The Bain 400
I recently read a white paper from Bain & Company titled “The value of Big Data: How analytics differentiates winners”. The paper explained the results of a survey Bain did with 400 companies from around the world, most with revenues of more than $1 billion. The paper explored their data, data policy and analytics capabilities. The results were fascinating because it reinforced my belief that companies of any size can use data and analytics to their advantage.
To put some of the data policy survey results in perspective, only 38% of these large companies were using state-of-the-art analytics tools. This doesn’t mean they weren’t using data and analytics, it shows that large companies are using tools that are tried and true. The tools they use are readily available, such as SPSS, SAS, and R. These tools are affordable, some free, for companies of any size.(the content for this shortcode failed to render [IHC ingagehub_attachment2])
Data Policy Defined
One part of the paper that really caught my attention was a section on data policies. In the paper Bain provided the following definition of a good data policy:
“A good data policy identifies relevant data sources and builds a data view on the business in order to – and this is the critical part – differentiate your company’s analytics capabilities and perspective from competitors.”
This resonated with me because this definition could apply to any company of any size, not just companies with $1 billion in revenue, but those companies with $500,000 in revenue. Let me demonstrate by breaking down this data policy definition into smaller pieces.
First “A good data policy identifies relevant data sources…” Your business has relevant data sources. For example, if you use a point of sale (POS) system, that POS is a relevant data source. Do you use QuickBooks? That is a relevant data source. If you use Google Analytics, that is a relevant data source. I do need to caution that just because you may have a relevant data source it does not mean it is a good data source; that comes later in the definition.(the content for this shortcode failed to render [IHC ingagehub_attachment3])
Next “…and builds a data view on the business in order to … differentiate your company’s analytics capabilities and perspective from competitors…” This is why good data is important. Once you have relevant data sources that also provide good data, then you can use that to differentiate your business from your competitors. Here’s an example:
A recent client we worked with was using a POS system for their single store retail operation. This POS system provided us with a relevant data source because we were able to access the data and the POS was used to record all customer sales transactions. After we exported the data, we analyzed the data to determine if it was good data that could be used for a deeper analysis (which it was), then we cleaned the data so it could be used to build data models. These models provided a way to differentiate how they marketed to their customers. Our client now has the capability to market to specific segments of their customer base, based on key metrics (how often they visit store, last time they visited store, how much they spend when they visit store, etc.). This is vastly different from what their competitors are doing (mailing monthly flyers) and much more effective.(the content for this shortcode failed to render [IHC ingagehub_attachment4])
A small company, with a relevant data source that provides good data and was used to make more effective decisions. This is the value of data and how analytics differentiates winners.(the content for this shortcode failed to render [IHC ingagehub_submit5]) (the content for this shortcode failed to render [IHC ingagehub_next_page6])