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Big Data 101: What Is It and How Are Organizations Using It?

  •  Everywhere we turn, we hear the term “Big Data.” It’s top of mind in the business press and conversations. Recent research by Accenture found that 90% of organizations believe Big Data will transform their business like the Internet did, and 83% are pursuing Big Data projects to gain a competitive edge.
  • You’ve probably heard the Big Data term too and perhaps you are wondering what all the fuss is about?In this three part blog series, we will try to demystify Big Data and how it is collected
  • In our first post, we’ll discuss what Big Data is and how HR teams in large organizations are using it. Next, we’ll focus on how and why small and medium sized businesses should use Big Data to address their HR and talent acquisition challenges.
  • Finally, we will discuss how HR leaders in small and medium sized businesses can build support for a big data initiative.So, what is Big Data anyway? Actually, the name is pretty descriptive.
  • Big Data is used to characterize very large sets of information. In terms of volume, we are taking about at least several terabytes of data.
  • To give you a sense of scale, one terabyte of data equates to around 472 hours of video or 2,000 hours of CD-quality audio.Although companies have always dealt with large amounts of data, that information has often been splintered into different siloed systems.


  • Big Data initiatives are different because they integrate data from many different systems into one, which enables organizations to analyze the information in its entirety and identify insights that could improve the way business is done.


  • The key to success with Big Data is finding correlations and connections across multiple disparate data sources. This may include information that resides within company systems and emails, or in external systems like social media sites that contain posts or videos

Big Data Analytics Training in Chennai

Big Data Analytics Training in Chennai


Big data should not be seen as an independent “processing silo.” Rather, it is important that any big data analytics applications (and their results!) be properly incorporated within the organization’s general data warehousing, business intelligence, and reporting framework. Although big data will combine its own specialty aspects of hardware, data management, software, data models, and analytical models assembled with practitioner expertise, the results should be aligned with other reporting and analysis activities as well as directly integrated within the business environment. That is the best way to derive the actionable insight driving profitable business value.

The following list gives a brief description of the three stages depicted in the preceding diagram:

  • Data Preparation: This stage involves activities from data creation (ETL) to bringing data on to a common platform. In this stage, you will check the quality of the data, cleanse and condition it, and remove unwanted noise. The structure of the data will dictate which tools and analytic techniques can be used. For example, if it contains textual data, sentiment analysis should be used, while if it contains structured financial data, perhaps regression via R analytics platform is the right method. A few more analytical techniques are MapReduce, Natural language processing (NLP), clustering (k-means clustering), and graph theory (social network analysis).
  • Data Visualization: This is the next stage after preparation of data. Micro-level analytics will take place here, feeding this data to the reporting engine that supports various visualization plugins. Visualization is a rapidly expanding discipline that not only supports Big Data but can enable enterprises to collaborate more effectively, analyze real-time and historical data for faster trading, develop new models and theories, consolidate IT infrastructure, or demonstrate past, current, and future datacenter performance. This is very handy when you are observing a neatly composed dashboard by a business analyst team.
  • Data Discovery: This will be the final stage where data miners, statisticians, and data scientists will use enriched data and using visual analysis they can drill into data for greater insight. There are various visualization techniques to find patterns and anomalies, such as geo mapping, heat grids, and scatter/bubble charts. Predictive analysis based on the Predictive Modeling Markup Language (PMML) comes in handy here. Using standard analysis and reporting, data scientists and analysts can uncover meaningful patterns and correlations otherwise hidden. Sophisticated and advanced analytics such as time series forecasting help plan for future outcomes based on a better understanding of prior business performance.

Business integration goes beyond the methods discussed for soliciting requirements. Rather, asking questions such as these will highlight the business process interfaces necessary to fully integrate big data into the environment:

• Who are the participants in the process?

• What are the desired outcomes of the process?

• What information is available to the participants?

• What knowledge is provided by big data analytics?

• How are the different information assets linked together?

• How are actionable results delivered to the participants?

• What are the expectations for decisions to be made and actions to be taken?

• How are results of decisions monitored?

• What additional training and guidance are needed?

• How do business processes need to be adjusted to make best use of big data analytics?

Reviewing the answers to these questions will guide the technical teams in properly leveraging big data within the business context.

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Using Big Data to Build Smart Cities

Smart Data CitiesRampant urbanization is a serious threat as the number of people living in cities is likely to double by 2050. Many experts believe six billion people will live in cities by 2050, as compared to the 3.6 billion now and this increase is likely to put enormous pressure on the available resources. Clean water, power, waste management, living space and clean air will be some of the issues facing these future urbanites.

The problems that come with rampant urbanization are also unique opportunities to build cities that are smart and sustainable. Many countries have already embarked on this idea of building such smart citiesand it is estimated that we will be spending a whopping $400 billion a year by 2020 to build them. One of the driving forces behind these smart cities will be technologies such as Big Data and Internet of Things (IoT). In fact, Big Data opens up a world of opportunities for urban planners, government entities and private players to develop a range of applications that will make these future cities more livable.

Big Data Market Expected to Grow Rapidly Through 2019

big data and ovum

The big data software market will grow nearly six-fold by 2019, according to IT research firm Ovum’s latest market size and forecast report on information management software.The report highlights the fact that while big data software in 2015 is just a small part of the overall market for information management, it is set to increase at a compound annual growth rate (CAGR) of 50 percent through 2019 and play an increasingly important role that will position big data analytics as a core capability for many enterprises by 2019.