5 Types of Personas You Don’t Want at Your Thanksgiving Table

Every year it’s the same old thing. The big juicy turkey on the center of the table, with its brownish color and a wine-honey-mustard smell to it, if you’re lucky. And except for the changing side-dishes, depending on who made them this year, there’s nothing new in this well-familiar dinner, especially when it comes to the guests.

You see, it doesn’t really matter who exactly was invited this year – because no matter how you look at this, there will always be the same types of people around the Thanksgiving table. Sure, with just a bit of wine, any seating arrangement could work, but there’s still those 5 personas you just never want to end up sitting next to.

Being a data analyst, my imagination led me to realize these 5 personas resemble 5 non-welcome types of data analysts, in a quite remarkable way…

The Know-It-Alls

It is needless to say people who think they know it all have one of the most annoying traits in the human arsenal. They’ll have something to say about e-v-e-r-y-t-h-i-n-g, regardless of whether they were asked or not. And you know what the worst of it is?? They’re not even right half the time.

This reminds me so much of those data analytics types thinking they know all there is to know about, well, everything! They know what’s relevant for the sales departments, what’s important for the marketing department to know, and of course, they exclusively hold the key on which data is crucial for the management’s decision-making. Yes, you know who I’m talking about- the guy with the perfect Excel sheet he won’t budge from.

The thing is, when you’re that certain you’ve covered all there is to know, most chances are because of your certainty, you’ve probably left things out, not taking into account all of the relevant data, because you’re so positive you know exactly what’s important. In fact, the only indicator we really have for making sure we’ve covered all relevant data, is deep analytics based on a broad view of affairs. Only after a comprehensive analysis can we talk so decisively about relevant data and important insights. It simply beats our instincts, and is a much more reliable source than those know-it-alls.

The Neurotic Type

With these types, you’re so fixed on the swiftness of their changing looks and their fidgeting moves, you almost feel neurotic yourself! And whether it’s constant talking, or non-stop moving, you can’t just sit and enjoy your food, not to mention a nice, relaxed talk.

This immediately relates, in my mind at-least, to the constant non-stop running for data so common in pharma industry. It’s as if we’re talking about the gold rush! People are running around trying to obtain more and more information to help expand their data arsenal, while in fact, we’re already drowning in data: behavioral data, consumer sentiment, clinical trials, reimbursement, EMRs, public health, costing and so much more… our constant scampering resembles those fidgeting, nervous neurotics.

Is it necessary to even articulate the serious problem here? We’re so busy getting new information all the time, we constantly shift our attention to the next set of data, neglecting to focus on how we use this data. We forget that to extract the valuable data, we need to produce processed insights.

The sophisticated analytical tools we have today allow us to process huge amounts of data, and so it’s good to constantly look for new, updated information- the analytical systems can handle it. The fact that we have the technology to process all that data doesn’t mean we have to neurotically look for more data to feed it with. The focus needs to be on extracting valuable processed insights from the analysis our systems provide us with.

The Whining Type

Now I know you recognize this type, since they’re often equipped with an unusually grating voice. You know, the “Oh I don’t eat any gravy… I guess I’ll just peel the skin-off” or the “Oh there’s vinegar in the salad..? I hate vinegar, it gives me the heartburn” and I’ll stop here since I think you get the idea.

Doesn’t this sound familiar in terms of excuses to why we don’t have any meaningful insights from our data? It sounds something like this: “we don’t have enough data to crack this problem…” or “the data we have just isn’t good enough” or the known cry “the figures just don’t match, there’s no one single source of truth…” and so on.

Well, rather than treating these, and other data-collecting issues with a defeated, whiny tone, why don’t we accept them as challenges to overcome; if our data inventory is lacking, let’s see how we can use the existing data and utilize it, and decide on how much more information we need by analyzing our current inventory. Similarly, if we think the data we obtained isn’t good enough, let’s check if our current analytical systems are providing us with the clear, insightful image of the data we need; maybe they need to be upgraded.

In other words, let’s find ways to overcome these data-related issues with the tools we have- the technology and analytical solutions available today, make it foolish, if not whiny, to get stuck on these issues.

The Messy Type

Ever sat next to the messiest guy at the table? Well, let me share the experience: even if you manage to get past the unappetizing sight of the actual eating, you find yourself covered in food leftovers and a bit of saliva, nevertheless.

Now, how can we not compare this to the messiest analytics guy in the office, the one mixing it all up so that the ever-organized data produced by careful analytics turns into a meaningless pile of, well, rubbish? You know, the whole bit: duplicated records, null values in the reports, shifts in columns when loading the data, inflated data as a result of his using the wrong joint data sources and wrong filters, etc.[DA1]

This is such an easy problem to fix though, especially when you have automated your analytics process. Once you have the system taking care of the donkey-work, all you need is to sit back and review the reports to identify relevant insights.

Last, but definitely not least, the Self-Centered Type

I saved this one for last because I find this type the most annoying, and if there ever was a type you don’t want next to you at your Thanksgiving table, it’s this one. These guys always find a way to talk about themselves, always in a pompous manner of course, never bothering to ask you one question, and even if they do, they’ll always manage to swing it back to talking about themselves!

This really reminds me of our tendency in pharma to look inwards when analyzing and processing data, often times neglecting to notice events and dynamics occurring outside in the real world. Sometimes we forget we’re dealing with consumers, patients, physicians, market shifts influenced by politics, and many more factors outside our warm greenhouse of numbers and charts. The world outside is full with changing dynamics, and if we get too tangled-up with our own data-processing, we risk failing to identify crucial events potentially affecting our business.

All of these types are fixable when it comes to data analytics. But when it comes to your thanksgiving table? Well, good luck with that…

And Happy Thanksgiving!