The great productivity leap?


Last Wednesday evening while waiting for a connecting flight at Vienna airport, I came across an interesting eBook by Tyler Cowen: ‘The great stagnation. Thanks to the iPad, I ended up reading the entire short book on the flight back home. To keep things short, Tyler Cowen looks at some of the reasons why the US economy is in a slump. One of the key points of the book is that the US (along with the majority of the Western world) have hit a technological plateau that results in slower growth, slower increases in median income and also slower productivity gains. While some of the earlier innovations such as the steam engine, refrigerators, air travel or TV had a huge impact on our overall quality of life and also our productivity, many of our recent innovations like the internet have had less of on impact (compare having or not having a fridge with having access or not having access to the Internet, or look at the options that air travel opened up for international trade).


This blog post is not intended to be a book review, although I do highly recommend this quick and insightful read. But there is an interesting aspect that Cowen mentions: productivity gains are slowing down. We have already grabbed the ‘low-hanging fruit’ by exploiting machinery for example. This got me thinking: What about all the great technology that we have implemented in the past 20 years? Could it really be that we are not experiencing substantial productivity gains by using this technology in a smart way? The statistics apparently say no (Cowen points out that the productivity surge in the US from 2009-2010 was mostly driven by a substantial reduction in headcount). And what about Business Analytics? Shouldn’t that help us be more productive and to drive profitability?


To answer that question let’s take a look at a very simple example: the office of finance. A few weeks ago, I posted the results from a survey we conducted. It showed that many professionals still conduct their work by copying and pasting data into spreadsheets. And that type of activity is error-prone and takes a lot of time. As a matter of fact, many finance professionals still spend a solid part of their week loading, scrubbing & massaging data. And the resulting reports often leave a lot to be desired. They are static and not interactive. The result? A bunch of highly qualified and highly paid individuals do work that they shouldn’t be doing. Wikipedia defines labor productivity as “amount of goods and services that a worker produces in a given amount of time”. Spreadsheet maintenance hardly qualifies as a service or good, wouldn’t you say? In other words: whether we like it or not, many of us have a very low level of productivity.


Business Analytics has a huge potential to help us increase productivity. And I would argue that there is plenty of ‘low-hanging’ fruit. Think about a story that happened to me a short while ago: A CFO decided to implement a dashboard of about 50-60 KPIs (let’s not start arguing about that part…). His team was excited and started building a 65 page Powerpoint master template. The CFO was delighted. Guess what – three people spent almost two weeks per month on preparing this dashboard. Lot’s of time was wasted on extracting data from different source systems. Lot’s of time was wasted on creating Excel lookups, queries and charts. To sum it up: A big waste of time. The low hanging-fruit came in the form of a traditional BI platform. We hooked up the source systems, created the reports once, linked different charts to Powerpoint and voila: the monthly reporting book. It took one person about a day per month to check & refresh the data (there was still some manual labor involved). Consider the difference: three people for 10 days vs 1 person for 1 day. 30 man days vs 1 man day. Not bad?


Based on my own experiences, I would argue that we have a huge opportunity on hand. Many companies have started Business Analytics projects but a lot projects are still highly focused and not pervasive. Once we start pushing capabilities out to a broader audience, we should be able to see significant increases in productivity. And this is not only about driving efficiency. This is also about driving effectiveness: just imagine what we could do, if we had the ability to understand our customers better. Just imagine what we could do, if we clearly understood which products are truly profitable. Business Intelligence allowed us to get to a lot of this valuable information. But I believe that we have only scratched the surface. The addition of predictive analytics to the mix offers completely new opportunities for all of us. I frequently work with some clients that have done some amazing stuff with the software. And their overall corporate performance is very impressive. I highly recommend reading the IBM CFO study which provides some great examples and insights about this topic. We just have to get started! What are you thoughts?