Do you know what p-values really means? It turns out it's closely related to the concept of the "null hypothesis." Both of these concepts are frequently misunderstood by analysts, data scientists, and researchers alike.
In my latest Towards Data Sciencearticle, I interviewed Dr. Robert Montgomery, an assistant professor in my data science grad program. He helps explain what the null hypothesis really means, what the p-value tells us about its probability, and when we can claim causality in statistics.
Many people believe that the best way for data science to have more impact on an organization is for the data scientists to learn better storytelling skills. Are they right?
In my Towards Data Science article, I interview data scientist Jasmine Samuel and Brent Dykes, author of Effective Data Storytelling, about how to use data storytelling to make a bigger impact with data science.
I recently read a book called the Good Jobs Strategy. It was written by an MIT business school professor named Dr. Zeynep Ton. In the book, she explains why retailers who invest more in their employees outperform their competitors.
These companies, she says, are following the “good jobs” strategy.
I highly recommend reading the book, if your organization is investing more in data. Much of her advice easily translates to the major challenges that face organizations trying to build enterprise wide data solutions.
Industry insiders have always claimed that the Great Recession was a good thing for marketing analytics. They believed that marketers would invest more in data to prove their value to their clients at a time when most companies are cutting their marketing budgets.
I think many of us assumed the COVID-19 downturn would do the same thing for marketing analytics. However, that may not be the case this time around.
I’ve noticed that many peers at different companies have lost their jobs in the past few weeks because of the COVID-19 downturn. It might be that the crisis forced marketers to evaluate whether the costly analytics practices were really worth the money and work involved. Or it might be that we (the data professionals) never delivered as much value as we thought.
In reality, both sides probably share the blame. Neither the analysts nor the marketers have ever really approached marketing analytics the right way.
For the past ten years, the marketing industry invested heavily in building data warehouses, implementing advanced tracking, and hiring data professionals to analyze and report this data.
But along the way, marketing analytics began turning into snake oil.
The benefits were widely overstated for the amount of money invested. The solutions built were flimsy on quality. And the goals were often improbable (if not impossible).
I don’t think the analysts or the marketers intentionally did something dishonest. I think they simply did what marketing people always do – sell the benefits of a product.
The main problem was that marketers may not have been the right people to use this particular product.
For starters, it pays well. The pay is often higher than other more technically challenging jobs in the data science profession. According to Glassdoor, the average salary for a Tableau developer is $81,514, which is higher than database developers, statisticians, and data analysts. (See footnote for more salary details.)
The work-life balance is also good. You can often work remotely and jobs in this field rarely require much overtime. If you become really good and market yourself effectively, you can work on a contract-by-contract basis, thus improving both flexibility and income.
Tableau development is also in high demand. After working for a single year as a Tableau developer, you will get calls from recruiters frequently. Sometimes multiple calls within a single week.
And best of all – it’s the most creative job within business intelligence. There’s no other job in this field that allows you to exercise more aesthetic flare and storytelling abilities than Tableau development.
Have you considered changing jobs recently? You’re not alone. The job market is hot right now and you’ve probably gotten more than a few phone calls from recruiters lately.
While this is a good thing for employees, high turnover is a problem for employers. And it’s especially bad for data and analytics teams.
Turnover disrupts the business ecosystem and the data and insights that come with it. It will impact both data and report quality, as well as team efficiency. Not to mention that any new employees will have to establish new relationships with stakeholders to learn the business needs. So what’s causing this high turnover? And how can employers prevent their people from leaving?
There’s a mindset in our profession that we have to make things easy for our audience. We don't want them to think too hard while reading our dashboards and analyses.
But do we take that attitude too far? When trying to make our work more accessible, do we dumb things down too much?
I've built reporting solutions for multiple organizations now and the one complaint I hear the most is that the data is useless. Dashboards never give much insight and the KPIs they display are usually found in the source tool.
While it's nice to have all that data in one location, dashboards never do what stakeholders are wanting the most – tell a story.
Telling a story is a bit of a cliche with data, but most people want to open a dashboard and see what’s unusual about the data they’re analyzing.
There's a great metric that gives the audience that ability and allows them to tell that story themselves. Only...it's not always intuitive for them...
Many businesses in the analytics space say they want more insights – and they expect their analysts to come up with those insights.
But not many analysts know how to deliver that. Most don’t do anything analysis related at all. They find themselves trapped in the world of reporting, where they crank out dashboards and KPIs on a regular basis, while begrudging their executive overlords for telling them to “include more insights!”
I call this the “reporting trap.” And businesses and BI professionals alike want to escape it. Only they don’t know how.