In October, IBM released a report from their Institute for Business Value titled Analytics – A Blueprint for Value. IBM releases these reports on a periodic basis, and this one is focused on the growing importance of analytics to business success. Through their analysis, they came up with nine levers that represent the sets of capabilities that most differentiated leaders exhibit:
- Culture: Availability and use of data and analytics within an organization
- Data: Structure and formality of the organization’s data governance process and the security of its data
- Expertise: Development of and access to data management and analytic skills and capabilities
- Funding: Financial rigor in the analytics funding process
- Measurement: Evaluating the impact on business outcomes
- Platform: Integrated capabilities delivered by hardware and software
- Source of value: Actions and decisions that generate results
- Sponsorship: Executive support and involvement
- Trust: Organizational confidence
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.
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.
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.
Business Analytics refers to the skills, technologies, applications and practices for the continuous exploration of data to gain insight that drive business decisions. Business Analytics is multi-faceted. It combines multiple forms of analytics and applies the right method to deliver expected results. It focuses on developing new insights using techniques including, data mining, predictive analytics, natural language processing, artificial intelligence, statistical analysis and quantitative analysis. In addition, domain knowledge is a key component of the business analytics portfolio. Business Analytics can then be viewed as the combination of domain knowledge and all forms of analytics in a way that creates analytic applications focused on enabling specific business outcomes.
In his “Building a Smarter Planet” Blog, Steve Hamm talks about Analyzing Data in Motion to save stroke victims. There is no better way to appreciate the power of emerging analytic technology than viewing it in the context of life saving applications. This example combines very powerful stream computing technology with predictive analytics and data mining. This combination enables high speed, scalable and complex analytics of data streams in motion. As the author describes, they are using this technology to look for patterns in data that could help them identify patients who are experiencing a severe complication to ruptured brain aneurysms.