Data driven processes: when data is the protagonist

In the age of digital transformation, from Big Data to the IoT and of Industry 5.0, companies are increasingly aware that their wealth and greater opportunity for success lies in their wealth of information: the data.

In reality, the data itself is of little use if it is not made available and operable through systems that extract the significant value in terms of strategic information. In the Italian panorama there are still many SMEs who see the need to manage the huge amount of data available as a burden rather than an opportunity to evolve towards a data-driven approach.

The companies that have already adopted an approach based on the valorization of the data are the data-driven companies (we have spoken about it here) and they are those that are already obtaining a great competitive advantage positioning themselves like leader in the various reference markets.

Data driven processes: what are they?

A data-driven process is a data-driven decision-making methodology, not only at the management level, but involving every area and business level: from CEO to marketing, from CFO to individual operators and executive roles.

In this way all those personal initiatives, based on personal intuitions and sensations, are excluded, every decision, from the arrangement of the products on the shelf to the location of new production plants, is guided by objective information.

Exploit the data

If we consider the data as a minimum unit, taken individually it can be represented by a number, information, an image or text, which already contains a meaning: the turnover year on year, the rate of loyalty of a customer, the preferences of a cluster of consumers, the text of a review, the graph of sentiment analysis, etc.

The value of this data can literally explode exponentially when we connect it with other information (external or internal data of the organisation) such as relating the rate of acceptance of a new financial product to the purchasing habits of customers in the target market and/or the behaviour and origin of their leads, or with new emerging trends in the market or with events at first sight little correlated (e.g. a pandemic can affect the sense of confidence in the future and shift the investment preference towards financial products more short-term and perhaps more risky).

So, at a time when the company has to choose whether and how to innovate a service or its mode of delivery, it needs to rely on objective data resulting from complex analysis. Not only that: having access to multiple sources and heterogeneous data allows you to find relationships that maybe you had not given any importance.

The value of meaningful data-driven decisions

A concrete example of a mistake born from a seemingly true relationship but in reality wrong is that of Blockbuster, the video rental chain that has dominated the market for many years, but that eventually got into serious trouble due to the growing popularity of the Netflix streaming service.

Blockbuster had noticed that his DVD and Blu-ray sales had started to decline rapidly, but mistakenly concluded that the reason was the growth of competitors. The company therefore decided to invest heavily in the opening of new stores and to acquire other companies to increase its presence on the market.

However, advanced data analysis would reveal that the real reason for Blockbuster’s drop in sales was linked to the growing adoption of streaming services such as Netflix and Amazon Prime Video. By acting this way, Blockbuster not only did not solve the real problem, but spent a significant amount of resources in an ineffective way, continuing to lose market share.

Blockbuster’s example demonstrates how advanced data analytics has the potential to deliver critical information that companies may not be aware of, leading to wiser and more effective business decisions.

This is a concrete example of data enhancement.

Features of the data-driven process

To enable a data-driven mode in the company it is necessary that this involves all the departments and all the roles of the organization (much to speak of data-driven culture). In the same way, IT solutions and infrastructures are indispensable, able to collect and analyze data efficiently, without wasting resources, aiming at the utility according to an agile development model: the maximum of the result with the least effort, in a continuous cycle of improvement.

Then there are other features that determine the success in the adoption of a data-driven model:

Usability of the BBI solutions introduced: visualization dashboards are fundamental to make the data readable and immediately significant, providing the necessary input to explore the data also following (and above all) new paths.
Analysis flexibility: linked to the previous point, this aspect is linked to the faculty granted to each company role to make analysis in complete autonomy (self-service analysis) without compromising the functionality of the implemented system.
Collaborative Analysis: Deals with the ability to access data from otherwise foreclosed business areas in order to discover new relationships, meanings, or paths of analysis.
Timeliness: get the strategic information when you need to make a decision.

In all this, the human component plays a primary role: from the selection and implementation of the most appropriate technological solutions, to the supervision of processes, People are the only component that can affect the success of any data-driven project.

Advantages of data driven processes

Being guided by data means making informed decisions. For companies this translates into countless advantages: better understand their customers and therefore their preferences and buying habits; developing better products and services; propose the offer in a targeted and personalized way; adapt rapidly to market changes (or better to anticipate them); improve performance, finding bottlenecks or low performing areas to act on; reduce costs due to inefficiencies; be more competitive, having first access to unpublished information.

Business Intelligence & Advanced Analytics solutions are therefore increasingly essential for companies that have the ambition to compete in markets. Until a few years ago, access to such technologies was prohibitive for most small businesses due to the high costs and long incubation times of projects. In such a scenario, leading companies have set the pace in their respective target markets.

Today, the spread of the data-driven approach, together with the deployment of cloud platforms, scalable offerings and ready-to-market solutions have lowered the threshold of access to these technologies, allowing even smaller companies to integrate them, even only on specific business areas.