Data Analytics: The Future
Updated: May 22, 2019
By Tom Goodwin
We have previously discussed several aspects of Data Analytics in a series of blogs, and now we conclude by looking at the future direction of Data Analytics, and the ways it is likely to evolve. Three notable areas that will influence its course will be addressed: The Internet of Things (IoT), the evolution of analytics architectures, and the new usages of the resulting information.
Impact of the Internet of Things
The Internet of Things (IoT) presents a challenge for organizations due its complex nature. Analysts must learn how to sense, interpret, and respond to data at rest and data in motion, data in real-time, and data scaled to various degrees. Data and analytics architectures must be configured to seamlessly ingest the tsunami of IoT data, combine it with data at rest for contextual insights, and act in real-time and at scale to maximize business value. The new generation of large data volume is expected to grow exponentially in the next few years, especially considering the number of handheld and Internet-connected devices being utilized.
Along with this generation of increased of data and large data networks with vast data stores - referred as advanced data networks - will become increasingly valuable for organizations. Additionally, the need for fresh data and real-time insights will increase in order to provide better business value. The days of nightly batch processing and waiting for hours or days for answers to critical business questions will disappear. The need for building real-time analytic pipelines to simultaneously ingest, analyze, visualize, and act on big data is of utmost importance.
Evolution of Analytics Architectures
Analytics architectures must evolve to address cloud and on-premise environments, data in motion and at rest, transactional and analytic databases, in-memory databases, spinning disk data, real-time and batch processing, artificial intelligence and business intelligence. All these functions need to co-exist and interoperate. With increased reliance on new tools for data analysis, coupled with an accelerated need to access external data stores, networks, and IoT devices, interconnectivity will be the key to building a cohesive data analytics machine for your business.
Moreover, data and analytics will have to move from control to a collaboration model. To establish a data-insight-driven culture, organizations will need to adopt analytic technologies that integrate analytics, data science, and machine learning. These technologies must also incorporate their analytic innovation with security, scalability, and availability.
Some of the emerging analytics tools that contain this advanced architecture include:
Behavioral Analytics: an effective tool to understand human behavior in controlled environments.
Graph Analytics: a set of analytic tools used to determine strength and direction of relationships between objects in a graph.
Prescriptive analytics: built-in business analytics software tools that use descriptive data to determine prescriptive actions.
Data transformation can be divided into the following steps, applicable based on the complexity of the transformation required.
Data discovery - apply profiling tools or manually-written profiling scripts to understand the structure and characteristics of the data for decisions on transformation needs.
Data mapping - define how fields are mapped, modified, joined, filtered, aggregated etc. to produce the final desired output.
Code generation - generate executable code to transform the data based on the desired and defined data mapping rules
Code execution - execute against the data to create the desired output; typically, this code is tightly integrated into the transformation tool.
Data review - ensure the output data meets the transformation requirements.
Usages of New Data Analytics
It is likely that real-time streaming insights will be the favored approach for the data analytics future. “Fast data” and “actionable data” will replace big data. To accomplish this, organizations will need to focus on asking the right questions and applying relevant use of the data.
Some of the new capabilities that are possible with evolved architectures include:
Hyper Personalization: Continuous analysis of data to refine user/customer persona and to provide services tailored to the needs determined by the analysis
Augmented Reality: Adding additional information to live video as a way of increasing situational awareness.
Journey Sciences: understanding the behavior of a customer or patient, or employee or a machine and applying analytics to determine their journey.
Do you have thoughts about the future direction of Data Analytics? Express your opinions and comments below, or on social media.
About the Author - Tom Goodwin is the Vice President of Marketing at HigherGround. His background in telecommunications and data networking has been augmented with work in data analytics and automated reporting prior to joining HigherGround. Click here for more information on Tom and the rest of the HigherGround team!
HigherGround, Inc. provides best-in-class, reliable data capture and interaction storage solutions that enable clients to easily retrieve critical information. Our interaction recording and incident reconstruction solutions transform data into actionable intelligence, allowing optimization of operations, enhanced performance, and cost reduction.