What is data management?
Data management
Data is essential to a company's operations and performance. Companies must make sense of data and find the relevant ones among the noise created by the various systems and technologies that support today's highly connected global economies. In this regard, data occupies a central place. On its own, data is useless -companies need an effective datastrategy, governance and management model to leverage all forms of data for practical and efficient use across supply chains, employee networks, customer and partner ecosystems... and beyond.
So what is data management? It is the practice of collecting, organizing and accessing data to support productivity, efficiency and decision making. Given the critical role that data plays in business today, a sound data management sound data management strategy and a modern data management system and a modern data management system are essential for all companies - regardless of size or industry.
The key elements of data management
The data management process includes a wide range of tasks and procedures, such as:
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Collecting, processing, validating and storing data
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Integrate different types of data from disparate sources, including structured and unstructured data
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Ensuring high data availability and disaster recovery
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Controlling how people and apps use and access data
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Protecting and securing data and ensuring data privacy
Why is data management important?
Every application, analytical solution and algorithm (the rules and processes that enable computers to solve problems and complete tasks) used in an enterprise depends on seamless access to data. In essence, a data management system helps ensure that data is secure, accessible and accurate. But the benefits of data management don't end there.
64.2 zettabytes
of digital data created in 2020 - IDC
80 %
of the world's data will be unstructured by 2025 - IDC
Turning Big Data into a high-value business asset
Too much data can be overwhelming - and useless - if not managed correctly. But with the right tools, Big Data can be harnessed to empower companies with deeper strategic insights and more accurate forecasts. It can give companies a better understanding of what customers want, and help them deliver exceptional experiences based on the data it provides. Or to drive new data-driven business models-such asInternet of Things (IoT)-based services and real-time sensor data-that would not be obvious or obvious without the ability to analyze and interpret Big Data.
Big Data are extremely large data sets that are often characterized by the five Vs: volume, variety, velocity, veracity and value.
It's no secret that data-driven organizations have a significant competitive advantage. With advanced tools, companies can manage more data from more sources than ever before. They can also leverage many types of data, both structured and unstructured, in real time - including data from IoT devices, video and audio files, Internet click streams, and social media comments - creating more opportunities to monetize and use it as an asset.
Laying the data foundation for digital transformation.
It is often said that data is the lifeblood of digital transformation-and it's true. Artificial intelligence (AI), machine learning, Industry 4.0, advanced analytics, internet of things and intelligent automation require lots of timely, accurate and secure data to do what they do.
The importance of data and data-driven technologies has only increased since the COVID-19 outbreak. Many companies are feeling intense pressure to better use their data now-to project future events, pivot fast, and build resilience into business plans and models.
Machine learning, for example, needs very large and diverse data sets to "learn," identify complex patterns, solve problems, and keep its models and algorithms up to date and running efficiently. Advanced analytics (which often leverage machine learning) also rely on large amounts of high-quality data to produce relevant and actionable strategic information that can be acted upon with confidence. IoT and industrial IoT operate on a constant stream of data from machines and sensors, at a million miles per minute.
The common denominator in any digital transformation project is data. Before companies can transform processes, leverage new technologies and become smart enterprises, they need a solid data foundation. In short, they need a modern data management system.
The continued survival of any business will depend on an agile, data-centric architecture that responds to the constant pace of change.
Donald Feinberg, Vice President at Gartner
Ensure compliance with data privacy laws.
Good data management is also essential to ensure compliance with national and international data privacy laws - such as the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act in the U.S. - as well as industry-specific privacy and security requirements. And when those protections must be tested or audited, strong data management policies and procedures are essential.
Data management systems and components
Data management systems are built on dedicated platforms and include components and processes that work together to help extract value from your data. These can include database management systems, data warehouses and data lakes, data integration, analytics and more.
Database management systems (DBMS)
There are many different types of database management systems. The most common include relational database management systems (RDBMS), object-oriented data management systems (OODMBS), in-memory and columnar database management systems.
Different systems for data management
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Relational database management system (RDBMS): An RDBMS contains data definitions so that programs and retrieval systems can refer to it by name, rather than describing its structure and location each time. According to the relational model, RDBMS systems also maintain relationships between data that improve access and avoid duplication. The basic definition and characteristics of an item, for example, are stored only once and linked to the customer order detail and price lists.
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Object-oriented database management system (OODBMS): An OODBMS is a different approach to data definition and storage, developed and used by developers of object-oriented programming systems (OOPS). Data is stored as objects, self-contained, self-describing entities, rather than using tables as in an RDBMS.
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In-memory database: An IMDB stores data in a computer's main memory (RAM), instead of using a disk drive. In-memory recovery is much faster than recovery from disk, so in-memory databases are widely used in applications that require a quick response. For example, reports that once took days to compile can now be retrieved and analyzed in minutes, even seconds.
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Columnar database: this type stores groups of related data together (a "column" of information) for fast access. It is used in modern in-memory applications and for many stand-alone data storage applications where speed of retrieval (of a limited range of data) is important.
Data warehouses and data lakes
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Data warehouse: a central repository accumulated from many different sources for reporting and analysis purposes.
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Data lake: is a vast reservoir of data stored in its raw or natural format. Data lakes are commonly used to store Big Data, including structured, unstructured and semi-structured data.
Master Data Management (MDM)
Master data management is the discipline of creating a trusted reference (a single version of the truth) for important business data, such as product, customer, asset, financial, and more. MDM helps ensure that companies do not use multiple versions of potentially inconsistent data in different parts of the business, including processes, operations, analytics, and reporting. The three key pillars of effective MDM include: data consolidation, data control, and data quality management.
