In 1815, they didn’t have access to all the information we have today, but sometimes they had the right information when they needed it. Take the Duke of Wellington, for instance. Knowing that Napoleon had 14,000 seasoned cavalry, what the French infantry tactics were, and capitalizing on the bad weather to contain the French, he had the right information at the right time to confidently act and win the Battle of Waterloo.
Arguably the most important gap in his intelligence for the day was whether his Prussian allies would arrive on time… Other helpful information could have been amassed about the very high-stakes decision he was about to make but this would have taken time. Wellington made his decision on aspects of the coming battle based on key indicators that were clear, not exact. Knowing the exact number of enemy cavalry or bayonets, sabres and muskets, the names of French Colonels and insignia of their units would have perhaps proved helpful. These were not critical when deciding to act on the threat of Napoleon’s army at Waterloo. He used the information he had to make the right decision at the right time to lead his troops to victory.
Today, data quality is something most organizations still grapple with, with varying degrees of success and consensus. Add to this the issue of having “all the detail,” rather than just the information that is needed for decision-making or core business process, and intelligence-based decision-making becomes a dream rather than a reality.
Data quality issues can result in mistakes being made when the wrong information is provided to the right person at the right time. Often this leads to a case of organizations being “once bitten, twice shy.” The practical realities of what data is relevant to whom, when they need it and what this is used for often do not gel with ideals of data quality perfection and having all enterprise data considered in decision making. There is no point having the cleanest, parsed and transformed data and “nice to know” information on a customer who is at risk of buying from another company once they have already left. Being confident in the quality of the information and being able to assess the relevant risks is what is most important when acting on intelligence.
The pursuit of Data Quality perfection and trying to get all the data available rather than what is needed now can slow (and sometimes cripple) an organization’s ability to respond to the near-to-real time demands of their operating environment. Knowing what is important and understanding the risks and opportunities and how quickly you need to act is the key. Predicting, knowing what will, or will most likely happen in your business before an event is the insight that gives some of Teradata’s most successful customers the competitive edge to act. The right information, on enough of the right indicators results in right-time action.
When is information too much and when is it not enough, and how do you know with confidence which you have?
David Bremstaller
