Leveraging Data Analytics and Internet of Things in Your Digital Transformation

Increasingly, connected systems and devices are generating data that produce insights for improving business processes and consumer experiences. IDC predicts that by the year 2020 there will be 44 zettabytes (that’s 44 x 10) of information, spawned partly by consumer activity but mostly from a myriad of growing devices. Introducing… the Internet of Things — an ecosystem of sensors, machines, and other everyday items that are producing data and, in many cases, interacting with each other. With more data come more opportunities. Big data powers new possibilities for organizations — to gather, analyze and act in near-real-time. Leveraging insights generated from all their data sources, organizations can monetize new opportunities, enhance customer experiences and optimize key business processes.

The benefits of data analytics are obvious. Transforming data into actionable insights to achieve business outcomes faster and more efficiently is becoming a differentiator. According to Bain & Company, organizations with advanced analytic capabilities are two times more likely to be in the top quartile of financial performers for their industry.[1] However, creating insights from data is only half of the battle, as now those insights must be operationalized back into the business in order for the impact to be made, and digital transformation realized.

Integrating data analytics into your digital transformation will be more effective when the challenges faced by most businesses are addressed early. There are three categories of challenges you need to focus on: operational, technological and organizational.

Before you embark on your digital transformation through data analytics, like any other project, or like a family road trip, you must determine what will be your end-goal — which use case or business outcome do you want to address. This sets your target and keeps your team focused while you plan the attack using technology and internal resources. Those resources include determining which data sets are more appropriate to work with. Understanding which data sets you need will help your organization to uncover where most of the data exists — often in disparate storage locations — and determine how to address consolidating, cleansing and wrangling them when needed.

Some businesses find choosing the first use case to be challenging. Here are the top three common use cases for data analytics, by vertical industry, according to IDC:

IDC also outlines common business outcomes across these same verticals: enable IT optimization; improve operational efficiencies, fraud, and risk management; improve business processes and operations; improve customer service; and support and implement regulatory compliance.

There are 3Vs that define properties or dimensions of big data: Volume, Variety and Velocity. Understanding the use case will answer what types and volume of data would be required for accurate analytics, and how rapidly that data will arrive to be processed. With use cases defined, businesses can use technology to determine the path to achieve the target and set up guide rails for the trip.

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