PROFITING FROM DATA
There has been much written in recent years of treating data as an asset, but much of the discussion has been excessively theoretical and lacking in practical application. So, how can we develop a more pragmatic approach to understanding how data can deliver value?
The first step is to accept the principle that data is only as valuable as the uses to which it is put. Simply having data does not by itself create any value, it has to be used for some purpose.
3 Ways Data Creates Business Value
In business, data is typically used in three ways: to execute transactions, to create information and as an input to analysis. Let’s look at each in turn:
The Transaction Value of Data
The most basic use of data is to execute a transaction such as buying or selling a product or service. Data such as a customer’s name, address and payment details are required to successfully execute a sale transaction. Errors in any of this data can result in a failed transaction, a disappointed customer or in some cases a fraudulent transaction. The value of correct data to process transactions is clear. It allows for timely and accurate execution of transaction. Inaccurate data can frequently incur increased costs for rework and error correction.
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In transaction processing, data is typically used in a linear manner with different data elements required to execute each stage of a transaction. For example, product and pricing data is required to present an offer to a potential customer; desired quantity, size and colour to fulfil a customer’s requirements; then payment type data and shipping information to enable delivery to a customer; tracking information to advise a customer of when to expect delivery and so on. The transaction value of data is measured in terms of accuracy, speed and reduced cost of rework.
The Information Value of Data
In addition to facilitating efficient transaction processing, data can be aggregated in order to provide information that reports on the performance of an organization and can guide future decision making. Aggregating data around different dimensions such as customer, product, geography, channel, time period and the like allows a business to understand what is happening and identify trends or patterns that can inform future decision making.
The information value of data is centred around relationships between data which can typically be expressed as questions. How much of our products did customer A purchase? How much did we spend on travel during the last period? How have our costs changed over time?
The information value of data can be measured in various ways. One of the most tangible is the management of working capital. Accurate, aggregated information around orders, sales, inventories, payables and receivables is fundamental to an organization’s ability to manage cash and capital. Informational value can also be reflected in impact of identifying positive and negative trends in a timely fashion and the economic value that is conserved by mitigating negative trends and enhancing positive trends.
The Analytic Value of Data
While the information value of data focuses on describing what has or is happening, the analytic value takes data and uses it to try and answer questions such as why is something happening, what is it likely to look like in the future and what can we do to influence it in the future. Data is analysed by applying tests, formulas, hypotheses and the like to try and understand why the data looks the way it does and what it could look like in the future under different sets of conditions.
The analytic value of data is measured most completely by the expected future value of an organization as this represents the expectation of growth and value creation based upon the analysis of currently held data. More practically, analytic value can be measured by looking at specific use cases such the likely outcome of executing a particular strategy with and without certain data, or the cost of acquiring data compared to the cost of already having such data. We are still in the early stages of developing both the uses and measures of analytic data value. However, as the adoption of advanced analytic tools such as artificial intelligence, machine learning and cognitive computing progresses harnessing the analytic value of data is becoming a competitive necessity.
As organizations plot their data strategy it is essential that all three elements of data value are addressed. The winners will be those that successfully unlock value in each area consistently over time.
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