Data management — definition, types, challenges, and more
Adobe Experience Cloud Team
05-12-2023
Businesses are awash in data, and it’s up to leaders to determine the best strategy for managing all of it. With decision-makers relying increasingly on intangible assets to create value, understanding data management is necessary to develop a strategy that can help them collect, analyze, and utilize data to their organization’s benefit.
In this guide, you’ll learn the basics of data management and tips to help you improve data collection, analysis, utilization, and management. After reading, you’ll leave feeling confident about creating your own data strategy — and knowing that your business can use the data appropriately.
Data management is the process businesses follow to collect, organize, and use data. The goal of data management is to balance a company’s needs for efficiency and organization with the equally important need for security and cost savings.
Instead of leaving the handling of information up to individual employees or departments, a proper data management approach creates official policies and workflows to develop a consistent standard across the business. This ultimately helps organizations make better use of their data at scale.
Benefits of data management
Data management is more important than ever. Organizations are contending with terabytes of information about their customers and products. Without data management, they have no framework to help them make sense of this data. This means businesses waste precious time and resources, as well as critical data that could improve their operations.
Effective data management allows companies to turn raw data into useful insights that can generate revenue and help them get more value out of their data for less effort. Businesses that invest in data management enjoy a number of benefits.
Visibility. What information do you have available across your business? Data management brings all data sources into one feed, giving you comprehensive visibility and control over your own data in a single, big-picture view.
Reliability. How reliable is your data, really? Don’t ask employees to sift through information to find what’s accurate and what isn’t. Data management allows businesses to ensure reliability while decreasing time to value.
Security. Unmanaged data is a serious security concern. By bringing your information under control, data management can help you secure it from unauthorized access. It’s one of the best ways to prevent the expensive headaches of data breaches.
Scalability. The good news is that your business doesn’t have to manage data manually. Data management solutions make it possible to undertake data management automatically and at scale. This is the best way to ensure data consistency and security across your enterprise.
Profitability. Data can help your business become more profitable, but only if you mobilize it. Data management makes it possible to find valuable insights into your business so you can make more profitable decisions.
Transparency. Research shows 70% of consumers don’t trust companies that sell or use their personal data. Building trust with your customers takes time, but being transparent with how you use their data can help. Sharing your data policies with customers can make it easier to lock down their data while earning their trust.
Consistency. Inconsistent information can lead to misunderstandings. But with data management, everyone sees a unified, centralized view of your raw data in one place.
Compliance. Businesses are required to give consumers control over their data. Data management allows you to stay compliant with GDPR, CCPA, and other data privacy regulations. This can help you avoid expensive regulatory fines while improving your customer relationships.
Types of data management
Every business is unique, which is why there are a variety of methods for managing data. Companies are free to create their own mix of data management practices, but these techniques are the most common:
1. Data pipelines
A data pipeline is a path for businesses to transfer information between two or more different systems automatically. For example, you might connect your sales enablement software to your website analytics to bulk up your lead profiles. Sometimes the data pipeline will change or enhance your data during the exchange process, but it can also leave the raw data as is.
2. ETLs (extract, transform, load)
An ETL is a type of data pipeline. It extracts data from a database, transforms it with formatting, and loads it into a new location for storage. The benefit of using an ETL is that it can take data from multiple sources and store it in a single solution.
3. Data architecture
All data strategies start with architecture. With data architecture, you build out the flow of information throughout your systems. This is a formal process to help you manage the flow of data through a solid data structure. It covers everything from storage to usage to compliance.
4. Data modeling
Data models are visual diagrams of how data flows through a system. They can help your team understand the flow of data within a system or between different systems. It’s common for businesses to create multiple data models for their various systems.
5. Data catalogs
Data catalogs store and organize data based on back-end information, which is called metadata. A data catalog makes important information searchable so you can find it quickly. For example, businesses can store inventory information in a data catalog and tag entries with labels that make it easier to find product information.
6. Data governance
Data governance is the set of rules you follow to standardize data. This helps with data quality and data compliance. Businesses will usually have a team in charge of data governance to hold the business accountable and make policy updates as needed.
7. Data security
The goal of data security is to protect your information from breaches, theft, and unauthorized access. This is usually an IT function that creates policies for software, access, backups, storage, and more.
8. Other data management types
Data is versatile, so businesses are free to create management policies tailored to their needs. Alternative data management types are less common, but many businesses add these to their data management mix for a more holistic approach:
Data lifecycle management. Every snippet of data goes through its own lifecycle with your business. Data lifecycle management monitors data from collection to deletion, creating policies for every stage of the lifecycle.
Data processing. Raw information isn’t actionable or helpful. Data processing takes this raw data and translates it into actionable insights.
Data integrations. If you have data from multiple sources, integration will bring these disparate pieces of information together in one place.
Data migration. If you’re upgrading your database solution, you’ll need to move data to a new home. The data migration process helps you move existing information into a new solution with as few errors or formatting issues as possible.
Data storage. This is the process of securely saving data. Some businesses store data in physical files, while others store it in the cloud.
Some businesses can use just a few types of data management, while large enterprises might need many or all of the types listed here. Evaluate the usefulness of these strategies to pick the perfect data management mix for your team.
The goal of data management is to balance a company’s needs for efficiency and organization with the equally important need for security and cost savings.
Data management challenges
Data management has proven benefits for businesses, but it isn’t easy to execute. Business today is fast paced, and the increasing expansion of available data makes it even more challenging to glean its true value.
While there are certainly other challenges that come with data management, these are some of the most common:
Lacking data insight. Businesses can collect more information than ever, but having terabytes upon terabytes of data can make it difficult to sift through, spot trends, and gain actionable insights.
