What is data management?

Data management is the practice of ingesting, processing, securing and storing an organization’s data, where it is then utilized for strategic decision-making to improve business outcomes. Over the last decade, developments within hybrid cloud, artificial intelligence, the Internet of Things (IoT), and edge computing?have led to the exponential growth of big data, creating even more complexity for enterprises to manage. As a result, a data management discipline within an organization has become an increasing priority as this growth has created significant challenges, such as data silos, security risks, and general bottlenecks to decision-making. Teams address these challenges head on with a number of data management solutions, which are aimed to clean, unify, and secure data. This, in turn, allows leaders to glean insights through dashboards and other data visualization tools, enabling informed business decisions. It also empowers data science teams to investigate more complex questions, allowing them to leverage more advanced analytical capabilities, like machine learning, for proof-of-concept projects. If they’re successful at delivering and improving against business outcomes, they can partner with relevant teams to scale those learnings across their organization through automation practices.

Data management vs. master data management

While data management refers to a whole discipline, master data management is more specific in its scope as it focuses on transactional data—i.e. sales records. Sales data typically includes customer, seller, and product information. This type of data enables businesses to determine their most successful products and markets and their highest valued customers. Since master data is inclusive of personally identifiable information (PII), it also conforms to stricter regulations, such as GDPR.?

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Types of data management

The scope of a data management discipline is quite broad, and a strong data management strategy typically implements the following components to streamline their strategy and operations throughout an organization:?

Data processing: Within this stage of the data management lifecycle, raw data is ingested from a range of data sources, such as web APIs, mobile apps, Internet of Things (IoT) devices, forms, surveys, and more.?It is, then, usually processed or loaded, via data integration techniques, such as extract, transform, load (ETL) or extract, load, transform (ELT).?While ETL has historically been the standard method to integrate and organize data across different datasets, ELT has been growing in popularity with the emergence of cloud data platforms and the increasing demand for real-time data. Independently of the data integration?technique used, the data is usually filtered, merged, or aggregated during the data processing stage to meet the requirements for its intended purpose, which can range from a business intelligence dashboard to a predictive machine learning algorithm.?

Data storage: While data can be stored before or after data processing, the type of data and purpose of it will usually dictate the storage repository that is leveraged. For example, data warehousing requires a defined schema to meet specific data analytics requirements for data outputs, such as dashboards, data visualizations, and other business intelligence?tasks. These data requirements are usually directed and documented by business users in partnership with data engineers, who will ultimately execute against the defined data model. The underlying structure of a data warehouse is typically organized as a relational system (i.e. in a structured data format), sourcing data from transactional databases. However, other storage systems, such as data lakes, incorporate data from both relational and non-relational systems, becoming a sandbox for innovative data projects. Data lakes benefit data scientists in particular, as they allow them to incorporate both structured and unstructured data into their data science projects.?

Data governance: Data governance is a set of standards and business processes which ensure that data assets are leveraged effectively within an organization. This generally includes processes around data quality, data access, usability, and data security. For instance, data governance councils tend align on taxonomies to ensure that metadata is added consistently across various data sources. This taxonomy should also be further documented via a data catalog to make data more accessible to users, facilitating data democratization across organizations. Data governance teams also help to define roles and responsibilities to ensure that data access is provided appropriately; this is particularly important to maintain data privacy.?

Data security: Data security sets guardrails in place to protect digital information from unauthorized access, corruption, or theft. As digital technology becomes an increasing part of our lives, more scrutiny is placed upon the security practices of modern businesses to ensure that customer data is protected from cybercriminals or disaster recovery incidents. While data loss can be devastating to any business, data breaches, in particular, can reap costly consequences from both a financial and brand standpoint. Data security teams can better secure their data by leveraging encryption and data masking within their data security strategy.?