Data economy: how to convert your digital data into a profit

The data economy is one of the fastest growing business sectors. During this year, the European data market is forecast to reach 739 billion euros, representing 4% of the European Union’s GDP. By comparison, in 2014 the data economy generated 1.8% of the GDP in the European Union. The value created by data will continue to increase. Therefore, both the private and the public sector have to learn to treat data as an asset that can be used to create a profit.

In order to employ a company’s data in this manner, several principles must be accepted. First, the data must be considered as an asset, while its value depends on the way it is stored and used. Most importantly, the data owner must be proactive in making decisions about a holistic approach to data usage.

Data is an asset

The phrase “Data is an asset” is not just a slogan – in the modern economy, data truly is a product that has a market value. The essence of the data economy involves using your accumulated data in planning your operations, making your processes more efficient or generating a profit from data exchanges or transfers.

For example, banks and insurance companies store large amounts of data that enables them to offer their customers customised services, while the GPS data from mobile phones can help experts to optimise traffic flows and plan routes. The value of this data lies in the fact that helps the user to understand consumer behaviour, much like the shopping cart details that enable a store to offer a consumer discounts or special offers on their favourite products, and thus grow their consumer loyalty. When used in the right manner, data can facilitate the planning of many internal processes. For instance, manufacturing companies can collect information about their stock balance and the product quantities they have ordered to optimise their manufacturing lines, and can adjust the production quantities according to latest trends to reduce their costs.

In any case, if you want to employ your company’s data, you will first need a system for collecting and managing that data in a way that can contribute to a profit. One of the best models is the data value chain, which has been designed to support companies in the process of creating their data management strategy and plans. The data value chain consists of four stages – when those stages are completed correctly, your scattered data will be converted into a valuable asset.

Four steps to managing data

Mastering data can solve several issues in a company. For instance, when asked which of your services is the most important, your employees will only answer right away. They will also understand why and how that service is currently popular, who is using it and who find the related information interesting. Managing this type of data starts with its collection.

Step One – data collection and integration. In order to use the data about your company, you first have to ensure that you are collecting the types of data that have value. In addition, all of the data sources (both internal and external) must be integrated. Before starting the data collection process, it is important to ask the experts what methods are the most effective for your requirements.

Step Two – storage and security of the collected data. The key objective at this stage is to ensure the quality and security of your storage method. Any data must be managed and protected, according to your organisation’s data management procedures. When these procedures are created, it is worth knowing about the several data management solutions available: data clouds, data lakes or data warehouses. Again, the experts can help you find the most suitable and efficient solution.

Once you have invested in the first two stages and created a suitable system for collecting and storing data, you can finally implement the third stage. This is where the real value is created, i.e., your digital assets will begin earning you profits. Through data dissemination and publication, will can organise your internal processes successfully and efficiently, because your staff will have access to a systematic and comprehensive database that is needed for the proper performance of their job duties. The organisation will clearly see the positive benefits, such as an increase in work efficiency and better accuracy when communicating with clients. The open data accumulated at this stage should also be considered a product: it can be sold, exchanged for something else, or shared with others, thereby creating added value for both the public and private sector.

Finally, you will reach the fourth stage of the data value chain – data usage and analytics. At this stage, the data and information is converted to knowledge and insights, while your strategic, operational and every-day decisions will be made on the basis of these insights. This stage also closes the loop and connects back to the first stage, because the analysis leads to a new data collection and storage stage. This analysis step is crucial and must not be skipped, as it is the only way to ensure that your data becomes a value-creating asset.

How can your data be useful?

Once you start to consider data as an asset, you will gain a better understanding of its possible uses. Like any asset, data creates value only when it is employed and prevented from depreciating. An interesting issue is that even today, some organisations are holding onto data without realising this: every user that visits your website or has clicked on your interactive advertisement, as well as every search in your internal or external systems, creates a point of data that belongs to the organisation that has recorded it. That organisation needs to appreciate its digital data and its potential. The value of that data will depend on how it is collected, its accuracy and relevance, and whether it is used in further processes. Every expert agrees that unemployed data does not have any value. Moreover, storing that data comes at a cost. Therefore, the only sensible solution is to start using this asset.