3 attributes that define a powerful analytics environment

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3d_speed_accuracyIn my last post I described "4 adaptability attributes for analytical success," and in the past I've discussed the strategic role analytics play in helping organizations succeed now and into the future. Now I'd like to discuss three attributes that define a powerful analytics environment:

  • Speed
  • Accuracy
  • Scalability

[NOTE: Any subliminal message you may see in the list above was solely the intention of the author.]

While most people agree that analytics are powerful, I'm focusing on what makes the environment in which you run analytics "powerful." As I did in my last post, I'll present what these three attributes mean from both a business and a technology perspective.

  • Speed
    • Business: The environment allows you to fail fast which means you have time to try alternative analyses, evaluate the results and choose to either move something into production (or take an action based on it) or move onto the next idea. If you did find an analysis useful then speed also means you can deploy it rapidly.
    • Technology: Speed is relative and is defined as having an architecture robust enough to service all of your users in a reasonable timeframe. It means the architecture is flexible enough to allow rapid movement from development to production so that value can be created as soon as possible.
  • Accuracy
    • Business: Accuracy often improves based on the number of iterations that can take place in a set time period, so speed and scalability both impact how close to the target you can get.
    • Technology: Garbage in = garbage out. Accuracy of data means reducing the opportunities for errors to be introduced when preparing the data for analysis. Data quality algorithms are used to ensure the data is as good as possible for analysis and reporting.
  • Scalability
    • Business: Your architecture isn't limiting your analysis. As the size of the data grows, your environment for processing grows to automatically accommodate your processing requirements.
    • Technology: Analytical workloads are variable, especially compared to repetitive query types of work loads, so the environment should be able to seamlessly add additional compute nodes or spill to disk on the fly to avoid job failures.

So, what are your next steps? Read more about what makes for a powerful analytics environment.

 

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About Author

David Pope

Technical Leader, Senior Manager US Energy

David leads the pre-sales technical team for SAS US Energy which solves business problems in the Oil & Gas and Utilities industries using advanced analytics. He earned a BS in Industry Engineering and a Computer Programming Certificate from North Carolina State University. Furthermore, he has over 27 years of business experience working with SAS across R&D, IT, Sales and Marketing in the Americas and Europe. He is an expert in working with data and producing insights through the use of analytics. David has presented at SAS Global Forum, the 2012 SAS Government Leadership Summit, IBM’s Information on Demand(IOD), EMC World, CTO Summit Conferences, is the author of the book: "Big Data Analytics with SAS", and he currently holds 11 patents for SAS in several countries: US, CA, Norway, UK, China, and Hong Kong.

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