Data is essential to how a business operates and functions. Businesses must make sense of data and find relevancy in the noise that’s created by diverse systems and technologies supporting today’s highly connected global economies. In this regard, data takes center stage. On its own data is useless – companies need an effective strategy, governance, and data management model to leverage all forms of data for practical and efficient use across supply chains, employee networks, customer and partner ecosystems … and much more.
So what is data management? Data management is the practice of collecting, organizing, and accessing data to support productivity, efficiency, and decision-making. Given the pivotal role data plays in business today, a solid data management strategy and a modern data management system are essential for every company – regardless of size or industry.
The data management process includes a wide range of tasks and procedures, such as:
- Collecting, processing, validating, and storing data
- Integrating different types of data from disparate sources, including structured and unstructured data
- Ensuring high data availability and disaster recovery
- Governing how data is used and accessed by people and apps
- Protecting and securing data and ensuring data privacy
Why is data management important?
Every application, analytics solution, and algorithm used in a business (the rules and associated process that allow computers to solve problems and complete tasks) depends on seamless access to data. At its core, a data management system helps ensure data is secure, available, and accurate.
Turning Big Data into a high-value business asset
Too much data can be overwhelming – and useless – if not managed properly. But with the right tools, Big Data can be harnessed to empower companies with deeper-than-ever insights and more accurate predictions. It can give companies a better understanding of what customers want and help companies deliver exceptional customer experiences based on the learning data provides. It can also help drive new data-driven business models – such as service offerings based on real-time Internet of Things (IoT) and sensor data – that wouldn’t be evident or obvious without the ability to analyze and interpret big data.
It’s no secret that data-driven organizations have a major competitive advantage. With advanced tools, companies are able to manage more data from more sources than ever before. They can also leverage many different types of data, structured and unstructured, in real time – including IoT device data, video and audio files, Internet clickstream data, and social media comments – opening up more opportunities to monetize data and use it as an asset.
Laying the data foundation for digital transformation
It’s often said that data is the lifeblood of digital transformation – and it’s true. Artificial intelligence (AI), machine learning, Industry 4.0, advanced analytics, the Internet of Things, and intelligent automation all require lots and lots of timely, accurate, and secure data to do what they do.
Machine learning, for example, needs very large and diverse datasets to “learn,” identify complex patterns, solve problems, and keep its models and algorithms up to date and running effectively. Advanced analytics (which often leverage machine learning) also depend on vast quantities of high-quality data in order to produce relevant and actionable insights that can be acted on with confidence. And the IoT and Industrial IoT run on a steady stream of machine and sensor data, flowing at a million miles a minute.
The common denominator in any digital transformation project is data. Before businesses can transform processes, take advantage of new technologies, and become intelligent enterprises, they need a solid data foundation. In short, they need a modern data management system.