How to embrace Big Data. A methodology to look at the new technology

Transcription

1 How to embrace Big Data A methodology to look at the new technology

2 Contents 2 Big Data in a nutshell 3 Big data in Italy 3 Data volume is not an issue 4 Italian firms embrace Big Data 4 Big Data strategies and operations need enhancements 5 The Big misunderstanding 5 How to approach Big Data effectively? 6 The Reply value offering 7 The technological perspective 7 Big Data as a Washing Machine 8 Traditional architecture as a data source for Big Data analytics 8 Traditional and Big Data architectures working together 9 Business perspective 9 Can Big Data help in detecting insurance fraud? 11 Big Data to improve churn analysis in the telecoms industry 12 New boundaries in customer profiling 13 Conclusion

3 How to embrace Big Data A methodology to look at the new technology

4 How to embrace Big Data. A methodology to look at the new technology Big Data in a nutshell In it s short life Big Data assumed a wide range of meanings: on the one hand it refers to the global phenomenon of information growth, resulting from the proliferation of activities and data generated on the net via the social network, smartphones or machine to machine interactions. On the other hand, Big Data describes a new generation of technologies and architectures, designed to extract value in a cost-effective manner from very large volumes of information, by enabling high-speed data capture, discovery and analysis. In the already well-established technical literature, three Vs are generally used to characterize Big Data: Volume: the total amount of data to be managed Velocity: the pace at which the data can be processed Variety: the complexity and heterogeneity of the data set Please forget it all! Big Data solutions cannot be defined by how you can measure data in terms of Volume, Velocity and Variety. The three Vs are just measures of data related issues. One firm s big data is another firm s peanut as velocity appreciation strongly depends on any single context behavior. Over the last twenty years, ideas of how to assemble a decision support system have coalesced around the concept of a data warehouse as a tool for navigating business issues but today the real challenge for business intelligence is to let emerge hidden value through intelligent filtering of low-density and high volumes of information, being them operational or unstructured data arising from sensors, transactions or either the web. Unfortunately the unstructured data sources may not easily and cheaply fit in traditional data warehouses, which may not be able to handle the processing demand imposed by unstructured data which for this reason remains largely untapped. To help connecting the dots of all the content that s out there by analyzing a huge data set and returning results in seconds a new class of technology has emerged; it includes new tools as NoSQL databases, Hadoop and Map Reduce. These tools form the core of an open source software framework that supports the processing of very large data sets across clustered systems. Let we show you how and why these technologies are gaining a leading position in the interest of companies So, what is Big Data? A nice definition says aloud: The frontier of a firm s ability to store, process, and access all the data it needs to operate effectively, make decisions, reduce risks and serve customers : that s probably the real essence of the paradigm change addressed by Big Data technology. 2

5 Big data in Italy Data volume is not an issue In January 2013, in collaboration with Forrester Consulting, Reply carried out a survey to evaluate interest in adoption of Big Data solutions in Italy. The aim was to explore not just the acceptance of Big Data but also the stage of maturity reached by organisations in building their strategy towards Big Data implementation. The study spanned vertical sectors across the country s top 100 organisations, with concentrations in financial services, telecoms, energy, utilities and waste management, retail and professional services. Key findings included the following, in such a way surprisingly, results: The results of the survey confirm that a high percentage of respondents do not have to deal with petabytes, zettabytes or yottabytes of data. The most of the Italian companies manage volumes of data that are relatively insignificant in comparison with the vast size of major enterprises, being them social networks like Facebook, Twitter or important retailers as Walmart and Target. Anyway, Italian companies seem to have realised that volume is not the only or primary characteristic of Big Data. The interest in Big Data technologies is then driven by the necessity to acquire and process heterogeneous data, while fastening computational time at a greater level of accuracy. This is pretty similar to the findings of a recent study conducted in the U.S., where it became evident that the amount of useful data generated inside and outside the company is not raising the hugeness of the major social network. ESTIMATE THE SIZE/VOLUME OF WITHIN YOUR COMPANY >1000TB TB TB 1-10TB <1TB None Don t know UNSTRUCTURED 14% 24% 26% 25% 7% 2% 2% STRUCTURED FROM TRANSACTIONAL SYSTEMS SEMI-STRUCTURED 13% 15% 44% 18% 6% 3% 1% 11% 25% 24% 26% 7% 2% 3%

