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Interesting summary of Microsoft’s new Q&A feature. Can’ wait to check it out.
A few weeks ago, I highlighted a new data-analysis tool from Microsoft that automatically analyzes and visualizes data as users type into a search bar. The feature, called Q&A, is an impressive piece of technology, hiding some complex computations under a deceptively simple user interface. That was no mistake.
“Microsoft was not a design-first company for many years,” explained Microsoft Technical Fellow and Q&A team member Amir Netz during a recent demo of the product. “… You see the design-first [mentality] now permeating even the highest-end enterprise products.” (We’ll be featuring the best in experience design at our RoadMap conference next month).
The past: Let’s call it suboptimal design
What he means is that Microsoft was once — pretty clearly — guilty of the classic feature-first business software mindset. Throw in everything a power user might want and largely ignore the fact that most users don’t want to wade through…
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Finally! 🙂 time to briefly introduce our SmartCharts app for Excel now available at Office Apps Store (download here).
So, yet another “chart” app/tool for Excel…what’s the point? 🙂 what’s it for?
Well, beyond being a DevScope research project and a place where we will be test driving lots of #dataviz features using the latest technologies, there were some other drivers to build the app:
- There’s so much data available these days, but still most people can’t even acknowledge that there’s lot of hidden value in data, being it small, medium or big data… so they don’t even start exploring it 😦
- Provide a data discovery tool that keeps user focus on the data, not modeling, not chart designing, just slice, dice & visual data mining
- There are a lot of patterns for dataviz, but there seem to be a lack of…
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Below is an Internet Excerpt detailing which NoSQL solution should be used based on various requirement scenarios.
Applications are getting bigger
Web applications are increasing in scale. We have to store more data, we have to serve more users and we need more computing capability. To handle this scenario we have to scale. We can scale in two ways. We can scale up, that is buying better machines, more disk, more memory and so on. Or we can scale out, that is buy a lot of small machines and use them in a cluster. In big applications scale up is not an option. Bigger machines are more expensive and they have a limit, we don’t have a machine that can handle the traffic from Google or Facebook. Given this context, we need new databases, since relational database are not designed to run on clusters. Yes, you have clustered relational databases…
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Data viz and Football?? Yes please!
What a great explanation of what date scientists are and what they may actually do in their careers. Such a broad amount of skill and knowledge needed when dealing with data. Amazing!
The Strata division of O’Reilly Media recently published an report titled Analyzing the Analyzers, which serves as “an introspective survey of data scientists and their work” . The three creators of this survey were motivated by an apparent miscommunication between data scientists (and their corresponding experience, abilities and ideas) and employers (and their corresponding needs, desires and resources). The miscommunication seems to stem from the fact that common buzzwords like “data scientist”, “data analysis”, “analytics”, and “big data” are vague and often misused or misunderstood.
In order to establish more useful and objective terminology to appropriately connect employees and employers, the authors of this study created a survey to literally analyze the analyzers. What they found is that data scientists can (roughly) be categorized by four groups;
Data Developer: Developer, Engineer
Data Researcher: Researcher, Scientist, Statistician
Data Creative: Jack of All Trades, Artist, Hacker
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