4 Mistakes to Avoid in Your Data Analytics Projects



hey everybody its Adam Jorgensen here with pragmatic works and welcome back to our everyday series and today we're talking about mistakes you want to avoid with your big data analytics projects and so we surveyed all of our customers that are doing big data using it for analytics using it for kind of scale out processing and we found ones that are really loving in we found ones that are you know almost on the verge of moving away from it they've tried it they're not they're not in love with it they're having problems and so we did kind of a deep dive with some of those customers that were having some challenges and what we found was there's a lot of similarities and so I want to share those with you today so you can avoid them or so you can identify them in your own environment and really there's four big ones that that we call out today and that's that they're not flexible they're not fast or accessible right and so we've created this Big Data environment and it's very technologically advanced but the organization can't use it it struggles with performance we we overcame a technology problem but not really a business problem and so keep in mind the whole reason to do analytics and to use maybe a technology like Big Data or a cloud service is to make it more accessible faster and more flexible so keep those things in mind if you're not achieving those step back take a look at how you're approaching it and maybe how you're aligned with those goals second it's not meeting the business needs so maybe we're not analyzing enough data maybe the business can't get to that data with something like power bi so the the clusters processing jobs are running or scoring data we're making predictions but the business isn't connected to that data and so being able to connect the business to that data is incredibly important and so make sure that that's happening make sure your business feels like they're a part of the process when the business is part of the process make sure the audience is appropriate the third area we found was that the audience was too narrow may be a very very small team that's great for a pilot to prove that the technology works but if you say hey we use big data analytics to run our business and you're only using it to help you know a couple of people in kind of a niche department maybe Manufacturing analytics or something like that that's great that's a good start but keep building on that because the rest of the company is gonna feel like they're not getting the same level of buy-in and that's super important we have to make sure that you know we continue to grow the audience for that type of data that type of analysis last but not least what are that what are the people gonna do when they get the data – are they data literate do they understand how to access and analyze and understand the data that they're getting what to do with it there's got to be a little bit of upfront investment to get them connected with something like either power bi or Azure blob stores or data Lake where they know how to connect and access that data they don't have to write a lot of code they don't have to be developers they get to do their job as analysts or managers or line workers what-have-you but they've got to be data literate to work with all of this wonderful technology and all this wonderful analysis that you guys are providing so it can be a little bit of a jumble sometimes to try to pull it together pragmatic works has a great proven strategy for how to do that we've helped over 7,000 customers over the last 10 years solve these kinds of problems so we'd love to talk to you let us know if you're interested if you want to know more we'll get you on the phone with some of our really incredible architects and they'll help talk you through it so click the link below for more information and we'll see you next time on Azure every day

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