In today’s digital economy, companies have access to more data than ever before. This data creates a foundation of intelligence for important business decisions. To ensure employees have the right data for decision-making, companies must invest in data management solutions that improve visibility, reliability, security, and scalability.
What is Data Management?
Data management is the practice of ingesting, processing, securing, and storing an organization’s data, which is then utilized for strategic decision-making to improve business outcomes. Over the last decade, developments within the 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 several data management solutions, which are aimed to clean, unifying, and securing 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.
Why does data management matter?
Data management systems help organizations provide information to the right people at the right time. With the appropriate controls and implementation, data management workflows deliver the analytical insights needed to make better decisions. Data management is a necessary measure to ensure that your business-critical information is secure, accessible, and scalable. Your data management process should:
- Generate and curate data across your infrastructure.
- Store and scale data in the cloud and/or on-premises.
- Establish high availability.
- Plan for disaster recovery.
- Secure and control access to data, wherever and however possible.
- Audit and destroy data to meet compliance requirements.
- Inspire the creation of intelligent apps through data services.
Data governance is a critical piece of any data management solution. While data management encompasses the creation, curation, and output of an organization’s data, data governance regulates the usage and security of data in accordance with an organization’s internal standards and policies, as well as any relevant external regulations.
Organizations experience several benefits when launching and maintaining data management initiatives:
- Reduced data silos: Most, if not all, companies experience data silos within their organization. Different data management tools and frameworks, such as data fabrics and data lakes, help to eliminate data silos and dependencies on data owners. For instance, data fabrics assist in revealing potential integrations across disparate datasets across functions, such as human resources, marketing, sales, et cetera. Data lakes, on the other hand, ingest raw data from those same functions, removing dependencies and eliminating single owners to a given dataset.
- Improved compliance and security: Governance councils assist in placing guardrails to protect businesses from fines and negative publicity that can occur due to noncompliance with government regulations and policies. Missteps here can be costly from both a brand and financial perspective.
- Enhanced customer experience: While this benefit will not be immediately seen, successful proof of concepts can improve the overall user experience, enabling teams to better understand and personalize the customer journey through more holistic analyses.
- Scalability: Data management can help businesses scale but this largely depends on the technology and processes in place. For example, cloud platforms allow for more flexibility, enabling data owners to scale up or scale down compute power as needed. Additionally, governance councils can help to ensure that defined taxonomies are adopted as a company grows in size.
Types of Data Management
Data management plays several roles in an organization’s data environment, making essential functions easier and less time-intensive. These data management techniques include the following:
- Data preparation is used to clean and transform raw data into the right shape and format for analysis, including making corrections and combining data sets.
- Data pipelines enable the automated transfer of data from one system to another.
- ETLs (Extract, Transform, Load) are built to take the data from one system, transform it, and load it into the organization’s data warehouse.
- Data catalogs help manage metadata to create a complete picture of the data, providing a summary of its changes, locations, and quality while also making the data easy to find.
- Data warehouses are places to consolidate various data sources, contend with the many data types businesses store, and provide a clear route for data analysis.
- Data governance defines standards, processes, and policies to maintain data security and integrity.
- Data architecture provides a formal approach to creating and managing data flow.
- Data security protects data from unauthorized access and corruption.
- Data modeling documents the flow of data through an application or organization.
What are some data management challenges?
Scale and performance
Organizations require data management software that performs efficiently even at scale. They have to continually monitor and reconfigure data management infrastructure to maintain peak response times even as data grows exponentially.
Compliance regulations are complex and change over time. Similarly, customer requirements and business needs also change rapidly. Although organizations have more choices in the data management platforms they can use, they have to constantly evaluate infrastructure decisions to maintain maximum IT agility, legal compliance, and lower costs.
Getting the data management process started in any organization can be challenging. The sheer volume of data can be overwhelming and interdepartmental silos might also exist. Planning a new data management strategy and getting employees to accept new systems and processes takes time and effort.
Understanding how master data management works and how businesses can put up the most effective policies and procedures to get the most out of it and apply best practices is the key to unlocking the potential of data as a driver for organizational success.