Systems supporting data analysis generated from IoT and cloud environments will continue to increase
Published2017.01.09 17:30Tableau Software (hereinafter "Tableau") has released a report on the "Top 10 Big Data Trends for 2017," which forecasts major analysis and solution trends in the big data field for 2017 based on an analysis of the big data industry this year.
Through this report, Tableau forecasts that systems supporting analysis of all types of data formats generated from Internet of Things (IoT) and cloud environments will continue to increase. In particular, as enterprises increasingly adopt machine learning and smart systems, it analyzed that demand for self-service tools that enhance end-user accessibility to this data will increase.
Additionally, it is anticipated that self-service tools will emerge that can reduce the time and complexity required for data preparation, enabling business users to process various data types and formats on Hadoop.
▲Tableau Software homepage.
According to the report, business users are identifying fast interactive SQL on Hadoop as a key element not only for faster and iterative KPI dashboard analysis but also for exploratory analysis. As a result, options such as databases for improving Hadoop speed, query acceleration technologies, SQL, and OLAP will continue to expand in the future. However, the data currently required for exploratory analysis in enterprises is dispersed across diverse environments ranging from Hadoop to record systems and cloud warehouses. Consequently, BI solutions isolated to Hadoop alone are predicted to evolve into platforms that are not restricted by data or sources.
The biggest reason companies invest in big data is the diversity of big data
Enterprises cite the diversity of big data as the primary reason for investing in big data, and this trend will be further reinforced as enterprises focus on analyzing various types of big data. Additionally, it is predicted that development of models and applications that process petabyte (PB)-level data for interactive applications or real-time stream processing will increase.
Accordingly, the capability of analytical platforms that provide live connections to heterogeneous data sources becomes important, and industry attention will focus on self-service analytics technologies that enhance accessibility to large volumes of real-time data. Moreover, increasingly large amounts of IoT device data are being provided to cloud services and stored in various database systems. Self-service analytics tools that support exploration and visualization of diverse data sources will increase enterprises' investment opportunities in the IoT field.
Agile self-service data preparation tools are improving business users' accessibility to Hadoop data. These tools are lowering the barriers to entry for enterprises that have adopted Hadoop late or are latecomers, and their use is expected to continue for easy and fast data exploration in the future.
Hadoop is establishing itself as a core component of the enterprise IT environment, and enterprises' investments in security and governance related to enterprise systems are expected to increase in the future while adoption barriers continuously diminish. Additionally, due to increasing demand for self-service exploration among enterprises seeking to find and understand data with analytical value, self-service analytics will expand further in the future.
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