Underutilization and the complexity of managing growing data sprawl have motivated several trends during the last several years. Data-as-a-Service (DaaS) represents an opportunity improving IT efficiency and performance through centralization of resources. DaaS strategies have increased dramatically in the last few years with the maturation of technologies such as data virtualization, data integration, MDM, SOA, BPM and Platform-as-a-service.
These questions are accelerating the Data-as-a-Service (DaaS) trend: How to deliver the right data to the right place at the right time? How to “virtualize” the data often trapped inside applications? How to support changing business requirements (analytics, reporting, and performance management) in spite of ever changing data volumes and complexity.
“Through 2015, more than 85 percent of
Fortune 500 organizations will fail to effectively exploit big data for competitive advantage” - Gartner BI Summit.
It doesn’t take genius to recognize that there is an increasing demand for information to improve shareholder value and gain competitive advantage by leveraging information, data and analytics as a strategic enterprise asset. The question is no longer about the importance of data but when, how, and where to leverage the asset. Read more
The Oracle BI stack illustrates the landscape changes taking place from hardware to mobile BI apps.
As I see it, there are two clusters of “parallel” innovation: (1) technology/infrastructure centric and (2) business/problem centric.
The interesting thing in the technology/infrastructure centric side is the multiple paths of innovation that are taking along different technology stacks shown below. The disruptive innovation is happening in parallel along 4 different fronts: Read more
Consider this…eBay’s “Singularity” Teradata warehouse exceeds 40 petabytes. According to eBay, the company’s data volumes are 50+ terabytes per day in new incremental data, processing 50+ petabytes
and tens of millions of queries per day, with 99.98% availability and more than 50 petabytes of online storage.
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data is challenging.
So, if you are a senior leader in a Fortune 2000 company. How do you structure your group to deliver effective BI, Analytics or Big Data projects? Do you have the right structure, toolset, dataset, skillset and mindset for analytics and Big Data?
Organizing for effective BI, Analytics and Big Data is becoming a hot topic in corporations. In 2012, business users are exerting significant influence over BI, Analytics and Big Data decisions, often choosing analytics and visualization platforms and products in addition to/as alternatives to traditional BI platform (reporting and visualization tools).
Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?
According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.
While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.
Machine data or “data exhaust” analysis is one of the fastest growing segments of “big data”–generated by websites, applications, servers, networks, mobile devices and other sources. The goal is to aggregate, parse and visualize this data – log files, scripts, messages, alerts, changes, IT configurations, tickets, user profiles etc – to spot trends and act.
By monitoring and analyzing data from customer clickstreams, transactions, log files to network activity and call records–and more, there is new breed of startups that are racing to convert “invisible” machine data into useful performance insights. The label for this type of analytics – operational or application performance intelligence.
In this posting we cover a low profile big data company, Splunk which recently went public. Splunk has >3500 customers already. Splunk ended its first day on the stock market with amazing 108.7 percent bump in price from its $17-per-share IPO.