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 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.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 main data management activities? ›
The data management process includes a wide range of tasks and procedures, such as: Collecting, processing, validating, and storing data. Integrating different types of data from disparate sources, including structured and unstructured data. Ensuring high data availability and disaster recovery.What are examples of 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.
- Business strategy.
- Operational strategy.
- Transformational strategy.
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.Which tool is used for data management? ›
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.
- Identify business objectives. Your organization creates billions of data points per day. ...
- Create strong data processes. ...
- Find the right technology. ...
- Establish data governance. ...
- Train and execute.
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 the stages of data management? ›
Data Acquisition: acquiring already existing data which has been produced outside the organisation. Data Entry: manual entry of new data by personnel within the organisation. Data Capture: capture of data generated by devices used in various processes in the organisation.
Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively.What is the 4 elements of management in a dataset? ›
Volume, velocity, variety, and veracity. Volume is how much data you are actually managing.How many types of data management systems are there? ›
Four types of database management systems
hierarchical database systems. network database systems. object-oriented database systems.
Customer information—such as names, phone numbers, and addresses—is an excellent example of master data. This data is less volatile but occasionally needs to be updated when a customer moves or changes their name.
What are the 8 strategies? ›
- 8 Strategies Robert Marzano & John Hattie Agree On.
- Strategy 1: A Clear Focus for the Lesson.
- Strategy 2: Offer Overt Instruction.
- Strategy 3: Get the Students to Engage With the Content.
- Strategy 4: Give Feedback.
- Strategy 5: Multiple Exposures.
- Strategy 6: Have Students Apply Their Knowledge.
It's an action plan to ensure performance targets are met, and the business continues to grow. Strategic management provides overall direction by developing plans and policies designed to achieve objectives and then allocating resources to implement the plans.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.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 different types of management strategies? ›
The five types of strategic management enumerated from most simplistic to most complex are linear, adaptive, interpretive, expressive, and transcendent. These five types of strategic management represent a continuum of organizational focus and action.What are the four tools of strategy? ›
- SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis.
- OKR (Objectives and Key Results)
- PEST (political, economic, socio-cultural, and technological) analysis.
- Balanced scorecard.
The marketing mix, also known as the four P's of marketing, refers to the four key elements of a marketing strategy: product, price, place and promotion.What are 10 types of data? ›
- 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. ...
- String (or str or text). Used for a combination of any characters that appear on a keyboard, such as letters, numbers and symbols.
- Character (or char). Used for single letters.
- Integer (or int). Used for whole numbers.
- Float (or Real). ...
- Boolean (or bool).
What are the 11 data types? ›
- For most data values, such as those stored in variables, the INTEGER , SHORTINTEGER , DECIMAL , SHORTDECIMAL , NUMBER , TEXT , ID , NTEXT , BOOLEAN , DATETIME , and DATE data types are supported.
- For dimension values, the INTEGER , NUMBER , TEXT , ID , and NTEXT data types are supported.
As a spreadsheet program, Excel can store large amounts of data in workbooks that contain one or more worksheets. However, instead of serving as a database management system, such as Access, Excel is optimized for data analysis and calculation.Is SQL a data management tool? ›
SQL Database Definition
Relational databases are built using the structured query language (SQL) to create, store, update, and retrieve data. Therefore, SQL is the underlying programming language for all relational database management systems (RDBMS) such as MySQL, Oracle, and Sybase, among others.
1. Tableau. A business intelligence platform, tableau can be accessed on cloud or as a software. This is one of the most common big data management tools that provides hassle free connection to different sources of data.What are some examples of data? ›
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 basic data types? ›
- Integer. An integer number, from -2147483648 to 2147483647.
- Double or Real. A floating-point value, for instance, 3.14. ...
- String. Any textual data (a single character or an arbitrary string). ...
- Boolean. A value that is either True , or False . ...
- Date/Time. ...
- Object. ...
Data Type Definition
Database Data Types | Computer Science
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 is strategies for data collection? ›
Both quantitative and qualitative data should be collected to answer the research questions. Types of quantitative and qualitative data collection methods include surveys and questionnaires, focus groups, interviews, and observations and progress tracking.
What are the four basic components of strategic management? ›
What are the four basic elements of strategic management? Four basic elements to create a tactical strategic management plan includes; situational analysis, strategy development, strategy execution, and strategy evaluation.What are the five 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.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 are main data management activities? ›
The data management process includes a wide range of tasks and procedures, such as: Collecting, processing, validating, and storing data. Integrating different types of data from disparate sources, including structured and unstructured data. Ensuring high data availability and disaster recovery.What are the stages of data management? ›
Data Acquisition: acquiring already existing data which has been produced outside the organisation. Data Entry: manual entry of new data by personnel within the organisation. Data Capture: capture of data generated by devices used in various processes in the organisation.
Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively.What are the 8 data collection strategies? ›
Different data collection strategies include Case Studies, Usage data, Checklists, Observation, Interviews, Focus Groups, Surveys, and Document analysis.What are 6 ways to collect data? ›
- Questionnaires and surveys.
- Documents and records.
- Focus groups.
- Oral histories.
- Step 1: Identify issues and/or opportunities for collecting data. ...
- Step 2: Select issue(s) and/or opportunity(ies) and set goals. ...
- Step 3: Plan an approach and methods. ...
- Step 4: Collect data. ...
- Step 5: Analyze and interpret data. ...
- Step 6: Act on results.