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The General Value of Using Data and Analysis in Decision-making
Are you reading 174 newspapers daily?
It is often claimed that we live in the information age. And it is indeed true that the amount of information available is not only staggering, but also rapidly increasing. If you were to reduce all information exchanges globally to digital information, research has shown that every person on the planet currently receives the informational equivalent of 174 newspapers every day. And this number is constantly increasing. According to classical economic theory, rational economic decisions are made by participants who not only have access to all relevant information, but who also are capable of digesting and understanding the information and placing it into the context of the decision being made. Clearly it does not seem we have a lack of information. The question, however, is whether the information is relevant – or how it can be transformed into something relevant. Some of the information available in the shipping industry today takes the form of detailed raw data, whereas other parts of the information takes the shape of mouth-to-mouth anecdotal data. This only partially fulfills the need for decision makers to have access to relevant information and it fundamentally fails to fulfill the requirement that the decision maker should be capable of placing the information in a meaningful context. To make matters worse, the human brain has a propensity to weigh anecdotal evidence supporting preconceived notions higher than detailed hard data. Without any means to analyze the information in a meaningful way – and do it fast – it is unlikely this vast array of data will be used effectively as part of a base for decision making. Consequently the information, in effect, provides no value.A Practical Example
Here is an example based on actual arrival data from an actual shipper: You are a shipper already using a range of carriers. You’ve managed to secure a new business customer and their shipments are extremely time-sensitive. You get a bonus for timely delivery, and a penalty for late delivery. Ideally you would like to use carriers that deliver your cargo on-time for 100% of your containers. Realistically, you know that none of your chosen carriers can deliver 100%, so you would like to know which are most likely to deliver close to 100%, or at the highest level. The performance information and shipment data you have to help you evaluate carrier performance includes your in-house booking data as well as booking confirmations received from your carrier partners. You have the carriers’ sailing schedules in different formats and arrival notifications. In addition, your people at the destination tell you that carrier A seems to be on-time twice as frequently as carrier B, which is confirmed by looking at their overall vessel reliability statistics. Theoretically, you have all the information necessary to make a decision. However – realistically – you don’t have consistent, unbiased information for all your carriers at the detail needed. And, extracting and combining this information will take days, if not weeks, to complete. Given the anecdotal input from the people at the destination you might be inclined to choose carrier A. However, combining all the available hard information across booking requests, booking confirmations, arrival notifications and gate-out notifications, reveals that carrier A delivers the containers +/-1 day compared to the booking confirmation in 70% of the cases, whereas carrier B is within +/- 1 day in 86% of the cases. Suddenly your decision might be reversed. By using the subjective information you might have chosen carrier A. However, after analyzing all the available data you would change your decision to carrier B.
How to extract value
From the practical example, it is clear that the key to maintaining a competitive and profitable business requires the ability to analyze a combination of relevant data in a timely fashion. The true focus is therefore shifted from the data itself to the combination and analysis of data. Having your information scattered in multiple systems – or even spreadsheets – prohibits timely and cost efficient analysis. Consequently, such scattered data is rarely used in realistic situations. This situation is seen in many global markets and businesses and has given rise to Business Intelligence. The providers of Business Intelligence solutions strive to combine a vast array of data from numerous diverse data sources for better decision-making. Business Intelligence has the ability to dramatically improve both profitability and the competitive position of companies. Business Intelligence solutions allow you to quickly extract the critical business insights you need, which ensures your decisions are based on solid, factual data rather than anecdotal evidence. One of the most detailed examples of utilizing such data is seen in the retail chain Wal-Mart where purchases are logged in detail at the customer level, and subsequently used to optimize sales – from knowing which goods to order for every holiday season right down to product placement in the individual retail outlets.OceanMetrics
OceanMetrics is a Web-based business insights platform from INTTRA for the containerized shipping industry. It delivers actionable data that provides you with the ability to analyze your own ocean shipment data and performance instantly, hence extracting value from the data. OceanMetrics provides shippers and carriers, who are in the INTTRA network, with one central repository of ocean shipment information. The key to OceanMetrics is the ability to extract meaningful data instantly, providing the information and insight to improve the decision making process for shippers and carriers alike. The following are a couple of examples of the insights OceanMetrics provides:iNews Subscription
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