DATA DRIVEN transformation and technology

DATA DRIVEN transformation and technology

The cultural question

In recent times, digitalization has climbed all the positions of the ranking of company priorities, very quickly becoming the challenge par excellence for anyone who wants to compete in the new normal. The necessary investment in new technologies has defined a new focus on the potential deriving from data analysis, and a new "data-driven" approach.

When we talk about a data-driven company, a data-driven company, we refer to the ability to make decisions based on objective facts, and not on personal feelings. Having a data-driven approach means exploiting the treasure of Big data in companies and effectively using data in the decision-making process.

Data-driven companies are those that consider data management not as a technical factor, but as a strategic pillar of the business. Making a data-driven company therefore means starting a path characterized by a strong human as well as technological component: the transformation into a data-driven company cannot take place with technology alone, but with a change management path capable of bringing culture data at all company levels. In the industrial field, i.e. in Production and the Supply Chain, the Internet of Things, which makes every object a connected and communicating device, has created countless opportunities related to access to new data sources. The data collection systems enable specific control over the process and the product, allowing CEOs and Managers to constantly monitor production and its costs, an increasingly important variable in today's industrial sector.

Why is a data-driven approach important?

In the real world, people relate to each other starting with data, and data skills are developed through practice and application. This is why a model is needed to be able to study them: the real challenge is in fact to associate them through analytics, that is, to connect them, identify relationships, analyze them in real time in order to be able to make targeted decisions. In this way the information takes on a value and can be transformed into suggestions and inputs capable of opening up new scenarios to support strategic decisions and to predict medium and long-term scenarios.

Managing the multiple and disparate data sources has prohibitive costs and times if it is a completely manual operation. The use of data requires a combination of processes and policies that provide for data governance and an agile approach. By data governance we mean a set of processes, roles and standards aimed at guaranteeing an effective and efficient use of information, which allows an organization to achieve the set objectives: Data Governance is therefore essential to manage huge volumes of data extracted from multiple sources, in different formats and at variable frequencies.

Once the significant data has been identified, companies must in fact govern, protect and analyze it, which implies understanding the role that Artificial Intelligence and Machine Learning, IoT and Advanced Analytics play in the management of large volumes of data, the so-called Big Data. We therefore need a Data strategy, that is to look at the business and the vision even before the data. In fact, the latter must support business objectives in favor of growth and to mitigate strategic risks.

The most advanced companies are now able to acquire and analyze data in real time, at the very moment in which it is generated, to create projections and hypotheses which, thanks to the application of Machine Learning algorithms, are increasingly accurate and truthful. In this case we are talking about Advanced Analytics, technologies that allow data to be used not only in descriptive mode, as was the case in traditional Business Intelligence systems, but also in predictive and prescriptive mode, anticipating problems and behaviors, needs and trends.

The key figures to make a company "data-driven"

What we expect from experts in the field is to be able to simplify data and manage technology in an optimal way, which have in synthesis an interdisciplinary approach between economics, engineering and statistics. These are the skills of data scientist, data engineer and data analyst.

The data scientist must in fact possess a global approach, with technological skills that can be acquired from engineering and information technology, statistical analytical skills, and managerial skills and knowledge of business processes to apply their skills to real problems. In general, not only data scientists are needed but also data engineers (systems experts) who allow IT to better choose the necessary technologies.

However, it must be considered that the inclusion of these new professional figures must necessarily include support for the internal team already present in the company, the only one able to share the expertise acquired over time: only in this way can the new skills achieve greater value.

Also of great importance is the figure of the dedicated operational manager who plays a central role in this change process, as he possesses the skills to seize opportunities and convey the importance of innovation, corporate culture and working method.

The fundamental skills of this figure are:

  1. speed of analysis and evaluation of a given situation;
  2. ability to define winning operational plans in a short time;
  3. aptitude to make the work environment motivating;
  4. management through example and direct operational involvement;
  5. propensity to direct the team to the meaning and value of the result;
  6. desire to transmit know-how and experience.


The ethical use of data is the basis

The culture of data also passes through security. Companies need powerful and reliable infrastructures and that intelligent data management cannot treat Data Protection as an extra but as an important pillar of the business itself.

Digitization is the way to recover the competitiveness of companies and to achieve it, simple and open solutions are needed, in order to integrate data that comes from different platforms and technologies. All this must be done with an eye always attentive to the objectives and results, while maintaining a balance between transparency and confidentiality.

Using data to make decisions in real time offers enormous opportunities but with the risk of trampling the rights to privacy and security. These are issues that cannot be considered a posteriori: the data-driven strategy must originally include the protection of the company and its audience from cyber-threats and a solid defense of customer data. Data is the new form of power but responsibility also comes from power.


IPREL Progetti is the ideal partner in the process of transition and corporate digitalization, continuously invests in new skills to be able to offer new ad hoc consulting programs.


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