Taking IoT and real-time analytics to the next level

Read on as we explain how you can push real-time analytics and IoT to a level that truly delivers on being a game-changer initiative for the business.

Sep 18, 2017 12:13:45 PM

Filipe Janela

Posted By Filipe Janela

Real time analytics coupled with IoT delivers a vast world of actionable data that is meaningful to your business. But how can you take advantage of that stream of information, making it work on your behalf to deliver consistent results?


Read on as we explain how you can push real-time analytics and IoT to a level that truly delivers on being a game-changer initiative for the business.



Organizations have been investing for quite some time on data analytics, using data warehousing and business intelligence tools. Undoubtfully, historical data analytics is an extremely important reporting and strategic decision-making tool, as it provides aggregated information on core corporate facts that exhibit medium to long term trends and, therefore, provide important baselines for corporate strategic guidance.



As mobile becomes ubiquitous and IoT becomes more and more prevalent in organizations, a new breed of data becomes available: data that represents the instant activity of entities that are related with core organization processes. This data is represented by real time events that occur when real world interactions are triggered and use or affect one or more organization processes.



A buyer posting on Facebook a comment on a just-bought product, an on-line purchase on a promotion, a missed delivery to a customer, a temperature variation on a delivery truck, a weather forecast update or a quality rejection on a production line are some examples of real time events that provide a data stream that has vital information but with a very limited life expectancy.



You can either capture a real-time event and act on it or it becomes obsolete. It certainly can be used for post-mortem analysis and root cause resolution, but if you don’t act on it as it happens, you lose the critical ability to prevent failure or maximize return. And missed opportunities are far more expensive than anything else you may do on your organization.








The recent digital transformation wave is trying to emphasize the relevance and value of capturing and processing this information. It’s more or less obvious that, if you can detect a specific condition in real-time and if you’re aware of it, then you can take preventive measures and escape a less than optimal situation. This has been the focus of most of the companies when applying real-time analytics and IoT: detect an exception condition or transient trend and signal it.



There is a significant level of technology required to process vast amounts of information in real-time, detecting exceptions or trends and using both structured and unstructured automated learning capabilities to provide better regression or prediction capabilities. Still, all of it does not actually deliver the value that it is supposed to deliver. Most of the time, companies are investing in specific, rigid real-time analytics solutions that are quite hard to change and to adapt to different business or operational conditions.



Additionally, companies are stopping on the edge of the information and they lack the ability to act on it on a fashion that mimics the real-time events that generated the initial information.

This means that, based on a signaled condition, companies are slow to react to that condition or have a very hard time in propagating that condition across all stakeholders engaged in handling that condition.



Taking real-time analytics further means that your organization must be prepared to react in real-time to the conditions signaled and that you’re able to change very quickly how you map the events that configure an actionable condition. When you’re trying to adjust your price to fluctuating demand, you’re probably better off. But when the physical world and your business management systems need to react with the same level of responsiveness that you’re getting from the analytics stream, then the challenge becomes huge.








Taking real-time analytics and IoT to the next level means that you must be able to link your operations and processes, including the activities that are mapped at the ERP, MES and CRM level to the real-time data stream. This will enable a real-time chain of activities and processes to be triggered, responding efficiently to a condition or trend that may disrupt your operation. A quality rejection on a production line should trigger a capacity planning review and a maintenance inspection. The capacity planning review may trigger a production order schedule review, which in turn may cascade into a supplier delivery plan reschedule and a customer delivery schedule change. All of these changes should be reflected in the ERP and both the customer and the suppliers should be notified of the schedule change. This is a very simple example, but I guess you got the picture. You have the information, you know what should happen, but still you’re constrained to the fact that you need to undergo a massive number of activities, most of them manual and not linked together, to actually prevent a disruptive situation. The likely outcome is that you know it’s going to hit you but you’re unable to prevent it.



That’s why you need to focus not only in setting up a flexible real-time analytics platform, that maps events and handles them using rules that you can effectively change in useful time, but also in building a process digitalization infrastructure that assures the integration of all stakeholders, the automation of all required activities and the integration between all systems required for the information to flow appropriately.



At Processware we’ve created a scalable, flexible, real-time analytics and process digitalization platform that allows you to become a responsive and efficient organization and we’ve deployed it in extremely demanding organizations with serious results. You can learn more about it here.

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