Researchers at Switzerland’s Competence Center Corporate Data Quality (CC CDQ) have been looking into how businesses can derive value from the rapid growth in data. They revealed their findings at a recent SAP Business Data and Analytics Info Day event, held in Essen, Germany.
Greater use of digital technology is driving the growth in data to volumes that were unimaginable only a few years ago. The Internet of Things, mobile devices such as smartphones and tablets, social networks, online shops, and cars and machines with hundreds of sensors generate vast amounts of data every day. More data was produced in the last two years alone than in all the previous years put together, and by 2020, more than 50 billion devices will be connected to the Internet.
The first question many companies ask is how they can manage this ever growing tide of data. Data is far more than just a byproduct of business processes; it has huge potential: “In traditional companies, data was an important, auxiliary resource used in business processes and for decision-making. In an increasingly digital world, it has an intrinsic value, since it is essential in digital business models and strategies,” writes Martin Fadler and Professor Christine Legner in their report “Managing Data as an Asset with the Help of Artificial Intelligence.”
Few Companies Have a Data Management Strategy
Researchers have been looking extensively at how companies can derive business value from the mass of data heading their way. They have found that perception matters. In their report, the authors conclude that companies should treat data as an intangible asset, and not as a burden or as a secondary means to an end. “Although data is so central to new technologies, so far only few companies are paying as much attention to managing their data as they do to other aspects of their business,” note the report’s authors.
Above all, few of them have a proper data strategy, aligned with their business strategy, that sets out the value and role of data to the business and how that data will be managed. Or they do not have a company-wide approach to data management that defines data products and their life cycle, and designates owners for data assets that belong to the company as a whole, for instance.
“Even if data has not yet made it onto the balance sheet, it does have all the features of an intangible asset and should be treated in the same way,” Martin Fadler and Professor Christine Legner believe. In and of itself, data does not automatically hold value to a business. Before its full potential can be discovered, there has to be intelligent data management so that the data can be used in value-adding scenarios.
Analytics: Creating a Data Value Chain
The researchers at CC CDQ have come up with a model for an end-to-end data value chain describing each step from data creation to its exploitation. At different stages, and in different scenarios, data can be used in different ways to create new value from it. However, more data does not automatically translate to heightened value. The data has to have sufficient quality, and companies have to actually use the data they have. Therefore, the key drivers of value in a data value chain are the volume of data, its quality, and its use.
This is why data management along the entire value chain matters, and where AI algorithms come in. They have two important jobs. One is to manage data systematically and improve control over it. The other is the smart automation of recurring tasks. Here, algorithms play a major part in deriving insights from the analysis of data.
The researchers at the Competence Center Corporate Data Quality describe a number of scenarios in which data can generate value: from creating digital products and services and supporting decision-making and business processes, to data-driven business models in which customers pay to access consumable data. Thus, the intelligent use of data drives and enables higher-level goals, such as increasing revenue and efficiency, reducing costs, and mitigating risk. “To get the most out of their data, companies need a well-thought-out approach that recognizes that the business value of that data depends on where it is in the value chain,” says the report.
The report was produced at the Competence Center Corporate Data Quality (CC CDQ). The Competence Center is a research association that works with the University of Lausanne, Switzerland, and 20 leading European companies on data quality and data management in organizations. Its research focuses on how to create a reliable and consistent set of data that provides the basis for technological innovation, the use of digital technologies, and process harmonization in global companies.
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