Developers And Their Business Counterparts Are Caught In A Trap
They swim in game-changing new technologies that can access more than a billion hyperconnected customers, but they struggle to design and develop applications that delight customers and dazzle shareholders with annuity-like streams of revenue. The challenge isn’t application development; app developers can ingest and use new technologies as fast as they come. The challenge is that developers are stuck in a design paradigm that reduces app design to making functionality and content decisions based on a few defined customer personas or segments.
Personas Are Sorely Insufficient
How could there be anything wrong with this conventional design paradigm? Functionality? Check. Content? Check. Customer personas? Ah — herein lies the problem. These aggregate representations of your customers can prove valuable when designing apps and are supposedly the state of the art when it comes to customer experience and app design, but personas are blind to the needs of the individual user. Personas were fine in 1999 and maybe even in 2009 — but no longer, because we live in a world of 7 billion “me”s. Customers increasingly expect and deserve to a have a personal relationship with the hundreds of brands in their lives. Companies that increasingly ratchet up individual experience will succeed. Those that don’t will increasingly become strangers to their customers.
We recently met with Huawei executives during the launch of its latest product in China, the S12700 switch. The product, which ships in limited quantity in Q1 2014 is designed for managing campus networks, and acts as a core and aggregation switch in the heart of campus networks. While wired/wireless convergence, policy control and management come as standard features, the draw is the Ethernet Network Processor (ENP). The ENP competes against merchant silicon in competitive switch products, and Huawei claims to be able to deliver new programmable services in six months, compared to one to three years for competitive application-specific integrated circuit (ASIC) chips. This helps IT managers respond quicker to the needs of campus network users, especially in the age of BYOD, Big Data, and cloud computing.
While it is a commendable product in its own right, Huawei will need to position its value more strategically against IT managers that have technology inertia, especially in ‘Cisco-heavy’ networks:
Tying the value of the switch to existing and future enterprise campus needs. In the age of cloud computing, big data, mobility, and social networking, IT managers need to solve network challenges like insufficient service processing capability and slow service responses. Huawei says the new switch is able to provide agile services and respond flexibly to changes in service requirements, on demand. For example, the switch has access control built in for wired/wireless access management. This is a good start. Enterprises will need to understand how the switch plays a central role in a campus network, and Huawei should continue to reinforce its agile network architecture’s storyline.
Big data gurus have said that data quality isn’t important for big data. Good enough is good enough. However, business stakeholders still complain about poor data quality. In fact, when Forrester surveyed customer intelligence professionals, the ability to integrate data and manage data quality are the top two factors holding customer intelligence back.
So, do big data gurus have it wrong? Sort of . . .
I had the chance to attend and present at a marketing event put on by MITX last week in Boston that focused on data science for marketing and customer experience. I recommend all data and big data professionals do this. Here is why. How marketers and agencies talk about big data and data science is different than how IT talks about it. This isn’t just a language barrier, it’s a philosophy barrier. Let’s look at this closer:
Data is totals. When IT talks about data, it’s talking of the physical elements stored in systems. When marketing talks about data, it’s referring to the totals and calculation outputs from analysis.
Quality is completeness. At the MITX event, Panera Bread was asked, how do they understand customers that pay cash? This lack of data didn’t hinder analysis. Panera looked at customers in their loyalty program and promotions that paid cash to make assumptions about this segment and their behavior. Analytics was the data quality tool that completed the customer picture.
Data rules are algorithms. When rules are applied to data, these are more aligned to segmentation and status that would be input into personalized customer interaction. Data rules are not about transformation to marketers.