15 years ago, British data scientist Clive Humby coined the popular maxim data is the new oil which has since been updated by Ted McCandless to data is the new soil. We should all agree on the importance on data to support decision making as there are plenty of examples out there (Moneyball anyone?) but what about the cost of having bad data? 7 years in the CRM and analytics industry pushed me to write an article about this topic, adding some tips/recommendations to change its vision: from business cost to business opportunity.
Given the amount of data available today, if you don’t put in place the best practice for your data quality management you could find yourself with a problem that grows exponentially. The following infographic from Neil Patel summarises it quite well:
Working on dozens of data related projects taught me marketing teams have plenty of information available, but it wasn’t until I googled for some stats to include in this article that I realized this opportunity/problem had a completely unexpected dimension. A couple of years ago Salesforce, the n°1 CRM software company, forecasted the median number of data sources marketers use to 15 in 2019. 15! This means there is a huge opportunity to deeper understand your customers and therefore be able to provide them with what they do want; but there may also be a huge problem if no best practice processes in data quality management are in place.
Working on dozens of data related projects taught me, marketing teams have plenty of information available, but it wasn’t until I googled for some stats to include in this article that I realized this opportunity/problem had a completely unexpected dimension. A couple of years ago Salesforce, the n°1 CRM software company, forecasted the median number of data sources marketers use to 15 in 2019. 15! This means there is a huge opportunity to deeper understand your customers and therefore be able to provide them with what they really want. There may also be a huge problem if no best practice processes in data quality management are in place.
Let’s move to a real-life scenario. The marketing department of a football club is being asked to drive more sales for a specific product (say a match kit) on the club’s official online store.
Instead of sending a bulk email to each contact in the database, the marketing team decides to send a personalized email to all the following audiences:
Data quality issue #1 (Existence). Information about visits to the “match kit” page on the online store is available on an aggregate level and therefore the marketing team can’t use audience A in the campaign
Data quality issue #2 (Consistency). Merging multiple Excel spreadsheets to build audience B results in the discovery of a consistency problem: first and last name for the same email address are different and therefore the marketing team should spend additional time to decide how to manage the communication to these contacts.
Data quality issue #3 (Validity). While all store owners shared the list of customers who made a purchase in the last 12 months, one of them also noted there is missing information as he didn’t collect it in the last 3 months due to internal processes issues. The result? There may be customers who actually bought the match kit recently but the marketing team could not include contacts from this data source in audience B.
Given the scenario presented the following are true:
There are three main benefits of introducing data quality management best practice into a sports organization:
Every organization has its own pathway to success, and this is not different when working on best practice processes in data quality management. Nevertheless, there are a few steps every company should follow. Below I’ve listed the activities the marketing department should take in order to reduce wastage in time, budget and energies to finally unleash the potential of their data. Starting from the organization’s high-level objectives the marketing team should:
If you’re a club or a sports organization you probably have recognized several aspects discussed in this article. Hopefully, your best practice processes in data quality management are already in place, and you can describe yourself as a “data-driven company”. If this is not the case, and you need some support in assessing your needs and planning your next steps to unleash your data’s potential, please feel free to get in touch.
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