Video: Business Analytics Fuels Data-Based Decision Making

With so much data being generated by social media and mobile devices, organizations have the opportunity to leverage these data points to make informed, strategic decisions and better serve their customers.
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>> Every 60 seconds there's at least 98,000 tweets, uh, 695,000 status updates, some 11 million instant messages. >> Terabytes, gigabytes, metabytes, even new terms of vocabulary. There's lots of data being, being generated. Uh, but the big deal about that is we can now take advantage of that, and we can get insights from that. >> We've seen so many examples of customers that are being able to take the tweets in real time, and change major things, like the manufacturing of the product, how the product tastes, how the product smells, and be able to change it over night and then see immediate positive reactions to it from consumers. >> It really opens up an opportunity to be able to bring in new data, create those relationships with our traditional data, and get a more holistic picture. >> Many businesses don't necessarily have big data. But they do have large data sets. They are trying to use those math and algorithm skills to gain real value from those huge data sets. >> I've been at CDW for 16 years. And I was the very first analyst that the company hired in the marketing department. CDW's a very data rich company, and I think it's taking all that data, and really understanding what that business problem is, and, and, putting together a story of, of what it means. >> There's the struggle to take the mathematical model and really relate it back to the business world. At the end of the day, I need to do X Y Z to drive profitability, or to drive revenue. >> There's a few things that I've learned over the years when working in the business analytics on how to get smarter in terms of delivering analytics to an organization. The first is, um, having a clear business problem that you're trying to solve. Making sure that you have that agreement with your business stakeholders, what that problem is. The second is having a strong relationship with your IT organization to make sure that they're supporting the infrastructure, and getting all the data in from those disparate sources in a single environment. Um, and then the third is, you know, having a skilled staff, um, that is experienced with a variety of different techniques to be able to quickly, um, process the business problem. >> The cool algorithm or the cool math is not what the business leaders want. They want to know what action should I take based on this data, based on what your models and your algorithms are telling you to do. >> CDW's in a unique position to be able to offer, ah, the best solution, the most appropriate solution for the customer's needs, to not just offering one particular solution. >> We provide a consultative and, and really a trusted adviser type of relationship. >> That's why we have data scientists or consultants, to come in and understand where your data is, is it really valuable or not. >> We can take a look at what the customer's requirements are. We can help them to find their strategy. We can help them find out where their skill gaps are. And then we can, depending upon where they are, we can then choose from the best of breed vendors. And then help them implement it. >> Most of the technology we're talking are additive. You can add on top, you can expand, you can pump data in or out. At the end of the day, when you use analytics, your end users have to be happy. >> It's a conversation that we're having, both with our business stakeholders and with our partners, to tell them what to do differently, what to do smarter. And what's working what's not is, is invaluable.

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