- 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).
BI is key to enabling companies to turn oceans of data into predictive models and actionable decisions. However, a survey of 353 executives in large companies, reported that their chief BI concern was the performance of various BI solutions.
Development, support and enhancement teams are typically deployed to address BI performance challenges with varied success. But most companies don’t have a dedicated focus on performance.
A BI Center of Excellence (BI CoE) measured by performance KPIs and service metrics is one solution to this problem. This is not an area that traditionally draws high-level attention or is featured in a dedicated CoE initiative, yet in the right circumstances it offers unique value. Read more
The financial crisis of 2007–2011 is driving widespread changes in the U.S regulatory system. Dodd-Frank Act addresses “too big to fail” problem by tightening capital requirements and supervision of large financial firms and hedge funds. It also creates an “orderly liquidation authority” so the government can wind down a failing institution without market chaos.
Financial institutions will be spending billions to strengthen, streamline and automate their recordkeeping, risk management KPIs and dashboard systems. The implications on Data Retention and Archiving, Disaster Recovery and Continuity Planning have been well covered. But leveraging Business Analytics to proactively and reactively manage/monitor risk and compliance is an emerging frontier.
We believe that Business Analytics and real-time data management are poised to play a huge role in regulating the next generation of risk and compliance management in Financial Services industry (FSI). in this posting, we are going to examine the strategic and structural challenges, the dashboards and KPIs of interest that provide feedback, and what an effective execution roadmap needs to be for every organization.
SAP AG recently released a new 4.0 version of its BI (business intelligence) and EIM (enterprise information management) solutions. They also released Enterprise Performance Management (EPM 10).
We think this will be a big deal for current customers who are struggling to build a robust enterprise foundation for BI for the diverse business initiatives. Having multiple BI projects with each on a slightly different data hubs leads to chaos and insights where people are unsure about what the numbers mean as they could be interpreted differently along the information chain.
We expect a significant upgrade cycle looming for the SAP community.
BusinessObjects 4.0 is a major release after the merger of SAP and BusinessObjects in 2007 and SAP and Sybase in 2010. It incorporates a significant capability enhancement that business users and CIOs are demanding around analytics – more real-time, better in-memory computing; BI for the masses – powerful BI in users hands, mobile BI capabilities across a wide spectrum of devices; combining structured and unstructured information and providing the tools to govern the information and finally dealing with the growing avalanche of social media data. Also a common “look and feel” and better data visualization provides a better overall User Experience.