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July 13, 2018
By Trey Johnson
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Data Access,Data Management,Data Preparation
I have had the great fortune of working with data in all shapes and sizes over my 25 years in the Business Intelligence, Data Warehousing, Data Management and AI/ML disciplines. And it goes without saying that – sometimes – a larger data set is of greater value.
However, is there a rush to think that things need to be bigger simply because they’re in the cloud? Yes, the cloud opens the door for data of all sizes to be valuable. But hopefully, the assumption the cloud is there to expand data sets (exponentially) isn’t the only assumption because, if it is, data which isn’t BIG may be getting very poorly served.
Getting Big Value from Small Data – Our Case (Study) in Point
Recently, ZAP entered into a relationship with PASS (The Professional Association for SQL Server) as a Knowledge Partner. One of our first undertakings was bringing forward a combined data management Case Study around multiple datasets. These datasets included:
There is not an overwhelming number of tables for these datasets. However, the value comes through the most prominently by enabling calculations and correlations even when the data doesn’t match up neatly! This was probably the coolest part and I’ll explore this a bit more deeply next…
In short, what ZAP’s case study for PASS showed very quickly was, yes, you need to do the work to give smaller data bigger value. But you absolutely can get value from it!
Enrichment + Calculation = Insight
One very cool scenario with our Case Study for PASS was the ability to use Geocoding information on Member, Event and Local Group data. The reason this was particularly good was, in the absence of a direct relationship, there still was an ability to infer an audience within a certain geographic area. The Power BI screenshot below shows the “Count of PASS Members in the Area”, where the Area is Boston, Massachusetts.
In fact, nearly every data point presented in the above analysis was calculated. Elements like the Average Tenure of each PASS Member, the Average Event Registrations, and the number of First Time attendees across the 12 events, each have value.
The astute observer will even notice that we did a little interrogation of Twitter from ZAP Data Hub to understand what the Twitter Count was for the local groups’ twitter handles, in this case @BostonSQL and @Boston_BI.
If you’re looking at all this and not seeing big value, consider how our analysis benefits the local group leader in talking to others about their achievements and consider the insight it just might offer for the local group leader trying to persuade a speaker or a sponsor to participate in their events!
Maybe the Reason is a Change of Season
As part of the case study, we knew other attributes would open the door to exploring interesting hypothesis and, as of this writing, I can genuinely say “We’ve only just begun…”
Augmenting our lesser volume of data with geo-positioning and weather elements will support unique questions over time.
One thing holds true – often, the data you collect is a down-payment on a future analysis.
Since this is a blog and not a full scientific exploration, we’ll address the ways we wanted to make these “down-payments”. We’ll again use another Power BI example over this information specifically looking at our SQL Saturday Event Analysis.
In the above, you’ll see we used a Historical Weather web-service to enrich the data and help us see what things looked like on one particular SQL Saturday (#015 in Jacksonville). There are many questions which can now be asked of the data (across all SQL Saturdays) and we’ll explore them in a future Blog or White Paper. Questions many CEOs, CFOs and CMOs might want to have instant, dashboard-based insight on, such as…
In our SQL Saturday analytics, the local leaders can also see exactly what they achieved and benchmark year over year. They can see sponsor tallies and what the reach was on Twitter through speakers, they can assess the percentage of first-time speakers (8 out of 23 in this case!) and first-time attendees (which generally runs 40-50% of many events and is also impressive!)
It might seem odd to have focused on SQL Saturday in the early days in Jacksonville. But as an interesting point of trivia, Jacksonville and Orlando were where SQL Saturdays were born! In those cases, similar to the points of this blog, BIG value came from a fairly modest start.
Trey Johnson is ZAP’s Chief Evangelist. Based out of Jacksonville, Florida, he brings experience from leading various boutique BI software and national consulting companies. A published author, speaker, and consultant, Trey sat on the PASS Board of Directors over multiple terms, concluding as their Executive Vice President. He was a long-term member of Microsoft’s BI Partner Advisory Council and has spent the last 25 years delivering business intelligence, data warehousing, and data management solutions to businesses of all shapes, sizes and “data challenges.” Follow Trey on Twitter and LinkedIn.
#Data | #PowerBI | #SQLSaturday | #PASSSummit | #SQLServer | #GeoCoding | #DataEnrichment | #BigData | #DataManagement
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