6 How to embrace Big Data. A methodology to look at the new technology Italian firms embrace Big Data Reply identified a significant amount of interest in Big Data technologies and solutions. It looks as the wish to take a competitive advantage from the analysis and integration of unstructured data is driving companies to adopt Big Data technologies. Notwithstanding only around a quarter of respondents have already implemented a solution, 40% were planning to implement in the next 12 months and a further 28% planned stretching out to a slightly longer time horizon. These companies are struggling with the data coming into their organisations and are looking for new methods to better leverage data to improve their businesses. BASED ON FORRESTER S DEFINITION OF BIG, WHAT BEST DESCRIBES YOUR FIRM S CURRENT USAGE/PLANS TO ADOPT BIG TECHNOLOGIES AND SOLUTIONS? (SELECT ONE) IMPLEMENTED, NOT EXPANDING EXPANDING/UPGRADING IMPLEMENTATION PLANNING TO IMPLEMENT IN MORE THAN 1 YEAR PLANNING TO IMPLEMENT IN THE NEXT 12 MONTHS 3% 19% 28% 40% or drivers organisation cares while overall orchestrating its business intelligence strategy, 34% of respondents pointed to improving data quality and consistency. But data quality is not the end goal. The whole idea of Big Data is to improve business success, through factors as customer insights, operational efficiencies and cost control. Business targets must come first and data quality is a prerequisite for these. Only 11% of respondents claimed to have a business case for Big Data with concrete KPIs and proven ROI. They represent the highest level of maturity in the Big Data initiatives. A further 19% reported having a business case with KPIs but no proven ROI. The majority (47%) have a business case with intangible benefits. As shown by the results, 70% of respondents are not yet able to translate the advantages of Big Data initiatives into tangible business benefits. This would indicate that further expertise is needed to lead Italian companies into the Big Data world. Starting small and demonstrating tangible benefits will enable organisations to prove the ROI on a small scale before going big. INTERESTED BUT NO PLANS 10% DO YOU HAVE A BUSINESS CASE FOR YOUR BIG INITIATIVE IN PLACE?" NOT INTERESTED DON T KNOW 0% 0% WE HAVE A BUSINESS CASE FOR BIG WITH MEASURABLE KPIS AND ALREADY PROVEN ROI 11% Big Data strategies and operations need enhancements WE HAVE A BUSINESS CASE FOR BIG WITH MEASURABLE KPIS AND A PROJECTED BUT NOT YET PROVEN ROI WE HAVE A BUSINESS CASE FOR BIG BUT WITH INTANGIBLE BENEFITS ONLY 19% 47% MATURITY Key goals focus firstly on data quality, followed by business objectives. The business cases that companies have developed do not measure concrete key performance indicators. Moreover, Italian organisations aren t pushing the envelope when exploring the potential of Big Data. Although the demonstrated significant interest for the new technology, Italian organisations need definitely to invest more attention in improving their Big Data strategies and operations. When asked about the most important goals CURRENTLY WE HAVE NO BUSINESS CASE, BUT WE ARE CURRENTLY WORKING ON ONE WE HAVE NO EXPLICIT BUSINESS CASE FOR BIG DON T KNOW 0% 4% 19% 4

7 The Big misunderstanding A cause of frustration for the customer trying to tap into the ability to design and embrace a strategy about Big Data is the fundamentally misleading view of Big Data as a social phenomenon on the net, generated by millions or even billions of pieces of information and backed by technologies that have been developed to extract the hidden value of that data. Too often technology key users (marketing or sales department, product development team, security and fraud offices, to mention just a few), are asking for solutions that will never come because echoing the traditional approach. It is not simply a matter of technology. Within the functional organisation Big Data demands new processes, a different way of interacting with the end customer, even new skills to leverage the increased power of the analysis. Simply Big Data requires a shift, in the corporate analyst behaviors, to leverage the potentiality of new information made available and in the IT departments, to deploy a new array of IT architectures that will enable companies to handle both the data storage requirements and the heavy computational processes needed to handle cost-effectively large volumes of data. We can segment company s behavior into 4 blocks: Inactive: Companies deal with Big Data issues as a storage problem and essentially deny that there is a problem. When issues come up, they just try to fix the problem using standard techniques. This approach results from a lack of business awareness and has several failings: it is expensive and unpredictable. Proactive: Companies have the technologies and the infrastructures to deal with Big Data but they still don t have business cases with measurable KPIs and proven ROI. Reactive: Companies have business cases and the maturity to start a Big Data project but lack of ability and expertise to address technological issues. Active: Companies view Big Data as an asset and own the necessary human resources, processes and technologies to gain insight into their data. These companies looks at Big Data as an opportunity to differentiate and gain competiveness, while well understand it is not the last technological hype. The final goal is then putting in place a comprehensive strategy to maximize the data value to business purposes. How to approach Big Data effectively? The survey, in line with our overall understanding of company s behaviours, suggests that the strategy to deal with Big Data challenges will strongly differ depending on the maturity of the organisation towards this topic. Reply has developed a Big Data maturity model to measure the organisation s aptitude in approaching Big Data. The real aim of this model is to help CxOs in better understanding the company behavior alongside the new technology and then properly identify the correct strategy for implementing a coherent and profitable Big Data project. 5