A technology-enabled discipline where the business and IT organization work together to ensure uniformity, accuracy, management, semantic consistency and accountability over official master data assets shared across the enterprise.
Gartner: MDM definition
Big Data Management
New types of databases and tools have been developed to manage Big Data - the huge volumes of structured, unstructured and semi-structured data flooding enterprises today. In addition to highly efficient processing techniques and cloud-based facilities to handle volume and velocity, new approaches have been created to interpret and manage the variety of data. To enable data management tools to understand and work with unstructured data, for example, new preprocessing processes are used to identify and classify data to facilitate storage and retrieval.
Data integration
Data integration is the practice of ingesting, transforming, combining and delivering data, where and when it is needed. This integration takes place across the enterprise and beyond - both between partners and with third-party data sources and use cases - to meet the requirements of all business applications and processes. Techniques include, but are not limited to, bulk/batch movement, extraction, transformation, loading (ETL), change capture, replication, virtualization, orchestration and data integration.
Data governance, security and compliance
Data governance is a collection of rules and responsibilities to ensure the availability, quality, compliance and security of data throughout the organization. Data governance establishes the infrastructure and names the people (or positions) within an organization that will have the authority and responsibility for managing and safeguarding specific classes and types of data. Data governance is a key part of compliance. Systems will take care of the mechanics of storage, handling and security - it is the people side of governance that ensures that data is accurate from the outset and that it is properly handled and protected before it is entered into the system, while it is in use, and when it is retrieved from the system for use or storage elsewhere. Governance specifies how responsible individuals should use processes and technologies to manage and protect data.
Of course, data security is a major concern in today's world of hackers, viruses, cyber-attacks and data breaches. While security is built into systems and applications, data governance is applied to ensure that they are properly configured and managed when outside of systems, and to comply with procedures and responsibilities.
Business intelligence and analytics
Most, if not all, data management systems include basic retrieval and reporting tools, and many incorporate or are bundled with powerful retrieval, analysis and reporting applications. Reporting and analytics applications are also offered by third-party developers, and will likely be included in the application package as a standard feature or an optional advanced add-on module.
The power of today's data management systems lies largely in ad hoc retrieval tools that allow users with minimal training to create their own data queries on screen or in print with amazing flexibility of formats, calculations, types and summaries. In addition, professionals can use these same tools or other more sophisticated analytics to do even more calculations, comparisons, advanced mathematics and formatting. New analytical applications are able to connect traditional databases, data warehouses and data lakes to enable the incorporation of Big Data for better forecasting, analysis and planning.
What is an enterprise data strategy, and why should you have one?
Many companies have been passive in their approach to their data strategy, accepting whatever their vendor has built into their systems. But now that's not good enough. With the current explosion of data and its importance to the operation of all businesses, there is a growing need to take a more proactive and holistic approach to data management. From a practical standpoint, that means establishing a data strategy that:
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Identify the specific type of data your company will need and use
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Assign responsibilities for each type of data
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Establish procedures to control the acquisition, collection and processing of data.
One of the key benefits of a data management strategy and infrastructure is that it unifies the organization - coordinating all activities and decisions to support the company's purpose, which is to deliver quality products and services effectively and efficiently. Having a comprehensive data strategy and seamless data integration eliminates information silos. This allows each department, manager and employee to see and understand their individual contribution to the company's success, and keep their decisions and actions aligned with those goals.
The evolution of data management
Effective data management has been critical to business success for more than 50 years - from helping companies improve reporting accuracy, to spotting trends and making better decisions to driving digital transformation and powering new technologies and business models today. Data has become a new kind of capital, and forward-thinking organizations are always looking for new and better ways to use it to their advantage. These are the latest trends in modern data management to watch and explore:
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Data fabric: Most organizations today have data deployed on-premise and in the cloud-and use multiple database management systems, processing technologies, and tools. A data fabric, which is a customized combination of architecture and technology, uses dynamic data integration and orchestration to enable frictionless data access and exchange in a distributed environment.
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Cloud data management: Many companies are moving some or all of their data management platforms to the cloud. Cloud data management takes advantage of all the benefits the cloud has to offer-scalability, advanced security, improved access, automated backups, disaster recovery, cost savings, and more. Cloud databases and database-as-a-service (DBaaS) solutions, cloud data warehouses , and cloud data lakes are growing in popularity.
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Augmented data management: this is one of the most recent trends. Noted by Gartner as a significant potential disruption by 2022, augmented data management uses AI and machine learning to make data management processes automatically configure and adjust. Augmented data management is automating everything from quality and master data management to data integration, freeing up skilled technical staff to focus on generating greater value.
By 2022, manual data management tasks will be reduced by 45% through the addition of machine learning and automated service level management.
Gartner
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Augmented analytics: Augmented analytics - another major technology trend identified by Gartner - is here. Augmented analytics uses artificial intelligence, machine learning and natural language processing (NLP) to not only find the most important strategic information automatically, but to democratize access to advanced analytics so that everyone, not just data scientists, can ask questions about their data and get answers in a natural, conversational way.
Explore additional terms and trends in data management.
Summary
We know that information is derived from data. And if information is power, managing and capitalizing on your data effectively could quietly be your company's superpower. As such, data management and database analyst (DBA) roles are evolving to become change agents-in driving cloud adoption, leveraging new trends and technologies, and delivering strategic value.
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