IBM’s Insight 2014 conference, held in Las Vegas from October 27-30, was largely dominated by the fleeting nature of Big Data and Watson analytics (which was partially implied by the conference theme of “Seize the Moment”).
The rapid velocity at which such data is generated and requires analytics in real time or near real-time was underscored by three principle points of focus that can significantly impact Big Data analysis:
- The recent partnership between IBM and Twitter
- The recent partnership between IBM and Apple
- The forthcoming release of Watson Analytics
The analytics applications underlying each of these areas strongly alludes to both present and future trends in this aspect of Big Data Management, and its impact on the enterprise’s ability to derive timely insight from data. According to IBM Watson Group Vice President Steve Gold:
“When we talk about Cognitive [Computing]…we’re not talking about traditional programmatic computing. We’re not talking about computing that we’ve known for the past 60 years: systems that are based on rules and logic that go against structured data. We are talking about a next generation system… systems that learn, that get smarter. Systems that navigate in natural language, so there’s no menu and drop downs; it’s intuitive…Its systems that are able to manage the natural amounts of Big data that we have to contend with.”
The alliance between Twitter (one of the most ubiquitous social media platforms) and IBM is noteworthy for several reasons, not the least of which is that the massive amounts of data generated in real time by the former provide ample opportunity to flex the analytics power of the latter. The partnership will involve IBM’s integration of data generated via Twitter on certain Cloud services, which emphasizes the importance of leveraging the Cloud for Big Data analytics. One of those services will include Watson Analytics, one of the chief analytics mechanisms of IBM’s well known Cognitive Computing platform.
Of the numerous capabilities for analytics that Watson provides, its ability to aggregate and analyze data from numerous sources—up to the minute an analyst desires them—is perhaps best suited for the onslaught of tweets regularly generated. In a separate announcement issued during the conference, IBM’s entire analytics portfolio (which involves Cognos Insight and SPSS as well) will be accessible through the company’s Cloud Marketplace. Watson Analytics will be issued through the Cloud.
The partnership will also enable users to implement tweets into other Cloud offerings such as IBM Bluemix and Watson Developer Cloud. The latter contains analytics options that augment those of Watson Analytics; the former is a Platform-as-a-Service (PaaS). The alliance between IBM and Twitter will also develop on an ongoing basis, as the pair foster vertical industry-specific consulting solutions. The organizations have presently formulated an application for mapping customer behavior and sentiment targeted towards marketers.
In addition to utilizing the power of Cognitive Computing (which has hitherto historically been used to address data intensive calculations in various areas of research and health care, such as genomics) to address business problems, probably the most compelling facet of the impending release of Watson Analytics is it easy deployment. IBM plans to release the technology as a SaaS offering for free. Users simply input their data to enjoy the advantages of predictive analytics with an Artificial Intelligence technology that learns the more it is used. Moreover, the product is a self-service analytics platform based on natural language with an ease of use designed for end users (not Data Scientists). Additional facets of Watson Analytics that could disrupt the analytics landscape include:
- Statistics Free Predictions: Most predictive analytics applications are based on the careful creation and calibrations of statistical models engendered by Data Scientists. Watson Analytics, however, automatically computes relevant variables (and in some instances even identifies what they are) so that the user is simply responsible for asking a question or generating a query via natural language. The predictive capabilities of this technology have an assortment of applications that can improve business and operations, partly due to the fact that they do not require a lengthy waiting process for the work of Data Scientists. Predictions are enhanced by the technology’s Machine Learning attributes, which can draw connections between seemingly unrelated data sets and attributes to inform analytics. Predictions also include explanations for how they were derived.
- Visualizations: The visualization aspect of Watson Analytics is useful for not only demonstrating the findings of a host of data sources for analysts, but also for persuading the most apropos parties that require the most convincing. The technology has an assortment of infographics that can accomplish these objectives. Other visual representations include depicting the salience of predicted outcomes with size, colors, maps and more through ‘word clouds’, and using spatial distinctions to illustrate the likelihood of events and results. Consequently, data-driven scenarios can be explored through visual storytelling.
- Critical Automation: Watson Analytics is able to justify its description as a self-service tool due to the large quantities of typical analytics processing that it automates. That includes finding data that is relevant (which may even include that which does not immediately appear so to the analyst) and incorporating a diversity of sources and data types. The technology was created to work with all forms of data. It also automates various aspects of Data Quality and cleansing to ensure that data is trustworthy. The objective is for analysts to spend more time actually analyzing their data than locating and preparing it for analysis.
Additionally, Watson’s Cloud deployment is pivotal to incorporating large amounts of Big Data and producing fast analytics results for end users. IBM Business Analytics General Manager Alistair Rennie commented that,
“Watson Analytics is a completely born in the Cloud, Cloud based tool. We’re very passionate about that because we think that the first wave of adoption is going to be individual business users and we want them to be able to access this tool with zero friction.”
IBM’s Partnership with Apple
The partnership between IBM and Apple is largely based on enhancing the mobile capabilities of workers through tablets (iPads) and smart phones (iPhones) that leverage IBM’s proprietary software and applications. The significance of this partnership is readily apparent—by facilitating mobility for the enterprise, the two organizations are actually attempting to redefine the way that work is carried out in contemporary settings and, perhaps by extension, even life itself.
The result of the partnership is IBM MobileFirst for iOS Solutions. iOS is the operating system utilized by iPads and iPhones; IBM Mobile First is an exclusive platform for the former with a number of different capabilities including apps that have been built specifically for business users across a variety of industries. These apps are designed to expedite and reduce the complexity of accessing Big Data analytics, workflow and device management, Cloud accessibility and storage, integration and security, and more. Users can access these apps either through their own device or through the Cloud via the IBM Cloud Marketplace (which will also deliver Watson Analytics). The apps have been tailored to fit vertical industries such as:
The unique nature of the collaborative effort is underscored by the fact that IBM will sell Apple’s mobile devices to the former’s customers with apps for the relevant industry, and by the introduction of AppleCare for Enterprise, a support service accessible at any time and with options to provision on premise service facilitated by IBM.
The analytics landscape is rapidly transforming. The cognitive capabilities of IBM Watson have been specifically tailored to provide intelligence for the business community with a number of different measures in place that drastically reduce wait time, data preparation, and the implementation of quality standards. The collaboration between IBM and Apple ensures a degree of accessibility that is virtually unparalleled, particularly when one considers the ubiquitous nature of mobile and Cloud technologies that are at the core of IBM MobileFirst for iOS Systems. The alliance between IBM and Twitter emphasizes the former’s commitment to provisioning analytics for Big Data in real time, the utility of which actualizes the full potential of both Big Data and analytics.
Rennie made the following observation about Watson Analytics and its extended functionality:
“In addition to putting the most compelling set of analytic discovery tools ever conceived in the hands of business users, we’re actually solving the other part of the problem which is giving them access to the data they need in a secure way. Without those two things, you’re back to the curse which is visualizing a spreadsheet that I currently have conveniently on a laptop, which is not going to drive the insight we need.”