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Riding the IoT Wave Without Drowning in Data

Your journey starts with a foundation in business intelligence, data analytics and data science.
By Dave Wagstaff
Jul 09, 2017

For most companies, the adoption of the Internet of Things means opening the flood gates to a wave of sensor data that needs to be collected, processed, managed and acted upon. This is a daunting task, and done incorrectly it can lead to failed IoT implementations. We know that business intelligence (BI) tools help display information, but it is the underlying data analytics, founded on data science, that makes IoT data valuable and able to be acted upon to meet your business goals.

Let's start with understanding the difference between business intelligence and data analytics, and then we can get into how this feeds into actionable data:

Business Intelligence
This refers to the Ts: tools and talent. BI tools convert business data into meaningful visuals, reports and dashboards that provide context for analysts and other subject-matter experts to make business decisions. BI can range from very general contextual awareness to very specific use cases; but ultimately, BI is focused on situational awareness.

BI can be a business performance enabler, but we need to recognize that it is not focused on solving specific business questions. It's a relatively passive solution, limited to descriptive statistics, pivot table type queries and standard data visualizations.

Data Analytics is a much more sophisticated evolution of informing business decisions through applied math, data wrangling and subject-matter expertise. At the most fundamental level, data analytics is the application of data science toward solving business problems. Data analytics certainly provide an input into BI solutions. For example, one could build a data analytics solution that enriches business data (for example, through business rules and heuristics that leverage the subject-matter experts in my business) and generates a higher-quality data to feed into a BI dashboard.

Perhaps we are generating risk scores for specific components of a process, and feeding those risk scores into a BI tool so that analysts can check on the daily status of the system. Under the hood, this solution would include both a data analytics solution (generation of the risk scores) and a BI solution (descriptive dashboard of risk scores, perhaps depicting changes in last 24 hours).

The bottom line—BI tools are only as good as the data that is being presented, and this is where data science comes to the rescue.

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