Last year, I read a very interesting blog post by Darwin Schweitzer a Microsoft technologist who discusses how to consider emerging technologies in the context of building sustainable enterprises. The blog posts relates the learning patterns organization adopt to newer data technologies replacing existing capability. The approach covers the strategic, organizational, architectural and technological challenges and changes with scaling enterprise analytics.
Three Horizon Model/Framework – strategic Data Mesh Sociotechnical Paradigm – organizational Data Lakehouse Architecture – architectural Azure Cloud Scale Analytics Platform – technological
Recently, I watched a webinar hosted by TDWI, Databricks and Carto. The topic was Unlocking the Power of Spatial Analysis and Data Lakehouses. A copy of the webinar and the slide deck shared is available here. What I liked about the session was the use of Databricks and a Data Lake to provide Spatial Data. There was also a brief discussion on the role of the Open Geospatial Consortium. This group is working on the specifications for creating a geoparquet file. For anyone with an interest in GIS, Mapping, Data and Analytics this is worth checking out!
For the last 20 years, ESRI has been capturing geographic and demographic data. In the 90’s Acxiom (and others) came up with Lifestyle Segmentation. ESRI started in 1969 and today maps “everything” down to the household level.
I’m surprised by the accuracy of Tapestry Segmentation. The top three segments in my zip code are: “Top Tier”, “Comfortable Empty Nesters” and “In Style”. Pretty accurate – with one data point. I’m an Empty Nester who would like to be “In Style” or “Top Tier” but feel very lucky and fortunate to be “comfortable”.
Differential privacy seeks to protect individual data values by adding statistical “noise” to the analysis process. The math involved in adding the noise is complex, but the principle is fairly intuitive – the noise ensures that data aggregations stay statistically consistent with the actual data values allowing for some random variation, but make it impossible to work out the individual values from the aggregated data. In addition, the noise is different for each analysis, so the results are non-deterministic – in other words, two analyses that perform the same aggregation may produce slightly different results.
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Recently I decided to move from AWS to Azure for the hosting of my “Sandbox” sites. With the move, I plan to add serve up live interactive data content highlighting different “data” projects of personal and professional interest.
Much of what I’ve worked on is contained on “corporate” portals and intranets. The move to Azure from AWS for “server based” content will allow more flexibility and access to Power BI, SharePoint and Microsoft Teams.
I had a chance to take a look today at Tableau Public. I’ve used it in the past but to be honest Tableau is my third favorite visualization tool (behind Qlik and Power BI). I was impressed to see they used an example mimicking the famous TED talk by Hans Rosling. If you haven’t seen this and you’re interested in data and analytics – its a must watch! 🙂