Delivering Data Science to the Business

Editor's Notes

• #3: Organizations understand the importance of machine learning and are exploring ways to implement it to improve their business. However every line of business has the challenge to find the best way of operationalizing machine learning for their business. Data gravity creates silos in the organization and it’s a challenge to bring all this data together for analyses. Even if the data can be brought together, using an ML model with data requires special set of skills and development effort. After operationalizing the machine learning model, businesses want to take actions on the discovered insights. These actions can be of variety and demand integration and development efforts. Businesses cant be agile and swiftly act on data unless these problems are tied together and addressed with a self service tool.
• #4: Lets meet Amy, who works for Outdoor Equipment Inc. Outdoor Equipment Inc is an sporting goods retailer. Amy works as a Marketing Director for this organization. Being an exec, her business objectives are to grow the business and her organization’s market share. She plans achieve her business goals by Real time marketing and improving ROI.
• #5: Based on competitive analysis, market trends and customer behaviors, Amy’s team has concluded that a prospective customer may convert into a paying customer if they are provided with proper incentive to shop. This key finding motivated Amy to come up with a sales campaign to send out product promotions to targeted users based on their interest in products. Amy is a well informed exec and understands the power of data science. She has decided to leverage it to get maximum ROI. She wants to put the right incentives in the hand of the right customer to convert them. She has put together a plan to run a sales campaign for 3 months with a variety of products that are available in the store.
• #6: Amy has to work with different teams that perform specific set of tasks in order to execute the marketing campaign. Chris is the data engineer that unifies the data which exists in different data platforms such as hadoop, db2 and other RDBMs. Chris pull all the data together into one single platform so that it can be used to operationalize the machine learning model to get predictions. Ryan is the data scientiest that creates the ML model based on Amy’s requirement so as to recommend the product category that a customer would likely be interested into. Once, Chris has used the ML model created by Ryan, they have a result set of customer and their interest. Finaly Nick integrates the result with mail gun app to send out emails to targeted customers with product promotions. This repeats everyday as the product promotions are refreshed and are extensive during seasonal sales.
• #7: With Big SQL, Amy’s team can start becoming self sufficient in operationalizing the assets on regular bases once they are created by Ryan and Nick. Amy’s team can leverage Big SQL’s federation capabilities to connect and query data that is stored in separate data sources in a secured way as its setup by Chris. So now Chris doesn’t need to ingest and bring the data into single data location. With Federation and Predicate pushdown, only the data that matters, travels over the wire. With Big SQL and Spark integration, Amy’s team can operationalize spark ML models without knowing the details of how Spark works or what Spark API’s. Finally, Amy’s team can push out the discounted sales promotions that are refreshed every day to the customers by leveraging Big SQL’s capability to call applications developed by users. Technical Meaning - Application is wrapped as a UDF and can be invoked by BigSQL Let me show you in demo that how Eric who is a marketing analyst and works for Amy is able to operationalize this whole effort in just couple of SQL statements. By using Big SQL, Amy’s team is more self-relaint in executing the marketing Campaign because of its capabilities to ties all these separate tasks together through a single tool. Amy still works with Ryan and Nick but only if she needs any changes in the assets.
• #8: After that exciting demo, I would like to summarize that how Big SQL can help you in making your team’s more productive and improve your business Big SQL understands different sql dialects so you can leverage your existing skills on Oracle and Netezza to build application on Big SQL or import enterprise workloads on hadoop platform and run it as is without any change. Big SQL’s can access remote databases and perform query pushdown to these federated data sources. Big SQL’s integrates with Spark Bi-directionally in memory to exchange data between Big SQL data sets and Spark Dataframes. This lets Big SQL call any Spark application and operationalize Spark ML models with enterprise data. Big SQL exhibits high performance even when data scales upto 100TB with complex SQL queries. It comes with a work load manager that lets the enterprise do a lot of plumbing with resource allocation and workloads. Big SQL also has a proven track record to support many concurrent users without degrading performance. Big SQL comes enterprise ready with build in security features and also integrates with Apache Ranger for centralized management of your hadoop environment. Details: SQL COMPATIBILITY SQL Compatible with: netezza, oracle, db2, etc Applications work as-is without any changes FEDERATION AND SPARK: Federates to more than 10 data sources: RDBMS, NoSQL and/or Object Stores Integrates bi-directionally with Spark, like no other Operationalizes ML models PERFORMANCE Exhibits high performance even when data scales up to 100TB with complex SQLs Handles many concurrent users without relinquishing performance ENTERPRISE & SECURITY Secures data using SQL with roles Integrates with Ranger for centralized management
• #11: We have some very exciting sessions lineup for you in this conference. Please attend these sessions to learn more. If you have questions about the demo or need any more information then please visit us at the IBM booth in the expo hall.