Power BI’s Advanced-Analytics Visualization Capacity is Huge for Unlocking Value

Microsoft’s Business Intelligence engine [aka “BI”] “Power BI” recently added what equates to a “mathematical-visualization filter” to its Desktop platform; allowing for instant analytics overlays for basic [and in some instances complex] mathematical calculations.

First, I should start this Medium by stating clearly that I’m not in any way compensated by Microsoft for this note [or any other coverage and/or promotional activity I engage in regarding Microsoft]. Microsoft did reimburse me for T&E regarding its Data Insights Summit in early-2016 — but I’m not compensated nor encouraged to be promotional of Power BI [which is Microsoft’s BI platform]. I am, however, a power-user of the platform and I use it frequently in helping diagnose, assess, and ultimately make capital allocation decisions when it comes to research and consulting provided by my firm [ATLAS Consulting]. So, in that sense I’m well compensated by Microsoft; especially given the incremental cost of the premium Power BI service to which I subscribe [the base Power BI platform is freemium].

With that out of the way, we can move towards the core focus of this short note: that Power BI’s recent addition of Advanced Analytics [or what I’m calling “Advanced Analytics”; knowing the marketing and branding prowess of Microsoft it likely has a much catchier name to it] is superbly helpful in unlocking the value of the platform as well as unlocking the value of the underlying data [being analyzed]. The video below will example exactly how Power BI is used at our firm and exactly how the Advanced Analytics comes into play when “unlocking value”:

In addition the video above — which I hope you viewed because seeing really is believing — I’ll also include a few static screenshots of data being illustrated [in this instance via a data-visual called a “scatter plot”] with a static Advanced Analytics “trendline” [a simple average of y-axis and x-axis data in this instance] being applied as a mathematical-visual filter:

This data-visual [again, a Scatter Plot] with a static Advanced Analytics “trendline” [a simple average of y-axis and x-axis data in this instance] being applied as a mathematical-visual filter uses ALL data points; not discriminating against [or removing] any outliers. Even using this this base-setup, you can see data outliers [with the mathematical visual filter being applied] easier than simply using a “quadrant”-based approach. While leaving much to be desired, from an outlier identification standpoint, there is definitely value being presented [to analyzing the underlying data] even at base-setup.
Secondary-setup removes only one data-point: data-point “VNR” [formerly in the top left of data-visual #1 above]. Immediately, and only after removing one data-point, we can see that the static trendline provides exponential value-add [to that being provided in data-visual #1 above]. Removal of this single data-point also exposes data-points “[NOG, PQ, REXX, CHK, AREX, HOS, CRK, RIG, JONE, SDRL, WLL]” as potential data-outliers. Reiterating, this wasn’t readily apparent in data-visual #1 using the static trendline and/or quadrant-based analysis.
Tertiary-setup removes a secondary data-point [in addition to data-point “VNR”]: data-point “TDW” [formerly in the top right of data-visual #1 and #2 above]. This also exponentiates the value-add of the static trendline as well as of the overall Power BI platform [clearly visible in looking at data-visuals #1 and #2]; because now we’re really working on filtering data-points down to the points that might still have [in this instance] something actionable associated with analysis [e.g. the ability to buy or sell the name via capital market exchanges; creating investor return as a result]. Again reiterating, this wasn’t readily apparent using base-platform analysis and/or quadrant-based outlier identification efforts.

Before Power BI added the capacity for mathematical-visualization filters — and the examples provided above are surely of the most basic [kept basic so as not to distract from illustration of the concept ]— the platform was still useful; it just wasn’t as useful as it is now. That’s a good thing — the changes having been made. Prior to the addition of mathematical-visualization filters both analysis and presentation [SEE BELOW] were simply harder than they had to be. Knowing this, the engineering team at Power BI remedied what was one of the longest running and [presumably] simplest to fix issues [my apologies if I’m underestimating the difficulty of fixing this issue — to be clear, I am NOT a software engineer]. Kudos to the Power BI team.

Prior to the addition of mathematical-visualization filters, basic calculations like “averages” had to be self-calculated and self-applied [via third-party applications] to data-visuals. Not only was this an “extra step” in analysis, this also required self-identification of outliers using the “eye test” or the basic quadrant-based approach. Again, kudos to the Power BI team for the remedy.

When it comes to data analysis, the far ends of the spectrum matter and granularity matters. Power BI gave users a bit more of both with the addition of mathematical-visualization filters. Given the Power BI engineering team’s history of innovation and of smart-evolution, I can only look forward to future iterations of the platform. This iteration, however, is highly useful and one in which I fully recommend a test drive.

Good luck, everybody.

DISCLOSURE:

[1] None of the companies/enterprises/entities mentioned in the above note were involved in any aspect of the above note preparation or in any aspect of the preparation of any data or information presented.

[2] I have not received compensation from any of the companies/enterprises/entities mentioned in the above note nor any other party for the writing of this note; and/or any other note written prior [regarding any of the companies/enterprises/entities mentioned in the above note].

[3] I may or may not have received compensation for providing research and/or consulting regarding one or more of the above companies/enterprises/entities mentioned in the above note from third-parties at some point prior to writing this note and/or I may receive compensation for providing research and/or consulting regarding one or more of the above companies/enterprises/entities mentioned in the above note at a later date. This includes providing subscription-based research and/or consulting services to both institutional and retail-based capital market participants.

[4] Dallas Salazar: I own, or my family or company owns, or I have influence over some amount of the outstanding shares of one or more of the following companies mentioned within this note.

[5] The article does not constitute investment advice. Each reader is encouraged to consult with his or her individual financial professional and any action a reader takes as a result of information presented here is his or her own responsibility.

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