What is big data analytics?

What is a big data analysis project? How to do it so that the results are guaranteed? This service describes how big data analytics tools to use and which skills and human resources to use for data management. We have outlined a guide on data analytics, which, also referring to the opinions of important market analysis companies is exhaustive, identifying, among other things, the four main categories of analytics: descriptive, predictive, prescriptive, and automated analytics. Furthermore, the latest market data relating to the double-digit growth of the big data analytics market are reported.

All the activities that are carried out daily on digital devices produce data. It is a huge amount of information that can be collected, analyzed, and enhanced from an economic point of view. It is the era of big data analytics. And the meaning of big data and analytics is respectively the set of heterogeneous data, i.e., obtained from multiple different sources and the discovery, interpretation, and communication of meaningful models in such data in order to initiate a more effective decision-making process. But let's proceed in order.

Big data what are they

So what are big data? The big definition data arises from the fact that the current already substantial amount of data will multiply in the future, examples of big data come from IoT devices - Internet of Things as well as from smart cars, but also from the use of social networks and etc.

Data sources are many and constantly increasing, and therefore, what characterizes big data is not only the quantity but also the complexity due to the variety of data types that can be recovered.

The concept of big data implies multiple factors, from the infrastructure necessary to collect and archive them to the tools to analyze them up to the skills necessary to manage them, starting with big data analysts.

What is Big data analytics

What is big data analytics? The definition of big data analytics refers to the process that includes the collection and analysis of big data to obtain useful information for the business. In fact, big data analytics techniques make it possible to provide companies with original insights, for example, on the market situation, on the competition, on the other hand, on customer behavior on how to refine customer experience strategies and so on. To carry out the activities aimed at providing these and much other valuable information to improve the business, software (from databases and tools useful for acquiring and processing information to applications dedicated to specific business processes), services (for example, for customizing technologies and successfully integrating them into pre-existing systems) as well as infrastructural resources (computing capacity, storage, etc.).

How to set up a big data analytics project

Setting up a big data analysis and management project in your company means addressing multiple aspects; you cannot, of course, limit yourself to technological ones, you need to evaluate the business needs to which you want to respond and set specific objectives, involving numerous skills. Defining and implementing a big data analytics strategy means having the opportunity to obtain valuable information to innovate, just think of digital analysis marketing, but you need to know how to start on the right foot. In this service, step by step, it is shown how to create a complete data management system, which is able to guarantee value to organizations.

The true meaning of big data and how to use it correctly through data analysis

It may seem obvious, but since it is the obvious things that are least thought about, the first question to be established is what business purpose, the big data analysis project should serve. If this is not immediately clear, the risk, and it is a high risk, is that the IOC and the IT go their own way by creating a big data architecture that may work very well, but which is then not aligned with the needs of the business and enterprise. And this is because this is what data analysis is: in the science of data analysis, it is the process of inspecting, transforming, and modeling data with the aim of extracting information that suggests and supports strategic business decisions.

The use cases of big data analysis, according to what the user companies told Forrester, fall into three groups:

- Efficiency and operational risks. Much of the examples of big data analytics built or planned to be soon are about reducing risk in financial analytics. Other areas where efficiency and risk reduction count are asset management (with a tip-in fraud analysis), personnel management, and the supply chain, where big data applications for preventive maintenance emerge. A global approach to these problems must consider the sharing of data and the exchange of ideas with business partners, as well as the tracking of the results obtained from the actions taken following these analyzes in order to start a virtuous cycle.

- Application security and performance. Predictive analytics and big data analysis on the functioning of IT serve to prevent problems in the provision of services and to monitor events to be able to respond to them in real-time. The analysis models, which need to be, discussed with security and service managers, using data logs generated by servers and network devices to assess performance levels, find bottlenecks, and so on.

- Knowledge and customer service. Solutions and applications for big data analysis are used for marketing and sales projects, for product development, but also for the optimization of the digital experience.

What are the priorities to keep in mind for optimal big data management?

