Data Analysis Trends: Big Data Gets Closer and Smaller
May 16th, 2016
Along with the social networking and mobile / cloud technologies, data analysis and related tools and solutions are among the main trends of the coming years. Moreover, data analysis can be called the cornerstone of the digital age that determines the development of several industry branches.
Even back in 2015, data analysis has transformed from test development into a huge industry that affects not just the IT industry, but also many other areas of activities and business trends.
We offer our readers and clients our own vision of the data analysis industry future for the next few years. The topic is so vast and comprehensive that we cannot even limit ourselves to one publication, so stay tuned for more posts about the data analysis to read and share your feedback.
Let’s begin the data analysis trends’ study from learning how big data will be developing. In point of fact, “big data” and the emergence of algorithms used to work with that big data laid the foundation for the entire data analysis.
Here’s our forecast about the future progress of the big data.
Big Data Will Be More Structured and the Results of Its Analysis Will Be More Qualitative
Big data will become smaller (we do realize that this phrase sounds rather paradoxical.) The fact is that the improvement of big data analysis algorithms, certain progress in that analysis and development of related areas will lead to the big data arrays to be less quantitative but more qualitative. In other words, companies will aggregate, or rather, store and process fewer data, but learn to do it better and the results of such analysis will be more effective and useful.
This change in working with big data views will be a result of natural processes. More and more companies realize that most of the data they collect aren't used, but simply takes storage space. Therefore, they will pay more attention to careful analysis. This will result in a reduction of data amounts that is collected, stored, and used.
This observed shift from quantity to quality of data can be called the “transition from big data to smart data.” It is only natural - just having a large amount of data is not enough. The key issue is data receipt regularity and its standardization as well as the extent to which they can be easily retrieved and analyzed. Data collection and usage make sense only when it is used for the decision-making and problem-solving processes optimization and automatization.
It Will Be Easier to Work and Analyze Big Data
The need to simplify the work with big data has been on the market for a long time. New ways are being developed for this on all levels: technical, consumer, business and so on. In 2016, significant progress in this direction will be achieved. Big data analysis will become much easier. This is possible thanks to many large companies and organizations that provide their data for analysis. For example, the European Organization for Nuclear Research (CERN) made available to public 300 terabytes of open data obtained during the Large Hadron Collider operation and proposed everyone wishing to analyze the data to do it - and they even provided tools for this purpose called CernVM.
This way, big data can be assessed by IT specialists as well as business analytics, security specialists, marketing experts, and everyone else.
Eventually, Big Data Will Become an Instrument Used by All
Big data are evolving from a “holy cow” available only to the select few (for example, data analytics) to a tool available to “mere mortals.” Even today, upon waking up, people can get comprehensive information about their sleep, research their spending trends, or get a detailed statistics of their favorite sports team's’ games.
Data literally surrounds us and people are becoming more and more aware of ways to use this data. This doesn’t mean everyone will become a data analysis specialist, but it does mean that more and more people will use data analysis in various spheres. The understanding of this technology will improve as well as its application.
As a result, the popularity of self-service data analysis tools will increase. Already there are many new solutions that are oriented on usual users’ needs, among them such instruments as Alteryx, Trifacta, Paxata, and Lavastorm.
Real-Time Data Analysis Will Be Used More and More
Real-time data analysis was a popular trend even in 2015. The thing is that using this type of analysis for some types of businesses helped them to stay ahead of their competitors. According to the latest announcements of Amazon, Cloudera, Confluent, Microsoft, MapR, SAP, and many other big market players, they are very interested in getting this kind of data analysis results.
Previously, the main consumers of data analysis were online advertising, marketing, and retail (including online trade). The companies used the analysis results to show ads, launch a new campaign, or change current proposals.
Real-time data collection and analysis is also helpful in fraud detection, risk analysis, the work of IoT-devices, security threats detection, and other scenarios where time is of the essence.
Big Data Will Extend Advanced Analytics Usage
Over 70% of companies actively use analytics in their day-to-day decision making. Next step is to use advanced analytics that will help to forecast future events and results.
More and more often companies collect historical data in order to concentrate on future trends forecasting. The demand for such analysis will grow exponentially.
Predictive analytics enables organizations to minimize the risks, identify frauds, and roll out new income-generating opportunities. For example, retail companies use behavioral and situational analysis to gain insight into the consumers’ behavior, their buying habits, and they use this knowledge to create client-oriented marketing strategies.
What would you like to find out about data analysis? Share in the comments!
