Often 10% of the data contains 90% of the relevant information to solve our problems. This means that we waste enormous resources by cleaning, migrating and managing this 90%. What's worse, we might even have missed the 10%.

"Eventually everything connects - people, ideas, objects. The quality of the connections is the key to quality per se."

Charles Eames

First comes the decision and afterwards the data

 

This view is in stark contrast to many AI and data-focused thought leaders who tend to start with data and then look for mechanisms to use it. Obviously, if our goal is to solve problems, starting at the decision making stage is much more cost-effective.

All this talk of data as the new oil has led to a situation where there is still far too much effort spent on accumulating and cleansing data prior to decision modeling. Nobody would have thought of pumping as much oil as possible first and then thinking about what it could be used for...

There are organizations that have tried for years to implement a consistent and homogeneous view of their operational data in their data warehouse. A huge effort was made to find the keys to merge hundreds of tables in a meaningful way and with the right time references.  What if an analysis of the relevant decision paths would reveal that linking only two tables would be sufficient for a large part of the required insights?

In many particularly volatile environments, there may not even be any data for the situation that has arisen, simply because it is brand new.  Here we have to make assumptions about the future and assess possible consequences. 

beeBlum can help you to save a lot of money

With beeBlum you somehow work backwards. You start with the problem, think about possible actions and then you gather the data. If you connect "the dots" between data and benefit you will probably notice that some data fields have a large effect, others a less large effect. So the price of your product might play a much more important role than the weight, at least if you don't sell any barbells. Although this is a rather simple example, it is clear that without some knowledge of how data relates to decisions, no distinction can be made between highly important and less important data.  But without this knowledge, your expensive data specialists are busy with just something and you wonder why your IT is so slow and expensive. At least, if you're not using beeBlum yet ;-)

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