Analytic Applications Focused on Connected Machines, Sensors, and Personal Devices Can Operationalize Insights and Monetize Data
Big data initiatives in most organizations have been largely experimental where large data lakes have been created for specialized, isolated analysis. Early production adoption of big data technologies was greatest in the large-scale e-business communities, such as Facebook and LinkedIn, and in the consumer products, entertainment industry, and banking industries. Much of the focus of the early adoption was clickstream analysis, social media analysis, and large-scale customer data archiving.
The Internet of Things (IoT) is bringing new validity to big data in the form of operational intelligence in many industries, as the amount of data generated by machines begins to exceed the data generated by humans. IHS forecasts that the IoT market will grow from 15.4 billion connected devices in 2015 to 30.7 billion in 2020 to 75.4 billion in 20251 – in addition to all the smartphones, computers, and tablets that are already connected to the Internet.
The good news is that consumerization of connected devices is driving a heightened awareness for their potential. Smart home products from Amazon, Google, Apple, Samsung, Philips, and others are bringing the value of IoT into our personal lives. Consumer adoption of technology, as we have seen previously with the Internet and mobile technologies often drives its adoption and viability in business. Most importantly, the Internet of Things is shifting the emphasis of big data from technology to real business value.
David Menninger, senior vice president and research director at Ventana Research, and a featured analyst in this issue, defines IoT as “the extension of digital connectivity to devices and sensors in homes, businesses, vehicles, and potentially almost anywhere.” This would include trucks, autos, appliances, shop floor equipment, electric grids, weather buoys, thermostats, and many other types of electrical and mechanical devices. Companies rely on these tiny instruments to monitor usage patterns, determine maintenance schedules, and enhance the quality of industrial processes.
Finding patterns in IoT data is important, but commercial IoT initiatives – and big data projects in general – also involve importing, profiling, contextualizing, integrating, and enriching the data. Forrester reports that 80 percent of the time spent in big data projects is devoted to preparing the data for analysis, 2 and Ventana says that IoT data sources are incomplete or inadequate in two out of five organizations.3
Information Builders is here to help. Our software solutions for big data do more than merely analyze, visualize, and display your data.
They also ensure its quality, as well as provide enrichment and context, as a prelude to deriving actionable intelligence. In addition, we make it easy to combine many types of data – from sensors, bots, and social media streams as well as from traditional enterprise sources – so you can turn ordinary intelligence into extraordinary insights. Together, our WebFOCUS and iWay products create the best platform to address the growing number of use cases for IoT initiatives.
For example, Cascades Tissue Group, which makes packaging and tissue products composed of recycled fibers, uses machine sensors to monitor temperature, humidity, and other operating conditions that affect the quality of its finished products. Information Builders’ WebFOCUS BI and analytics platform helps managers gain insight into the safety and status of Cascades’ delivery fleet.
Cascades also uses our iWay tools to integrate IoT data with enterprise data from an SAP ERP system and legacy business applications. This real-time exchange allows maintenance personnel to keep shop floor equipment running optimally, as well as monitor the quality of finished goods on the production line, improving forecasting and removing waste from the supply chain. In a related IoT project, the company’s trailers are equipped with door sensors, fuel sensors, and other monitoring devices that continually transmit data from drivers and vehicles. Fleet managers use WebFOCUS dashboards to supervise the fleet, predict upcoming repair needs, and provide insight into safety issues.
Spreading Insight with InfoApps™
Some IoT use cases require data sharing across systems, as is becoming prevalent in aircraft, automobiles, manufacturing process control, and home automation. In other cases, deriving maximum value means making the data and insights available to people – often many people. This is the type of targeted operational data that employees need as well, from call center agents and sales reps to bookkeepers and customer service technicians – not to mention customers, suppliers, and business partners. Line managers may want analyze the manufacturing process data, Location data for logistics may be shared with external supply chain partners and online advertisers, and CPG may deliver this data directly to customers.
According to a KPMG analytics survey, fewer than 20 percent of organizations are very satisfied with the value of their analytics tools.4 That’s partly because it’s so difficult to quantify the value of the BI tools and dashboards that people use in the back office.
