Most companies are collecting massive amounts of data yet many do not know how to turn that data into an asset that generates money. While selling data is one obvious way to monetise it, most other opportunities are less direct. They include making processes run more efficiently, incentivising certain types of behaviour or revealing the true value of an asset.
Teradata suggests five steps towards monetising your data:
Start with Questions.
To analyse data effectively, find out which questions, answered at the right level of detail in the right timeframe, would most impact your company’s performance. This can help assess whether the data at hand is sufficient or if more is needed.
Next, examine ways of analysing the data and extracting insights. Inspiration for data monetisation can come from questions, from data and from analytical methods.
Look for Patterns
Velocity of data, new forms of precision and opportunities for combining different data sets can lead to data monetisation. Velocity is relevant when information is valuable for a short amount of time. For example, if a shopper who checked a price online is now using an organisation’s app inside one of its stores, then there is a distinct window of opportunity to target that person with an in-store offer and make the sale.
Precision refers to examining more granular data. Using a microscope on important data can create high-resolution models that can lead to valuable insights. Data fusion is the idea of combining data from many sources to create a more valuable view of an asset. For example, a real estate company combines many different data sources, such as location, inflation and property features, to provide an estimate of the value of a house.
Search for External Data
Internal data is important but the addition of external data increases its relevance. Organisations should consider dedicating one team member to searching for valuable external data. This could include open data and data from partners.
Sharpen your Analytics Skills
Big data is not just new because of its size; it’s new because it is impossible to analyse it using traditional methods. Trying to gain insights from billions of records using small, handcrafted tools will yield a small amount of information very slowly. Machine learning and advanced analytics are needed to profile big data sets and extract insights. This will smooth the path towards data monetisation.
Understand your Data Monetisation Identity
Organisations usually fall into one of three groups: expert consumers of data; aggregators of data; or creators of new data products. By understanding which role is most natural for the organisation, businesses can determine ways to monetise data.