Maintaining data-management performance levels. As databases add more information, it becomes much harder to ensure performance. The challenge is maximizing data integrity at scale without affecting the level of quality.
Complying with changing data requirements. Ever-changing compliance requirements make it hard for businesses to commit to a data management strategy. As soon as you achieve compliance, new requirements might make your past data management practices unusable. To make matters more daunting, businesses targeting an international audience need to comply with a complex web of international, national, and local requirements.
Processing and converting data with ease. Raw data usually doesn’t have a lot of value on its own. Processing and converting data into the right formats for analysis makes it more actionable, but doing so can be difficult — especially at scale.
Storing data effectively. Data warehouses can store data, but it isn’t unusual for businesses to store their information in multiple warehouses or data lakes. Data scientists might need to reformat data to perform an analysis, but its stored format can limit analysis. Security concerns also make it difficult for companies to store data in the cloud effectively.
Optimizing its agility and costs continually. Data storage comes at a cost. The more data you store, the more you pay. Larger quantities of data can also affect your business’s data agility. It’s up to your IT department to manage the agility of this data while balancing costs.
Drawing value from new analytics and data. As you collect larger volumes of data, finding meaning from the deluge of information can become even harder. If businesses don’t have the right management solutions, they risk missing out on insights from new analytics and data.
Integrating disparate databases. Most data management platforms pull information from multiple sources. Bringing data into a single repository is helpful, but it has problems. Not all software or storage solutions integrate seamlessly. Integration headaches can cause inaccurate, incomplete, or incorrectly formatted data, hurting accuracy and productivity.
Training employees. Regardless of your employees’ expertise, there’s a good chance they won’t have the skills necessary to handle all aspects of data management. Businesses either need to hire new employees with specific skillsets or train their internal teams in data management. That takes time and effort, which can throw off your time to value.
Data management best practices
While data management certainly has its challenges, businesses can mitigate the effects of these challenges by embracing best practices. Here are some of the best practices that will give your business a great place to start:
Clearly identify your business goals. Your company collects an immense amount of information, and you need a strategy for managing it. After all, not all data snippets will be valuable to your team. It’s important to set business goals that your data management team can follow. For example, if your goal is to reduce customer churn, what specific data will be most beneficial? This can help you focus your data management efforts on the most relevant KPIs to maximize your time and resources.
Create your data management processes. To fully embrace data management, your team needs an approved playbook to guide its data management practices. You have to create documented processes that control how your business collects, organizes, uses, and analyzes data. Work with multiple stakeholders in your organization to ensure your data management practices are efficient, compliant, and secure.
Focus on the quality of data. Your business must collect enough data to spot trends and form actionable insights. But there is such a thing as too much data. If you collect a large amount of unhelpful, inaccurate information, it will only distract your team. Instead, set standards for data quality. For example, ensure all raw data automatically maps to the correct fields in your storage solution. You might even need to set up data verification tools to ensure the accuracy of customer information.
Data management can help you develop a strategy for properly collecting, analyzing, and utilizing information to benefit your business.
Find the right software. Whether you run a small or medium-sized business or a large enterprise, it’s impossible to do data management manually. You need the right technology platforms to support your data strategies. For example, use data management solutions that make it possible to see your cross-channel customer profiles in real time.
Store your data. Your team can’t mobilize data if it isn’t stored in an organized fashion. Create policies for how your company will collect and organize data. Most businesses store their data in a data warehouse, a data lake, or in the cloud. For the sake of backups and disaster recovery, it’s a good idea to store your data both on-premises and in the cloud. Ensure that everyone on your team has access to your stored data. This way, if a team member leaves, your business maintains continuity.
Allow the right people to access the data. While your data team needs to have access to your data, it doesn’t mean that everyone should have equal privileges. If a hacker steals one employee’s credentials, you don’t want that employee’s account to have unfettered access to your data. Access management is a security best practice, but it’s also a data management best practice because it keeps your data clean and ensures your team has an appropriate level of data access.
Data management software
All businesses need policies in place for data management, but policies alone won’t help you manage the huge amount of information in your organization. You need the right mix of data management software in your tech stack to effectively use this data at scale.
Data management protects your data and keeps your team both productive and secure. Good data management software can help you do all of this automatically, significantly reducing inefficiencies and losses.
Businesses can choose from a mix of several different data management solutions. These are three of the most common types of software available for managing data:
Master data management software. These solutions offer a little bit of everything, ranging from data collection to verification to organization. Master data management software pulls all of your information sources together into a unified view. You can get better visibility with these solutions, which ensure your data are accurate and actionable.
Storage management software. Data storage can be expensive and complicated. Fortunately, storage management software helps businesses regain control of their information. With a storage management solution, you can find the best ways to store your data, whether that’s on-premises, in the cloud, or with a hybrid solution.
Integration software. Chances are that your business needs to connect different data collection, storage, and analytics solutions. Not all of these solutions work together properly out of the box, though, which is why many organizations invest in integration software. Use these solutions to integrate your data management solutions more seamlessly.
Be part of the evolving data management scene
Data management changes as quickly as data technology changes. It’s hardly set in stone, but it’s critical for businesses to create a plan for managing large amounts of data.
Instead of simply collecting more and more information that you can’t mobilize, use data management to regain control of your data — and generate value from it. Data management can help you develop a strategy for properly collecting, analyzing, and utilizing information to benefit your business.
When you’re ready to get started, check out the advanced features of Adobe Real-Time Customer Data Platform. Real-Time CDP collects B2C and B2B data from across systems and unifies it into real-time profiles ready for activation across any channel. With best-in-class usage governance, brands can use data more responsibly and transparently so consumers have greater control over their information.
