Big data powers the digital world. In fact, the world produces somewhere around 2.5 quintillion bytes of data every day (2.5 followed by 18 zeroes). And the projection is that by 2025, global data creation will reach over 180 zettabytes!
The numbers aren’t super important, though – most of us can’t wrap our heads around such massive figures. What does matter is that companies have a way to tap into all the data created and use it in a way that serves them. That’s where a data management application comes into play.
Data management gives data purpose. It turns it into a usable resource instead of a bunch of numbers and facts. Without a way to make sense of the data you collect, what’s the point?
This guide to data management covers why it matters. It explains how to empower your strategy with the right data management tools.
Table of Contents:
What Is Data Management?
What is a Data Management Plan?
Why Is Data Management Important?
What Are Some Key Elements of Data Management?
What Is a Data Management System?
Understanding Data Management Platforms and Tools
How to Choose the Right Data Management Software Solution
What Is Data Management?
Data management is the intentional practice of collecting, storing, organizing, and using data. It makes dealing with structured and unstructured data cost-effective and efficient. It accounts for legal requirements for data usage (such as GDPR). It also includes robust internal policies for using data.
Another similar term you'll hear is master data management. Master data management involves creating a single master record for each person, place, or thing in a business's database. It generates a single source of truth, coordinated across the entire enterprise.
What is a Data Management Plan (DMP)?
A data management plan (DMP) is a formal outline detailing how the data you gather will be used, sorted, stored, and it's overall purpose.
Having a data management plan is essential as a CYA as well as a way for companies to keep track, as the amount of data available continues to proliferate. Companies rely on intangible data assets to make decisions and generate revenue. Creating a data management infrastructure helps companies:
- Use their data properly
- Make decisions
- Take action with confidence
How do you draft a data management plan?
To create a data management plan, start with a list of important questions that need to be answered and clearly laid out. These include: the purpose of the data, how it will be documented and used, date ranges for gathering the data, when it will be active, budgets for your data management project, roles and responsibilities, who has permission to use the data at any given time, and finally it should include metadata information as well — that is details about the type(s) of data itself, formats used, etc.
Why Is Data Management Important?
Because of the vital role of data in today’s organizations, data is now considered a form of business capital. It has real value that enables businesses to operate.
Banks rely on data to:
- Fight financial fraud
- Target new customers
- Fuel their market research through media intelligence
- Offer new products and services
- Open new locations
- Verify customer identities
- Create better customer experiences
- Approve customers for loans and more
Managing data takes an organization’s data needs to the next steps. It includes a range of tasks and priorities, including:
- Systematizing data collection and storage
- Choosing an on-premises or cloud-based approach, or both
- Updating data as new information becomes available
- Deleting or archiving data that is no longer relevant
- Backing up data for disaster recovery
- Making data available at all times
- Setting and maintaining data access privileges
- Ensuring data privacy and security
- Helping companies harness data for use in business decision making
- Choosing data management solutions
However, data management practices are constantly evolving. Data regulations are continually being introduced. New data management products emerge to simplify the work of data professionals.
Data management strategy has become mission-critical. It’s important that companies treat it as a priority and not a one-off investment.
What Are Some Key Elements of Data Management?
There are a few primary elements to data management that businesses should understand before getting started with implementing their data management strategy:
- Product data management (PDM)
- Data quality management (DQM)
- Data lifecycle management (DLM)
What is product data management (PDM)?
Product data management refers to the use of a centralized data system to manage product data, and systematically communicate it across teams that may be in several different locations around the world. This type of data management is popular in the manufacturing industry.
What is data quality management (DQM)?
Data quality management is a specific process in place to constantly asses and quality assure the integrity of your data. It prevents your data from being out of date, incorrect, incomplete, or damaged. Good data quality management is essential for not only maintaining accuracy, but can help you catch if there are any storage issues, or purposeful tampering, causing your data to be corrupted.
What is data lifecycle management (DLM)?
Data lifecycle management is the management of specific data points from the beginning to the end of their usage. Lifestyle management is essential for data security and privacy, keeping the flow of data aligned with business goals and purposes
What Is a Data Management System?
A data management system offers a unified approach to data. Data management systems are sometimes called data management platforms or data management solutions. A data management system enables the entire data lifecycle, including:
- Collection
- Storage
- Accessibility
- Archiving
- Destruction
Such systems help companies think about their data architecture and data governance procedures. They may include databases, a data warehouse, data lakes, data analytics, and more. Together, these components enable users and algorithms to create and leverage data.
