The evolution of Business Intelligence (BI) and analytics began in the back offices of IT departments where the proverbial gatekeepers lorded over reporting technologies, while disseminating largely historic data.
More recently, the business was able to take control of these tools due to developments in Data Discovery, Cloud Computing, Open Source analytics, and Big Data technologies that considerably expedited the process of supplying insight to influence decision making. Organizations were not only able to analyze what had happened, but also what was taking place at that moment and, in some instances involving advanced analytics, what was forecast.
Looking forward, however, the evolution of BI and analytics will directly impact the consumer—those who are on the receiving end of the information and discernment that business users can gain regarding their products and services. In this respect BI and analytics is stepping out of the office altogether and, courtesy of mobile, Cloud, and Big Data technologies and the rapidly multiplying data streams found in the Internet of Things, is showing up in watches, phones, home appliances, personal computers, and everywhere else consumers demand access to their data.
Enterprises that are able to take advantage of this trend and shift the way they utilize their BI and analytics to a customer-driven focus can effectively monetize data to the point where they no longer merely support decisions and courses of action, but actually provide the consumer with what he or she needs most. As noted in the Gartner Business Intelligence and Analytics Keynote, 2014 “…business analytics is moving from internal support to a sellable asset. It is the task of business analysts—as specialists in the form of analytics—to advocate these changes to the business.”
Advocating these changes ultimately requires personalizing BI and analytics into formats, products and services that customers desire most, which makes these technologies as ubiquitous as data themselves.
Personalizing BI for Customers
Personalizing an enterprises BI so that it targets customer experience and data monetization involves reshaping business models and analytics deployments to prioritize the needs of consumers. Doing so effectively requires organizations to place higher valuations on:
- Dark Data: Enterprise data involves that which are already readily used for analytics purposes and that which are not. Organizations generally know what types of data are most valuable to customers; it is also key to determine what other data might have additional auxiliary value and to test customer demand and delivery methods for them. Virtually all data have value; one must simply determine the appropriate use case.
- Transparency: Although this term still applies to regulatory agency compliance, it is increasingly becoming used as a way to facilitate customer interaction by extending BI and analytics to consumers. Organizations are sharing their data more and more with customers, enabling greater customer agency, interaction, and satisfaction. Allowing customers to access more enterprise data enhances trust and company image while personalizing customer experience.
- Customization of Customer Experience: Whether analytics are deployed merely to facilitate recommender engines and predictive widgets on web sites or to determine insurance company rates, the personalization of BI should facilitate a unique customer experience. Customization is not only based on an organization offering its data to customers, but also on enabling customers to create and keep track of their own data via any range of apps and remote devices that involve about connectivity and access.
Internet of Things (IoT)
Use cases for the personalization of BI and analytics almost always involve the IoT, the demand for which is currently projected by an Infoblox survey to exceed the network bandwidth of the majority of enterprises planning to utilize it. Cloud, mobile, social media, and Big Data technologies have created the environment in which more and more utilities, appliances, and devices outside of traditional desktops are accessing and generating data in a tightly interwoven web largely hinging upon semantic technologies.
One of the most eminent of the emerging application of personalized analytics is the conception of a smart home, which extends the notion behind smart phones and smart watches to household devices—including thermostats, clothes dryers, door locks, and more. These devices utilize personalized analytics to determine what settings are most appropriate for cycling clothes based on utility rates, when to disarm and arm home security systems, and other conveniences that both analyze and generate data.
Cars and trucks are increasingly being equipped with gadgets and sensors that can transcend GPS systems and not only detect collisions but also call for emergency services. More common use cases of personalizing are found in the bevy of hand held smart device applications that provide analytics for everything from counting calories to measuring jogging/walking distance, as well as dashboards that service providers (utilities, financial, health care) offer in which customers can scrutinize their data and make changes to their services.
One of the consequence of personalizing BI and analytics for customers is that it provides the enterprise with a viable means for the most effective advertising—directly to its customer base—in a way that transcends mere recommendations. Apps that enable end users to generate and monitor their own data provide valuable data that can in turn be analyzed by the enterprise to not only develop future customized products and services, but also to package them appropriately. An article from ReadWrite denotes this advantage to personalized analytics with the following excerpt from a recently released industry report that states:
“Much like Apple bundles its devices with a million apps in the App Store, Google bundles its online services with Android devices. Through these services, Google collects user intelligence and creates opportunities to expand its ad inventory. Amazon as well bundles its e-commerce services with subsidized Kindle tablets (and soon smartphones) to drive user traffic to its virtual store shelves.”
In addition to fueling advertising and tailoring products and services to specific customer interest, personalizing analytics and BI to suit the needs of customers also facilitates an interactive web in which these products and services are integrally linked. By personalizing BI for customers and additionally analyzing which of a company’s services and products are resonating with which customer base, enterprises can effectively innovate other related ones. Doing so can engender a snowball effect in which customers are effectively dictating the business output of a particular enterprise. An article in The New York Times describes this potential use of personalized analytics as:
“The more people use your technology, the more likely other people are to use it. Economists call this the network effect…It can be any software that attracts a following of developers who write applications that link to it or work with it. The company behind a successful platform creates a technological and business ecosystem and reaps the benefits through increased sales of software or hardware.”
The most daunting caveat for the degree of transparency and sharing of data with customers—and analysis of the most widely used or potentially lucrative of such data—is issues of governance. These concerns are exacerbated by the fact that in many instances, governance is driven by regulatory and compliance protocols. Yet when applied to rapidly generated Big Data provided by the IoT and other uses of personal analytics, these issues are heightened by ramifications regarding security, privacy, and ethics.
To account for them from a governance perspectives, organizations must scrutinize their data discovery process (even if its automated), implement security measures such as masking and encryption, and ultimately shift the focus of governance to include the customer in much the same way that they has shifted its BI and analytics to do the same. The result is a set of governance roles and responsibilities that reflect the driving customer-first focus.
What’s at Stake
There is a level of excitement about the contemporary data sphere and its ability to personalize BI and analytics for customers and effectively allow them to dictate the business climates and models of the enterprise. This zeitgeist is witness to the convergence of a number of highly influential technologies—analytics, social media, the Cloud, Big Data, and semantics—manifested in the Internet of Things and other applications of personalized analytics that endow organizations with a greater responsibility to their customer and, simultaneously, a greater responsibility of the customer to the organization.
The most sapient of enterprises will leverage this reality to their advantage. As stated in a Gartner article from Frank Buytendijk and Jay Heiser: “Big Data, like most innovations, is a double-edged sword. It brings huge benefits. It allows organizations to personalize their products and service on a massive scale; it fuels new services and even business models, and can help mitigate business risks.”