The COVID-19 pandemic brought many uncertainties involving intra-personal relationships in companies and relationship between the companies and their customers. There was an urgent need to improve the digital communication channels, and this brought a real acceleration of the digitization of customer interaction.
With the increasing intake of data from external sources (IoT, social media and mobile) it has been a constant challenge to filter and locate the most trustworthy data. At the same time, the key stakeholders demand more transparency on how the data was obtained and how it is being used.
All these factors are pushing the limits of the decision makers on how to improve data discovery, data analysis and make better data driven decisions, while at the same time being compliant with the regulations related to privacy and security.
The answer to these challenges is not only related with the investment on new tools, but also in the adoption of agile methods to improve the development of projects.
Last month IDC held an online event, where Passio was one of the digital partners with Denodo, with the topic Data Monetization and Management, which focused on these new challenges that companies faced due to the COVID-19 pandemic, regarding data management and data analysis. Several expert speakers gave their insights into what data monetization is, how it can be improved, and the impact Machine Learning has on it.
Data Monetization
Data monetization is defined by Gartner as “(…)the process of using data to obtain quantifiable economic benefit.”. In 2017 the Economist published an article saying that the world’s most valuable resource is data and companies know it. The data monetization landscape in changing rapidly and companies need to adapt if they do not want to be left behind.
A distinction can be made internal monetization and external monetization. The internal monetization is the use of data to improve internal processes to improve productivity and increase value and the external is the exchange and commercialization of companies’ data, with all the difficulties it brings (security and privacy).
There has been a great emphasis in the quality of the data and its use for ML models and the need for decision makers to be knowledgeable in ML to understand what can and cannot be done with the existing data inside each company.
The use of AI and data is continuously highlighted and its importance in companies’ businesses, be it retail, communications, energy, or entertainment is undeniable. It can be used to monetize data indirectly by allowing the use of descriptive and predictive analytics to optimize the business processes and discover new opportunities.
Energy companies use computer vision to evaluate structural damage at a distance and protect their employees*, entertainment companies develop highly evolved recommendation systems to deliver the best possible content to their users**. All these innovations are driving the data monetization scenario forward and allowing companies to generate useful data and use it very efficiently.
Although the construction of these models require highly technical manpower, data monetization is an interdisciplinary effort that must bring together decision makers, sales marketing, and IT***.
In summary we can no longer use data only to improve processes inside the company, but it must also be used as an effective source of income for companies, this can be done by implementing the right data monetization strategies and, consequently, increasing the competitive edge.
Key take-aways:
Data monetization is not only about improving internal processes but must also be an effective source of income for companies.
Decision makers must be knowledgeable in Artificial Intelligence to implement realistic ML projects in their companies, considering the data that exists.
Artificial Intelligence has a very large role to play in this area, namely in the form of Machine Learning algorithms.
Data Monetization requires a highly specialized multidisciplinary team, that must bring together decision makers, sales, marketing, and IT.
by João Raposo
Consultant @ Passio Consulting __________________ References
*. Jalil B, Leone GR, Martinelli M, Moroni D, Pascali MA, Berton A. Fault detection in power equipment via an unmanned aerial system using multi modal data. Sensors (Switzerland). 2019;19(13):3014. doi:10.3390/s19133014
**. Gomez-Uribe CA, Hunt N. The netflix recommender system: Algorithms, business value, and innovation. ACM Trans Manag Inf Syst. 2015;6(4). doi:10.1145/2843948
***. Findling S, Strohlein M, Schneider L, Vesset D. IDC PlanScape: Data Monetization.; 2018. Accessed May 12, 2021. https://www.idc.com/getdoc.jsp?containerId=US43627918