Silos are one of the primary reasons organizations are not deriving value from data. The effort for business users to obtain one set of data in a usable format and then manually correlate it with other data in other formats is extreme…

With necessity being the mother of invention, the necessity of breaking down data silos and facilitating real-time or near-real-time decision-making across an organization has given rise to a new category of software called situational intelligence. Situational intelligence combines business intelligence techniques with operational and location intelligence to give businesses a 360-degree operational view. Key to the success of situational intelligence implementations is the application of advanced analytics to large volumes of disparate data and the rich visualization of that data in a single dashboard.

The above quote is from an interview Paul Hofmann did with the Enterprisers Project.  He goes on to say in the second part of the interview:

Situational intelligence approaches the problem as follows: Data is analyzed as it is streaming in, significantly shortening the time from sensor to decision. In addition, only the data that users need to be concerned with is presented to them. If a rapid response to certain conditions is required, there’s no need to burden users with data where those conditions are not met.

IT should realize that data is extremely important to the success of analytics initiatives. Every piece of data history that is abandoned on the roadside could have implications down the road for an organization’s ability to understand how and why assets and resources behave in certain ways, and how they might behave in the future.

He then goes on to give this example of situational intelligence in action

users might want to understand which assets (such as machines or pieces of equipment) are currently operating under stress, access the details for those assets, determine whether temperature or other environmental conditions should be considered, assess the impact on service delivery if one of those assets fails, pull up the cost and revenue implications of such a failure, and take action to prevent that scenario from taking place.

To pull this off in the past would have required multiple people accessing multiple systems and manually correlating the data. With situational intelligence, it all takes place in one application, and users in different roles all have access to consistent information as they collaborate to address issues…

If a user is trying to understand which of millions of assets are at risk of failure for example, it doesn’t make sense to show all the assets on the screen and let the user sort through them. Instead, analytics can identify and prioritize the most critical assets, helping the user focus on the task at hand. With an approach like this, it is possible to derive immediate value from data, justifying further investments in big data and analytics infrastructure.

I would highly recommend reading the two part article if you are looking for more information on Data Analytics, IoT and Big Data

It’s not just about how, when and where data is captured and stored. It’s about how, when and where value is derived from that data.

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