written by Eugenio Ciamprone
It is mandatory today, for a company, to be present and competitive on the markets, to build the decision process on data analysis instead of founding its decisions on observations or intuition.
Data-Driven Decision Making is indeed the process with which the management bases its strategies and decisions on measurable and verifiable numbers and data.
The paradigm change needs to begin at the top management level. It’s above all the top management who needs to trust a data-driven business approach based on analysis and evaluation of data.
The value and trustability of decisions based on data obviously depends on their quality as well as their correct analysis and interpretation.
Only this way a data-driven business approach can help organizations to obtain a competitive advantage, improving at the same time efficiency as well as obtaining substantial cost reductions. The implementation of a data-driven decisions approach allows the management to make decisions based on collected data, their analysis and interpretation. It is the first step towards digital transformation of a company. But it cannot be obtained without a correct use of big data and machine learning techniques.
According to the definition published by Gartner, Big Data are high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Using innovative Machine Learning techniques allows the automation of the learning process and the autonomous provisioning of solutions and scenarios that support decisional processes.
Thanks to the evolution of AI and Business Intelligence platforms, management can currently extract and examine data and their analysis, consult reports and take data-driven decisions.
Companies willing to implement a decision process based on the collection and analysis of data need to follow a path and proceed by steps: they first need to identify their goals, and business areas most important to them from the point of vue of their digital transformation strategy.
Once the goals are identified, they will need to plan the base of data upon which the analysis will need to be performed and the ways to acquire it.
Following the identification of the dimension and composition of the dataset the step of Data Preparation is where data are organized and cleansed, ready for being analyzed once their quality has improved.
Only after that the appropriate Data Modeling techniques will be identified that better adapt to the reference context and are able to produce the best performance with a vue to transform results into decisions and concrete insights.
Advantages of a decision process based on data
What are the advantages of basing a business decision process on evidence emerging from data analysis?
First of all, it will be easier to make a decision once data is analyzed. Besides that, decisions taken following the evidence shown by data analysis are part of the company strategy to pursue a path of data-driven transformation which allows one to follow this approach without worrying about making wrong decisions.
For sure, decisions based on data will not always be correct; but data analysis tools allow us to constantly monitor the economic and non-economic impact of decisions taken, that way orienting remedial actions that will eventually need to be taken. Relying on data analysis for decisional processes also allows competitive advantages on the market in terms of costs and times besides the identification of business opportunities prior to competitors or the identification of menaces for business before it will be too late.
As already said, two consequences of implementing a data-driven decision process in the company are the ability to make choices faster, based on more updated data; this drives to improvement of operational efficiency as well as cost-reduction.
The role of data modeling in decision processes
The implementation of company decision processes based on data analysis cannot disregard a correct usage of Machine Learning techniques.
A particularly important phase is therefore that of Data Modeling, which also means choosing the algorithms to be used inside a specific context to allow for good performances in results obtained.
Furthermore, Data Modeling is the representation of rules and relationships that characterize the whole of the data analyzed and is therefore a crucial phase for organizations that decided to use the analysis of data as a strategic company asset upon which decision processes will be built.
It allows for automating operations, reducing costs and making activities more efficient thanks to the access to clearer and more precise information in conspicuously reduced times.
Finally, data modeling gives the company the opportunity to organize and combine data of disparate provenance and consistency with a goal of extracting value and transform the analysis in actions and insights immediately activatable.
