Design Thinking balances analytical thinking and intuitive thinking, enabling an organization to both exploit existing knowledge and create new knowledge. A design-thinking organization is capable of effectively advancing knowledge from mystery to heuristic to algorithm, gaining a cost advantage over its competitors along the way.

Analytics is about corporations learning to drink from the fire hose of cheap data that the modern IT systems wrapped around their operations can generate. So what is new? People have been championing a variety of data-driven approaches to management since Frederick Taylor, so what difference does, say, a modern point-of-sales (POS) or RFID system make? Here’s what’s new, per the authors:

Unlike data mining, analytics is about operational interpretation and visualization, not collecting or reporting. This distinction can range from significant to irrelevant depending on the case you are talking about.
Analytics is to be viewed as a subset of business intelligence (BI), within which it lives next to its older, stupider sibling, reporting. I am not sure this contextualization helps anybody, since BI is a massively overloaded term.
Unlike ideas like lean six sigma and its predecessor, total quality management, analytics (when mature) is i) truly global in scope rather than globally-local, ii) about an enabling information infrastructure for all processes and functions (managed at an enterprise level) and most importantly, iii) about responding opportunistically in real time to some sort of systemic variability via feedback, rather than about reducing process variability via episodic process measurement and re-engineering.
Unlike most management doctrines, analytics needs an enabling technology, since it relies on continuous data flows to support high-frequency operational decisional making, rather than low-frequency interventionist decision-making. Without some sort of technology that provides a breakthrough cost structure for data generation (such as remote diagnostics, RFID, retail POS data aggregation or Web services), the data flows required for competing on analytics would simply be prohibitively expensive. That explains why Web-based businesses lead in best practices.
Unlike related technology paradigms like “Service Oriented Architecture” and “Software as a Service,” analytics is about a business competence that has its locus in people, not (just) software.
Perhaps most important: unlike previous data-driven management paradigms, analytics can be a competitive differentiator. This is such a critical claim that it deserves some probing, so I give it its own section.

So if you are championing analytics-based competition, and someone asks you how it is different from what failed before, your canned answer: “Continuous data flows, cheap, operational measurement systems, enterprise-level presence, differentiating capability.” Keep examples handy for those untalented in the area of business abstractions (a.k.a the “all buzzwords=bullshit” crowd).

Wal-Mart and Target aren’t direct competitors due to having sharply different customer demographics, with the former leaning towards low income and the latter leaning towards solidly middle class. Perhaps the easiest way to illustrate the socioeconomic differences is to note that Wal-Mart’s financial services business revolves around check cashing, money transfers and money orders, while Target’s is focused on credit cards. In other words (even though there is undoubtedly some overlap) Wal-Mart and Target aren’t direct competitors because they’re pursuing markedly different customer bases.