By Gary Allemann, MD at Master Data Management

Making money from data – whether selling data to third parties or developing data products – has become an essential revenue stream in a digital world. However, while there are numerous ways of data monetisation, they all have one critical element in common – they rely on the quality and integrity of the underlying data to deliver value. Not only is data integrity crucial for data monetisation, getting the foundations of data management right will have other positive business impacts, from reducing costs to improving quality and facilitating better decision-making abilities. 

What is data monetisation?

The phrase ‘data monetisation’ typically suggests the thought of selling data to third parties. However, while this is one method of making money from data, it is actually not common.

Most businesses do not want to sell their data. Instead, businesses are using their data to create new products and services, such as personalised recommendations or predictive analytics capabilities. Data can also be used to optimise business processes, which can help businesses save money or create additional revenue. Data can be licensed for use to other companies, and it can also be harnessed to improve the targeting and effectiveness of advertising and marketing spend, in turn increasing revenue. It can also be combined with machine learning and artificial intelligence to create data models and data sets for data scientists. 

Data can be converted into a revenue-generating asset in many ways, depending on the type of data and the industry, but at its core, data monetisation centres on extracting value from data to turn it into a source of income.

Data integrity drives value in your data

No matter whether you are selling data or developing data products, investing in the data itself is critical to driving value. If the quality and integrity of the underlying data cannot be guaranteed, the insights and data models derived from the data cannot be trusted, and can lead to incorrect conclusions, poor decision-making, and negative business outcomes. When ML and AI come into play, there is the added risk of data bias and incomplete information corrupting the models. 

Apart from improving data monetisation prospects, maintaining data integrity can also have numerous positive business impacts. While investment into data integrity is often a difficult business case to sell, research from IDC shows that higher levels of data integrity positively impact numerous important business metrics. These include customer satisfaction, innovation, time to market, employee retention and productivity, adherence to regulations, operational efficiency, risk and costs, profit and revenue. Lower levels of data integrity can also negatively impact these metrics, particularly with regards to operational efficiency and cost. 

Trust is key

The importance of data integrity comes down to trust – without trust in data, it cannot be sold, used for data products and revenue, or harnessed to enhance business operations. Data integrity is also essential for maintaining data privacy and security, and for compliance with a growing body of data legislation. Ultimately, it is the missing link in creating enterprise intelligence, which is vital for competitiveness in a digital world.