I recently Google searched “the value of data”. I was not at all surprised when it returned about 1,550,000,000 results. That got me thinking. Most organizations consider their data to be an asset, but how do you really quantify its value? How do you determine if the value in the data is worth the investment in data governance, analytics and other data initiatives? What is the return on those investments? As I dug deeper into this I started thinking about the relationship between data and knowledge. After all, the ultimate goal is to provide the information and knowledge required to make timely and appropriate decisions.
The Evolution of Analytics
Before I jump into the relationships between data and knowledge, I want to talk about the evolution of analytics. Over the past few years we have moved from descriptive analytics and business intelligence, to big data and real time predictive and prescriptive analytics.
Business intelligence provides an analytical model that allows users to analyze trends in master and transactional data to support the decision making process. The BI datasets are smaller and less volatile than big data, and contain primarily structured data from disparate enterprise systems. The static nature of this data (compared to big data) often limits the BI analytic to historic trends and current conditions. Typically, the descriptive analytics available from business intelligence must be combined with institutional and industry knowledge to drive business predictions.
Enter big data. As computing power and data storage technology improved, companies began to amass different types of data. Companies like Google and Amazon began to analyze massive amounts of data from the internet and other publically available sources. This shift provided forward looking companies like Facebook and Uber the opportunity to leverage huge sets of data for real time analytics, giving them an enormous competitive advantage. With big data, companies can now analyze both structured and unstructured data from within the enterprise and across the globe. This provides the opportunity to derive not only historic trends and current conditions, but real time insights into possible outcomes and recommended courses of action.
Real Time Predictive Analytics
Analytics has advanced beyond the purely descriptive method of analyzing past and current data. We can now combine our descriptive analytics with big data and machine learning to provide real time predictive analytics. Predictive analytics does not predict the future, but if the analytics and the datasets they rely on are factual it predicts what the likely outcome will be.
What’s next? We are now entering the era of prescriptive analytics – the ability to do predictive analytics in real time and have that analysis prescribe what measures should be taken to reach the desired outcome. Prescriptive analytics can tell us the answers to the questions we didn’t even know to ask.
The point here is not to define or debate the pros and cons of each analytical model, but to point out that each analytical model has one thing in common; the goal of analytics is to provide fact based information and knowledge that can be used to make decisions.
The Relationship between Data & Knowledge
Ok - back to the relationship between data and knowledge.
Data is “facts and statistics collected together for reference or analysis”. We combine and analyze data to obtain information. For example, we can analyze the sales data for a specific product to obtain information on the average retail price of that product.
Knowledge is gathered by using different types of information and data to develop an awareness or understanding. We can combine our analysis of the retail price of a product with the cost to manufacture the product, regional labor cost, shipping rates, and other information. This provides knowledge. It provides an awareness of future profitability, and knowledge of what conditions should be changed to reach the desired result.
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Factors that Drive the Value of Data
Knowledge is essential to decision making, and the knowledge derived from data can be incredibly valuable, but the potential for knowledge from data does not in itself define the data’s value. So, if the potential for knowledge does not directly drive the value of data, what does? To answer that question let’s take a look at a few of the factors that contribute to the reliability of data, and our ability to derive accurate, timely and actionable knowledge.
- The accuracy of the data. We all know that if the data is not accurate it is of little value, and basing decisions on inaccurate data is often worse than making no decision at all.
- The suitability of the data. We must be sure the data we are considering is suitable, and it will enable us to obtain the information and knowledge we are looking for.
- The completeness of the data. Obviously, incomplete data may not be useful, but we also need to consider if the combination of our data will provide a complete set of information. If not, we risk making decisions without seeing the full picture.
- The facts about the data. In addition to the definition of the data, how the data is used across the enterprise, and the relationships between the different data, the “facts about the data” must include a complete understanding of every piece of data that is relevant to the information and knowledge we are after.
Understanding the “Facts about the Data”
The challenge we face is that we cannot effectively address the accuracy, suitability, completeness and overall value of the data until we fully understand the “facts about the data”. This starts with our understanding of what we are looking to achieve, and the information and knowledge required to reach that goal. From there we must identify all of the data required to derive that information, and completely understand the definition of that data, its relationships, its use, and how it is maintained through its lifecycle.
When big data comes into the picture the “facts about the data” become even more convoluted – and more important to understand. The data we gather from outside sources is defined and stored to support a specific purpose within the source systems, and those purposes often differ from our intended use. Understanding how that data was originally defined and used by the source, and how it relates to our internal information is essential.
Data has the potential to provide extremely valuable knowledge and insights. We see this in business time and time again. But we cannot quantify the value of our data, or determine the value of the information and knowledge we derive from it, without first fully understanding the “facts about the data”. In fact, if we do not fully understand the “facts about the data”, and we rely on that data for decision making, we could easily do more harm than good.
It all starts with “the facts about the data” – that’s what really drives the value of our data.
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