Data Management cannot be approached as in the past when priorities 'boiled down' to data governance at the IT level and its use by some 'restricted' users. Today the scenarios have changed, and the correct definition of the Data Management strategy should take into account some important considerations:

Big data sources continue to evolve and grow: 'waves' of new data continue to be generated not only by internal business apps, but by public resources (such as the web and social media), mobile platforms, data services and, more and more, from things and sensors (Internet of Things). According to analysts who are experts in this area, the strategy of a data analyst cannot fail to take into account these aspects, often linked to the characteristics of volume, speed, and variety of Big data in continuous growth and evolution. For companies it becomes essential to be able, according to a logic of continuous improvement, to identify new sources and incorporate them into the Data Management platforms;

Capture, manage, and archive all company data to preserve history and context: 

The impoverished data of the context would be of little use, in the era of big data management it is essential to be able to 'capture' and archive all the data useful to the company, and since their usefulness is often not assessable a priori, it becomes a challenge to be able to have them all available and then draw the meaning of the big data collected. Until a few years ago, the efforts and costs to be able to capture and maintain all this data were excessive, but today innovative and low-cost technologies such as Apache Hadoop have made this approach possible.

Scientifically analyze data to 'enrich' them with useful and 'non-obvious' meaning: 

The goal of big data analytics projects is not to generate reports on what has happened but to understand how this can help to make better decisions. This means changing the data analysis model by opting for 'descriptive,' 'predictive,' 'prescriptive' approaches, i.e., exploiting big data analytics applications through which to generate 'insights,' knowledge useful for decision-making processes ( for example, anticipating customer needs knowing in real-time preferences and habits ). Succeeding in this goal requires new skills: data scientists, in particular, who, using ' machine learning algorithms' and 'advanced visualization tools' can generate usefully and' undiscovered 'information to support corporate competitiveness and profitability.

Release data quickly and freely:

To all those in need: it may seem obvious, but we know how the history of IT has shown how much the 'silos' approach is also valid for data, with multiple examples of big data residing in unshared and difficult to integrate databases. To overcome these barriers, it will be increasingly necessary to equip the big data management platforms with innovative functions through which data can be made available and accessible along with all company levels.

- The value of the Analytics market in 2019 and the main trends for 2020

- 1.75 billion dollars is the value of the Analytics market in 2019, according to the Big Data Analytics and Business Intelligence Observatory 2019, a figure up 23% compared to last year and which marks an increase of more than double compared to only five years ago (in 2015, revenues were 790 million), a period in which the average annual growth rate was 21.3%.

- In total turnover, the main expense item is related to software (47%). In this context, tools for data visualization and analysis account for 53%, while 47% consists of data ingestion, integration, preparation, and governance tools.

- The 20% of the spending is devoted to infrastructure resources, solutions to enable Analytics and provide computing power and storage to enterprise systems, first of all, the cloud.

- The 33% of investments were destined to services for software customization, integration with business systems, and consulting for process re-engineering.

- Among the sectors that have distinguished themselves in their commitment on this front, banks rank first in terms of market share with 28% of spending, followed by manufacturing (24%), telco and media (14%), services, GDO and retail (8%), insurance (6%), utilities (6%) and PA and healthcare (5%).

- The gap remains between large companies and SMEs in terms of investments and data science skills. 93% of large companies invest in Analytics projects, compared to 62% of SMEs.

- As regards the trends identified by the Observatory, first of all, a rethinking by the business intelligence companies oriented towards the implementation of advanced initiatives is noted.

- Secondly, we are increasingly looking at software that promotes the interaction and involvement of actors other than IT managers to introduce the concept of collaborative data science into the company.

- Again, the public cloud, but even more so, the multi-cloud represents in this period an arena of experimentation in the analytics field.

Finally, there was interest in actionable analyzes, that is, attributable to rapid action.

Applications for data analysis and data mining: three macro areas of offer

The available Analytics tools can be divided into the following classes:

Aggregators:

Collect and organize data, both corporate and mainly from external sources, so that they can be used by business users for their work. Many vendors also add data management, cleaning, and enrichment services to it. These solutions are especially necessary for companies that want to enter new or unknown markets, need to manage various types of big data, for example, 'dirty' internal data (redundant, equivocal, uncertain), or whose data structure on customers has uncovered areas.

Enrichers 

They enhance and complete the amount of data relating to marketing and sales activities with elements from different sources, mainly feeds and clickstreams collected from the Web and social networks. Many tools pre-process the data to obtain information targeted to the needs of the company-user and tend to enter the field of real analysis. These tools should be considered by those who want to refine the segmentation of the market, do direct marketing with personalized messages, and (in business-to-business) interact with specific customers.

