Inevitably the trend of the ‘big data analytics’ market can only be positive: the digital transformation, internet, have led to an abnormal production of data, from which it is possible to extract a lot of value to improve services, products, business, life quality. How to extract value is the focus of ‘big data analytics’, a market in strong growth also in Italy, according to the photograph of the Big Data Analytics & Business Intelligence Observatory of the Politecnico di Milano: in 2018 it reached a total value of 1.393 billion euros, increasing by 26% compared to the previous year, even if the gap between large companies, accounting for 88% of total spending, and SMEs, which represent 12% of the market, remains very wide.
45% of spending in Analytics is dedicated to software (databases and tools to acquire, process, visualize and analyze data, applications for specific business processes), 34% to services (software customization, integration with corporate information systems, process redesign consulting) and 21% to infrastructure resources (computing capacity, servers and storage to be used in the creation of Analytics services). Software is also the area with the highest growth (+37%), followed by services (+23%) and infrastructure resources (+9%). Among the sectors, on the other hand, the first in terms of market share are banks (28% of spending), manufacturing (25%) and telco – average (14%), followed by services (8%), large-scale retail/retail (7%), insurance (6%), utilities (6%) and PA and healthcare (6%).
In the insurance industry the impact of big data is undoubtedly expected to grow: as we report also in a recent article dedicated to claim management, the data are crucial for a range of changes in the insurance industry, they are the ground on which to develop artificial intelligence solutions to improve all phases of the insurance process.
“The Big Data Analytics market keeps growing at a fast pace, more than 25% – said Carlo Vercellis, Scientific Director of the Big Data Analytics & Business Intelligence Observatory – “Fast data” initiatives are growing, involving the analysis of data in real time, integrating various streaming information sources and exploiting the potential of the Internet of Things: these include real-time advertising, fraud detection, predictive maintenance and new product development. But in order to fully reap the benefits, Big Data must be analyzed in a smart way, using sophisticated machine learning algorithms capable of identifying patterns and correlations in the data and transform this knowledge into tangible actions allowing companies to gain functional strength”.
The digital transformation driven by data is not easy at all, mainly in large organizations, and is very profound, as it affects the process of data collection and management, the technologies supporting the data life cycle and the development of new skills for data enhancement.
The issue of skills is a particularly delicate one. The need for data science skills is growing: 46% of large companies have already included Data Scientist positions among their staff, 42% Data Engineer, 56% Data Analyst, professionals who are still not very widespread. The lack of in-house skills is still the main hindrance to the development of Big Data Analytics projects. 77% of large companies report a lack of in-house resources dedicated to Data Science: among these, 29% believe they can fill these gaps with the support of third party consultants, while 48% consider it necessary to integrate Analytics skills in the short term.
“The dynamics of growth of the market are different depending on the size of the company – said Alessandro Piva, Head of Research of the Big Data Analytics & Business Intelligence Observatory -: while SMEs are struggling, among large companies there is a widespread belief that the time has come for action: companies having already started projects are experiencing the benefits and are driven to continue to invest, those left behind feel the urgency to equip themselves. At the same time, there is a growing number of organizations with qualified professionals for the management of analytics, such as data scientist, data engineer, data architect data analyst, and introduced organizational models to make the most of opportunities, standardizing technological choices and creating structured coordination mechanisms”.
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