A new Small Multiples visualization feature has been released for public preview in Power BI. I was thrilled to see this first announced on the roadmap and am looking forward to putting it through its paces now that it is out in preview.
“Small Multiples” use multiple similar views to show different partitions of a dataset. Small multiples are sometimes called trellis or grid charts and until now, you have had to use custom visuals or some SVG tricks to get the functionality in Power BI. Continue reading “Power BI Small Multiples Preview Feature”
We recently put together a video highlighting the features of Productioneer’s Daily Production BI Report. We are proud to include this BI Report as a standard offering in every Productioneer subscription. Our Productioneer customers can access it using the Productioneer Report Portal.
One aspect of the report that the video does not directly touch on is the BI Report was crafted with performance in mind. We tuned the data model and report design to offer a responsive user experience. Here are a few of the best practices and strategies we used to optimize the report:
Using stock visualizations instead of custom visuals
Reducing the number of slicers on the canvas and utilizing the Filter Pane
Specifying interactions across all visuals to avoid unnecessary background calculations
The report works out-of-the-box for all Productioneer customers and has many advantages over standard paginated reports.
As mentioned in the video, the report can work with other Oil and Gas Production software as well. If you are interested in utilizing this report or other BI Reports in your organization, we would be excited to talk with you.
Productioneer has an extensive paginated report library for our customers to utilize. We have everything from simple data exports to paginated reports that highlight variances and trends.
One of the reports we have had in our library for quite a while, is the Tank Stock report. The user selects a day and the report returns the tank stock and tank production for each tank in the organization for the selected day. It aggregates the stock and production at the tank, battery, field, and organization level and provides the tank strappings (feet and inches) for each tank. It is a standard and widely used report and I know it has not had any significant changes requested to it for at least 3 years.
As an end-user of a Power BI report, a chart that looked great at first might look not so great once you start applying filters or using slicers. Very large values in the data might throw off the scales and now your line chart might be suddenly all squeezed at the top or the bottom. Does any of the above sound familiar?
Usually it’s the report designer who has all the power, this article is about giving more power to Power BI end users…
End users’ ability to change the scale, appearance or formatting of that chart is limited. That’s why I started creating the Mi4 Line Chart Power BI custom visual that lets you switch scales on the fly, and eventually have more overall control of the visual without having to edit the report.
As we wrote about earlier this month, the Texas Railroad Commission (RRC) released a treasure trove of data freely available to the public on their site. It was like Christmas in the Mi4 office. After we sang some carols and drank some hot chocolate, we realized that there was so much data. We didn’t know where to start.
Christmas in September
As my colleague @Talal wrote last week, we decided to get Lat/Long coordinates for every Texas well. In his post, he explained, there are many use cases for this data, so it seemed like an excellent place to start.
Last week I wrote a blog post talking about the TX RRC publishing data to the public that previously was only available via a paid subscription.
After downloading the different data sets and examining the various types of data and formats, I decided to take a closer look at the data that might prove to be useful to us and to our Productioneer clients.
It is not uncommon for location and depth data, as well as other well-header and meta data to be incomplete or non-existent during a software migration, after all this data might not be considered crucial for the daily gauge sheets. Especially in the case of Excel gauge sheets, where additional columns are a waste of “prime real estate” and might be considered as cluttering that particular production report.
It would be nice if we have a quick way to pull the Lat Long data in bulk to speed up the on-boarding process.
Welcome to the second post in our miniseries: “Are You Developing Power BI Reports the Right Way?” In the two-part series we are designing a sample Power BI report visualizing the weather on Mars and using some real world techniques.