Optier presentation for open analytics event
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1. The document discusses how traditional analytics processes are flawed and inefficient due to the way application data is stored. 2. It introduces OpTier's patented technology, which can collect data from applications in real-time as transactions are processed, without changing the applications. This data is tagged and put into context to enable useful real-time analytics. 3. OpTier claims its solution can significantly reduce the time and money spent on analytics projects by capturing transactional data in real-time and near-real time using proven technology, decreasing reliance on ETL tools, and leveraging the power and economics of Cassandra databases.
Optier presentation for open analytics event
- 2. YOU MUST FUNDAMENTALLY CHANGE THE PROCESS TO ACHIEVE MASSIVE RESULTS + ≠ INCREMENTAL IMPROVEMENT ONLY WORKS SO FAR…
- 3. THE PROCESS OF ANALYZING DATA IS INEFFICIENT AND CAN BE FUNDAMENTALLY OVERHAULED
- 4. ACCENTURE: SERVING THE NON-STOP CUSTOMER Source: Accenture, October 2012 ACCENTURE’S NON-STOP CUSTOMER EXPERIENCE MODEL REDEFINES CUSTOMER INTERACTION • Digital technologies are driving this change • Customers no longer enter a channel but are continuously in the channel • Transactional, dynamic, ex perimental • UNPREDICTABLE Analytics are Getting More Complex
- 5. • The data is first translated using a tool like Informatica and then stored in a Data Warehouse. • The contextual relationship is NOT defined, and the transactions are NOT established. • Financial reporting is stable & predictable. • Does not require access to real time data. • This process works well. Sophisticated data and modeling tools are used to create models, infer context and create enhanced data sets. The Data Warehouse stores massive amounts of different data – but it’s not storing contextual, transactional data. DATA TOOLS (ETL) FINANCE MARKETING Mobile App Database E-Commerce App Database Call Center App Database Retail Branch App Database DATA WAREHOUSE DATA & MODELING TOOLS DATA WAREHOUSE OPS TODAY’S ANALYTICS PROCESS (TXNs & CONTEXT - AFTER) ENTERPRISE APPLICATION DATA ENHANCED DATA SETS
- 6. The Marketing team wants to analyze data that flows through the applications, but is not stored anywhere. 3 Marketing wants to analyze data that is already captured by the application, but is not stored in the data warehouse. 2 Marketing wants to analyze data that is not being stored in the enhanced data set, but is being stored in the data warehouse. 1 DATA TOOLS (ETL) FINANCE MARKETING Mobile App Database E-Commerce App Database Call Center App Database Retail Branch App Database DATA WAREHOUSE DATA & MODELING TOOLS OPS TODAY’S ANALYTICS PROCESS (TXNs & CONTEXT - AFTER) ENTERPRISE APPLICATION DATA ENHANCED DATA SETS
- 7. WHY IS THE TRADITIONAL ANALYTICS PROCESS FLAWED? THESE PROJECTS COST SO MUCH TIME & MONEY WE OFTEN JUST GIVE UP Problem Time To Fix Data not in enhanced data set 5 Days Data not in data warehouse 1 – 3 Months Data not in application database 3 – 12 Months3 2 1
- 8. THIS PROCESS IS RIPE FOR A FUNDAMENTAL OVERHAUL BECAUSE OF THE WAY APPLICATIONS STORE DATA + ≠
- 9. REAL-TIME TRANSACTIONAL ANALYTICS - WITH OPTIER ENTERPRISE APPLICATION DATA DATA TOOLS (ETL) Mobile App Database E-Commerce App Database Call Center App Database Retail Branch App Database DATA WAREHOUSE DATA & MODELING TOOLS ENHANCED DATA SETS FINANCE MARKETING OPS