Until you can bring business intelligence (BI) to operational workers, you won’t easily see a dramatic impact on costs, revenue, or efficiency. That’s why David Menninger recommends not only extending reports and dashboards to operational workers, but also embedding operational intelligence apps throughout the organization. We need to bring decision-making capabilities to customer support representatives, account executives, field technicians, delivery personnel, and many other types of workers in the context of their day-to-day activities. All these people make operational decisions, but they don’t necessarily want BI “tools.” They want simple, intuitive apps – what we call InfoApps™ – that allow them to perform very specific functions with their data.
Information Builders encourages a broad view of self-service that accommodates all potential users. Our analytics platform gives developers and power users the tools they need for intensive datadiscovery tasks, but it also helps them create and distribute InfoApps, so business users can answer questions with a few clicks.
Big Data Integrity and Integration
According to Gartner, 40 percent of all business initiatives fail to achieve their targeted benefits due to poor data quality.5 Generally there aren’t many data quality problems with machine-generated data, but there certainly are with other data sources – and heavy-duty integration needs as well.
When an event occurs in business, politics, or entertainment, positive or negative, people are likely to turn to social media and comment on it. Social media was one of the first sources of data that organizations targeted for big data analytics. There are many standalone tools for analyzing social data, but the real value is derived when you can correlate this data with other sources of information. For example, if a consumer entertainment company has a problem with one of its products, it should not only look at what people are saying, but also how that event impacted customer support, customer defections, and related revenue over that time period. Data tells a story, but more data tells a more complete story of what is happening.
Monetizing Your Data
Clickstream data is often the starting point for big data monetization projects. For example, Yellow Pages customers use a WebFOCUS dashboard to measure key performance indicators (KPIs) related to their online advertising campaigns, helping them compute ROI and track revenue on the best-performing ads. These analyses involve massive amounts of information – approximately 50 billion rows of data representing 275,000 advertisers – all of which Yellow Pages can easily handle using WebFOCUS, Hadoop, and an Infobright’s analytic columnar database platform.
Information Builders developed iWay Big Data Integrator to apply our rich iWay legacy to these big data projects. You can even put big data integration processes inside of Hadoop, which allows you to apply parallel processing capabilities to IoT and other big data integration projects. You can also run predictive models and scoring algorithms against IoT data with WebFOCUS RStat, or run iWay Service Manager to integrate and enrich large volumes of data.
The amount of data generated by second-by-second readings of temperature, vibration pressure, and other industrial variables can add up to many terabytes per day. The airline and automotive industry understand this well, as the new products they deliver are generating massive amounts of data for onboard use across multiple systems. Aircraft and vehicle operators often use the sensor data generated by parts and equipment for preventative maintenance analysis and fleet scheduling. This data is of value to the manufacturer, suppliers, and the customer.
Location data is also creating a growing opportunity for IoT data. Retailers analyze shoppers’ locations to analyze campaign effectiveness. Logistics organizations analyze critical inventory locations and provide the data directly to manufacturers and retailers. The transportation industry is placing beacons and readers on vehicles, along rails, and along highways to monitor everything from performance to emissions. Government is also leveraging IoT to automate tolls, enforce safety, and address other concerns for the public.
Consider your personal involvement. Your smartphone is constantly generating location data and providing it to the app vendors to whom you have granted permission. When you tell Alexa to turn on your lights, useful data is generated to your energy company and perhaps Amazon. I now have over 40 connected devices in my home. You might be surprised how many you personally own when you take inventory.
From studying jet engine performance to monitoring the biometrics of the human body, IoT data allows businesses to detect issues, spot trends, make predictions – and ultimately take corrective action. Once you have the right systems in place to integrate, validate, and analyze IoT data, there is no end to the potential benefits. Those benefits become real when more people interact with the data. IoT has given big data new purpose. When it comes to big data or any data, it’s not about the data or technology, it’s about what you do with it.
1“IoT Platforms: Enabling the Internet of Things,” IHS Markit, March 2016.
2 Data Preparation Tools Accelerate Analytics,” Forrester Research, February 2015.
3 “Internet of Things Requires Operational Intelligence,” Ventana Research, April 2016.
4 “Widespread Adoption of Data and Analytics Among Businesses, Yet Many Challenged to Derive Value From the Data,” KPMG, July 2015.
5 Friedman, Ted; Smith, Michael. “Measuring the Business Value of Data Quality”, Gartner, January 2013.