Next up in this transformations series is the sixth enabler: sense and respond systems. These systems are critical to the transformation agenda, as most of the disruptive technologies likely to impact the enterprise in the next decade have data at its core. The resulting data explosion promises to complicate information management for most companies. As the speed of business accelerates and the amount of data flowing through company ecosystems expands, the need to sense stimuli and enable a real time response intensifies. Fortunately, rapid advancements in the price and performance of technology make realizing this sense and respond paradigm achievable and economical for a wide range of use cases – but this is arguably one of the most difficult components of transformation road maps.
This recent Big Data Article from the Mckinsey Global Institute focuses on three key themes – some or all of which will impact every company in the future.
- Big Data will increasingly form the foundation of competitive advantage for years to come.
- Big Data will drive new business models across every industry.
- Decision processes will forever be changed.
A company’s use of Big Data will increasingly be driven by competitive pressures from those that effectively leverage its insight. As these companies mature in their use of data, they will shift from competitive response to competitive advantage. Decisions will improve, driven by an ability to simulate and model various scenarios that enable optimal outcomes.
The really interesting aspect of Big Data – and the analytics that help us derive insight – is the potential impact on complete value chains. This article provides some good examples of this phenomenon at work. In essence, data will drive new business models. Members of a value chain that own data may have an ability to monetize it. Those that have a proven ability to deliver insight from this data can monetize a core competence. Some companies may find themselves driving revenue from a business model that was never envisioned.
Whether it is new business models, better decisions, or enabled actions, the effective use of Big Data requires a level of analytic excellence that few companies have with any level of scale. This Mckinsey article echoes an earlier report that identifies a scarcity of analytic resources as a key obstacle to Big Data success. As I talk to companies about their digital strategies, I continue to focus on Big Data as the centerpiece of the strategy.
The explosion of data and content is not limited to social media and represents a top of mind issue for many companies. The opportunity exists to create unprecedented business value – but there are significant hurdles like greater risk exposure, more complicated risk management, and difficulty extracting relevant insight from large volumes of data.
As volume grows, automation is critical. For example, social media monitoring is a common practice today, one that becomes increasingly ineffective and costly as the social web expands. Monitoring tools that enable the analysis of dialog on social networks like LinkedIn, FaceBook and Twitter provide a basic level of insight. But a deeper level of insight still requires a manual process, where irrelevant content is filtered before finding meaningful insight. Information management is therefore a growing challenge.
I find myself talking a lot lately about the slow evolution from basic social listening to a more robust use of analytics to truly gain actionable business insight. I have long felt the evolution was inevitable – of course I often think these things and they take years to materialize – a story for a different day. This Recent Forrester Blog Post touches on the notion of moving from social listening, to integrating social and customer data. It also presents a roadmap for how to move through the crawl-walk-run-fly stages.
I am sure the authors realize that although this is a piece of the evolution, there are other steps along the path to actionable business insight. I’m already seeing the movement from basic social media monitoring to the broader use of text analytic platforms. Companies that started their journey focused on brand mentions are evolving to new use cases that deliver considerable business value. One of the signs that we are reaching an inflection point can be found in a growing move towards evaluating text analytics software for a broader set of use cases.
This very good Article by Anand S. Rao discusses the growing use of predictive analytics in the Insurance Industry. I believe Mr. Rao is right on the mark – although I continue to emphasize the expanding role of Text Analytics in the analytic value equation. In this article, he identifies some of the drivers of predictive analytics adoption.