8 How to embrace Big Data. A methodology to look at the new technology The Reply value offering As the model demonstrates there are several challenges on both technical and organisational side that must be carefully addressed to achieve the full potential of Big Data, while finding the right solution involves more than a simple evaluation of price/performance of any specific tool. Reply has built an integrated a consistent methodology to support clients in the development of suitable strategies to let them able to benefit of best of breed solutions. A multidisciplinary team of business analysts and technologists has been established to address the main issues related to a Big Data project implementation within a comprehensive approach. Additionally, to help business people in challenging value from data, has been founded a data scientists team. The goal is to help customers by proposing the most appropriate business and technology model fittings to their needs. This heterogeneous team can help companies at any stage of the Reply s maturity model: Inactive: The first stage, where the organisation has no expertise in Big Data. Technologists provide the architectural solution, while business analysts and data scientists collaborate with business in discovering new patterns from the data, to create a business case with a proven ROI. Proactive: Organisation has already gained experience in the technology but do not know how to apply it to a real business case. Reply s business analysts can help supporting the development of a Big Data roadmap, to transform customer s needs into a real business scenario. Data scientists work with business analysts in finding new insight and perspectives, helping the company to improve data value. Reactive: Organisation has established a business case with measurable KPIs but it lacks the technical experience necessary to develop a Big Data architecture. Reply s technologists can help customers in finding the best architectural solution. The first step is to analyse the organisation s data sources and IT infrastructure. The overall overview of the technical environment enables technologists to develop a Big Data infrastructure tailored to the customer s goals. Active: Organisation has here reached a high maturity level in both the technology and business issues. Finetuning job can still be done, however, to make it easier to better develop business opportunities: this is the typical environment where data scientists can, use their expertise to refine the logical approach to data discovery and modeling to deliver more detailed insights. In summary, Big Data may be approached by two different roadmaps: starting from the business issues, using Big Data as a very powerful tool to redesign and improve data analysis processes and from a technological perspective, looking at Big Data solutions to reshuffle best practices and infrastructure in order to provide faster and cheaper results. 6

9 The technological perspective Big Data as a Washing Machine The most important value offered by the Reply s approach to Big Data implementation lies in the development and delivery of solutions which strongly fits with the customer s technological architecture. The goal is addressing the resolution of specific business problems while maximizing the safeguard of the current investments in technology, through a gradual integration of the Big Data architecture into existing legacy systems. Founding on the distinctive competencies and wide operational expertise of the Group companies, Reply developed a framework to tailor Big Data implementation in three different scenarios. A major problem when approaching a data warehouse solution is represented by extracting data from outside sources, transforming and loading it into the data warehouse. ETL processes (extract, transform, load) can involve considerable complexity while significant operational problems can occur with improperly designed ETL systems, whereas for the business purposes they represents a null-value, expensive and time consuming set of activities. Big Data can solve this problem by substituting the traditional ETL process with a new kind of storage architecture and, on top of this, a processing layer able to quickly transform data and load it into a data warehouse. This approach can appreciably lower the overall time needed to satisfy the necessity of building the base of reporting/analytics value chain, at a fraction of the cost incurred by the traditional approach. It will also introduce a faster management of the quality and coherence of the data ignited into the systems. PRESENTATION CALCULATION ENGINE MART DWH ANALYTICS & VISUALIZATION STAGING AREA CALCULATION ENGINE STORAGE INGESTION & STORAGE Unstructured Structured 7

