By Carlo Caloisi
The new challenges of the future mean that companies are becoming increasingly structured in order to compete for greater market share or to consolidate existing markets. Among the many structural aspects necessary to ensure the organizational efficiency of a company, there is a need to manage and interpret information in order to better understand what the future holds. Therefore, being data-driven means considering data management not only as an aspect among the areas that define a company, but as one of the pillars that characterize its business strategy. The process of business strategy based on Data Driven Decision Making (hereinafter DDDM), identifies and connotes a company that has the ability to make decisions by relying on the data in its possession and then working towards key objectives through an adequate competence in interpreting and analyzing them correctly. In order to operate at its best, with the aim of obtaining the maximum result, it is necessary to possess a series of skills both technical and, so to speak, transversal (soft skills) which are:
- Data extraction (bringing together and organizing input from different archives);
- transformation and homologation of data into a reference standard: data are “cleaned” and standardized for use;
- basic mathematics and statistics, data science and data understanding;
- Systemic thinking: helps decision-makers to better understand the collective mindset on why one type of orientation is taken and not another;
- critical thinking and active listening: you analyze the amount of data obtained by evaluating the pros and cons of the decisions made net of your personal preconceptions;
- communication with the data: necessary to allow the person being addressed (e.g., employee, investor, client) to accept the decisions made.
To get the most out of the data in order to successfully pursue the required objectives, the decision-making process should follow the aspects listed below:
1- identification of business objectives; understanding the objectives will support in the next step to choose the Key Performance Indicators (KPIs), in order to better focus which data to analyze and which questions to ask in order to be in line with the objectives;
2- creation of questionnaires to be submitted (e.g. to employees, customers, etc.), through the use of different sources (e.g. social, corporate web, etc.);
3- data collection and preparation: this is perhaps the most delicate point of the whole process. In fact, 80% of the time is dedicated to cleaning and organizing the data and only 20% to the analysis phase. The “80/20” rule clarifies the importance of this aspect;
4- execution of the statistical analysis: once the data have been cleaned, statistical models are created to test the data obtained in order to ensure that they are in line with the objectives set;
5- development of possible insights: aspects that may emerge during data collection and not previously considered;
6- visualization and sharing of detailed data: effectively communicating the data collected through multimedia supports such as graphs, images, block diagrams, etc..
Among the many contexts in which this business “discipline” can be used, we can mention:
• the financial sector, where to study which is the best method to promote a new product or to hire new staff;
• marketing and sales, to decide which advertising channel can guarantee the best profitability or which sales activities obtain the highest number of interactions;
• customer service, how response times can be improved, including handling support tickets.
The DDDM allows to acquire a series of economic and managerial advantages
- reduced costs even through a more or less significant initial investment (e.g., hiring data scientists);
- improvement of the decisional ability; with concrete data it becomes difficult to discuss the effectiveness of a plan;
- Stimulate continuous improvement: the adoption of this system leads to the modification of the method of analysis in a perspective of improvement, also changing the way in which the resources operating in the company are chosen;
- more accurate decision-making: data analysis also increases the ability to choose the strategy to be undertaken in a safer way, reducing the possibility of making mistakes.
In fact, companies using this approach have seen an 8-10% increase in profits and a 10% reduction in overall costs (e.g. Google, Starbucks, Amazon). Although there are objective results by adopting this method, to date 91% of companies say that DDDM is important for their business growth, but in reality only 57% of them practice it.
The European Union incentivizes companies to use this method that has proven to be virtuous so much so that they have been fired, first in 2014 and then in 2017, some working documents of the Commission in which it is proposed the implementation of a data economy that sets as an objective the improvement of the digital single market.
Just last February 23, the European Commission presented the proposal for a European Data Act, aimed at encouraging and supporting the implementation of data-driven business models.
With this law, available for all economic sectors, the aim is to make more data available for their use and to establish clear rules on who has access to data and consequently on how and for what purpose to use them. The new rules should enable an increase of 270 billion euros in the GDP of member states by 2028.
Companies and consumers will be able to benefit from lower prices for after-sales services, new possibilities for using services thanks to better access to the data collected.
SOURCES AND IN-DEPTH ANALYSIS
- Why should leaders acquire data-driven decision-making skills: https://www.productleadership.com/why-should-leaders-acquire-data-driven-decision-making-skills/
- How can you benefit from data driven decision making?: https://www.productleadership.com/how-can-you-benefit-from-data-driven-decision-making/
- 10 competenze richieste per il data driven decision making: https://www.productleadership.com/10-skills-required-for-data-driven-decision-making/
- How to create data driven decision making in 6 steps: https://www.productleadership.com/how-to-make-data-driven-decision-making-in-6-steps/
- 10 skills required for data driven decision making: https://www.productleadership.com/10-tips-for-enhanced-data-driven-decision-making/
- Towards a thriving data-driven economy (July 2014): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52014DC0442
- Building a European data economy (January 2017): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2017:9:FIN
- On the free flow of data and emerging issues in the European data economy (January 2017): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52017SC0002
- European data strategy: https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/european-data-strategy_en