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Managing the multiplication of data, or Big Data, means having a data driven approach to business, to make informed decisions and anticipate the future. Here are what technologies and what skills are needed to become more competitive.

What is Big Data? What are Big Data analytics?

These are questions that are heard more and more often today, where big data has recently become a must for all digitized companies that aim to transform themselves into a data-driven company, to make informed decisions based on relevant data. So, let's see in detail to give a definition and a simple explanation to the concept of Big Data .

Big data is a growing amount of information that the digital transformation of business is creating and circulating inside and outside companies. The Big Data,  for example, come from the sensors integrated in thousands of objects that are connected to the Net, now we call the Internet of Things ; according to the McKinsey Global Institute today there are already more than 30 million, networked and used in the automotive, industrial, utilities, or retail sectors and the number rises by 30% every year.

Beyond the flows of data produced by information systems and infrastructures supporting the production, distribution and provision of services, big data is a phenomenon associated with a massive evolution in people's uses and habits. Every time we use a computer, turn on the smartphone or open an app on the tablet, we always and in any case leave our fingerprint made up of data.

In 2001, Doug Laney (now vice president and distinguished analyst of the Gartner Chief  Data  Officer Research and Advisory Team) described in a report the  Model  that succinctly describes  Big Data with

3V: Volume, Speed ​​and Variety. A simple model to define the new data generated by the increase in information sources and more generally by the evolution of technologies. Today the Laney paradigm has been enriched by the variables of Truthfulness and Variability and for this reason we speak of 5V.

Big Data Analytics: from browsers to social networks what is the meaning

The Big Data , in fact, are also increasingly pushed by multimedia that originated by the proliferation of fixed and mobile devices we use to live and to work. Familiarity with video sharing and an image culture that leads people to share all kinds of photographic shots will help those who manage this amount of data to understand tastes and trends even better, orienting decisions better.

Big Data also comes from social media, and from all the information traffic that passes through the various CRM systems , from the cash desk of a supermarket swiping a loyalty card to a phone call that arrives at a call center .

Unlike many technological fads, in fact, Big Data is not a trend but a management necessity. And they are for any type of organization. Those growing data sets that seem to blow up corporate databases will be the keys to competitiveness, business growth, and innovation. How?

  • Helping to understand the reactions of the markets and the perception they have of brands
  • Identifying the key factors that move people to purchase a certain service or product
  • Segmenting the population to customize action strategies as much as possible
  • Enabling new experiments allowed by the availability of unpublished data
  • Gaining in predictivity, thanks to an information history so wide-ranging and timely that it allows simulations that are much more than likely
  • Enabling new business models

The use of Big Data in the Covid-19 emergency

The pandemic caused by Covid-19 has highlighted the importance of enhancing data to make decisions quickly and guarantee business continuity in times of crisis, but it has also forced many companies to rethink their investment plans. 

This has widened the gap between the most innovative companies, which have rationalized investments by managing to reinvent or accelerate the data-driven strategy, and the more conservative ones, which have interrupted or postponed investments. The result is a slowdown in the growth of the Analytics market, which in 2020 reaches 1.815 billion euros, showing a + 6% compared to 2019, slowing compared to the + 23% recorded in 2018 and + 26% in 2019.

( Source:  Big Data Analytics & Business Intelligence Observatory) 

The business, therefore, exists and has not stopped.

Despite the difficulties related to Covid-19, 96% of large companies did not interrupt their activities to improve the collection and exploitation of data and 42% moved, in terms of experiments and skills, in the Advanced Analytics area . Among SMEs, on the other hand, 62% have some data analysis activities underway, of which 38% are advanced.

“The pandemic has led to rethinking some data analysis activities, paying greater attention to efficiency, the presence of internal skills and governance of data and Data Science. - explains Alessandro Piva , research manager of the Big Data & Business Analytics Observatory - Covid has been a stress test: while the most immature companies have seen a reduction in interest in the topic, those oriented to the data-driven approach have reinvent itself ".

Other relevant trends in recent months concern the application of Machine Learning in the entire data life cycle, the industrialization of Advanced Analytics and greater organizational maturity.

Also, real-time analysis ( fast data ) is emerging : it means that various streaming information sources are integrated in real time ,  especially in the IoT field : among these we remember real-time advertising (programmatic) , fraud detection (fraud detection), predictive maintenance , new product development

What are the technologies for Big Data Analytics?

