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The Future of Business Analytics: Mobility, Collaboration and Big Data

Sean Salleh 24 Jul 2013 Modeling methods

Imagine a world where business analytics are as common on a manager’s PC or mobile device as, say, word-processing applications. Picture business information nervous systems that spread out over the Internet to probe for big data inputs, in order to bring the information back for processing for immediate recommendations for action. Or that connect with social networking capabilities to allow collaborative business modeling. Even if this sounds like science fiction, the components for these futuristic visions exist today.

Technology trends towards mobile computing and other changes Image source: Flickr.com

PC and Mobile Business Analytics

Although mobile applications have also been built where nothing previously existed, many desktop PC programs also have been the starting point for mobile apps. Business software is transformed into mobile versions that often pare down functionality to the essentials to increase ease of use and take account of the small screen size. In the same way that business analytics is available as a cloud computing service for PCs (Analytica Cloud Player, for example), it becomes available to tablet PCs, smartphones and phablets as well. The clarity of presentation of Analytica also makes it that much more suitable for business people who are not business modeling experts.  As an example, the potential power and simplicity of ‘analytics democracy’ is making Singapore encourage the use of analytics with a government drive to involve solutions providers in as many industrial sectors as possible.

How big data from social networks feeds into business models Image source: Flickr.com

Business Analytics Collaboration – Being Forced to Improve?

Giving useful information to a competitor doesn’t seem like a smart way of doing business. Give and take business analytics sharing however may be not only possible, but in some cases necessary if enterprises are to continue to hold their own. It’s a model that has already been applied in areas such as product development (models of automobiles that differ only in brand) and supply chain (shared warehousing for pharmaceutical firms). Correctly applied to business analytics, collaboration can increase organizational agility and meaningful insight. Models that use the right technology for speedy modification and adaptation (Analytica’s Intelligent Arrays, for example) allow sharing of robust core models with easy customization by parties concerned.

Big Data – Big Deal?

Big data is a talking point because people have woken up to the fact that they can now obtain data on practically anything, anywhere: from customer buying patterns in Shanghai to the status of its company coffee machines in Sydney.  What is less clear is how such coffee machine information contributes to running a profitable business. Just because the data is out there does not mean that organizations should feel obliged to use it.

Smarter Use of Big Data in Business Analytics

By targeting the specific data that can be used to answer key questions, organizations can get to the real value of big data. The quantity of data can still be considerable, for example, linking hitherto isolated databases of information from marketing, sales, customer service and e-commerce. Distributing processing of this information using Hadoop is a solution already being used by a number of enterprises. Risk modeling, sentiment analysis, customer churn analysis and new product research directions are just some of the potential uses of data that is cleansed and transformed into usable datasets that can be used by Analytica to model different situations and outcomes.

If you’d like to know how Analytica, the modeling software from Lumina, can help you to forecast and plan for the future, then try a thirty day free evaluation of Analytica to see what it can do for you.

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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