Data management is the practice of managing data as a valuable resource to unlock its potential for an organization. Managing data effectively requires having a data strategy and reliable methods to access, integrate, cleanse, govern, store and prepare data for analytics. In our digital world, data pours into organizations from many sources – operational and transactional systems, scanners, sensors, smart devices, social media, video and text. But the value of data is not based on its source, quality or format. Its value depends on what you do with it.
History of Data Management
Some say the need for data management began in the 1890s with mechanical punch cards that recorded information (data) on a thick card. But the concept of data management wasn’t widely discussed until the 1960s, when the Association of Data Processing Service Organizations (ADPSO) began providing data management advice for professionals.
Data management systems as we know them today weren’t common until the 1970s. These data management systems were strictly operational. They provided records (reports) of business operations at a given point in time, pulled from a relational database that stored information in rows and columns (typically a data warehouse).
- Batch processing and extract, transform, load (ETL).
- Structured query language (SQL) and relational database management systems (RDBMSs).
- Not-only SQL (NoSQL) and nonrelational databases.
- Enterprise data warehouses, data lakes and data fabrics.
- Data federation and virtualization.
- Data catalogs, metadata management and data lineage.
- Cloud computing and event stream processing (data streaming).
Data Management in Today's World
Taking charge of your data requires tackling a wide range of data management concepts, technologies and processes. Learn from data experts what it takes to master this approach.
Data Quality: What You Need to Know
Outdated or unreliable data leads to mistakes and missteps. Yet many organizations distrust the quality of their data. Learn about the key features of data quality, why it’s so crucial and how to fix data quality dilemmas.
The SAS Data Governance Framework
Today’s barrage of data demands critical governance decisions. An overarching approach to collecting, managing and storing data across the enterprise helps you keep pace with changing technologies, trends and regulations.
Build a Data and Analytics Strategy
Wondering how to build a world-class analytics organization? Make sure information is reliable. Empower data-driven decisions. Drive the strategy. And know how to wring every last bit of value out of your data.
Self-Service Data Preparation
Imagine the results if business users could prep data for analytics without relying on IT– no coding or special skills required. SAS Data Preparation lets business users access, cleanse, profile and transform data on their own.
Who's Using Data Management?
Data management powers the processes for every successful organization, across all industries. With more data and easier access to analytics comes the chance to seize more opportunities, ask more questions and solve more problems. Learn how industries across the world are using data management to support their goals.
Understanding customers and responding appropriately to expectations requires having an accurate, up-to-date view of all the data – whether it’s streaming, cloud based, or stored in a data lake or warehouse. From marketing to merchandising to sales, trusted data management is essential to taking charge of retail data.
In the manufacturing industry, nothing speaks success like quality. With solid data management and data quality technologies, manufacturers can efficiently manage product inventory, and integrate structured and unstructured data from all sources to get an enterprise view of performance, drive better outcomes and make well-informed business decisions.
More than ever, issues around data privacy, compliance and digitization require banks to have a trusted data foundation. Only with a complete, integrated view of all their data – and sound techniques for quality, governance and personal data protection – can banks can gain customers’ trust and pursue forward-looking digital transformation efforts.
Enterprise data management is a must-have in the health care industry. The industry counts on being able to integrate data from all formats and sources – including data from outside of the organization – all while spotting duplicate data, fixing data quality issues, and adhering to strict regulatory and compliance requirements for protecting personal data and privacy.
Local and national governments are responsible for a vast range of services and programs. Reliable data management technologies support all those efforts – from fighting fraud and improper payments to ensuring citizen safety to overseeing population health outcomes, economic development and smart city initiatives.
Small and midsize business
As small and midsize businesses work toward digital transformation, they need to implement data-driven business models and modernize legacy IT so they can be competitive with their larger counterparts. One way to get there is with reliable data management technology that can be catered to the needs of smaller businesses.
We overplay the robotics side of AI – it’s truly more about the data. It’s more about the sensory inputs and making decisions based on those inputs, similar to the way people make decisions. It’s all about the patterns, the trends and the anomalies in the data. Kirk Borne Principal Data Scientist and Executive Adviser Booz Allen Hamilton
Augmented Data Management
This approach uses artificial intelligence or machine learning techniques to make processes like data quality, metadata management and data integration self-configuring and self-tuning. For example, SAS can:
Generate a list of suggestions for how to improve data. Actions taken over time will continue to improve results.
Profile data and automatically find personal information, which can be flagged to influence behavior – such as only allowing specified users to access personal data in a table.
Suggest data transformations, then suggest improvements over time using machine learning – done via a discovery engine that analyzes data and metadata.
Provide recommendations to users and suggest next-best actions during the data preparation process.
More About How Data Management Works Today
- Data management for artificial intelligence (AI) and machine learning (ML). Many business processes rely on AI, which is the science of training systems to emulate human tasks through learning and automation. For example, AI and ML techniques are often used in making loan and credit decisions, medical diagnoses and retail offers. With AI and ML, it’s more important than ever to have well-managed data that you understand and trust – because if bad data feeds algorithms that adapt based on what they learn, mistakes can multiply quickly.
- Data management for the Internet of Things (IoT). The data that gushes from sensors embedded in IoT devices is often referred to as streaming data. Data streaming, or event stream processing, involves analyzing real-time data on the fly. This is accomplished by applying logic to the data, recognizing patterns in the data and filtering it for multiple uses as it flows into an organization. Fraud detection, network monitoring, e-commerce and risk management are popular applications for these techniques.
- Bidirectional metadata management. Bidirectional metadata management shares and connects metadata between different systems. SAS, for example, is committed to being part of the open metadata community through its involvement in the OPDi Egeria project – which underscores the need for metadata standards to promote responsible data exchange across varied technology environments.
- Data fabric and semantic layer. The term data fabric describes an organization’s diverse data landscape – where vast amounts and types of data are managed, processed, stored and analyzed, using a variety of methods. The semantic layer plays an important role in the data fabric. Like a business glossary, the semantic layer is a way to link data to commonly defined business terms used across the organization.
- Data management and open source. Open source refers to a computing program or infrastructure in which the source code is publicly available for use and modification by a community of users. Using open source can speed development efforts and reduce costs. And data professionals can thrive if they can work in the programming language and environment of their choice.
- Data federation/virtualization. Data federation is a special kind of virtual data integration that lets you look at combined data from multiple sources without needing to move and store the combined view in a new location. So, you can access combined data exactly when you request it. Unlike ETL and ELT tools that show a snapshot at a point in time, data federation generates results based on what the data sources look like at the time of the request. This gives a timelier and potentially more accurate view of the information.
Data Management Solutions
Trusted data leads to trusted analytics – which is important for the success of every business. And trusted data starts with having a solid data management strategy supported by proven data management technology. SAS Data Management includes all the capabilities you need to access, integrate, clean, govern and prepare your data for analytics – including advanced analytics like artificial intelligence and machine learning. It’s all part of the SAS Platform. Learn how to transform your analytics programs into big opportunities.
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