Data Scientists Paving The Path For BI

As the number of data scientists within BI teams increase, we see what difference they are making


Business Intelligence (BI) is not a new field, but with the spread of data science it has taken on far more depth and meaning.

The reason for this is that they can used far more advanced methods to find useable data. BI teams have tended to focus on traditional metrics such as web traffic, sales etc, whilst data scientists can utililze their knowledge of complex programming language and run more in depth reports to uncover more relational or disguised data. 

The usefulness and potential insight that can be found from the additional data has meant that the metrics that can be used for business intelligence have vastly increased. Creating relational data that can add to the understanding of how people are interacting with your company can create not only a better way to understand the best way to sell, but also to understand you customers and users better.

For instance, we all know about the Amazon ‘you might like’ recommendation engine, but data science can be used for far more insightful and useful operations. For instance, the OKCupid founder, Christian Rudder, uses the data from the site to show general social trends.

He has used it to pinpoint how different racial group’s dating preferences change on the site and to show the most successful words to use in an initial message when contacting somebody for the first time.

These may seem insignificant to overall company performance, but in reality it means that people can better associate with the company and find the posts interesting, making them more likely to use them. For instance, the top 5 articles written on oktrends have around 9 million combined reads.

The kind of data in required for the posts would simply not be available through traditional BI and the basic knowledge that website analytics can create.

This combination works for more than just peaking interest in your company, but by simply making incremental but well researched changes it can make a significant difference to the performance of a site. YPlan for instance, found that simply by changing the copy on one button, they managed to improve their conversion rate by 15%.

Experimentation with your data and more importantly with the people who are using a website is sometimes frowned upon. Facebook’s recent report onto emotional manipulation of their users through filtered news feeds on their site is a prime example. However, it is at the basis of everything a company needs to do to maintain successful.

Even a new product being released is technically an experiment for a company and BI has the role of making sure that these experiments are unlikely to be a disaster. With the added insight that data scientists can get from their use of complex algorithms and enhanced data gathering techniques, it adds to the pool of knowledge that can be used.

We have seen that this relationship is working in several companies and as the numbers of data scientists across the world increases, we are going to see this relationship become more common in companies across the world. 


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