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So Much Data but No Information

Ian Duffield
Mar 8, 2019 3:50:08 PM

It is a frequent complaint of many in business, that the information they want is just not available. This may be true in some instances, but in many other cases the truth is that the information is just not accessible. This article discusses some of the truths about the nature of data and information in our companies.

First a few terms to understand. You might be familiar with the DIKW model shown below.

Data is discrete, objective facts or observations, which are unorganized and unprocessed and therefore have no meaning or value because of lack of context and interpretation.*

Information is organized or structured data, which has been processed in such a way that the information now has relevance for a specific purpose or context, and is therefore meaningful, valuable, useful and relevant.*

Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information.*

Wisdom is the ability to increase effectiveness. Wisdom adds value, which requires the mental function that we call judgment. The ethical and aesthetic values that this implies are inherent to the actor and are unique and personal.*

* Definitions from Wikipedia 

In businesses, we typically have masses of data; it’s collected everywhere. In the fresh food supply chain paradigm, it may be the number of cases of romaine packed each day, or the temperature sensor data from IoT devices in trucks, or the line item details on purchase orders or the amounts owed to suppliers. On its own, each piece of data is interesting but of limited value. For example, if the temperature sensors in a truck, driving cross-country with a load of leafy greens, showed that readings taken every 5 minutes were between 39 and 40 degrees, you might think that was great the temperature stayed constant during the entire trip. – except that it needed to be kept under 34 degrees and the truck might as well drive to the landfill.

Analyzing the data turns it into useful information and giving it context turns it into knowledge. Wisdom is knowing that it needs to go to the landfill!

In business we tend to collect data that is helpful to our particular roles. The invoice and payments team want information about all the money that is owed to suppliers, operations people want to know all about the trucks coming inbound, sales people want to know what’s available to sell. Sometimes this data is collected in systems that don’t talk to each other which makes cross-functional analysis virtually impossible. Another scenario might be that valuable analysis is completed on a block of data, but the analysis is not available. This apparently was discovered during the aftermath of 9/11, which led to the government restructuring the data systems of many of the country’s security agencies.

Going back to the IoT temperature monitoring example, getting a thousand readings of 34 degrees is OK, but not if you miss the 5 readings of 45 degrees that were buried in the details, when the truck stopped in the middle of the desert. We need systems that allow us to identify the exceptions from the volume, and that identify trends that show things going up or down, inside or outside exception criteria.

Data that is not available for analysis, in a separate system has no value, and the expense of capturing it was wasted.

All data has a cost. It costs money to capture it and to store it. If it is not useful to the business, it’s money that is wasted.

At Procurant we’re building solutions that work on the same platform; they allow users to access any data (subject to security protocols). We use modern tools like Casandra, because it is elastically scalable, while maintaining performance. Some of that data is in blockchain which means it’s immutable. Data can be downloaded into Excel to leverage that tool’s excellent analytical capabilities, or our proprietary tools can be used to perform the analysis. You can spot the exceptions and the trends. You can use your data, turning it into information and then knowledge and hopefully, wisdom.

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