Predictive analytics represents the culmination of technology initially pioneered in Business Intelligence tools, especially when one considers their prescriptive capabilities that not only determine the likelihood of future events, but also prescribe the most viable courses of action to deal with them.
When combined with sets of Big Data—which are constantly updating and impacting tactical data—there are several reasons why the Cloud provides the most suitable environment for performing predictive analytics:
- Scalability: The Cloud’s extreme scalability can readily accommodate the enormous quantities of Big Data, even for continuously running applications.
- Integration: Data can be easily copied from the Cloud to on-premise environments and vice versa, which enables the integration of a wide variety of data sources to provide a comprehensive overview of one’s data assets (and, courtesy of analytics, their relevance to business problems).
- Accessibility: Cloud resources can be accessed anytime, anywhere, and by anyone with an Internet connection. They facilitate an ease of accessibility that is virtually unparalleled regardless of how many data centers in physical locations an organization has.
And, according to RapidMiner CEO Ingo Mierswa, running predictive analytics in the Cloud has one other benefit that immensely assists the effectiveness of predictive analytics for Big Data:
“The real problem is you never know how much number crunching you need to do next… We wanted to find a way to outsource the computational demands for the number crunching of our customers. The Cloud is the ultimate solution for that.”
RapidMiner Cloud
RapidMiner Cloud was created to address those computational demands. There is a plethora of aspects about the solution that are notable in regards to predictive analytics and their utility for Big Data. It reinforces the growing trend to utilize the Cloud as the de facto environment for performing analytics with its three core features:
- Elastic Computing: Elastic computing enables organizations to replicate data from on-premise sources to the Cloud to perform analytics.
- Cloud Connectors: The product comes with connectors for some 300 of the most widely used Cloud applications such as Salesforce, Amazon Web Services (AWS), Twitter, and others, which substantially enhances the ease associated with taking data from those sources and running predictive analytics on RapidMiner’s Platform.
- Cloud Repository: Cloud Repositories function as places in which one can store data and analytics processes, and share them with other users. This feature may be of particular use for consultants.
“For good reasons, many companies have all their data on premises; moving data in general is a bottleneck,” Mierswa observed. “Since that is true, at the same time not all analytics will happen in the Cloud, but it seems to be going there. More and more data is living in the Cloud in the first place. Customer data right now such as Salesforce and other CRMs are all Cloud based uses; the data is already there. If you’re doing a lot of comparisons, marketing calculations, and predictive lead scoring, those are scenarios where the data is already living in the Cloud.”
Reducing Data Science/Analytics Complexity
The growing proclivity to access the Cloud for analytics purposes ultimately benefits upper level management and business users, since it provides them with a means of aggregating and analyzing data in a timely manner that can influence decisions and actions. The offerings of RapidMiner and other providers of Cloud analytics are effectively reducing the difficulty associated with analytics by not only improving the logistics involved (pertaining to the data’s environment and the means of accessing them), but for actually running them as well.
RapidMiner is especially useful in this regard. RapidMiner Cloud (the functionalities of which are also included in another product, RapidMiner Studio) is endowed with Wisdom of Crowds Operator Recommendations—an application of predictive analytics that provides the proverbial “next step” for properly implementing analytics on data sets. With this feature, laymen with no understanding of code or programming can get recommendations for the most useful algorithm, predictive model, or telemeter settings to deploy to solve their business problems. This feature greatly improves time to insight and helps to provide demonstrable business value for predictive analytics to end users who may be intimidated by the statistical rigors of Data Science.
Spawning Machine Learning
The capabilities of the Wisdom of Crowds feature of RapidMiner Cloud allude to another aspect of predictive analytics that is swiftly gaining credence within the data landscape—particularly with the emergence of Cognitive Computing. Machine Learning algorithms are responsible for the ability of certain applications to take established precedents (whether in regards to a customer’s profile in the form of personal data, or in regards to data attributes in general) and make logical inferences based on them that affect future processes related to such data. Examples include everything from personalizing widgets for first-time e-commerce customers to an organization’s website to correctly categorizing data types and attributes according to established governance procedures. Machine Learning is engendered by predictive analytics, and is an application of the latter that is emerging in a number of contemporary and future Data Management platforms and services.
The Internet of Things and More
The uses for predictive analytics span nearly every vertical industry and include, most saliently, areas of research and development related to science and government. From a business standpoint, they can readily inform marketing and sales processes (both related to ecommerce and that found in conventional brick and mortar stores) by identifying cross-selling opportunities and means to reduce churning. They also play a vital role in the monitoring of equipment, its maintenance, and parts sales responsible facilitated by the Industrial Internet.
In addition to assisting original equipment manufacturers with fleet management, predictive analytics can impact the daily lives of those with devices connected to the Internet of Things. RapidMiner is currently working with a customer in the automobile space in which the former’s technology is used to predict when drivers are apt to fall asleep while in transit, and take preventative action to assist them before any substantial damage is done.
“For the end user, there’s so many predictions that you can’t actually tell that there’s prediction happening in the background,” Mierswa said. “If the car predicts you falling asleep and keeps you awake by vibrating the steering wheel for example, the prediction itself is somewhere hidden in the car. For the customer [churning] example the same thing is true. You create the prediction that those are the clients who might go away, and then you feed those predictions into the user work environment—in this case to save people.”
Additional use cases for predictive analytics involve providing personalized news services for people which scan the internet and determine what articles might be of interest (and which are not) for a particular consumer’s profile.
Advancing Analytics
The advent of RapidMiner Cloud is indicative of some of the most prominent trends to influence Data Management today. It empowers the business and myriad other non-technical users by extending the power of predictive analytics beyond the narrow region of Data Science and democratizing it for the enterprise in general. It incorporates a bevy of ways to make the most of the Cloud and its applications for marketing and sales data, as well as for Big Data applications in general.
It also underscores the growing need for analytics in a world in which the Internet of Things and ubiquitous, continuous access to data are the new normal. Finally, its Cloud connectors, repository, and elastic computing capabilities are indicative of the impending reality that the Cloud will play in facilitating that access.
