5 Mistakes When Automating Pharma Analytics

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How tempting is it to sit back and enjoy while our programmed business processes and analyses are automated to just… do their magic!

Automation is indeed a significant factor of every good analytics solution, and constitutes the future of Pharma analytics all together. But let’s hold our horses for a minute there, and consider the potential risk of separating the actual users from the analytical process.

Never lose sight of the process

When relying too heavily on automating our analytical processes, we often neglect our crucial part as active users in making the system really work as a comprehensive analytics solution. Our automated analytics processes should, if designed correctly, incorporate all of the relevant data into a holistic analysis, to produce insightful reports for us. But at the same time, it should be flexible enough to allow us, the actual users, to add fresh data from the field, change outdated process definitions, etc. That’s why a really great Pharma analytics solution combines automation with user entries and changes.

So in order to help you check whether you’re relying too heavily on automated processes alone, we’ve identified some very common mistakes made when neglecting the vital role of the active user:

1. Forcing analytics on the field.

The order defining the relationship between the analytical processes and the field should be very strict: first identify, analyze and understand processes in the field, and only then build and define analytics that support those processes. Any analytics drawn from the top down is bound to be inconsistent with the field in one way or another.

2. Allowing the production of irrelevant reports.

Ever heard of utilization? Not every report received on data is of value to you! If you don’t define which analytics are vital and which are irrelevant, you might find yourself with a bunch of reports you have absolutely no use of. You have to check, on a regular basis, whether or not the reports are being used.

3. Not allowing changes from the field-up.

Now, this might sound funny when discussing analytics solutions that are supposed to equip the field with valuable market and product data. So why even bother relying on data coming from the field teams? Because field, informal insights are crucial for the analytical process. In fact, often times it is the field that produces valuable current data more than any other source!

4. Thinking reports are static, or set in stone.

A brilliant analytics solution should be a dynamic one, just as flexible and subjective to changes as the business itself. When you trust the same reports for too long, you risk relying on outdated data.

5. Designing your analytics infrastructure without thinking strategically.

Will you be adding additional sales forces in the future? Will you be selling more products? Will you have more data sources? These are vital questions that should be asked at the very beginning of the design process, to allow versatile analytics capabilities to fit your changing business needs.

When it comes down to it, most of these mistakes are derived from the notion that setting up an analytics solution is a one-time, automated action, when really it is a dynamic user-based process that continuously escorts the business and the field.

So, are you making these mistakes? Maybe it’s time to look at Pharma analytics a little differently.

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