Brief Views on Trends in Data Analytics

trends-in-data-analytics
Unlike other areas, the rate of disruption in the data platforms and analytics is not so disruptive. Looks like it may take its own course of journey and for now the Presence of large existing vendors shall loom large. There are some evident trends which shall remain significant in affecting the growth of disruptions –

The trends shaping data platforms and analytics in the coming year –

  • Gradual enhancement of maturity index of predictive analytics;
  • Clients users interest in a self-service methodology for data readiness
  • Open source vendors and users are recognizing various parameters of data governance;

Of course, most of these trends have been visible for some time now and none of them has the potential to disrupt the market in 2016, but all of them can be viewed as potential dashboards for some real time and imminent   changes that are happening within enterprises as they look to take advantage of the opportunities for generating business intelligence.

The massive rate of data production has been long recognized as the key aspect of big data, and the accelerating consumption of Internet of Things (IoT) is driving more companies to consider how they can take advantage of data produced by digital devices like sensors and other data-generating machines. Also much significance is being given to the fact that how much of this shall be available for businesses to analyze, and the way in which they may like to analyze it.

Business may most likely want to have due enablement so as to be capable to act on data .Predictive analytics tools and techniques enable the business to not only react to this data, but proactively anticipate changing circumstances in order to remain competitive. Again, predictive analytics is in no way new, but it is being more widely adopted, to the extent that many enterprises now expect to take advantage of predictive analytics in their decision-making process. Customer who fail to consider the complexities of predictive analytics are likely to miss on opportunities to drive market adoption and awareness. Similarly Vendors who may ignore the need to provide a cleansed data access w.r.t extraction from named and unnamed sources ,  profiling the same appropriately and then  transforming as per the business rules applicable . Essentially the need of the different stakeholders needs w.r.t IT  , data analysts and business users when it comes to data management have to be recognized and executed so as to serve them the required chunks of data on a platter

There is quite some journey to go – predictive analytics is still within the firm understandings of the folks from statistics , data troubleshooting teams,   data scientists and other highly technical professionals  – but we increasingly see companies  absorbing the results of predictive analytics in applications and tools that are used by less skilled pyramid in their teams , though more business-savvy client teams

One thing to be kept in mind of a realistic implementation and absorption of resultant output is that not only right data but data teams should also be able to give rightful insights to the business decision-makers so that they can make optimum decisions. Experiences have amply demonstrated that tools like ETL: Extraction – Transformation and Loading are also key to the story as it prepares the data for consumption by advanced analytics.

Truly most of the open source vendors and users are recognizing the true potential about data governance and applications like Hadoop. The data preparation tools always have governance related capabilities that can be leveraged to sanitize the data stored in Hadoop.  The Hadoop vendors themselves are also getting more serious about data governance as Hadoop is increasingly being consumed as a data lake accessed by different users, applications and groups using different tools for different purposes. The customary caution here is to avoid a sudden rush into  building data lakes to avoid errors

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