What is “Big Data”? What is “Big Data analytics” ? These are questions that are being heard more and more often today, especially in Italy, where big data has recently become a must for all Italian companies aiming to transform themselves into a “data-driven company” to make informed decisions based on pertinent data . So let’s see in detail how to give a simple definition and explanation to the concept of Big Data.
Big data (data in large quantities) constitutes an increasing amount of information that the digital transformation of business is creating and circulating within and outside companies.
Big Data, for example, comes from sensors integrated into thousands of objects which, connected to the Net, we now call the Internet of Things ; according to the McKinsey Global Institute today they are already more than 30 million, connected to the network and used in the automotive, industrial, public services, or retail sectors and the number rises by 30% every year.
Beyond the data flows produced by IT systems and infrastructures supporting the production, distribution and provision of services, big data is a phenomenon associated with a massive evolution in the uses and habits of the population.
How does Big Data help companies?
Big data can be used in companies to:
- help understand the reactions of the markets and the perception they have of the brands ;
- identify the key factors that move people to purchase a certain service or product;
- segment the population to personalize action strategies as much as possible;
- Exploiting the power of predictive algorithms, thanks to such a wide-ranging and punctual history of information as to allow much more than likely simulations;
- enable new business models.
Examples of Use: Smart Cities and Retail Companies
Smart cities are becoming a shining example of Big Data Management and Big Data Analysts. Thanks to the sensorized street lamps, the Public Administration is able to better manage traffic peaks and monitor pollution. Police can reconstruct suspicious car routes by analyzing the ubiquitous closed circuit television (CCTV) cameras outside clubs and banks. For separate waste collection, RFID tags are used which make bins, tubs and bags connected and communicating.
There are many companies that have started a Data Driven strategy . By analyzing purchasing behavior, i.e. the receipt associated with the loyalty card and the various interactions with promotions, announcements, e-mail marketing, any newsletters that are periodically received, companies operating in the retail sector are deepening the customer knowledge.
All of this represents a mountain of information to be collected and analyzed in order to define an increasingly customer-friendly offer of services and products.
Using the features related to geomarketing 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 and managing loyalty programs better by monitoring the feedback recorded by each individual promotion, product launch, initiative but also being able to manage warranty requests or complaints, reaching a 360-degree view of customers, products and any commercial operation.
Technological trends
The technology trends that are transforming the data analytics landscape in organizations are:
Hadoop , an open source software platform for processing large amounts of data in parallel mode, which has established itself as a technological standard in recent years. Today, however, the open source ecosystem has been enriched with technologies and complexities, in order to better manage real-time analysis and Machine Learning. Thus new technological standards emerge, such as Apache Spark (processing engine in the Hadoop cluster ) and Apache Kafka (stream processing system). Skills of this type are still rare in large Italian companies.
Hybrid Cloud and the Machine Learning Revolution
The Hybrid Cloud, i.e. the possibility of connecting your private environment with one or more Public Cloud systems, creates greater flexibility, limits data movements and allows the execution of Analytics where the data is stored. Numerous benefits derive from this: cost reduction, greater agility and better management of legal requirements in terms of data privacy and confidentiality. Furthermore The topic of Edge Computing is of growing interest, i.e. an architecture with distributed resources that brings specific processing and analysis closer to the place where the information is actually collected, in particular by sensors.
Developing Machine Learning algorithms means both extracting value from data and using traditional sources in a new way. For example, using Machine Learning can lead to anticipating customer behavior, increasing the effectiveness of the fraud prevention system; intelligently analyze images or videos.