The era of big data has witnessed a paradigm shift into analytics. Today, it’s no longer sufficient to simply gather data from social media, IoT, and wearable devices, and be unable to manage or filter it. It is more about delivering the right data to the right person, at the right time.
This trend is growing crucial as data is multiplying every day and pouring in from various devices and smart machines including wearables, electronic gadgets, and other devices. Such factors call for the treatment of vast pools of structured and unstructured data with care and precision. This is precisely where invisible analytics come in.
By far, big data has remained as an enabler of the new wave of analytics solutions. However, the challenge for big data analytics lies in the traditional hardware storage capacity and processing rates that execrably lag during operations, thus becoming inefficient in supporting the demand for handling large amounts of data.
As we look into the future, more products and technologies are leaning towards what can be possibly done with the large amounts of data that is already present, without the need of harvesting more data. This time, analytics will be the enabler. Market experts predict that analytics will become deeply, but invisibly embedded everywhere. The increasing invisibility in analytics is in the same breath as the growth of the volume of data and the rising trend toward embedded business intelligence (BI).
Pervasive BI is gaining immense traction these days. The adoption rates of BI hover at approximately 30 percent in a typical business or enterprise environment.
Analytics will continue to grow due to the Internet of Things (IoT), creating large pools of data. Analytics will be deeply embedded and virtually invisible in the coming years. It will be the major highlight in the future as the volume of data generated by various embedded systems is rapidly increasing. Every application will need to be an analytic application and the value will be in the answers, not the data.
With the help of advanced analytics techniques such as natural language processing, data mining, text analytics, statistics, predictive analytics, and machine learning, organizations will utilize big data analytics and similar analytical tools to gain deeper insights and make significantly better business decisions.
Hadoop is an open source software platform that provides quick and reliable analysis of both structured and unstructured data. Given its capabilities to wield large data sets for scalable distributed computing, Hadoop is often associated with the phrase ‘big data’.
Hadoop has been the highlight for the past couple of years and is further projected to be the biggest attraction this year. Spread across Asia, Europe, and other parts of the world as an effective breakthrough, Hadoop’s ability doesn’t only lie in leveraging powerful analytics tools, but also in sifting through an avalanche of data and understanding big customer data.
Nevertheless, not many market predictions say that Hadoop will realize its huge potential through 2015. Some of the most cited reasons that are pinning down the growth of Hadoop are its ‘security concern issues’, ‘potential stability concerns’, and ‘not fit for small data’. While many research companies and analysts are still readjusting their thinking regarding the level of maturity Hadoop is expected to achieve, the need for an advanced platform during this hiatus is much needed to realize the big data dream.
Customer experience is growing beyond tracking details, turning into a more responsive environment based on data input. Moreover, many new applications today are trying to answer the ‘what more?’ question and creating new avenues to explore the factors driving deep analytics.
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