10 How to embrace Big Data. A methodology to look at the new technology Traditional architecture as a data source for Big Data analytics Traditional data warehouses can handle many situations but they do have limits. The volume of data imported into a data warehouse is a critical issue in terms of costs to upgrade the system and in data elaboration time. The higher the volume, the greater the impact on processing performance. The usual solution for this problem is to back up the data but in most cases this is tantamount to losing the information. Big Data architectures can load data from existing data warehouse systems and process it along with data from sources such as data streams or unstructured data that are not easily managed by the traditional data warehouse. There are many benefits from using this approach; it is possible, for example, to combine classical structured data with other sources, enabling new insights and achieving a better granularity in the data analysis. Furthermore, having a Big Data storage structure means that data coming from the data warehouse will never be lost; it will always be possible to use historical data and analyse it. Traditional and Big Data architectures working together In some cases, thinking of replacing or supplementing IT architectures can be valued as a disruptive approach, so that the Companies prefer to keep their incumbent systems despite the loss of the information that - if properly used - could dramatically upgrade their competiveness or the revenue streams. Big Data architecture, more than others solutions, allows companies to implement a parallel infrastructure to exploit new data sources in counterpart with the traditional B.I. ones. The cost-effective hardware jointly with the open source software, which represents the foundations of the Big Data solutions, enable a company to manage both scenarios at a very marginal differential cost. Moreover, some of the tools that belong to the Big Data ecosystem (e.g. analytics, presentation and data integration layers) can be substituted by or integrated with resources already present in the traditional architectural stack. PRESENTATION CALCULATION ENGINE STORAGE ANALYTICS & VISUALIZATION PRESENTATION ANALYTICS ANALYTICS & VISUALIZATION DWH STAGING AREA ETL - ELT INGESTION & STORAGE CALC. ENGINE STORAGE DWH STAGING AREA ETL - ELT MANAGEMENT & STORAGE Unstructured Structured Unstructured Structured 8

11 Business perspective Our daily confrontation with CxOs make clear that many organisations start in claiming how business intelligence solutions are failing to meet their current business needs; this is the major push to accept looking at Big Data as the ultimate instrument to design a new, more effective information strategy. From being the sort of tool that was only needed for meteorology or mathematical simulations, Big Data has pretty recently moved into the industry mainstream as the easiest and cheapest way to overcome traditional costs and implementation times of complex data management systems, essential to encompass and manage heterogeneous and multi source data sets. Not all industries are likely to benefit from Big Data projects equally and not surprisingly, the first movers were internet companies; in fact, the most popular Big Data platforms has been built on top of software originally used to batch process data for search analysis but now retail, telecom, financial services and media sectors are quickly recovering while manufacturing and process industry are definitely approaching. But just having the Big Data tools isn t enough: enterprises need to know what questions to ask, actually ask them and then translate that into strategy or tactics. It will be important for enterprises to develop new policies around privacy, security and intellectual property. Big Data isn t just about technology and employees with the right skill sets, it will also require businesses to align work flows, processes and organization to get the most out of it. It is important to note that enterprises should not concentrate on destructured data at the expense of current data or business information as normal. There is still a lot of value to be extracted from the information inside their traditional databases! Reply can help customers in designing and addressing the right path to define an appropriate strategy, by identifying business cases where a Big Data approach can create a true difference to meet unsolved organisation s needs. Below are summarized some of the most common usage patterns explored by Reply; while the explanation of the usage patterns may be industry-specific, the rational basis can be applied across industries to bring new sparks that ignite the change. Can Big Data help in detecting insurance fraud? The technology that most insurers have currently in place to help to fight frauds is a blend of business rules and database searches, where the results rely heavily on the sensitivity of the claims auditor. While these techniques have proved being successful in detecting known fraud patterns, insurers today need to invest in new analytical capabilities to help them to spot unknown and complex fraud activities. These analytical capabilities include incongruity detection, predictive modelling, unstructured data mining and social network analysis. Anomaly detection aims at discovering fraud by identifying those elements that vary from the norm. Key performance indicators associated with tasks or events are baselined and thresholds set. When a threshold for a particular measure is exceeded, then the event is reported. Outliers or anomalies could indicate a new or previously unknown fraud pattern. 9