In 2020, most of the spending was concentrated on software (52%, + 16% compared to 2019, includes databases and tools for acquiring, processing, visualizing and analyzing data, applications for specific business processes), in particular for Artificial Intelligence and the Data Science Platforms . Followed by services , which represent 28% of the market, and infrastructural resources (20%, + 7%), i.e., systems for enabling Analytics capable of providing computing and storage capacity. Spending on Analytics in the Cloud grows by + 24% and this component accounts for 19% of spending (+ 2% compared to 2019)

How has the sale changed today? 

Most people have only a vague idea of ​​how much Google has a deep understanding of everything we search online, or how much Facebook knows (about everything and more) about friends, feelings, preferences, dreams and needs of its great. community?

Even if we have never told them, Google knows how to recognize our personal details, profiling us based on our navigation methods to offer us absolutely targeted advertising that borders on tailor-made personalization. For all that half of the sky that has chosen Android, always knows where we have been, where we have traveled, stopped, eaten or stayed overnight.

Facebook , on the other hand, with its one billion subscribers, even knows when a love story has reached a tipping point. Based on bulletin board status updates (3.3 million posts are published every minute), the company can predict whether a relationship is going to last, with disturbing accuracy. 

Not to mention Twitter that every 60 seconds moves 347 thousand tweets and which, like the other Big Tech has developed an API (Application Program Interface) that allows third parties to access each of these (by definition all public): it is unstructured data, probed by new sentiment analysis techniques who are able to understand the emotions contained in the textual information, helping decision makers (business and politicians) to understand where the wind of public opinion is going.

Examples in the world: the analytical side of smart cities

Turning to public order, smart cities are becoming a shining example of Big Data Management and Big Data Analysts. Thanks to the sensorized street lamps, the PA is able to better manage traffic peaks and monitor pollution. Police can reconstruct suspicious car routes by analyzing the increasingly ubiquitous closed-circuit cameras (CCTV) outside clubs and banks. For separate collection, RFID tags are used which make bins, tubs and bags connected and communicating.

According to McKinsey analysts, in Europe public administrations can obtain savings in the order of 100 billion euros by a good management of Big Data, increasing operational efficiency. A figure that could increase dramatically if Big Data were also used to reduce fraud and errors, targeting fiscal transparency.

Examples of Big Data: Big Data Analysis also in retail

There are many companies that have started a data driven strategy, such as Leroy Merlin and Cattolica Assicurazioni.

By analyzing the purchasing behavior , or the receipt, associated with the loyalty card and the various interactions with promotions, announcements, e-mail marketing, any newsletters that are received periodically and periodically open, retail is deepening its knowledge of customers. All this represents a mountain of information to be collected and analyzed to define an increasingly customer-friendly offer. From the point of view of the services associated with geo-marketing and geolocation   (beacons, NFC, apps, interactive touch points), the opportunities are significant.

Big Data Management means going beyond order processing, implementing new systems to support marketing campaigns, managing loyalty programs better by monitoring the feedback recorded by every single promotion, product launch, initiative but also being able to manage the warranty requests or complaints, reaching a 360-degree view of customers, products and any commercial operation.

The new professions of Big Data

The issue with Big Data is not so much their quantity, but the ability of companies to be able to correctly analyze the data available. The formula is conversational and develops in three stages: questioning, answering and detailed vision . More and more sophisticated algorithms make it possible to intercept and interpret every digital stream. This is the technological advance that is revolutionizing business models. Data-driven companies need new data science profiles, now widespread in large companies: in 2020 there is stability in the spread of Data Analysts, now present in 76%, and Data Scientists , present in 49%, but Data Engineer (58%, + 7%) and Data Visualization Expert (52%, + 31%) are growing , while Analytics Translator (present in 30% of companies) is establishing itself as an emerging profile .

To this end, McKinsey's latest report ("The age of analytics") identifies four types of profiles that will be increasingly requested by companies:

  • Data architects, i.e., those who design data systems and related workflows.
  • Data engineers, able to identify data-based solutions and develop targeted scouting and analysis products.
  • The scientist data , analyzing the data, thanks to increasingly sophisticated algorithms.
  • Business translators, bimodal figures who have technical knowledge and business-related skills.

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Speed ​​emerges today as a major source of competitive advantage. Carrying out analysis in real time allows you to have internal visibility of automated processes and actions and allows the development of new products and services, from wearable devices to self-driving cars.

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These solutions thanks to the Big of the web already play a leading role in our daily life, but they are in their infancy in organizations.

Sarah Ahmed is associated to Reckon Media LLc serving as a sales consultant and also a freelance writer and blogger.