- Data-as-a-Service: Data Provisioning, Management, Lineage, Quality
- Reporting-as-a-Service: Dashboards, KPIs, Drilldowns/Aggregates…. Descriptive
- Analytics-as-a-Service: Predictive Modeling and BI… Prescriptive analytics
- Information-as-a-Service: Threshold based Alerts, Exceptions, Mobile Prompts
- Insights-as-a-Service: ML/AI based…automated learning – ambient intelligence, Next best Offer/Action
Which strategy are you implementing?
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).
Machine data (Internet of Things) 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.
Machine data comes in many forms. Take for instance, what the Bosch Group is doing in Germany and Schnieder Electric in France.
The Bosch Group has embarked on a series of initiatives across business units that make use of data and analytics to provide so-called intelligent customer offerings. These include intelligent fleet management, intelligent vehicle-charging infrastructures, intelligent energy management, intelligent security video analysis, and many more. To identify and develop these innovative services, Bosch created a Software Innovations group that focuses heavily on big data, analytics, and the “Internet of Things.”
Similarly, the Schneider Electric focuses primarily on energy management, including energy optimization, smart-grid management, and building automation. Its Advanced Distribution Management System, for example, handles energy distribution in utility companies. ADMS monitors and controls network devices, manages service outages, and dispatches crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use analytics to monitor the network.
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. Splunk has >3500 customers already. Splunk’s potential comes from its presence in the growing cloud-analytics space. With companies gathering incredible amounts of data, they need help making sense of it and using it to optimize their business efficiency, and Splunk’s services give users the opportunity to get more from the information they gather.