From data to decisions: aspects and benefits of data-driven decision making

It is increasingly clear today how information can be a guide of high strategic value in business decisions. Indeed, valuing the "right" information in a decision-making process creates the basis for sound and informed planning that can flexibly meet the competitive challenges of an increasingly uncertain and changing world.

A company's management can derive useful information to guide business processes from a variety of sources, as described in the article "From data to decisions: the case of the purchasing office".
However, the step that needs to be taken for one of these sources to turn into real information is through the integration of it to managerial and specialized skills that can compose a "puzzle" from the individual components identified.

In order to best clarify this fundamental logical step, we will focus primarily on data analysis, given the increasing awareness of its potential and the consequent development that this topic has been experiencing in recent years.

The data-driven decision-making process

Decision making based on data and its analysis can be called data-driven decision making (DDDM), and is depicted in the infographic below.

Dati

The first foundational building block of this process is, of course, data. Digitization has made it possible for companies to collect a vast amount of data, which can basically be divided into data internal and external to the company. The former aim to summarize all the entities on which a company's life is based (customers, suppliers, employees, purchase orders, sales orders, etc.), the latter, on the other hand, allow the company to monitor the external context in which it operates and competes.
Especially in the case of external sources, the clarity of the source of the data, that is, the facts of which they are a measure, is crucial. The data collected and systematized can, in fact, be direct or indirect measures of the facts of interest, depending on whether they are primary or secondary data. Primary data are those data collected directly for a specific purpose (think, for example, of specific market surveys specially commissioned by the enterprise), while secondary data exist and are systematized for other purposes, but lend themselves to being of primary utility to the enterprise the greater the quality of the relevant measure. Of the two, secondary data are becoming an increasingly efficient tool for business purposes because of their lower costs.

Analysis

The step that makes data usable and interpretable is that of analytics, which enables the identification of meaningful trends and patterns and can enable predictive analysis techniques based on historical data. On this front, Artificial Intelligence (AI) has laid the groundwork for a significant expansion of analytical capabilities, introducing new levels of automation and predictive capability, improving the performance, efficiency, and scope of information extracted from different data sources.

Information and evaluation

As anticipated, the construction of useful information for business purposes enhances all the previous steps and integrates them with the specific skills of management to derive a clear direction consistent with business objectives. This step may involve many individuals with specialized skills or a few individuals with multiple skills.
A data-driven information is thus a more complete view of the fact of interest, resulting from the processing and interpretation of data, dropped into a given context, as a result of extraction and processing operations married to the manager's expertise, sperience and vision.
It is evident that in this process the evaluation stage is particularly important, in order to verify the consistency, quality and reliability of the information produced. If, for example, the facts to which the data refer are unclear, or they are roughly measured, the relevant information extracted from their processing will be of little use and may lead to wrong decisions.

The benefits of data-driven decision making

We have gone over the steps through which a DDDM leads to data-driven decision making, but what are the benefits of this type of process?

Objectivity

Data-driven decisions are guided by quantitative, measurable and objective aspects, rather than by intuition or personal opinion. This increases the accuracy of decisions and reduces the risk of errors due to subjective judgments.

Planning

DDDM processes enable more accurate forecasting and more effective planning of the enterprise's strategic activities in the medium to long term.

Monitoring

Data play a crucial role in assessing the effectiveness of current business strategies. By monitoring key metrics and performance indicators, strengths and areas for improvement can be quickly identified, enabling timely adjustment of operational strategies.

Flexibility

A data-driven company is therefore more flexible and able to adapt quickly to changing market conditions.

The case of internationalization

The collection of data, their analysis, and the extraction of relevant information can be more or less effective depending on how clear and shared the characteristics and objectives of the decision are. If we wanted to drop this process into the case of business internationalization, the possible decision-making processes are different:

1. Definition and choice of a new market;
2. Monitoring sales performance in foreign markets;
3. Identification of international competition drivers;
4. Formulation of the commercial budget;
5. optimization of its market portfolio.

For each of these cases we will go on to define ExportPlanning measures useful for capturing the facts of interest to extract relevant information and improve decision outcomes. This analysis will be the subject of subsequent articles, each devoted to a specific decision-making process.