How to monetize the data in your company?
“Strategists and business leaders must be fluent in the information economy”
Laney, Douglas B. Infonomics: How to monetize, manage and measure information as an asset for competitive advantage
“Information is our most valuable asset”, countless meetings revolve around this premise. The production and innovation teams, meet in different work tables to define what to do with their data and how to build value from them. However, if it is such a valuable asset, the accounting areas should be able to give an economic value in the balance sheet, right?, but the accounting areas are better prepared to count the chairs as an asset, than the data and its potential to increase the value of the brand.
What is the value of Facebook or Google ’s information, or how much is the value that can be created when extracting the data from the sources? Despite the fact that “information is our most valuable asset”, companies are not prepared, for the most part, to answer this.
There is a reasonable explanation for this phenomenon: the rise of big data, neural networks and data exploitation models are not widely spread and are still very incipient in Colombia.
Now, how can a company monetize its data and follow the path of giants like Netflix, Walmart or Monsanto?, Douglas Laney, in his book Infonomics (2018), recommends a seven-step approach to achieve it:
- Organize a dedicated team: Whose mission is to think how much value can be extracted from the data and find creative ways to use it, but not before facing the following challenges:
- Think big and different.
- Identify and really understand customers.
- Swim against the corporation´s rigid processes.
- Test hypothesis with possibilities of failure.
- Evolve the value offer of the company.
- Measure progress and communicate transversely.
It is recommended that the team leader be a person with experience in developing new products or R & D, including someone who has worked in a company based on data such as: Experian, Nielsen or Kantar.
2. Make an inventory of data: It must go beyond the typical ones that the company has (databases, excel reports and CRM). Use the available data, those that can be purchased, the unstructured or those of social networks.
The following figure shows examples of this type of data.
3. Select the type of data monetization: There are two ways to do it.
- Direct: Sale and licensing of information, this model is viewed with caution in organizations, because it could be misinterpreted as “selling confidential information”, but in reality, it is about finding data that can be licensed like Google’s.
- Indirect: A much richer version for the creation of value, because it is designed to solve business problems such as:
– Improve the maintenance of plants. – Increase production. – Detail the quality of products. – Detallar la calidad de productos. – Increase sales. – Strengthen the supply chain. – Create new products.
Did you know that indirect monetization was what led Netflix to launch in 2013, House of Cards. Netflix based the scripts, selection of actors, duration and number of episodes of this successful series in data, which gave enough information about users as: schedules, themes, repeated or omitted scenes.
Indirect monetization also helped americans farmers to move away from the idyllic vision of manual agriculture, to now be a data driven industry. The possibility of predicting the climate, measuring the supply and demand of the products, obtaining satellite images to identify the type of ground, influenced the increase in costs by 800%, but its profits also increased by 50% compared to previous years (Lanley, 2018).
4. Look for inspiration from other companies, in different industries: You have to learn from many companies that have already traveled this road, they have surely had successes and failures. Additionally, find inspiration in different industries from yours and understand how they are facing the data world.
Think of Sony, a company that by its path, should own the mp3 market, but three years after the release of Apple’s iPod, Sony was relegated to oblivion.
5. Test ideas: A judgment value must be made on the ideas that the team has created and discard the inadequate ones, based on the following table.
6. Extract, transform and load (ETL): Now that the ideas have been segmented, the team is aligned, we have seen what is in other industries and we have an inventory of data, we must perform a technical work to ensure that the data collected can be used. This can be as simple as homogenizing a table or as complicated as creating a unified repository of structured and unstructured data, with prediction and autoclassification capabilities.
7. Test the market: Before taking out new products and services, you should think about prototypes “friends and family”, either for internal or external users.
How do you see the success of monetizing the data?
Finalmente, para saFinally, to know if the monetization project is going in the right direction, verify the following aspects:ber si el proyecto de monetización va en la dirección correcta, verifica lo siguiente:
- Economic Attribution: At this point you are able to answer how much a fraction of your data is worth.
- New products: The new proposals are the result of that investigation.
- Internal resistance: Typically, the teams that seek to monetize the data meet with areas in charge of compliance with laws, which openly disagree for fear of violating customer data.
- Market reaction: It can come in different ways: copy of your ideas, sabotage of competitors or discovery of a new market segment by consumers.
- Reaction of the investors: Exploitation of data, design of services and new products that attract the eyes of the investors, because they give solution to the flaws that the companies have.
- Increase in profits, reduction of risk and operating costs.
While these two checklists are only recommendations and observations, the logic behind them is what allowed companies like Walmart, UPS, UBS and Morgan Stanley to become companies that monetize every available data to the maximum.