12 How to embrace Big Data. A methodology to look at the new technology Predictive models use past fraud events to produce fraudpropensity scores. Adjusters simply enter data and claims are automatically scored against the likelihood of them being fraudulent. These scores are then made available for review. Use of predictive modelling makes it possible to understand new fraud trends. Since around 80 percent of claims data is unstructured, the use of tools able to mine unstructured data enables insurers to analyse information arising from medical chronicles, police records, external and internal database sources or even s. Social network visualisation tools allow investigators to actually see network connections so they can uncover previously unknown relationships and conduct more effective and efficient investigations. By using Big Data technologies companies are able to manage all of these issues and to learn from experience to improve their fraud detection and pattern identification capability. This learning characteristic enables the software to adapt and increase in sophistication as more and more intelligence is gathered over time. The more analytical the tools, the higher the chance of detecting fraud in the early stages and predicting potential areas of abuse before fraudsters discover the opportunity themselves. Automation also means less reliance on the human element, and provides greater accuracy and homogeneity in fraud discovery activity. Reply has established a proven methodology to apply a Bayesian model in fraud recognition combined with Big Data analysis techniques. This is a comprehensive approach, which includes data discovery through all the available internal and external structured and unstructured data sources, combined with the powerful computational capabilities of a Big Data infrastructure to support the claims manager in every phase of the investigation. First of all, a network analysis will identify any historical relationship between the actors in a specific claim, revealing any connection in the past that could suggest a propensity to commit a fraud. Then a clusterization of the actors and related behaviors based on a self-learning statistical model let emerge similarities in the data model, to better represent relations and attitudes to plausible fraud existence. While this technology is still in its early stages, the bottom line is that new Big Data analytics can be used to explore large volumes of networked data, using high-speed processing with configurable data entry from multiple internal and external sources, to reveal fraudulent behaviour. Can you imagine how far you could go using a so strong paradigm change in tracking frauds? USERS ACTUARIAL CLAIM MANAGER RISK MANAGER Internet data base Risk/Tarif Evaluation Claims Managements Fraud Monitoring External data base Contracts Customers Claims Real time evaluation BRMS Case Manager Workflow Mgmt Case Analysis Case Assignment Big Data Analytics Dashboard Fraud reporting Data Matching SOGEI MCTC ANIA / ISVAP Frauds black list Clustering Scoring Network analysis Data Certification Others 10

13 Big Data to improve churn analysis in the telecoms industry Today s customers want competitive pricing, value for money and, above all, a high quality service. They won t hesitate to switch providers if they don t find what they re looking for. So particularly in mature markets or where regulations and service dematerialisation makes churn easier, it is absolutely crucial to put in place a sustainable and robust strategy for customer retention to preserve customer lifetime value. The telecoms market provides a good example of why the high acquisition costs and slim profit margins for each customer make churn analysis vital to help companies identify and retain the most profitable among them. In this context, the paradigm change more is more is in tune with the main aim of Big Data analytics. The uncovering of hidden value, through the intelligent filtering of low-density and high volumes of data, can become a real differentiating factor. The more data you have, and the more recent and accurate it is, the faster you can learn from it and the more predictive you can be. The value of Big Data can then be exploited in two different directions: to decrease the capital expenditure (CAPEX) or operational expenditure (OPEX) associated with the computational infrastructure needed to address the huge amount of data used to feed predictive analytical models; and/or to increase the data sources used for the integration and leverage of new kinds of unstructured information, enabling companies to better describe and understand customer behaviour. One method now emerging to enable an operator to move from reactive churn management to proactive customer retention is to use predictive churn modelling based on social analytics to identify potential churners, thereby enabling the operator to act on such predictions, rather than waiting for explicit trigger points (e.g. credit on prepaid card running down), by which time the churn is most probably inevitable, irrespective of any act or offer on the part of the operator. Big Data analytics offer the opportunity to process and correlate new data sources and types with traditional ones, to achieve better results more efficiently and receive insights that will set alarm bells ringing before any damage has been done, so giving companies the opportunity to take preventive measures. Pricing analytics and next best offer recommendations in particular are classic examples of how, by analysing structured data (such as CDRs) and unstructured or semistructured data types (such as log files, IVR tracked calls to call centres, clickstreams and, ultimately, text from s), telecoms operators can provide more accurate, personalised offer recommendations. Last but not least is the issue of timing. It is true that traditional business intelligence solutions have allowed enterprises to move forward by consolidating data sources into centralised data centres. However, this data is used simply for reporting. We are now moving into a new era where information can and must be converted into realtime actionable insight, to enable the company to respond in real-time to behavioural changes in the customer mindset or to react quickly to threats on the competitive horizon. This is exactly why and where Big Data analytics can win the battle against old BI tools. Feedback VOICE NETWORK CRM TOOLS MOBILE WEB NAVIGATION CUSTOMER INTERACTION HDFS & MAP REDUCE REAL TIME ANALYTICS INSIGHTS AD SERVER CAMPAIGN MNGT CELL TOWERS CALL CENTER BIG PLATFORM OPERATIONAL STACK 11

