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.
Quality checking is one of those things we all say we love but secretly hate. We send our work over to another developer, believing they won’t find anything wrong with it, only to discover there are A LOT of things wrong with it.
You then go through the various stages of grief - denial, anger, bargaining, depression - before reaching the final stage, which is accepting that you are not the great genius you thought you were.
While I can’t take that emotional roller coaster completely out of quality checking, there are things you can do to make a better experience for both you (the developer) and the person who quality checks your work.
Something I’ve learned working in corporate America is people don’t like to talk directly with one another about workplace conflicts. If someone doesn’t like the way someone else acts around them, they never address it directly with that person.
Companies are investing heavily in data. That means they need to hire more people to work on that data. There’s not enough talent in the industry to fill all the open positions, so employers naturally have to start from scratch with some people.
Since you can’t always hire for experience, hire for personality types.
There’s six types of people who will do well in data and analytics, even if they have no BI experience. My handy list below tells you what those six types are. It’s unlikely you will find someone with all these characteristics. But the more of these traits a person has, the better they’ll do.
Business intelligence development often moves at a slow pace. For any given project, a developer will spend weeks and sometimes months with stakeholders to determine requirements. That means a lot of meetings, emails, and slack conversations, all of which are meant to move stakeholders towards a consensus on what they want.
That seems to be the opposite of what a developer should be doing. But there’s one part of the process that makes it all worth it – the part where they actually get to build the dang thing.
For me, there’s usually a routine to that. I block four to five hours off on my calendar, sit down with a cup of coffee, and zone out to Final Fantasy music as I write SQL, program R, or build a Tableau dashboard to fulfill the requirements.
And I feel great while doing it. I become completely immersed in what I'm doing and thoughts about deadlines, relationships, or the future completely vanish for those four hours.
A couple of weeks ago, I received an email from Tableau announcing that Salesforce completed its purchase of the company. I went on Yahoo! Finance and, sure enough, Tableau’s stock was no more. This made me sad because I enjoyed watching Tableau's stock volatile swings in the market.
What would I look at now that it was gone?
So I researched a few other business intelligence stocks to find a good replacement. I looked at the big players, such as Oracle and SAP, and a handful of smaller companies, such as Alteryx, Cloudera, Domo, Splunk, Talend.
While looking, I noticed one stock dramatically outperformed the rest. That stock was Alteryx (Ticker: AYX).
Most managers agree that feedback is important for employee growth. Most employees agree with that too. Despite that, many managers actively avoid giving feedback. Just like how your body has a fight-or-flight response to criticism, giving feedback creates a similar response in managers. Instead of facing that anxiety head-on, managers often delay feedback until annual reviews. By that point, the feedback is the least effective.
We all have that friend who talks about the new diet or new workout routine they’re doing. Maybe you are that friend. The one that never seems to lose weight, but talks about how this new diet might change things.
Good data quality is the foundation of your data solution’s success. It doesn’t matter if you have a great personality, build beautiful dashboards, or present engaging analysis – if your stakeholders stop trusting your data, they’ll stop trusting you.