Benefitting from the combination of Analytics, Big Data, Cloud and Multi-Platform development

According to Gartner Inc., one the world's leading information technology research and advisory companies, the top 10 fastest growing strategic technology trends include Analytics, Big Data, Cloud and Mobile applications. To the latter might be added Multi-Platform support, since users may want to view your Web applications anywhere, at any time. [1]

However, trends come and go, without necessarily having practical significance. Can businesses benefit from the combination of these technologies? The answer is yes and here’s a brief step-by-step explanation of how. We’ll start with the definition of Analytics.

The three aspects of Analytics

There are actually three aspects to Analytics: descriptive, predictive and prescriptive. The descriptive part involves illustrating the meaning of data. For example, this could mean displaying an explanatory message (text), sets of rows and columns (tables), bar charts, histograms, etc. (graphs). 

More advanced programs will depict the information using sophisticated 2D and 3D graphics, such as the realistic rendition of a cockpit in a flight training simulator. This would be analogous to the simulations that are provided in games, but in this case they would be intended to accurately represent reality, rather than some imaginary world.

The next step in Analytics is to predict the likelihood of a given set of possible outcomes, by forecasting the results for various scenarios. This is common in finance, but it is also applicable to simulations of equipment, technical training and marketing situations (e.g. depicting a given customer in a specific scenario, such as buying a condominium or a car).     

Finally, there is prescriptive Analytics, which involves recommending some prescribed action, based on the above results. In other words, you must ask: “How can my client save money, or otherwise benefit from the above predictions?”  

Therefore this is the key to profiting from Analytics … it is the value-added feature that accompanies the treatment of data in fields where information overload is common (e.g. energy management, financial risk management, healthcare management, etc.).

Applying Analytics to Big Data

             

This brings us to the term Big Data, which is bandied about freely these days, although not everybody who uses it actually knows what it means. So, what is it and what is its relationship to Analytics? The key here is information overload: there is simply too much data being collected for most applications, such that it becomes a challenge just to make sense of it.

Thus the treatment of Big Data involves extracting what is relevant to the user, much like the objective of Analytics. However, in the case of Big Data the amount of data is huge, such that the application of Analytics is intended to both display and analyze a relevant portion of that data coherently.

In particular, a given outcome can be predicted with a specific confidence interval (e.g. “there is a 76% likelihood that this unit will fail within 2 weeks”) and remedial action can be prescribed accordingly.  Thus the 3 aspects of Analytics can be applied to the Big Data, in order to recognize, predict and correct problems as they occur.       

Deploying Cloud applications to treat Big Data using Analytics

Typically the above scenarios are addressed via a Web-based Portal or Dashboard display, which allows registered users to monitor their systems and alerts them if there is an unusual situation.  This usually involves a customizable graphic user interface (GUI), which can be divided up into parts that correspond to the specific points of interest for users.

For example, critical data might include:

- Operating parameters of HVAC equipment for an energy management application

- Fuel consumption for a fleet of vehicles or

- Physiological measurements for a medical monitoring system

All of these systems might be accessed from a central server that provides customized Portals or Dashboards for a given set of registered users. This is, by definition, a cloud application and it is increasingly the preferred choice for deploying robust Web applications.

Dealing with government/industry Regulations and Security

After these users log on securely, they should be presented with the specific data that is of interest to them, in a format that makes sense. In other words, they are not expected to waste their time trying to understand superfluous information that is not relevant to their goals.

As a result, the treatment of Big Data via Analytics usually requires considerable subject matter expertise, which may or may not be regulated by government authorities.

This information is typically sensitive, such that measures to deal with security and encryption then become necessary. Furthermore, the presence of large numbers of simultaneous users will require the system to be both scalable and optimized for performance.

Finally, in addition to accessing the information that is available on their personalized Portal or Dashboard via a browser, users will also likely want access to this via a mobile device or tablet instead.

Developing mobile and platform-independent applications

In order to make these applications accessible on either mobile or other platforms, they must be developed with multi-platform use in mind. With a Cloud-based application, the server-side software becomes transparent to the user, who can access it via a browser on a PC or Mac, as well as a tablet or mobile device.

Thus the server can run the development environment (be it Microsoft C# or Open Source Java) and the operating system (such as Linux or Windows) of choice. Hence tools like Knockout GS and Kendo UI can be used to make the applications portable, in order to ensure that each user’s platform is identified and the available screen space is optimized.

Designing the application with a Model View Controller (MVC) paradigm helps ensure that there is a separation of concerns for the development team. In other words, the various developers on the team might focus exclusively on the client side, back-end or middle-ware.

Fine-tuning the user interface

Development of the GUI can be made even more granular, by using the Model-View-View-Model (MVVM) approach, which initially grew out of the Windows Presentation Foundation (WPF) initiative. This approach further breaks the GUI layer down into its own model, view and controller paradigms.

The use of specialized Java Script tools (such as JQuery and Ajax) allows further fine-tuning of the client-side software, thereby diminishing the load on the server and enhancing the response time for end users. For example, JQuery allows “accordion” type screen sections that dynamically select only relevant blocks of information for display on the screen.

Similarly, Ajax can be used to dynamically refresh only specific sections of the display, as required, thereby avoiding the need to annoy users by frequently refreshing the entire page. The result is that the user can experience roughly the same kind of performance on whatever platform he or she is using at any given time.

Aiming for a satisfying user experience

Thus the bottom line is that users must feel that their time is optimized and that they are as comfortable as possible with the application. For example, a surgeon checking a patient’s status while golfing on his day off can access the same information on his smart phone that he’d see on his PC or tablet at work.  

Similarly, a salesperson in the field can use her smart phone to access the same mission-critical information that she’d have at her desk, back in the office. By the same token, the system should be intuitive enough that new users can benefit from it as soon as possible, without the need for a steep learning curve.

That is a critical factor for sales as well, since your sales force will need access to easily understandable and demonstrable prototypes of the system, in order to show it to potential or existing clients. Whatever the circumstances, the use of these technologies and development strategies can result in significantly increased profits, by providing your customers with value-added features that zero in on precisely what they need to know.  

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[1] From: apmdigest.com/gartner-top-10-strategic-technology-trends-for-2013-big-data-cloud-analytics-and-mobile