Data Analysis Algorithm is a must!

As already introduce in our article about Big Data, Iot & OoT, all companies has a growing awareness of the need to develop an integrated approach to use the large mass of data collected by any connected DiDe.

The new frontier of competitive differentiation is in the data and, obviously, the way to analyze it among the millions of information that every day are produced.

“Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.”

Margaret Rouse

What is the relationships between different data? Which actions may result from the ability to interpret a phenomena?
Amount of data is rapidly growing as well as the sources that produce it. This means that the complexity of data analysis will grow as well. Not only structured information coming from transactions, but also data received from GPS, from an infinite variety of sensors (RFID, Bluetooth, NFC, …).

Furthermore, the world of social information is a mine from which companies can find many ways to generate profit. Obviously, it is still difficult to understand how and when… Indeed, these data are also protected by PRIVACY policies and it is not always so simple find the way to analyze it!

Beyond any definitions there is growing awareness, especially in large companies, about the need to develop an integrated approach to use large masses of data, rapidly acquired from sources inside and outside the company.

Essentially, we are talking about big data, ranging supported by technological solutions and analytics, able to build up dedicated mathematical models to understand the information and provide a guide to use it in a strategical way. Just these issues are priorities for investment by 44% of CIOs intercepted by the Italian Observatory Research.

In 2015 the italian market for Analytics increased by 14%, reaching a total value of 790 million euro: 84% by Business Intelligence and 16% by Big Data.
The growth of big data, although still marginal in volume, however, is much more robust with an annual growth rate of + 34%, while the Business Intelligence stops at a + 11%.
Analyzing the market Analytics for industry as it turns out in 29% of cases is common in banks and 21% by industry, followed by telco and media (14%), makes PA and health (9%), other services (8%), department stores (8%), utilities (6%), insurance (5%).
The higher rate of growth, however, concerns insurance, banking, telco and media with growth rates between 15% and 25%, followed by utilities, retail distribution, services.

In 2016 Analytics will be the main priorities for investment (44%) for italian CIOs.
As expected, during current year, the Big Data management skills are considered the most significant organizational challenge for this actual digital businesses transformation, according to 22% of CIOs.
A scenario that clearly shows how companies of our country have clearly understood the importance to extracting data meaning. But most organizations do not yet have a business strategy driven by the value associated with the data.
The big data, which is worth 16% of the total, had an increase of 34%: three times that of business intelligence.

Ref. Results of the Observatory’s research Big Data Analytics & Business Intelligence School of Management of Politecnico di Milan
The research involved a survey through 91 CIOs, IT managers and 160 c-level functions of other medium and large organizations, and analyzed over 100 player offering through direct interviews or secondary sources.

Banks, manufacturing and telco-media covering today more than 60% of the analytics application; the insurance sector, blocked at 5%, however, marks the highest growth rate (over 25%).

Despite the amount of transaction data is set to drop in favor of the sensors’ one, open data, localization and social, less than the half of this amount of data is analysed using the analytics systems. Based on the research of new skills and new governance models, which begin to appear among the most sensitive companies, it seems to be clear that the scenario of big data is rapidly changing. However, the ability to achieve an effective strategy for data analysis is often undermined by poor overall view of methods and technologies for analysis of business data.

If CIOs are still the main references for the control and management of analytics systems, the new functions of CDO (Chief Data Officer) and Data Scientist, begin to appear as part of the organizational structure. Strong specific skills, for example, in management of multifunctional teams or in the modeling of complex problems starting to be a needs for any IT company.

All markets are going to be involved into this revolution: infrastructure (processing, storage, and data analysis), analytical technologies (platforms for the extraction and the graphical display of data, tools for the analysis of social and information relating to the geolocation of users) and, finally, of the applications (data analysis platforms for specific functions such as marketing, security, finance, …).

Semi-structured and unstructured data may not fit well in traditional data warehouses. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually. As a result, many organizations looking to collect, process and analyze big data have turned to a newer class of technologies that form the core of an open source software framework that supports the processing of large and diverse data sets across clustered systems.

Nowadays, the ability to work faster with DATAs gives to any organizations a competitive edge they did not have before. This means that any good analysis algorithm’s will be something that could bring companies to success.

As results of this continuous changing there is an opening prospect versus a New Economy, based, now more than ever, onto skilled people. In fact, as example, an RF Engineer that before was just looking at RF KPI, now he should try to anticipate customers trends. e.g. Think about a street in the city where customers could move rapidly from A point a to a point B. In this case the capacity offered from a MNO, should be able to satisfy this rapid transfer from an eNodeB to another. Furthermore, think about what a social could create into a city just offering some “Special Offer based on customer position”… in this case the RF Engineer should base the Network Optimization not only on network KPI but even on information coming from the Social itself. In fact the Engineer, knowing offers coming from Social, could anticipate the optimization based on customer position. So, this Engineer should have the right skills to read information coming from Social in some ways.

Naturally, this is just an example about all new data interconnections that could create differents labor position, but, of course, for skilled people! So, as never before…