Analytics 3.0老人与海感悟
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Tho of us who have spent years studying “data smart” companies believe we’天文望远镜看太阳ve already lived through two eras in the u of analytics. We might call them BBD and ABD - before big data and after big data. Or, to u a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analysis 2.0. Generally speaking, 2.0 releas don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul bad on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information - big data - that was surely the ca.
Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings - to embed data smartness into the products and rvices customers buy.
I’ll develop this argument in what follows, making the ca that just as the early applications
of big data marked a major break from the 1.0 past, the current innovations of a few industry leaders are evidence that a new era is dawning. When a new way of thinking about and applying a strength begins to take hold, managers are challenged to respond in many ways. Change comes fast to every part of a business’s world. New players emerge, competitive positions shift, novel technologies must be mastered, and talent gravitates toward the most exciting new work.
Managers will e all the things in the coming months and years. The ones who respond most effectively will be tho who have connected the dots and recognized that competing on analytics is being rethought on a large scale. Indeed, the first companies to perceive the general direction of change - tho with a sneak peek at Analytics 3.0 - will be best positioned to drive that change.
The Evolution of Analytics
My purpo here is not to make abstract obrvations about the unfolding history of analytics. Still, it is uful to look back at the last big shift and the context in which it occur
red. The u of data to make decisions is, of cour, not a new idea; it is as old as decision making itlf. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could.
Analytics 1.0 - the era of “金克斯符文business intelligence.” What we are here calling Analytics 1.0 was a time of real progress in gaining an objective, deep understanding of important business phenomena and giving managers the fact-bad comprehension to go beyond intuition when making decisions. For the first time, data about production process, sales, customer interactions, and more were recorded, aggregated, and analyzed.
New computing technologies were key. Information systems were at first custom-built by companies who large scale justified the investment; later, they were commercialized by outside vendors in more-generic forms. This was the era of the enterpri data warehou, ud to capture information, and of business intelligence software, ud to query and report it.始乱终弃
New competencies were required as well, beginning with the ability to manage data. Data ts were small enough in volume and static enough in velocity to be gregated in warehous for analysis. However, readying a data t for inclusion in a warehou was difficult. Analysts spent much of their time preparing data for analysis and relatively little time on the analysis itlf.
More than anything el, it was vital to figure out the right few questions on which to focus, becau analysis was painstaking and slow, often taking weeks or months to perform. And reporting process - the great majority of business intelligence activity - addresd only what had happened in the past; they offered no explanations or predictions.
Did people e analytics as a source of competitive advantage? In broad terms, yes - but no one spoke in today’挨挨挤挤s terms of “competing on analytics.” The edge came in the form of greater operational efficiency - making better decisions on certain key points to improve performance.
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Analytics 2.0 - the era of big data. The basic conditions of the Analytics 1.0 period predominated for half a century, until the mid-2000s, when internet-bad and social network firms primarily in Silicon Valley - Google, eBay, and so on - began to amass and analyze new kinds of information. Although the term “big data” wasn’t coined immediately, the new reality it signified very quickly changed the role of data and analytics in tho firms. Big data also came to be distinguished from small data becau it was not generated purely by a firm’s internal transaction systems. It was externally sourced as well, coming from the internet, nsors of various types, public data initiatives such as the human genome project, and captures of audio and video recordings.
