It is a cliché that data is for the company like sap for a plant, but it is now a reality in many companies that data is not just a necessary component, but the very essence of the business. In modern data-driven companies, data is part of the production chain, all strategic actions and directions are dictated by insights from market data analysis, partners, customers, IoT sensors and a multitude of different sources.
Data science for business is therefore configured as the set of methods, processes, algorithms and technologies that allow to extract useful knowledge from the multitude of structured data (databases) and especially unstructured data (text, images, videos, etc..), which the company has available internally or for external access in the form of data warehouse, data lake or big data.
The most advanced statistical and analytical methods, together with artificial intelligence(AI) and machine learning(ML), are the basic resources of modern data science for business; resources that allow to understand correlations and trends concerning complex phenomena such as consumer preferences, the evolution of the demand for a specific product or service, the analysis of competition on the market.
How data science works for business
According to the most accredited definitions, data science for business is characterized as a process that underlies the three phases of data collection, data modeling/analysis and decision support. While the first two phases (data collection, modeling/analysis) concern aspects of an IT nature that also include specialist mathematics and statistics skills, the last one (decision support) concerns the ability of the organisation to understand the meaning of data and therefore to use it in the best way to gain an advantage on the market.
The data driven company is the one that is best able to combine the ability to collect and obtain insight into data with the ability to use the information in its processes to continuously redesign them according to the needs of change. It goes without saying that the key to success in data science projects for business lies in the ability to create a close link between data scientists and the end users of information in the line of business (LOB).
For data to be useful, you need a high level of communication and collaboration within the company. Collaboration that allows data specialists to transfer knowledge of the contingent needs of the business and LOBs to participate in the definition of AI analytics and algorithms, so that they can trust the results for the decisions that need to be made.
The use of AI/IV in the data driven company
Today, artificial intelligence technologies are the basis of many innovative businesses that are redefining entire market sectors, from online retail to transport services, from home automation to insurance and banking. AI and machine learning techniques make it possible to understand the customer’s needs, get real-time information from the navigation on an e-commerce site to propose the products that are most likely to be bought, cut out insurance or financial offers tailored, understand from a set of sensors the premonitory signs of a failure and then send the assistance on the spot before the disservice is created.
With ever-increasing amounts of data and business requiring real-time actions, AI and ML techniques become the only way to extract useful knowledge from data and thus to trigger automated and rapid actions. AI techniques are now essential not only to improve customer relationships or create new predictive maintenance services, but also for IT management.
AI and ML today allow the company to defend itself against external attacks (cyber security) and to realize automatic optimizations in the functioning of IT systems, taking into account historical or contingent workload trends. Intelligent systems can solve problems quickly, long before team members intervene, in other cases they suggest the best actions to be taken, based on best practices or previous situations.