14 How to embrace Big Data. A methodology to look at the new technology New boundaries in customer profiling Customer analytics start with data. To get better customer insight, most companies begin by analysing their structured transactional data, which typically includes information such as demographics, purchase history, complaints and retention information. Statistical algorithms can help companies to create meaningful segments and gain insight into buying patterns. These insights and tendencies are then encapsulated in models which are used as a basis for future predictions; basically, an extrapolation of past history. Is this enough in today s markets? Probably not! In recent years every one of us has become a powerful walking data generator, delivering personal information (that reflects daily changes in our habits) through many different channels. Information sources include call centre records, communications and transactional data as well as usage patterns on company websites. Very few enterprises, however, are in a position to probe this gold mine of information. In their quest to make these models more accurate, companies are starting to embrace new sources of data; but most of this data is unstructured and it is quite expensive to have it integrated into traditional data warehouse and data-mart infrastructures, both in terms of cost and time. Moreover, analytical algorithms are continuing to evolve to deal with the changing landscape brought about by new trends (such as mobility, social media and e-commerce), while the need for a very fast computational time is increasingly becoming a necessity to help companies to segment their customer base more effectively, attract more profitable customers, improve campaign handling or reduce customer churn. Propensity models are also becoming more dynamic to deal with the geo-spatial and temporal dimensions, acknowledging the fact that location and time events impact people s propensity to react to external stimulation; in this case, the ability to react in real-time or near real-time becomes a must have feature. As demonstrated by a recent Reply project, Big Data technologies provide a very powerful tool-set to address all of these issues. The ability to digest and elaborate in real-time huge amounts of data as single cash lines in till receipts, and compare them with the purchase history of each customer in order to generate promotions in real-time is without any doubt a capability that would be extremely hard to achieve using traditional analytics solutions - which would in any event be prohibitively expensive. The more data and information to be analysed, the longer the process required (days); while Big Data solutions allow retail companies to analyse huge volumes of data, with more granularity, in a shorter period (hours vs. days). Retailers can now get insight into customers seasonal trends and use it to improve the management of stock or create tailored pricing and promotions. While embracing this new customer approach companies must be aware there is a very fine line between using customer analytics to create value by serving customers with customised precision, and destroying value by surprising customers with actions that erode trust. Privacy policies and a consistent execution across the enterprise are essential and must be properly performed to understand the ever-narrower segmentation of customers and so deliver much more precisely tailored products or services. It is worth it, however, and the reward will surely overcome best expectations. 12

15 Conclusion While other business metrics come and go, growth continues to be the most important criterion used to measure companies value, the measure by which the market assesses companies and managers evaluate their performance compared to competitors. Daily we appreciate as competiveness passes more and more through a better understanding of the huge amount of data organizations collect and store about employees, customers, finances, vendors, inventory, competitors and markets, to name only a few. The amount of data needed is important because people generally make better decisions if they have more data available to them. In parallel, even more in the coming years we will appreciate the increasing volume and detail of information captured by enterprises as the rise of multimedia, social media and the Internet of Things will fuel exponential growth in data for the foreseeable future. The real issue is data have swept into every industry and business function and are now an important factor of production, alongside labor and capital. As organizations will definitely understand this pattern and invest to become more dependent on information, the processes of gathering, managing, and utilizing data will become more central to operational success, because data is only as valuable as our ability to access and extract meaning from it. This is probably the main reason why Big Data solutions have definitely left their primordial field of application, entering to its own right the industrial world. Also if there could be reasons to be skeptical about the Big Data expansion we can say without risk of contradiction that a disciplined, targeted approach to Big Data deserves a very focused attention; when organizations will recognize that Big Data s ultimate value lies in generating higher quality insights looking in a different way to available data to enable better decision making, interest and related revenues will accelerate sharply. Albeit in this field Big Data is still in its infancy, the rapid and constant growth of attention to this technology suggests that industry begin to embrace the challenge and is ready to take on transformative measures, using the next generation of Big Data industrial solutions. Then, the final and most important question is: are you ready to harness the power of Big Data? 13

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