written by Marco Belmondo (Chief Marketing Officer at Datrix group)
In the last few years, there is much talk of the need to extract value from the large amount of available data (the so-called Big Data) to improve business decisions, acting proactively and optimizing results. Despite good intentions, companies often encounter different problems related to data-driven transformation, both problems related to the necessary skills, technologies to manage and analyze data and the necessary cultural change to become data-driven. To that aim, it is necessary to get managers and operators used to decisions based on data.
Let’s try to understand in this article how to best reconcile data intelligence and decision-making strategies.
From data to Data Intelligence
Let’s start from the more traditional aspects, let’s think about all the operations that are carried out in a company (e.g., invoices, payments, production). The tracking of all these processes in motion naturally generates data. Business Intelligence means extracting valuable information from this data.
For Business Intelligence (or BI), we can refer to all the technologies that allow data to be stored, analyzed and displayed. In a more complete way, we can refer to BI as the set of techniques, technologies and processes that lead to extract business-relevant messages from the data.
In large companies, analysis on corporate operational data is more or less consolidated. On the other hand, it can be much more complex if you try to work with heterogeneous data by source and format or that need speed much higher analyzes. The first issue to be addressed is therefore technological: adequate infrastructures, high-performance storage spaces and, above all, data governance and data integration (topics that should not be consolidated). A necessary prerequisite is the presence of governed and integrated data. Data, even though heterogenous, should be integral, searchable, of good quality and secured.
In addition to the issues just addressed, as a second step we focus on Data Science software or methodologies (e.g., which algorithm to use to answer this business question?). In order to be data intelligent, it is necessary to take a step forward. It is important to know programming languages and algorithms, but it is even more important to know the data. If you know the data, you can foresee which questions they can answer. On the other hand, it is important to know your business, in ordr to ask questions that are actually relevant to the company at that precise moment.
Data Intelligence is not (only) a Data Scientists’ matter, this is the main point to understand. Business figures know their own process and, consequently, they can ask the right questions. Data Science experts are able to reformulate business questions using variables and algorithms. The collaboration between business figures and Data Science experts is crucial to ensuring that decision-making strategies become data driven.
To speed up this step, data visualization tools can play an important role. These tools – among the best known we can mention Tableau, PowerBI, Google Data Studio – are growing rapidly and are enriched every day with new features, thanks to the fact that companies they also allow non-specialist figures, without technical skills, to get closer to data.
International analysts name this trend Self-Service Data Analytics, i.e., the set of technological and organizational mechanisms aimed at providing greater autonomy to business users in interacting with data. It is sufficient being able to create a simple graph or to carry out a simple analysis to enhance day by day a great cultural shift.
To summarize, in order to become data intelligent, it will certainly be important to have adequate tools to store, process, govern and analyze more and more data, but the real step to take is the promotion of a cultural change that get even the most operational figures used to data driven approach.
Having a strong organic strategy is even more important if you manage online data. The amount of data that can be pulled out from a website, an eCommerce or even just a page on social networks is huge and if you are not prepared, there is the danger of being overwhelmed.
Transforming decision-making strategies through Data Intelligence
Once talked about cultural aspects, one last point must be addressed: using data intelligence can lead, in practice, to totally rethink some processes.
Data, in some cases, can even automate processes. Some examples are chatbots, to mention a well-known application or advertising, in which the programmatic approach (i.e., online auctions for the purchase of advertising space) has revolutionized the market. Another example is dynamic pricing, where the price of a product changed automatically on a website.
It is obvious that automating a process, totally or partially, leads to a great transformation of data-based decision-making strategies. To be ready to address this transformation, you must evolve both from a technological and from a change management point of view.