In the past, data management systems were manual. Today, AI-powered technologies help to automate many tasks. They still need some manual intervention due to the size and complexity of the data systems. But, as automation and data technologies continue to evolve, manual intervention may lessen.
Understanding Data Management Platforms and Tools
Data management platforms and tools bring all your company's data under one umbrella. They help businesses ensure no part of data lifecycle management has gone overlooked.
Data tools fall under one of the following categories:
Master Data Management
Master data management (MDM) tools centralize a business’s data to create a single source of truth. These tools typically include:
- Customer data
- Employee data
- Product data management
- Operations
- Regulations and more
Master data is helpful when organizations must synchronize data across many locations. It ensures a high level of data quality and accuracy.
One example of an enterprise data management platform is Meltwater Display. Meltwater Display is a single solution designed to ensure a high level of data integrity via:
- Data integration
- Data governance
- Data quality
Tip: Check out our dedicated blog to <a href="https://www.meltwater.com/en/blog/master-data-management">learn more about Master Data Management.
To create a single source of truth, our Global Media Intelligence Framework draws insights from many sources such as:
- Benchmark data
- Social media
- News sources
This helps reduce the complexity of relationship management. It provides a 360-degree picture of your company’s digital presence.
Cloud Data Management
Cloud-native data tools help organizations manage and integrate data across multi-cloud environments. They’re beneficial for accessing and managing large data volumes in the cloud.
These cloud-based tools integrate easily into your existing ecosystem via APIs.
Amazon Web Services is one of the most recognizable names in cloud data management. Its ever-expanding toolkit encompasses:
- Data analytics
- Data backup and recovery
- Temporary storage
- Dashboards
- Data warehouses
- Data visualization and more
Larger enterprises with technical know-how and tons of data are common users of AWS.
Reference Data Management
Reference data management is sometimes called research data management. It is an important part of using data for research purposes. It’s a slice of the master data management picture. It classifies data and creates permissible values to maintain data integrity. For example, if a field asks for the name of a person’s city or state, a user would not be able to input numerical values.
As an example, Collibra is a research data tool used by data scientists, stewards, analysts, and other business stakeholders. It’s designed to help them perform accurate data mapping and reference rules. It uses automation to create new code sets and ensure seamless data flow.
Data Integration & ETL
Data integration and extract-transform-load (ETL) tools take data from a source and send it to a destination. For example, an eCommerce business might need to access historical data as well as up-to-date sales data. ETL can pull data from many sources, compile it in a “staging area,” and then send it to its destination.
One example is cloud-based Stitch Data. True to its name, it helps companies “stitch” data together from disparate sources. It alerts users to errors in the data and automatically resolves them when possible.
Data Transformation
Data transformation tools allow companies to turn data into different formats. This automated process includes structured and unstructured data from various sources. Data transformation tools cleanse and enrich the data without the risk of data loss.
Airflow is an example of an open-source data transformation tool. Originally designed for Airbnb, the tool helps users schedule, organize, and track ETL processes via Python.
Data Analytics
Analytics and visualization tools turn information into insights for businesses. These tools fuel the business intelligence aspect of big data. Data models and analytics allow you to:
- Make quick takeaways
- View real-time trends
- Gain a better understanding of what your data is telling you
Almost all the above data management tools offer analytics and visualizations. But they limit customization. Using purpose-built data analytics tools can give you more flexibility and function.
One example is Tableau. This business intelligence platform connects to your data sources and fosters collaboration. Users can create interactive dashboards and maps to liven up their data findings in minutes.
How to Choose the Right Data Management Software Solution
You may be using your data for market research, product development, sales, or daily operations. No matter how you use it, data is an indispensable resource. Data quality management matters if you want to stay competitive, efficient, and moving forward.
As you grow, you’ll want to make sure your data management system can grow alongside you. It should be able to perform and scale, offer ongoing availability, and support your business as you get more customers. Data becomes even more valuable with age and quantity.
Last, make sure you treat data management as an ongoing priority. New technologies and automation capabilities can improve data management over time. They can increase its value to your business.
As you explore your options for better data management, we invite you to see how Meltwater simplifies your approach. Meltwater develops custom solutions for enterprise companies. Our solutions help combine data integration, risk mitigation, and digital transformation in one platform. Backed by robust data management solutions, companies can enjoy reliable data without curating their own data solutions from scratch.
Get a demo and see how Meltwater helps you turn external data into valuable business capital!
