In an era where the business model of big Internet companies is entirely based on data, traditional companies such as credit, telecommunications, energy, insurance and retail are also looking for ways to trade the data they collect. Advanced data processing capabilities, recruiting data scientists and the maturity of new business models mean that such data products already exist in the market and companies are stating a new line of revenue in their P&L reports.
Let's start from the end - a mobile-phone company in Singapore has developed a data product based on its customers' location data, to help public transport agencies optimise their services. In Israel, a large retail chain sells a data service that allows its product providers to analyse the behavioural profiles of customers who buy each product in each store. At the same time, a European credit company allows each store to purchase a product that provides an analysis of all the marketing segments that a store purchases, and another insurance company offers to collect its health insurance premiums in accordance with health data coming from the insured's smart watch.
As you can understand, the good news is that many traditional companies, such as credit, insurance, telecommunications and retail companies have amazing data on their customers, one that not even the biggest Internet companies like Facebook and Google have access to. This is a unique and highly valuable data that includes characterising purchases, locations, watching TV and networking behaviour, where this "raw data" can be converted into products and services for third parties, from which one can generate a lot of money.
The less good news is that for many traditional companies, the way to create profitable data products is even harder. While Internet companies have developed the monetisation capability from the data they hold and mastered it, most traditional companies are still relying on traditional commercial models in their fields and have not worked enough to convert their data into new business routes that will significantly increase the companies’ revenue. Here, too, there is an optimistic angle to the story, as more and more companies are establishing monetisation departments and launching data products into diverse sectors such as advertising, transport, security and more.
The Data Monetisation Era - As mentioned earlier, when technology monsters realised that the data they were collecting had a much greater profit potential than traditional business models that charge a monthly registration fee for a particular service. By doing so, users of Google, Facebook and similar platforms receive a service at no cost, but with the cost of authorising them to use their data for advertising purposes, selling information to third parties and more. Given the tremendous success of these new business models, traditional companies are also looking to integrate data products as part of their product portfolios, thus increasing their value proposition to sectors with which there has been no connection so far (because who would have believed that a mobile company could enter the field of transport thanks to its data?).
The way to launch successful data products has three major challenges that every company has to face: technological, business and legal, each of which we will discuss in detail in the following articles.
In the technological aspect, many data monetisation ventures include advanced data analytics capabilities. These capabilities are required because these new products usually include a huge amount of data analysis that traditional companies have not faced in the past. These analyses usually dominate the company's location / acquisition / browsing traffic (and this, my friends, is exactly what Big Data means), which is far larger than the traffic on which the classic BI analyses that the company analysts have performed so far are based.
In addition, a substantial part of the added value that data provides, is the product of various artificial intelligence models that require data architecture that will allow data scientists (which we will discuss in detail below) to fail cheaply, to run fast and to allow for the constant changes required in product model development. What does all this mean? That data monetisation ventures bring many organisations to the cloud-based architecture that runs alongside the limited and cumbersome on-prem infrastructure used by most of the company's core services. This change in architecture has many significant implications relating to purchasing model, information security and new knowledge that the company needs to recruit / teach its employees.
Despite all that, the most dramatic change that the data monetisation field brings, is precisely in the business and financial aspects, as it removes most of the BI and analytics from their comfort zone and - for the first time - engages in the productisation of data, driving business development processes, pre-sale, drafting contracts and engaging in legal negotiations. This is much closer to a start-up than to a classic data organ whose clients have so far been employees of the organisation who need dashboards and pieces of information. It is, therefore, a significant shift in the capabilities of the data personnel in the organisation, in terms of business and financial understanding and familiarity with the markets in which the company's data products intend to operate. In this aspect, the correct pricing model for each data product must be carefully discussed. Is the right pricing model a monthly model? A fixed-price? Perhaps, freemium? Will it include a free ads-based service? Many of the ventures whose data-based core is producing, discourse on new pricing models that must be tailored to the nature of these ventures.
Another critical aspect to address, is the one known as the "party pooper" of the data ventures: privacy issues, GDPR and data security. In a data product, collecting customer data and analysing it for the benefit of the product / service, actually constitutes the core of the product, without which the product has no right to exist. Therefore, the ever evolving regulation in the areas of privacy protection and data security is much more than a "winging it" during the work on the product envelope. These are often issues that constitute GO / NO GO for the entire product, and - at the very least - an issue that must be taken seriously as part of the technological and marketing characterisation of the product. Many products and ventures failed during development, or did not even launch, due to incorrect management of the legal issues, which must be studied in depth.
Many companies have learned – the hard way - that to overcome all these challenges and develop high-quality data products, they need to establish a designated team that will operate independently of the company’s core business. This team will be responsible for both the product, commercial and business side of the activity as well as the development, data science and the required e infrastructure. It is actually a start-up within the company, that has the privilege of access to data that every external start-up would only dream of accessing. The successes and failures in the field, show that traditional CEOs have to adopt this new way of thinking and manage their data products, using the configuration discussed above.