Can a traditional analytics approach empower business operations in the IoT?

advanced data analyticsThe Internet of Things (IoT) is about digitization of physical assets encompassing sensors, devices, machines, gateways and the network. This creates possibilities of significant value creation and revenue generating business models through data democratization across IoT networks.

However, digitization is not simple. Connecting devices or things to the network and to the cloud will not only drive complexity but also scale at unprecedented levels.

Customers can only realize the value of such complex networks through the application of advanced data analytics to extract timely insights and actions from ‘dark’ data.

Let’s look at how the traditional analytics approach might address these complex IoT scenarios.

Data Management and Analytics

Traditional data warehouses and data management solutions were based on schema-on-write, where the data model and analytic frameworks are designed before any data is loaded.

With the advancements of Hadoop-based technologies, managing semi-structured and unstructured data such as sensor data, machine data, social data, content, and images is now possible. However, the primary focus of the analytics is centered around descriptive and diagnostic analytics with focus on hindsight type use cases – why did it happen, what is happening.


data types

Such analytics are often developed independently and have multiple connection points to the various sources of data.

The structured, semi-structured, and unstructured data that is often stored in different data warehouses and logical locations is connected independently and requires multiple connectors to consolidate all the relevant information.

This is time and cost prohibitive and makes it difficult to build IoT applications quickly and meet business imperatives for timely action. It also significantly delays time to value and is not scalable from an economic point of view.


In the traditional model, there are typically three distinct roles or groups involved – business analysts, Information Technology (IT) group and business operations. Analytics is rarely considered a strategic asset for the organizations, and often there is not a clearly defined business owner.

business roles

Sometimes data scientists or analytics professionals are primary and focus on technical capabilities that solve narrow problems within their domain. Often IT is a driver and will focus on applications and data that is central to their job. Business Operations is often focused on the Operational Technology (OT) part of IoT.

What is needed for IoT?

The explosion of data in all forms in IoT requires a more robust and broader lens in order to enable smarter timely actions and better outcomes.

In IoT, business operations becomes the key driver of applications & projects. They need better analytics tools for rapid development and innovation to drive timely value out of IoT networks.

The first step is to remove the silos such that analytics are used across a broad spectrum of valuable data. The second step is to provide analytics capabilities that will address hindsight, insight and foresight use cases by unifying the analytics layer to include historical analytics (descriptive & diagnostic), real-time streaming analytics, predictive analytics, and prescriptive analytics.

The payoff is that end-users will now be able to spend more time on insights and business outcomes that matter most and avoid the time and distraction of creating or managing a complex infrastructure.

In the upcoming blog you will hear more on how to make analytics a key strategic asset in IoT operations.

Interested to Learn About Advanced Analytics for IoT?

IoT Analytics White Paper DownloadAdvanced Analytics for IoT helps you drive better business outcomes faster. Find out how you can overcome the limitations of traditional approaches to analytics for IoT with Advanced Analytics designed for the IoT era.

Click here to get the Advanced Analytics for IoT White Paper

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