IBM Big Data Study


The IBM Institute for Business Value recently completed Big Data research and released a report titled Analytics: The real-world use of Big Data. As the report states, companies have been dealing with large volumes of data for years (think billions of call center records collected by Telecommunication companies). But the report also identifies the two trends that make this era of big data different:

  • The digitization of virtually “everything” now creates new types of large and real-time data across a broad range of industries. Much of this is non-standard data: for example, streaming, geospatial or sensor-generated data that does not fit neatly into traditional, structured, relational warehouses.
  • Today’s advanced analytics technologies and techniques enable organizations to extract insights from data with previously unachievable levels of sophistication, speed and accuracy.

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The Social Ecosystem


I recently viewed a video titled The Future of Social Inside the Enterprise, a thought leadership presentation from the recent Dreamforce 2012 conference. The presentation is delivered by Dion Hinchcliffe of the Dachis Group, and Alan Lepofsky at Constellation Research. This is a fifty minute journey through the past, present and future of social business. You’ll find some content on the business value associated with social, and some good examples of how social is evolving to support the way we work.

You can start to see how systems of record may integrate with systems of engagement. Two examples are given by Mr. Lepofsky. The first describes a stream level integration, which allows system of record events to be broadcast into the activity stream. This stream level interface is envisioned to be the place where people spend time doing their jobs. It is pointed out however that stream level integration has its issues, the biggest in my mind being the loss of context. Comments made in the activity stream do not work their way back to the system of record, so context is lost. The other critical issue is the noise level associated with these streams. Without robust intelligent filtering, these streams become worse than email. This filtering – finding the actionable insight among the noise – is critical to the effectiveness envisioned by future systems of engagement. I had a discussion this week with senior executives from a large Financial Services firm, and the general belief is that this critical filtering will take years to develop and optimize. It took IBM four years to tune IBM Watson to compete in the Jeopardy challenge. This is not simply a sentiment analysis exercise.

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TCS Digital Mobile Consumer Study


Tata Consultancy Services recently conducted a major study to understand how large organizations in North America, Europe, Asia-Pacific and Latin America have been revamping their strategies, products and processes to win the loyalty of consumers who use mobile devices to do business with them– the so-called “Digital Mobile Consumer”. The Study focused on how companies are coping with this mobile consumer. Some key findings are summarized here.

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Big Data and the Emerging Era of Engagement


I often tell the IBM Watson story as a way of describing the future of analytics. Watson – with a large quantity of Big Data behind it – beat the two biggest Jeopardy winners of all time. Although that became the story, the bigger story for me was the business application of what I had just witnessed. Watson showed us how analytics will mature from descriptive to prescriptive. Most companies I talk to are still in the descriptive stage – reporting on that which has happened.

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The Early Stages of the Digital Enterprise Journey


I have had the pleasure of attending and presenting at several CIO forums in the past couple months –and with all the talk of their future demise and the changes ahead for Enterprise IT, it’s good to get a view from the CIO themselves. They all seem very interested in the dialog around their role changing in the next 3 to 5 years, and the panel sessions on the topic are mobbed. But I don’t see this group buying into the notion that their role will change – aside from the more progressive CIOs. Actually, at a recent event, it felt like very little was changing – as I sat through presentations that could have easily been given in 1998. Some of the same challenges that traditionally drain the resources of an IT organization are still front and center. 

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From an Extended Enterprise to a Digital Enterprise


I find myself reflecting on a common phrase as I watch the digital enterprise unfold: “history is repeating itself”. So much of what is happening today, feels like a second and more attainable version of what was happening about eleven years ago. Back then, I remember developing a framework for the extended enterprise – a popular way to describe the inclusion of other value chain members in end-to-end business process. The Internet was going to change the game by providing the infrastructure required to extend a company’s inward-focused business processes to the value chain. It was to become the catalyst for value chain optimization.

I thought back then about the delivery of innovative product and services comprised of differentiated internal services and value-added external services. The Enterprise would in effect be a functional specialist within their value chain, focused on connecting with partners to gain access to information and services. There would be a divestment of assets, as companies focused on their core competency, making external optimization, synchronization and integration critical success factors. Companies would realize that the ability to adapt to market change was inversely proportional to investment in fixed assets.

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Is Data and Insight a New Source of Revenue?


In this world of Big Data and advanced analytics, many companies are starting to wonder about the revenue potential of their data. An opportunity to create new revenue streams is an attractive scenario – if data assets can become information products. Although not new, the number of companies that pursue information related products could grow considerably. Most companies upon inward reflection will find that the analytic core competence required to deliver insight – the higher end of the monetization scale – does not exist internally.

This introduces some interesting scenarios that underscore the market need for analytic outsourcing and the establishment of analytic centers of excellence. The evaluation process for this opportunity involves a market analysis that identifies the value-added information products for the target market. Understanding the business outcomes enabled by insight is a critical step in determining which products make sense. In addition, the data required to enable these products must be clearly understood, available, and clean enough to deliver the required insight. The process is then enabled by the appropriate analytic methods, taking advantage of advances in computing power and in-memory capabilities. The ability to include insight from unstructured data through the use of text mining expands the opportunity for value creation.

I envision a number of relationships emerging between technology companies and value chain players. These relationships enable new, more insightful information products by combining traditional data related skills with deep analytic and domain expertise. Companies will look to supplement existing offerings with analytic oriented products and services, or pursue new innovative offerings altogether. Insight as a driver of business and decision processes will be enabled in the future by internal movement towards analytic excellence, the use of external information products and services, or some combination of both. This is a natural response to the “Big Data” phenomena and the need to improve the speed and quality of decision making. It introduces a new era of information brokers that deliver new innovative products and services enabled by business analytics.