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Data management encompasses the processes of gathering, organizing, storing, handling, and securing information according to specific needs, and in compliance with any applicable regulations. While data management can be challenging under the best of circumstances, doing it well can be particularly difficult—especially for enterprises whose increasingly complex data burdens involve massive amounts of data from numerous sources. In this article, I provide a brief guide to understanding and following the associated best practices to help reduce risks.
Types of Data Management
There are numerous data management strategies, and most organizations can expect to use several depending upon their specific needs. Here are eight of the most common types of enterprise data management.
Data Cleansing
Data cleansing involves analyzing collected information for inconsistencies and errors and getting it into the desired format. For example, a database with phone numbers in various formats could contain duplicate entries or other errors that will skew the results if not addressed—data cleansing would mean deduplicating entries and reformatting all the telephone numbers to meet a consistent standard to ensure the reliability and accuracy of the data.
Data Architecture
Data architecture is a visual representation of how information flows through an organization. What are all the sources of data, where is it all stored, which teams handle it, and which applications and devices process it? Detailed answers to these questions help create an applicable data strategy and identify weak spots that could make it challenging or impossible to handle and use the information effectively.
Data Modeling
Data modeling is similar to data architecture, except a data model relates to a specific type of information rather than all of them. These straightforward diagrams show people how specific data moves through the organization, which systems process it, and all the involved departments. A data model might examine information related to customers, third-party partners, employees, or any other relevant group, or show which systems store particular types of information within a company. Those specifics make it easier to find the data later and ensure it’s stored and handled correctly.
Data Pipelines
Data pipelines are an organization’s information pathways. They’re essential for getting information to the desired locations after ingestion. Extract, transform, and load (ETL) is one of the most widely used data pipelines, and involves pulling information from a database, altering it to meet an organization’s standards or formatting needs, and loading it into a new location. People commonly use data pipelines to increase productivity, since they can automate many associated processes. They also optimize usability since the information can move quickly from one place to another.
Data Cataloging
A data catalog details a company’s information resources and helps users search through them–for example, through a search bar interface that accepts keywords, short phrases, tags, or labels. Most catalogs allow further specificity, such as letting people find information that fits certain parameters. That might mean someone can only search for active customers versus all of them in the database. A 2023 survey of company leaders found 41 percent lack the understanding to fully benefit from their organizations’ data assets, and 30 percent said the amount of information overwhelmed them. There’s no single solution for those issues, but creating and maintaining a data catalog could help make that information more visible, actionable, and manageable.
Data Access Control
For companies grappling with the best ways to capitalize on data, access control is often part of the discussion. Who gets access to what data can become a battle to find the balance between accessibility and security. Data breaches don’t always happen intentionally. A 2023 insider risk study showed accidental data breaches were the most concerning for the business leaders polled. Additionally, 93 percent of respondents said hybrid-remote work arrangements have increased the need for data security training. One common approach is to restrict access according to role, giving everyone the information needed to do their jobs while mitigating security risks. But “mitigating” is not “eliminating,” and companies need to remain responsive and alert in case of a data breach—some companies only have 72-hour windows for reporting severe incidents to appropriate authorities.
Data Processing
Data processing involves turning raw data into usable information through a variety of methods. It includes collecting, manipulating, and transforming data, and can be done manually, but increasingly it is being automated to accelerate workflows. Advanced technologies like artificial intelligence and optical character recognition can process higher volumes with fewer errors than humans working manually. Data processing makes it possible to identify patterns and trends, discover new information, make the most of available resources, improve efficiencies, and make better decisions. It can be used to track consumer trends, measure consumer behavior, and create customer segments.
Data Governance
Data governance is the process of creating and enforcing policies and standards for data in an organization to ensure its security, protect privacy, and meet compliance requirements. To be successful, these policies require comprehensive frameworks that rely on participation from people at all levels of the company. As businesses store and use more and more data from diverse sources, including Internet of Things (IoT) devices, data governance programs become increasingly important to improve quality, remove silos, and make data accessible and secure.
Benefits of Data Management
Data management offers enterprises a wide range of advantages. Here are just a few.
- Avoiding regulatory fines. Data management can reduce companies’ chances of being fined for improper data handling. Consider an April 2023 case in which regulators in Britain fined TikTok £12.7 million for insufficient screening to prevent underage users from using the platform. Data associated with minors, medical patients, or credit card details requires specific processing to comply with regulations.
- Protecting critical files. Data management can reduce the chance of important files being deleted accidentally by users who don’t recognize them by name. Because effective data management shows the purpose of information and how it moves through an organization, users are less likely to delete things they believe are unimportant.
- Facilitating responsible information-sharing. Some decision-makers start emphasizing data management to encourage information sharing across entities—for example, the World Health Organization requires research data-sharing for all initiatives it funds or conducts, a policy it says supports science and public health.
- Improving engagement and security. A 2023 survey found 80 percent of public sector entities have started creating collaborative ecosystems where users can share data, which has improved citizen engagement and strengthened cybersecurity efforts. Enterprises can achieve similar results among employees.
Data Management Challenges
Even with well-thought-out, detailed plans, enterprises can expect data management challenges. A 2022 Deloitte study revealed that 45 percent of tech industry leaders cited gathering and protecting growing data volumes as their top challenges, while 32 percent of respondents cited the changing worldwide regulatory landscape.
Emerging technologies can present another challenge. For example, some employees have unintentionally revealed company secrets by feeding proprietary data into the ChatGPT chatbot to get help fixing broken code. OpenAI, the company behind the tool, has since introduced a feature that allows users to turn off their chat histories, preventing ChatGPT from using information in those messages to train future algorithms.
Lack of visibility can also make data management more difficult. Cloud tools facilitate access to data, but they can compromise visibility—a 2023 study showed only 40 percent of parties using the cloud have total visibility into their data’s location, which is particularly problematic if the information requires specific handling due to its content.
Solving problems within your organization starts with understanding the issues and associated ramifications. Take the time to get feedback from people at every level of the organization. Ask them how the identified obstacles affect their workflows and what they’d do to improve the situation.
Bottom Line: How to Implement Data Management
If your enterprise is ironing out your data management plans, you should remain mindful of best practices, including being transparent with customers about why you need their information and how you protect it. Stating the relevant information in easy-to-understand language can boost consumer trust and confidence. You should also thoroughly vet external service providers handling data. In the eyes of your customers, and possibly authorities, I think it’s important to remember that the responsibility—and the blame—is yours, not the third parties’.
Businesses should consider continuing education a foundational part of any data management plan. New processes, product launches, and expanding teams can change how a company handles information, and training employees on the latest preventive measures can help everyone abide by company data-handling rules and reduce mistakes. They should also clarify how data usage connects to the organization’s goals.
Organizations are most likely to optimize outcomes by fostering a data-driven culture. Everyone must understand how their actions can influence a company’s success and reputation. Adhering to an agreed-upon file-naming structure can make information easier to find and use later.
Data management can become complex, especially as information volumes rise. However, becoming familiar with various types, anticipating the frequently seen advantages, and recognizing common pitfalls will help you get the most out of your efforts.
To learn more about how to implement a successful data management strategy at your organization, read Top 10 Data Management Best Practices for 2023.
