It’s a fact. If you have manual data entry, there will be errors. I found this out the hard way when working with our completions team on a Spotfire KPI project. We built the Spotfire KPIs and were attempting to tie out to spreadsheets. The numbers didn’t match. Discrepancies consistently traced back to bad data entry. We would fix the bad data, but without proper controls to keep it out, we were chasing our tails. So, we addressed bad data with a QAQC or error report. The first version was all Spotfire, but it had flaws. Version 2 performed error reporting with Alteryx. Ultimately, I wound up with a combination of Alteryx and Spotfire. To see what it looks like and how it was implemented, read on.
As regular readers know, I attended the Gartner Analytics Conference in Orlando a few weeks ago. Since then, I’ve been synthesizing my key takeaways in blog posts. My first key takeaway centered around Organizing the Chaos. Last week, I followed that up with a post on Governing Self Service BI. This week, I am writing up the last key take away — drive analytics innovation with efficiency.
A few weeks ago, I wrote the first of three posts on my key takeaways from the Gartner Analytics conference. The first message focused on Organizing the Chaos. It’s taken me a bit longer than anticipated to write post no 2 on governing self-service BI. However, hopefully it was worth the wait because I will tie back to a post I wrote on Spotfire Skills – Historically Speaking. In this week’s post, I will explain more about the skills users will need to have in the immediate future. Read on to find out what you can do to put a guard rail around self-service analytics.
One of the most common problems I run into in while building Spotfire projects is requests that are too large. They begin simply with a request for data or a modification to an existing project. However, it quickly balloons into more and more until we’ve created a monstrously huge project. One of these monsters jumped on my desk last August, and I’ve been working on it since then with a few stops and starts. Last night, I wrapped up a week of work in Midland. I was about to shut down my machine when I realized I wanted to write up what I’ve learned about how to increase the speed of delivery in these monster project situations.
I always enjoy writing Python code. It’s fun, expressive, and my go to language when starting a new project. Python has exploded the past few years becoming the language of choice in many areas. No area has been affected more than data science and analytics.
Last week, I attended the Gartner Analytics conference in Orlando. It was my first time attending a Gartner event. Granted, I have previously read their research. The scale of the event impressive. The speakers were top-notch, and the content was relevant to the challenges I deal with every day. To summarize the most important things learned, I developed 3 key messages from Gartner. They contextualize the 7 presentations I attended into important themes important for analytics at any company. Read on if…
- Your company is struggling with delivering data to users in a timely fashion.
- You would like to use analytics to be more innovative.
Youranalytics environment seems confusing and chaotic.
A Spotfire user reached out to me on LinkedIn last week asking about developing Spotfire skills. He wanted to know what skills to focus on and how to develop them. I have LOTS of ideas and thoughts on this. Therefore, this week’s post looks at building Spotfire skills based on roles in the oil and gas industry.