Kevin Bartley

Companies have access to more data than ever, but leveraging this data profitably is growing increasingly difficult as the size and complexity of data expands. Consider some recent statistics:

Data is more abundant than ever, but it is also more unwieldy and more challenging. In order to actualize the value of data, companies must coordinate data operations and systems to deliver the right data, at the right time, to the right stakeholder. This is the process of data management.

In the past decade, countless tools and solutions have emerged to streamline the data management process, but the fundamentals are still critical. Read on for a full overview of data management, including why it matters, what the process entails, and the technologies that power it.

Why is Data Management Important?

With the rapid transformation towards a digital economy, data has become an economic factor of production for digital goods and services. Data is also a corporate asset and tool that supports critical decisions, improves business functions, reduces costs, and drives marketing campaigns.

However, unprecedented data volumes, inefficient data processing, poor data quality, proliferating data silos, and other issues limit effective data usage. Mishandling these complex issues can lead to data chaos, disrupt data models that produce BI and analytics, wall off business users from the data they need, and in the worst cases, even damage the reputation of a company.

Companies must exercise firm control over their data to avoid these common pitfalls. That’s where data management comes in.

What is Data Management? An Overview.

Data management is the process of effectively and securely ingesting, storing, organizing, and maintaining an organization’s data. The data management process employs protocols and technologies to ensure the accuracy, accessibility, and availability of data throughout an organization.

For many companies, the data management process optimizes data usage to maximize decision-making, strategic planning, and other business initiatives. In all stages, the data management process must manage data assets efficiently and enforce organization-wide data policies.

Data management deployments often combines a number of tasks and procedures into an automated framework, including piping raw data into data systems, storing data in cloud and on-premise servers, cleaning and transforming data for usage, pushing data back out to 3rd party systems, ensuring data privacy and protection, and preparing data for algorithms, analytics, and BI. Other core components of data management include:

  • Data architecture: In most organizations, a blueprint is developed to deploy database management systems (DBMS) and other data platforms according to the technical specifications for specific applications. Developing such blueprints, or ‘data architectures,’ is the first step in data management.
  • Data modeling: Data modeling explains how data is connected and corroborates the relationship between various data elements. The model maps these elements to meet business requirements for transactions and analytics. Data models govern how data elements are stored and processed.
  • Data integration: Data integration involves extracting data from data sources, converting the data into a consistent format, and loading the data into a data warehouse so that queries can be performed on it. Commonly used data integration techniques include extract, transform and load (ETL) and extract, load, and transform (ELT).
  • Data governance: Data governance is a set of common data policies and corporate standards for creating, formatting, and using data. With data governance, companies develop policies and plans-of-action to ensure consistency of data throughout an organization.
  • Data quality management: Data quality management monitors data sets to guarantee that the needs of end-users are met. Inconsistencies are rectified, via several subprocesses, including:
    • Data profiling: Data profiling scans across data sets to identify errors and outliers.
    • Data cleansing: Data cleansing fixes data errors through the modification and deletion of outliers. This process is also known as data scrubbing.
    • Data validation: Data validation compares data quality against predefined standards and ensures that data sets abide by the preset rules.
  • Master data management: Master data management (MDM) organizes, centralizes, localizes, manages, and synchronizes master data across an organization. MDM maintains a central registry of master data for a set of data domains, termed golden records. Data is stored in an MDM hub that transfers the data to analytical systems for consistent reporting.

Companies frequently combine and iterate upon these core components to build the data management process that works best for them.

Challenges of Data Management

Data management improves business decisions, democratizes data, and unlocks the competitive advantage of data. However, as data grows in complexity, companies will encounter unique difficulties and limitations when implementing data management processes. Some common obstacles include:

  • Generating insights at scale – With the constant expansion of organizational data, companies have a harder time deriving insights as growth outstrips capacity.
  • Performance deficiencies – Performance suffers as operations expand, since data acquisition occurs at a faster pace, and companies must control and monitor the increasing number of queries made by the database.
  • Maintaining a single source of truth – Companies struggle to maintain a single, unified source of organizational truth as data sources and processes grow in number and kind.
  • Lack of resources – A lack of data personnel, solutions, and time disrupt streamlined data management.

For these reasons and more, companies often leverage data management technologies to navigate the process. Here are some examples.

Data Management: Technologies & Best-Practices

With today’s modern data stack, companies must unify several different systems and tools to build a single source of truth that can execute analytics at scale. Data management, across this many platforms, is an onerous, fragmented process. That’s why a wide variety of tools have emerged to streamline and automate data management, including:

  • ETL tools: An ETL tool extract raw data from sources, transform the data on a secondary processing server, and then load the data into a target database. These tools are ideal for compute-intensive transformations, systems with legacy architectures, or data workflows that require manipulation before entering a target system, such as erasing personal identifying information (PII).
  • ELT tools: An ELT tool extract raw data from sources, load the data directly into a database, and then transform the data inside the database. These tools deliver faster data ingestion, simultaneous loading and transforming, and complete raw data sets that are endlessly queryable.
  • Reverse ETL tools Reverse ETL pushes data back out to 3rd party systems, business apps (CRM, ERP, etc.), and organizational stakeholders.
  • DataOps platforms: A DataOps platforms expands on the functionality of data management tools by adding operational features such environments, version control, programmatic data modeling, and more.
  • Data lake: A data lake stores both structured and unstructured data. It is commonly referred to as a dumping ground for data. Moreover, it organizes massive volumes of data from diverse sources, and is most suited for centralizing data and broad data exploration.
  • Data warehouse: Data warehouses usually contain structured data from relational databases, but the data can also be unstructured. DWHs enables analytics, business intelligence, and performing queries on larger and more diverse volumes of data.
  • Data lakehouse: A cross between a data warehouse and data lake, a data lakehouse combines the best of both to offer data teams tremendous flexibility and superior performance for certain use cases.

The right data stack will enable companies to build out the best-practices and operations that drive superior data management. These look different for each company, but common use cases include:

  • Developing a discovery layer on top of the company’s data tier, thereby improving data usability.
  • Creating an automated data science environment for efficient handling of data transformation.
  • Continuously monitoring database queries employing autonomous tools such as AI and machine learning.
  • Using a converged database that supports all modern solutions and state-of-the-art development tools.
  • Implementing a standard query layer for coordinated management of repositories and handling diverse forms of data storage.

Data Management: Driving Business Results with Data

According to Statista, 74 zettabytes of data will be created in 2021, up from 59 zettabytes in 2020. As the volume and convolution of data continue to increase, companies must create a data management process that can accentuate speed and efficiency of operations, while enabling business users to achieve business goals and make decisions.

Read our eBook on trends and strategies in data management to learn more.