Enterprise Business Intelligence (BI) project failure can happen for a variety of reasons. Sometimes it’s due to frequent scope changes with a fixed schedule constraint, unexpected and unplanned-for “must-have” requirements changes, loss of key team members onshore or offshore, chronic effort under-estimation, lack of proper work breakdown structure, lack of QA, and so on.
Regardless of the causes, BI, Analytics, performance management failed projects waste billions of dollars (and hours) each year.
Over the years, I have seen a lot of well-intentioned custom development, commercial, off-the-shelf package customization – SAP, Oracle, Peoplesoft ERP, CRM, SCM – and other enterprise data-warehouse projects get into trouble for a variety of reasons. Troubled projects usually need triage, recovery, and turn-around skills to straighten things out quickly.
I am afraid that BI and Corporate Performance Management is reaching a phase in its hype cycle where we are beginning to see growing demand for troubled project recovery. It doesn’t take genius to realize that BI/Analytics project demand is growing as it is one of few remaining IT initiatives that can make companies more competitive. However, demand doesn’t imply project success. Read more
“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.