Big Data and the five “W”

The increase in the last years in the creation of data, the new gold of companies, tempts interest and research for opportunities to exploit them. The value of these data, being in raw state, is zero, being necessary to work on it to generate benefits. Nowadays the words Big Data, Machine Learning, IoT and many other acronyms reveal how backwards we are in organizations in the use of information for making decisions.

Most organizations are backwards in the adoption of analytical practices. Included the Digital Transformation, which proposes the use of technology to add fundamental changes in the business, generating value through the integration of systems, optimization and automation of processes, products and services.

There is a lot of curiosity by companies on how to use and what benefits the Big Data generates. But despite this interest, they can’t generate value using emerging practices in handling and processing data. Factors such as the short lifespan of technologies, lack of trained professionals, zero or little support from business areas and the lack of a culture oriented to decision-making based on data are unpromising and create mistrust in different levels of organizations.

With the objective of simplify the term Big Data, with a look towards its utility in a language closer to the business, we have developed the 5 W (double u) of Big Data.

The "5 W" refers to the questions in English that begin with W: What, Why, When, Where and Who. This way intended to help companies and people to understand what factors to keep in mind in the implementation of Big Data.

What

Information and Communication Technologies (ICT) are based on data: they generate, store, process, transmit and visualize information. This way data is the basic unit of almost all technologies. We can see from the transactional systems, cell phones, wearables, sensors and many others. The only thing in common that these examples share is data.

On the other hand, we have seen the evolution of technologies throughout the digital era. From the punched cards, which could store 960 bits, through magnetic tapes, floppy disks, CD / DVD's and portable storage units, until the "cloud" era, where the capacity to store information is unlimited and can be accessible from any device with internet connection. Each improvement comes to solve a previous technological problem, inspired by business needs. Systems and infrastructure need new capabilities and features to solve new challenges.

In this sense, organizations are facing a Big Data problem when they fail to deliver required information within the time needed by the business units to add value to their activities. This information can be in the form of a report or KPI for a decision maker, or using advanced analytical techniques, generate automated actions.

Fortunately, we are experiencing the development of various emerging technologies nowadays that come to solve these challenges.

Why

In order to establish the reasons for implementing Big Data technologies in organizations depends on many factors and challenges that they deal with. Although it is important to choose which Big Data technology to implement, the most important thing is to define business strategies that are organized and supported by products, services and analytical initiatives.

The correct question for this "W" would be: "Why Big Data is so important for organizations". Many professionals, with macro or micro making-decision power, who question the "why", should use emerging technologies with an analytical approach. The little or almost no knowledge of the potential and benefits of Big Data make them doubt their actual application, as many of them have implemented or have been users of traditional analytical projects, usually Data Warehouse or Business Intelligence, with very little impact for the business.

The answer to this question is easy, predict. Using any combination and source of existing data, both internal and external, generate enough degree of knowledge allowing to include these "insights" in the current business model of the sale of products and services, and the same for operational efficiency.

By predicting the behavior of clients individually, companies will be able to adjust their product and service offer in a specific way for each need, detecting new sources of income in a timely manner, or even developing new business models.

The next step is the prescriptive analytical, which allows to simulate scenarios, creating recommendations on what actions the business should perform to achieve the proposed objectives. In this way it is allowed to advance in the maturity scale, leaving behind the analytical that tells "what happened", giving way to the analytical that allows predicting and optimizing the operation.

But reaching this level of maturity is not easy and neither does it happen overnight. Even it’s not enough to just implement some of the thousands of Big Data platforms available in the market. It requires a learning process that goes from detecting the needs and problems of the business areas, finding opportunities where analytics add value to implement a process that allows discovering new valuable perspectives.

When

The decision of when to use Big Data joins the definition of it, which is to determine if the organization has a Big Data problem. When detecting that the business strategy requires delivering information in a timely manner to internal agents and areas, the next step is to validate if current systems allow working at the pace of the business or analyze if there may be budgetary restraint that limit the technological investment required.

