What are the characteristics of Analytics 3.0? Davenport: the most important aspect is that analytics is driving not only major operational and strategic decisions, but also the creation of new products and services for companies in every industry – not just online firms, as in the big data era. the easiest way for me to explain the other attributes is to give you a list they include: In a synthesis of traditional analytics (1.0) and big data (2.0), organizations are combining large and small volumes of data, internal and external sources, and structured and unstructured formats to yield new insights in predictive and prescriptive models. Analytics is central to the organization’s strategy. The “Hadoop-alooza” (excitement about big data technologies) continues, but often as a way to provide fast and cheap warehousing or persistence and structuring of data before analysis. Faster technologies such as in- database and in-memory analytics are being coupled with agile analytical methods and machine learning techniques that produce insights at a much faster rate. Many analytical models are being embedded into operational and decision processes, dramatically increasing their speed and impact Companies are beginning to create chief analytics officer roles or equivalent titles to oversee the building of analytical capabilities. Tools that support particular decisions are being pushed to the point of decision making in highly ta Tools that support particular decisions are being pushed to the point of decision making in highly targeted and mobile analytical apps. What defines Analytics 1.0? Davenport: Analytics 1.0 represents an era in which enterprises start assembling business intelligence systems and expertise to drive reporting and descriptive analytics. During this era, very few enterprises view their systems as capable of generating predictive or prescriptive analytics. Enterprises focus on the internal, structured data that they generate without giving much thought to other types or sources of data. in this era, most organizations do not view their data as a valuable asset, like equipment or inventoryrgeted and mobile analytical apps What defines Analytics 2.0? Davenport: the primary difference from Analytics 1.0 is the emergence of big data: fast moving, external, large and unstructured data coming from various new and interesting sources. As such, it has to be stored and processed rapidly, often with parallel servers running technologies like Hadoop. the overall speed of analytics increases, and visual analytics (a form of descriptive analytics) gains prominence; however, predictive and prescriptive techniques are still not the main use of analytics. the users are primarily online firms. in this stage, a new community of data scientists emerges that fosters experimentation, hacking and data mashups. Regardless of industry, most enterprises are discussing new data product business opportunities that may lie ahead of them. Big data is still Among these three analytic eras, where do you see most businesses operating? Davenport: the majority of companies today are still operating within Analytics 1.0 and, in online firms, 2.0. But industry leaders are entering the Analytics 3.0 world. to achieve competitive advantage, firms must prepare for and embrace Analytics 3.0. moving business intelligence from the periphery of operations to the center. GE is another early adopter. they’re investing multiple billions of dollars in a new center for software and analytics. the goal is to offer new services based on the analysis of big data from industrial products – they’re putting sensors in gas turbines, jet engines and locomotives. companies need to have the technical infrastructure to manage the volume of data. this includes technologies like Hadoop, in-memory and in-database analytics, and enough computing power to handle the complex calculations. in addition, companies need appropriate tools to effectively support decision making at the front lines, such as mobile and self-serve analytical apps. Data scientists are a critical element of the shift to Analytics 3.0. they have the skills to extract and structure the complex, high-volume data sets that organizations use. However, they need to work closely with it and traditional quantitative analysts to develop insights for the business. it is critical that there is close collaboration and communication between the business, it and analytics teams. companies can prepare for Analytics 3.0 in several ways. they need to start with discussions among senior management about how they play in the data economy and what resources they already have. they will also need to create a chief analytics officer (or equivalent role) to oversee the strategic deployment of analytics. next, they’ll need to invest in the technology needed to manage big data and provide insights quickly. Finally, companies will need to recruit, retain and effectively use analytical talent. they’ll also need to ensure that analytics groups are aligned with the business and focus on critical business questions.
Posted on: Wed, 19 Mar 2014 10:12:15 +0000
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