Modelers – Apply algorithms to data that highlight the patterns and compare them to probability criteria (the rules by which an event is estimated to occur) in order to build forecast models. The problem with these solutions is that they are often made by start-ups whose technology (and whose very fate) can change in the short term. They are suitable for companies already experienced enough in digital data analysis marketing that have to fill gaps in their demand management.

- The advantages of big data analytics: how and why they can help the business

- The benefits that big data analysis can give are many. It should be remembered that the meaning of big data analysis is the ability to analyze, extrapolate and then relate a large amount of heterogeneous data, whether structured or not, in order to discover links and correlation between phenomena and even get to predict them. We recall the main ones, citing them in order of profitability for the business:

Increase in turnover:

Sometimes data alone is enough if they are the right ones, summarized in a simple quantitative analysis to grow a sale, evaluate the size of a market, enrich a customer profile, and calibrate the management of an account.

Make the development of demand predictable:

Relying on customer behavior as a mirror of the propensity to buy is a risk: who can ever say that they will do tomorrow what they do today? The definition of big data analysis and, more specifically, the analysis of big data unrelated to what concerns the sale of the company's brands and products can instead reveal the intentions and interests of potential customers that are not evident and allows to evaluate the 'fitness' of the offer, i.e., the degree to which the things we know about the customer's life cycle are coupled with those we come to discover.

Give account management more value:

By analyzing the transactions between sellers and customers and integrating them with information on what customers do outside the business relationship (mergers, acquisitions, financing, hiring, legal issues), the B2B relationship can be focused on mutual objectives, better serving the client and helping account managers optimize their work.

Predict what is best for any customer:

It involves using big data, predictive analytics applications for account management. In practice, what direct sales companies do in B2C with targeted promotions are brought to B2B. For example, data analysis marketing uses the amount of data internal and external to the sales relationship to be ready to satisfy a request or better still to prevent it with a suitable offer. And there are many examples of big data analysis in this sense.

Open up new business opportunities:

It is often talked about by referring to new products or services that big data analysis suggests doing. But this is also true, and it is a more frequent case, for those who want to expand the market by focusing on relatively new customers. Typical case: the company active on large users that intend to turn to small businesses and must study a different business model calibrated on SMEs.

- The skills needed to manage a big data project, the importance of data scientist, data engineer and data analyst

- In the 2018 edition of the Observatory, the focus was particularly on skills, as the lack of skills remains the main obstacle to the development of Big Data Analytics projects. In general, a big data analyst is the one who deals with exploring, analyzing, and then understanding the data that is collected to obtain useful information, as we said, for the decision-making process.

- "The scarcity of skills in the field of Data Science - recalled Alessandro Piva, Head of Research - and more generally in the ability to manipulate data, has characterized the phenomenon of Big Data since the very beginning."

- As a demonstration of this, 77% of large companies declare an undersizing in terms of human resources dedicated to Data Science.

- “In addition to the imbalance between supply and demand, companies struggle with the search for non-standardized roles, whose core skills are unknown. For this reason, the 2018 Research - explained Piva - has investigated the topic of professional figures not only through dissemination data but also through the analysis of job offers on Linkedin, in order to obtain more qualitative insights on the main skills and activities performed ". These are the main pieces of evidence that emerged:

- Data Scientist - The figure of the Data Scientist is entering the concrete daily activity of many companies. In 2018, the data on the diffusion of this role within large organizations recorded a further, albeit slight, increase. Large companies that have at least one Data Scientist internally, formalized or not, are 46% (+ 1% compared to 2017).

- Data Engineer - The extraction and delivery of insights are linked to a series of preliminary activities which consist of the design of the infrastructure and the construction and maintenance of the data pipeline. These operations are the responsibility of the Data Engineer, a role of absolute importance, long underestimated in favor of the more popular Data Scientist. In 2018, 42% of large companies declared that they had an internal Data Engineer.

- Data Analyst - The Data Analyst, which deals with researching quantitative evidence within large amounts of data, supporting business decisions in this world, is present in 56% of large companies and about half (44%) of companies that do not yet have this figure plan to include it by 2019.

Author: Vicki Lezama