4 Data Strategies for Companies in a Post-Covid World

Covid-19 has really shaped our world in ways that I wouldn’t have expected just 6 months ago, which feels like a lifetime ago. I personally have been hiding out my garage for the past few months. Time seems to have both sped up and slowed down simultaneously. Covid-19 has changed the landscape for data professionals and smart players are moving to make some strategic changes. Here are 4 that I see smart people doing.

Data Professionals Are Cheap Right Now

Many companies are not holding onto their data scientists in a corona-virus induced pandemic. These professionals are high-cost and ultimately not viewed as direct drivers of profits. The nice thing for smart players in the data space, and everyone should be a player in this space, is that for the first time in a decade there are more data scientists looking for work than jobs to fill. Translation: Demand is low(er) than it has been, and supply is high(er) than it has been. All else equal, the price a company has to pay to have one on the payroll is lower than ever before.

Obviously, with a recession going on, it is hard to justify adding heads to the workforce. Smart players are going to realize that this recession is temporary, and of a transient nature. It was a big shock to the economy, for sure. In fact, it was a bigger shock received than the one that kicked off the last recession. That being said, the last recession was caused by weakness in the economy. This was more like a natural disaster. We should see a quick recovery, once we have control of the virus. Therefore, smart companies are going to be hiring these data scientists while they are cheap and relatively abundant on the market.

Now is the time to adjust models

Machine learning and statistical analysis depend on stationary data. Over the last decade, we’ve been building really cool models in various industries. I think the reason why so many data scientists are being put out to pasture is that these models that they touted were completely useless in a pandemic economy. How could these models be useful? The last time we had a pandemic economy, it was 1918! The data profession didn’t even exist back then.

There is no way to train our models for this. We weren’t even trained for this!

As data people, we are actually in a very cool moment right now. I call it the grand 2020 experiment. Large businesses with customer bases in multiple localities can take this grand experiment, and learn something very useful about their customers. They can become far more effective in a post-covid world, because this is the first time that they get a true experiment for the demand for their products.

Let me explain. Usually data science is based off of what I would call, hand-waving correlational modelling. This is because getting at causation is really hard. You essentially have to wait for a natural experiment to fall into your lap to get a causal model. And guess what differing responses to the pandemic have given us? Yep a big ole, global natural experiment.

Now is the time to have a good data scientist that understands this and is ensuring that the data from this experiment is being collected. Your models will be more effective because you will be able to get at what truly drives demand for your products, costs from your suppliers, or any other variable you wish to measure. So going hand in hand with my first point is that now is the time to start collecting and using data, more than ever you need a data scientist, and a really good one.

Invest in data science infrastructure

Okay, so you may not be ready to put a permanent data scientist on the payroll. Now is the time to invest in the infrastructure at least. Here’s why: Many jobs that I have taken, I am shocked at how immature the data systems are at many companies. I think that data science is an after thought. A lot of companies will try to hire a data scientist, thinking that, well, they will come and sprinkle magic pixie data dust on the organization and it will be more efficient. Incidentally, I think that this attitude is one of the reasons for the spat of layoffs for data scientists as well.

The truth is that like a good plumber would be hard pressed to do his job if you took away every single tool that he had, a data professional needs the tools of his trade as well. You need to invest in databases, and in collection procedures, and in software.

A good consultation, like with Barnes Analytics would help. Feel free to give me a call. I will spend some time with you and give you a free assessment of the level of maturity of your data operations. Give me a call at (801) 815-2922.

If you haven’t been investing in data infrastructure. It is better to be late to the party, than not go at all.

Double Down on Data Quality

Garbage in, garbage out. One thing that you should do in this economy as it relates to your data is to ensure that the quality of your data is there. You need to make sure that you are measuring the right things. Spend some time thinking about your metrics. Really, think about your metrics. I’m serious, now is the time to be thinking about whether or not that is the result that you want to drive.

The metrics that you use today, will determine what happens at your organization for the next decade. The reason is that the metrics you measure, are the ones that will improve. Start measuring the right data, and your company will grow. Measure the wrong data and well I hope you like being friends with the dodo.

Be Brave

Some parting advice. None of these strategies are going to be easy to implement in the current environment. In fact, doing this will look like staring into the abyss.

Don’t blink. Don’t hesitate. Now is the time to be a leader in data. Be Bold! Be Brave!