The Internet of Things (IoT) is viewed as a huge opportunity by industry. Many believe that with the data generated from IoT devices, tailored, improved products and services can be delivered to end customers in many industries. Businesses can improve revenue, save costs, energy, and fuel as well as improve productivity. To realize these benefits, IoT data needs to be properly harnessed, which is difficult, mainly because it is unstructured and complex.
An integrated analytics platform has an important role in delivering the right analytics from a set of unstructured data. To deliver meaningful analytics, you need a combination of tools in one place that can store, query and process complex data. An integrated analytics platform does just that.
What is an Integrated Analytics Platform?
An integrated analytics platform is a unified solution that provides meaningful analytics out of any data, even unstructured and complex data. The traditional relational database management system (RDBMS) is unable to provide contextual or tailored analytics out of stored data. Large companies depend a lot on meaningful and actionable data to drive their business. The integrated analytics platform integrates different tools such as execution engine, database management system (DBMS), data mining capabilities and capabilities to obtain and prepare data that is not in the database. And the platform is updated to handle complex and unstructured data, like big data. There is no need for any other tool to process data. This platform can be delivered to end customers as an application or on the basis of the software-as-a-service (SaaS) model. Companies can subscribe for a period and then renew (or not). In a report, Merv Adrian and Colin White of BeyeNETWORK defined the analytic platform as “an integrated and complete solution for managing data and generating business analytics from that data, which offers price/performance and time to value superior to non-specialized offerings. This solution may be delivered as an appliance (software-only, packaged hardware and software, virtual image), and/or in a cloud-based software-as-a-service (SaaS) form.”
What Does IoT Data Look Like?
IoT data can be extremely complex and is definitely unstructured. Think of the millions of devices, each with an IP address, talking to one another. Millions of servers are collecting the data these devices are sending. Let's look at some examples. Think of smartwatches sending health data such as pulse and blood pressure, or devices fitted in electronic appliances such as air conditioners or refrigerators that store data such as temperature and food habits. The total amount of data is huge, and it is multiplying. The data received is complex because of the different configurations of devices and sensors, parsing done midway between sensors and servers, technologies used to capture data, file formats and several other factors. So, data volume and format make IoT data analytics an extremely challenging task.
In a survey, it was found that of the total data generated, 44.6% is XML data, 23.8% is unstructured file data, 23% is weblogs and the remaining comprises package application data, rich media data and other file types.
An Integrated Analytics Platform + IoT Data
It is clear that volume, complexity and unstructured format make IoT data analytics a challenging proposition. What compounds the challenge is the requirement that the analytics need to be delivered quickly. So, you need a solution that can not only deliver meaningful IoT analytics, but also deliver them quickly. This is something that cannot be addressed by isolated tools and technologies. Therefore, you need a unified solution. As stated earlier, an integrated analytics platform combines a database management system, data collection and storage system, and processing capabilities in one place. Here are some reasons why an integrated analytics platform is your best bet.
An analytical platform is designed to process new data types such as IoT data. It can retrieve data by columns and encode data to ensure superior compression. Some platforms use “smart storage” to free the analytical processor from heavy analytical lifting, which improves performance and speed. The platforms can bind together several commodity processors that have large storage spaces. This helps to scale operations.
Analytics platforms are capable of doing advanced analytics on data. For example, regular analytics tools will struggle to do a simple comparison of profitability of the past week of the top ten traders in New York City because of the gigantic volume of data it needs to process within a limited time. Integrated analytics can do that and more. It can build predictive data models and then compare the data model against real-time data, do geographic visualizations and more.
Performance and Scalability
Given the enormity of the IoT data volume, it is likely that there will be several people engaged in data analysis. This means more resources are required. Modern analytics platforms are capable of engaging multiple commodity servers and increasing the bandwidth when required. Since the platforms use inexpensive hardware, the cost is not high.
Traditional data center setups and analysis technologies are an expensive proposition, more so when you try to deliver IoT analytics with these resources. You have to invest more in the setup as the data volume and the analysis requirements grow. Analytics platforms can cut these costs significantly. License costs of open-source software are significantly lower. These platforms use cheaper commodity processors so hardware is easy to upgrade. Since appliances are pre-integrated and pre-configured, it reduces setup costs.
Facebook is a prominent case study of how an integrated analytics platform made a difference. Facebook and Google provided limited and standardized analytics. Deeper analysis, though possible, was time consuming and could be costly and ineffective. The solution was an integrated analytics system that combined Facebook analytics, Google Analytics and custom analytics with the ability to slice and dice data in any way required. This created a versatile, effective solution. As a result, the analysis time was reduced by 90%, budgets for test campaigns and minimum sample sizes were reduced by 75%, conversion rates increased by 100% and the average campaign pausing time came down to one day from four days. The table below shows how isolated metrics from Facebook and Google were integrated by the analytics platform.
IoT data presents a strong case for integrated analytics platforms. It will be extremely difficult for businesses that depend a lot on data to persist with traditional analytics methods and technologies because of relative inefficiencies and cost issues. However, it needs to be noted that moving to an integrated analytics platform also reflects a change in mindset for many businesses and change is usually slow. Integrated analytics platforms are still being viewed with a lot of caution and a lot of debate is going on about the returns on investment. This is natural because the modern platforms are at a nascent stage and it will take some time for these platforms to gain wider acceptance. But soon, this promises to be the dominant data analytics platform.