5 Things to Consider When Selecting IoT Analytics
In a recent report, Gartner estimates that 5.5 million new things are getting connected every day during 2016, and will reach 20.8 billion by 2020. The sheer amount of data that these devices will generate is simply mind-boggling – but it is data that businesses need to process in order to make insightful decisions that will give them an edge over their competition.
As Gartner mentions, “New analytic tools and algorithms are needed now, but as data volumes increase through 2021, the needs of the IoT may diverge further from traditional analytics.” Enterprises need a scalable, robust, and secure Analytics solution to keep up with the vast amounts of data that will be processed in the IoT age. Whether you are early in your IoT analytics journey, or looking to expand on the benefits and resulting business value, consider these five factors to help you decide on the right IoT Analytics solution for your business.
1. The Nature of IoT Data
IoT data, by nature, is “more dynamic, heterogeneous, imperfect, unprocessed, unstructured, and real-time than typical business data.” Businesses struggle to understand the complexity of mining machine data from connected devices . Combining multiple data formats and structures from different data sources, including unstructured log files, is a huge challenge when analyzing and generating value from machine data. The IoT Analytics solution that you should invest in should have established techniques and processes for ingesting, parsing, and transforming heterogeneous and unstructured IoT data from geographically dispersed devices.
One of the factors that businesses consider when selecting a solution is the time required to launch it and realize business value. Building and deploying an IoT Analytics solution can be difficult especially if it entails device connectivity, machine data parsing and translation, and integration with your legacy systems. Furthermore, machine data is messy, complex and needs enormous amount of time (over 60-70% in a typical analytics initiative) to transform and prep for meaningful analytics. On top of this, the time it takes to develop applications that utilize the data and insights from analytics can impact time-to-value. Choose a solution that can be easily implemented and integrated, and significantly reduce data transformation and application development time has a huge impact on time-to-market, allowing companies to gain competitive advantage in a matter of only days or weeks using customer intelligence gleaned from your machine data.
3. Insights Generation
This complements point number one. Insights generation is the primary purpose of Analytics. The solution should be able to shore up data analysis and presentation and provide effective mechanisms for data preparation, data visualization, and reports generation. The IoT Analytics solution should also allow for predictive analytics as enterprises should be more proactive in dealing with issues that can potentially impact their business. The key will be for organizations to operationalize these analytically driven insights into their everyday business activities. This is how real time insights turn into competitive real time action.
4. Scalability and Flexibility and Maintainability
According to IoT Data Analytics Report 2016, a joint report by Camrosh and Ideya Ltd., scalability, flexibility, and maintainability influence purchase decision. IoT data by nature is heterogeneous and ever-changing, and having the ability to scale and maintain your analytics solution over time is critical. Invest in an IoT Analytics solution that can enable you to adjust easily to changes in data volume and variety when removing or introducing new devices, operations, functions, and services. By doing so, you can be more agile in generating value from your machine data and making informed decisions.
5. Build vs Buy
Organizations that want to search and mine the information contained in machine data have two choices: obtaining a purpose-built analytics system, or building one in house. While the latter option may sound appealing at first, in-house solutions often face a series of challenges, and require a variety of committed resources for an extended period of time. It is inherently time-consuming and risky if not planned properly with appropriate resources needed to not just design and implement, but also to maintain and manage its life cycle on a continuous basis.
PTC and Glassbeam have partnered to address an industry pain point – the ability to transform unstructured machine data to drive complex machine learning and real-time advanced analytics. Glassbeam has integrated its core set of adaptors and data transformation engine with the ThingWorx IoT Platform and ThingWorx Analytics – thereby providing a holistic, end-to-end application to deliver everything from connectivity to data transformation, visualization, and predictive analytics. This combined solution is already being deployed at joint customers in the medical devices, smart grid, storage and data center industries to dramatically improve support and engineering operations.