Both positions are valid and it happens quite frequently. The important thing is to be aware that there is a Big Data problem, and that the way to solve it requires new strategies and technologies, which does not have a traditional or known proposal.

The implementation of these platforms, in addition to the technical capabilities to handle a high volume of distinct data at high speed, add agility to analytical cycles, bringing technology and business in a scenario never before seen, and decreasing the "time-to-market" of products and services.

Where

The major impacts that organizations should consider to implement Big Data are not strictly linked with the well-known SVV (Speed, Volume and Variety). Many initiatives like this one are born today due to problems that organizations deal with in data management and timely information delivery. But in turn, they also appear from the lack of agility in the analytical cycles, both in a productive scheme and in "data discovery". Another point is the demand for cost-effective technologies, versus traditional high-cost solutions, but recognized and approved in the market.

But beyond the economic and technological factors that inspire the implementation of Big Data, many questioned where they can implement this within their organizations. It is precisely at this point that conversations and activities that lead to define business cases are established.

There are endless success stories depending on the sector and the vertical structure of the company, which allows to understand the definition of "roadmap" and the implementation plan, according to the needs of the areas, such as operations, marketing, sales, logistics and security, which today have had tangible and high results, from the application of these technologies.

Finally business cases are opportunities that are detected and justified with real benefits in organizational processes and in different levels using analytics to support decision making process. The process of detection and replace of use cases is usually carried out by a group of Business Analysts, involving business users from its conception. Many times, analysts carry out small, limited projects, commonly called Concept Verification Tests (CVT), to validate assumptions and the value of the business case, aiming to improve the economic benefits or reduction of company costs.

Who

Big Data promises to unite business and technology, so the entire organization benefits from the results by focusing on use cases for the business. Therefore, to impact the organization transversally, these initiatives must be leveraged and sponsored by the management and executive lines, or the "C-suite level". When these initiatives are supported and aligned with the CEO's vision, the objectives are clearer and, therefore, it is easier to maintain the motivation of the people who, within the organization, should adopt these new initiatives. This is crucial to take an essential analytical position in the future.

From the viewpoint of macro decision making, managers must be able to define business strategies by incorporating relevant analytical services that makes possible to include value to internal activities, or processes toward customers and consumers.

In this way, a Big Data implementation will have a strong relationship with the business, and will not be just a technical solution.

This simplifies the justification of the investment, both in qualified personnel and platforms.

From this strategy plan should be divided the priority of business cases that will work with a clear "roadmap" within a reasonable time, understanding that this is a process that requires a long learning, as well as be prepared to operate the business in a new way. It is heavily dangerous to rely on data without suitable controls and without general supervision. Preventing these possible dangers in advance is also the job of the executives who sponsor the corporate Big Data initiative.

Future and Present

Even though Big Data has been known for several years, it’s barely starting and just a few companies are willing to implement it. Some have ventured on these initiatives, understanding little or nothing, about how to obtain benefits from this type of technology. The era of the Data Warehouse or Business Intelligence was only the beginning that allowed organizations to understand the value of data, beyond the product that produces enables to see "what happened".

Big Data is going to be the core of the organizations, and it will revolutionize the way business will be done. People will be the central axis, allowing the creation of new ways to obtain benefits.?Companies should consider data as a high value asset in this future, which can be profitable and monetized.

It is important to detect opportunities within organizations that allow processes to be optimized and tools to be delivered for decision making in the business context. The Big Data era will allow "to identify what will happen", in a scenario where all the factors influence to generate benefits.

Sources:

[1] Advanced Analytics and Big Data Adoption Report 2016, International Institute of Analytics, 2016.

[2] IDC White Paper, sponsored by Oracle, Six Patterns of Big Data and Analytics Adoption Infographic, 2016.

[3] IDG Data Analysis Survey, 2016

[4] IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions, 2016

[5] Data Lake Adoption and Maturity Survey Findings Report, 2015