Data management includes a wide array of functions and activities involving a broad range of people, from chief compliance officers to IT architects and data scientists. Some key focus areas are:
- Data governance: Data governance is a set of functions and approaches to manage organizational data through its life cycle. Data governance aims to ensure that data is accurate, secure, available and usable throughout the organization. It includes the development of data management principles, policies and standards. Data governance should be an ongoing activity. It’s best driven by cross-functional teams representing the organization’s business and technology groups.
- Master data management: Master data encompasses the core data that’s essential for running the business, including the company’s charts of accounts, suppliers, locations, employees and sales prospects. Master data management involves business experts and IT working together to ensure the uniformity, semantic consistency and accountability of this data. It enables organizations to create a consistent set of data attributes and identifiers, which helps to ensure accurate reporting.
- Data stewardship: Data stewardship is a subset of data governance that focuses on the enforcement of policies that maintain the company’s data governance principles regarding data quality, security and integrity. A company’s data steward typically plays a leadership role within the data governance team and is responsible for making final decisions in line with the firm’s data governance policies.
- Data quality: Data quality is the area of data management responsible for unearthing and eliminating inaccurate, duplicative, and out-of-date data. Data quality practices also aim to ensure that data is being used appropriately to obtain correct answers to business questions.
- Data security: With the continuous onslaught of cyber threats, data security has never been more important. Data management techniques enable you to apply more effective security because they help you gain a better understanding of what data you have, where it is stored and who has access to it. These key pieces of information allow you to focus your security efforts on that data, applying appropriate access controls, encryption and other security controls as necessary.
Why Data Management Is Important
Data analysis is a key function in every organization. Companies use data analysis to make decisions and discover new ways to streamline operations, create marketing strategies, adapt to unforeseen market changes, open new lines of business, enhance customer relationships and improve security. That’s a key reason that data management is so important. Well-run modern businesses don’t make decisions unless they have data to support them. Without solid data management practices that create a reliable source of data for analysis, an organization’s decision-making suffers.
An effective data management approach plays a critical role in creating a single source of information that everyone in the organization can rely on for making decisions. Many larger organizations face the common challenge of siloed data — the organizational intelligence that managers need to make decisions is so spread out that it can take weeks to pull it together for analysis. Siloed data also means there is no shared version of the truth. As a result, decision makers have a hard time reaching consensus on important issues because discussions are based on each person’s view of the data, not the problem or the solution.
Data silos are an organizational issue, not simply a technical problem. A cross-functional data management team that centralizes the administration and management of data can build consensus and gain understanding of the company’s data challenges. The team can take advantage of modern data management tools to help standardize the data so that it can be used by multiple systems and users without worrying about its provenance or accuracy.
Data management also helps organizations extract valuable information from huge amounts of raw data. The sheer number of applications and devices today means organizations are bombarded with a firehose of data. Data management strategy and software can help you understand which data has the most value so you can filter data early in the process. This helps reduce the data to a more manageable size for processing and analysis. Consider the case of a sensor on the manufacturing floor that records temperature every 5 seconds. If the value of the temperature is within the normal range, you could discard those records and replace them with an average over 15 minutes. If you start to receive out-of-range values, you could collect all the records for a shorter period.