- 10. User Transaction Web Server Authentication Application Server Message Bus, ESB Middleware Server Data Base Mainframe The end-user initiates a transaction, such as checking their bank balance. User Transaction Each transaction is uniquely tagged so useful transactional data can be collected as it flows through your architecture. Web Server Authentication Application Server Message Bus, ESB Middleware Server Data Base Data is collected at each step of the transaction. This granular approach enables us to pinpoint and resolve problems quickly and predict potential problems. Mainframe Active Context Tracking Each piece of data is put into context to deliver useful real-time analytics. This unique and powerful concept is at the heart of OpTier’s technology. 3rd Party Web Services3rd Party Web Services Data is collected at each step of the transaction. OpTier Real-time business transactional dataset OPTIER’S PATENTED TECHNOLOGY COLLECTS DATA WHILE TRANSACTIONS RUN, WITHOUT CHANGING APPLICATIONS CASSANDRA DATABASE WE DO THIS MILLIONS OF TIMES A DAY FOR THE WORLD’S BIGGEST COMPANIES
- 11. Marketing wants to analyze data not saved by applications. 3 Marketing wants to analyze data that applications process but not saved in Cassandra. 2 REAL-TIME TRANSACTIONAL ANALYTICS - WITH OPTIER ENTERPRISE APPLICATION DATA Mobile App Database E-Commerce App Database Call Center App Database Retail Branch App Database DATA WAREHOUSE DATA TOOLS (ETL) CASSANDRA DATABASE Marketing wants to analyze data that’s not being stored in the enhanced data set but is in the data warehouse. 1 CASSANDRA DATABASE ENHANCED DATA SETS FINANCE MARKETING OPS DATA & MODELING TOOLS OpTier 1. Capture Transactions & Create contextual data in near- real time using proven technology. 2. Decrease the reliance on ETL tools. 3. Leverage power & economics of Cassandra. OpTier has created a code-free drag and drop tool that empowers Business Analysts to Actively engage in analytics and visualization – without time-consuming & expensive IT Projects.
- 12. CREATES MASSIVE VALUE Problem Time To Fix Data not in enhanced data set 5 Days Data not in data warehouse 1 – 3 Months Data not in application database 3 – 12 Months3 2 1 ____ 1 Hour ______ 1 - 2 Days _______ 1 - 2 Days REAL-TIME TRANSACTIONAL ANALYTICS - WITH OPTIER
- 13. REAL-TIME TRANSACTIONAL ANALYTICS: WHY IS THIS IMPORTANT? 1 Supports routine changes & net-new business requests quickly - without IT involvement. 2 Deliver complex analytics – in days or weeks (not years). Reduces “Big-Bang” Project risk. 3 Saves huge amounts of money. Easier to start now and get results sooner.
Editor's Notes
- #5: The traditional demand funnel - that describes a linear and generally predictable customer path to buying - has lost its relevance. It’s too slow, too static and too generic to be used as the foundation for marketing, sales and service strategies.Now, while buyers still go through the same stages of awareness, consideration, evaluation, purchase and use, they no longer enter a channel but, instead, are continuously in the channel.Nonstop customers today are frequently re-evaluating their decisions, and the alternatives.
- #6: Data stored in the application data bases lack the context and uniformity to enable analytics to quickly make use of it.Data is the bricks and context is the cement that holds the data together. Context is defined as the relationship between different data elements that answers who, what, where, when and why?
- #7: Data stored in the application data bases lack the context and uniformity to enable analytics to quickly make use of it.Data is the bricks and context is the cement that holds the data together. Context is defined as the relationship between different data elements that answers who, what, where, when and why?
- #13: The traditional demand funnel - that describes a linear and generally predictable customer path to buying - has lost its relevance. It’s too slow, too static and too generic to be used as the foundation for marketing, sales and service strategies.Now, while buyers still go through the same stages of awareness, consideration, evaluation, purchase and use, they no longer enter a channel but, instead, are continuously in the channel.Nonstop customers today are frequently re-evaluating their decisions, and the alternatives.