While certainly exciting, buying a new house, can also serve as a revealing exercise in understanding data science.
A couple of weeks ago I went to my bank to investigate my financial options for buying a new house. To my surprise, my account manager gave me a stack of paperwork to fill out—and I soon realized that my bank was already in possession of 90 percent of the information I was being asked to provide. So why was I having to take the time to fill in information the bank already had, or could easily acquire? And more importantly, why couldn’t my account manager quickly access information about my client status and my personal preferences, and immediately provide a tailored offering, decreasing the chance that I would look elsewhere for this service?
Figure 1. Centralized, Decentralized, and Distributed Networks. A distributed, virtualized approach to database management enables quick combination and analysis of large volumes of data—where and when it is needed.
Source: Paul Baran, Rand Corporation.
I wrote in one of my recent blogs about the issues and solutions related to quickly combining data that comes in large volumes by focusing on data virtualization and cloud. This can enable seamless customer interactions and decrease client churn, be it in financial services or in the telecom sector. But what is required at an organizational level so that people, process, data, and things come together to enable a superior customer experience and create entirely new revenue possibilities?
In today’s enterprise world, three major centers of influence—and budget—are coming together in the data analytics space:
- IT departments manage the data infrastructure and tend to focus on an infrastructure-centered view. Their focus is in understanding how the large volume of data available in the IT system can be effectively transported, stored, and managed. They focus on the inside-out question: “how can big data help solve critical business issues?”
- Business units tend to focus on outside-in business intelligence, focused around particular client use cases. Their focus is in trying to find out whether their customer issue can be solved by leveraging existing and new sets of available data.
- And a new figure, often referred as the Digital or Big Data Czar, now tries to mediate those two views. The focus here is on accelerating the organization’s capabilities to extract value from data through an analytics approach, often acting as a Center of Competence supporting the two other parties
The issue is that these parties often do not come together effectively around specific business issues. They do not speak the same language and do not align along a clearly agreed upon operational model—in a way, very similar to how eCommerce managers initially struggled to work with their CIOs and business unit leaders 15 years ago, when the eCommerce revolution took place.
In Cisco Consulting Services we focus on helping customers make rapid progress in the digital space by accelerating their ability to develop new offerings or services and to use data to create replicable business insights, enabling fast and effective decisions. Today, unlike 15 years ago, the Internet of Everything allows us to quickly bring together a dynamic ecosystem of data scientists and user experience designers who can focus on developing small, iterative analytics use cases that can tangibly “move the needle,” on top of an analytics platform. Our clients’ Centers of Competence quickly become a rapid prototyping environment that adds immediate value to the business: use cases quickly move from prototype to rollout once tested in a “sandbox” environment, and new use cases are continually developed.
This iterative approach allows the three parties to more effectively come together and execute quickly on tangible and critical use cases. By leveraging a rich combination of business analytics, a virtualized data architecture, and a globally deployed inter-cloud platform, we enable intelligent access to information anywhere, any time, and on any device. When the network becomes the database, decisions are taken in real time and at the edge of the network, adding immediate and critical value to the work field personnel need to perform—be it in the branch of a bank in Amsterdam, at the front desk of a citizen services center in Rio, or at a retail store in Shanghai.
Back to my home-buying experience in Amsterdam: with data analytics capabilities and an inter-cloud model, my bank of choice will be able to see what real estate sites I have been browsing and which mortgage options I have reviewed on their website. They should be able to combine that information with data they already have to quickly provide me with a tailored offering, cleared and approved before I even walk into the branch. This approach applies the prescriptive analytics capabilities of digital retailers to the brick-and-mortar world to transform my experience and help me make better, faster—and more profitable—decisions.
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