“More firms will adopt Amazon EC2 or EMR or Google App Engine platforms for data analytics. Put in a credit card, by an hour or months worth of compute and storage data. Charge for what you use. No sign up period or fee. Ability to fire up complex analytic systems. Can be a small or large player” Ravi Kalakota’s forecast
Big data Analytics = Technologies and techniques for working productively with data, at any scale.
Analytics-as-a-Service is cloud based… Elastic and highly scalable, No upfront capital expense. Only pay for what you use, Available on-demand
The combination of the two is the emerging new trend. Why? Many organizations are starting to think about “analytics-as-a-service” as they struggle to cope with the problem of analyzing massive amounts of data to find patterns, extract signals from background noise and make predictions. In our discussions with CIOs and others, we are increasingly talking about leveraging the private or public cloud computing to build an analytics-as-a-service model.
Analytics-as-a-Service is an umbrella term I am using to encapsulate “Data-as-a-Service” and “Hadoop-as-a-Service” strategies. It is more sexy 🙂
The strategic goal is to harness data to drive insights and better decisions faster than competition as a core competency. Executing this goal requires developing state-of-the-art capabilities around three facets: algorithms, platform building blocks, and infrastructure.
Analytics is moving out of the IT function and into business — marketing, research and development, into strategy. As result of this shift, the focus is greater on speed-to-insight than on common or low-cost platforms. In most IT organizations it takes anywhere from 6 weeks to 6 months to procure and configure servers. Then another several months to load, configure and test software. Not very fast for a business user who needs to churn data and test hypothesis. Hence cloud-as-a-analytics alternative is gaining traction with business users.
“Dissatisfaction is the basis of progress. When we become satisfied, we become obsolete.” J. Willard Marriott
We talk to customers often about their dissatisfaction with things as they are and hear the same pattern of complaints. Despite increasing adoption of BI and data analytics tools, the current sets of tools are inadequate to meet the needs of users.
The market of BI is enormous. According the recent Census 2010, there are over 20,000 large and medium-sized enterprises (organizations with over 500 employees) and ~ 7 million small businesses (organizations with ten to 500 employees) in the United States alone. Now include Europe and Asia and you can see the potential.
However, most organizations face the following limitations: Read more
“There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot cards, or crystal balls. Collectively, these methods are known as “nutty methods.” Or you can put well-researched facts into sophisticated computer models, more commonly referred to as “a complete waste of time.”
Scott Adams, The Dilbert Future
Are you clear on your objective? What is the most important value proposition that you want to achieve through BI and analytics enabled strategies?
- Reduction in operating expenses
- Increased profitability
- Improve growth, competitiveness and market position
- Customer acquisition, loyalty and retention
- Product development and differentiation
The mis-alignment between what C-suite wants and what IT is capable of delivering is quite extraordinary. Many CFOs, CEOs believe that IT is unable to deliver results where it counts: the top line and bottom line. At the same time, IT organizations spend an incredible amount of time, money and resources simply reporting the obvious data within their business processes and workflows. The data overload is making find the obvious in the increasing tidal wave of structured and unstructured data a full-time job. As organizations emerge from the deep recession of 2008, the competitive pressures are putting even greater demands on the decision-making, KPIs and performance management processes of organizations.
To stay competitive means making better decisions more quickly. It means accelerating the “raw data -> clean data -> information -> insight -> decision cycle.” It dictates widening the scope and scale of the data management domain, the analytic landscape and the technological infrastructure.