Around 6% of annual revenue is being lost through poor quality data, according to new research released recently. All sales and marketing teams are attempting to provide their customers with the right content, in the right channel at the right time for that individual, in a compliant way. However, at the same time, regulations are getting tighter, and physician access is reducing. It is possible to achieve nirvana with big data and AI, but in order to do this, good data is critical.
The problem is due to several factors.
1. Siloed data.
Customer data is everywhere but the systems used to store it are siloed and disparate so combining and consolidating it becomes difficult as the systems are working in isolation and often not shared between systems. Without being able to consolidate all the views of the same customer across an organization it is easy to have multiple instances of the same person recorded as different people. Many projects we have worked on include customer data stored in databases from IMS, Veeva, Siebel, Oracle and more. The challenge is combining and cleaning these disparate sources. The world is changing and these days it is about collaboration for the best interest of the customer. To have excellence in customer analytics all the customer data across all channels relevant for the customers (not only those relevant to the company) must be cleaned and integrated and then standardized into a single view of the customer.
2. Dirty Data.
Errors in data lead to more problems that are difficult to identify than anything else. Everyone knows the saying ‘Garbage in, Garbage out’. Nowhere is this more of a problem than in analytics. Most of the data exists but often is not clean and if it does not get cleaned, the results will of course be poorer which is no fault of the sales and marketing teams. This contaminated data is an issue for most companies. Let’s consider the issues with errors that creep in. In Pharma sales and marketing it can mean wasting resources on the wrong targets, inaccurate strategy or tactics leading to reduced revenue and profit. It is a huge problem in Pharma. If you look at CRM data alone, most data we receive and have to clean is dirty.
For example, it has issues such as inaccurate addresses, multiple instances of the same physician, outdated physician information, and retired, and even dead, physicians. When people use this data without cleaning it in their analytics, it is clearly going to lead to erroneous conclusions and recommendations, not to mention compliance risks. If you consider physician payment transparency requirements, and you have dirty physician databases, you could be running risks in compliance. If your data is unreliable, you will make inaccurate decisions and senior management will stop believing what you say. Everyone will fall back into using gut feel and intuition, and will be more likely to reject counterintuitive implications that arise from strong data and analyses. The need to clean data and improve data quality is critical to pharma if the teams are to have reliable insights and recommendations to work from. However, the solution is simple. Every team needs to ensure that the people working with the data are aware of potential issues, that they understand where errors could occur and know enough about the data they are using to be able to find and correct any issues uncovered.
Missing data is another issue in databases we see that need cleaning. If data is missing, an analysis could assume that the missing field means 0 rather than simply being missing. Each of these things must be corrected prior to any analytics.
3. Lack of insight and results.
No matter how good your analytics, if you do not correct the data issues, your results will be suboptimal. By simply uploading your CRM data into an analytics platform you will be uploading missing values, repeated values and other dirty data errors that will lead to erroneous analytic conclusions and lower quality insights and poor results.
When you are next looking at your results and why they are not as good as you expected, go back to your data and examine the cleanliness of it. Until you deal with that issue, you will be constrained in the results you achieve.
For more information on getting more from your customer data, contact the author at Eularis http://www.eularis.com