Seeing New Insights From Data - Thanks to Data Lakes

Geo-Data + Social = New Business Intelligence & Insights

Whereas much of the enterprise business analysis has been focused on RDBMS and Big Data sales transactions to test hypothesis and create reports, the adoption of geo-data on business transactions is an area of huge opportunity. Several companies and industries have already adopted geo-data and are reaping financial benefits. For example, UPS is using geo-data to optimize truck delivery routes, aiming for as many right turns at traffic intersections as possible. This will result in an anticipated $50M saving per year. 




Enterprise Insights Exploration of Geo-Data + Social

If you looked at your favorite social media apps, you will find that they want to track your location. These apps take your location—combined with what you are doing, how you feel, who you are with, and why you are there—provide invaluable and difficult to obtain insights about you. For example, if on January 21, 2017, between 2PM-8PM, you were at location 37.79° N, 122.39° W, and you tweeted that you were feeling happy and civic, you were probably part of the Women’s March in San Francisco. Hence, a certain marketing profile can be built up on you for target marketing.

Enterprise Insights   Exploration Hampered by a Lack of Data Diversity

A business analyst, seeing the value of geo-data, wants to perform an ad-hoc query. She has data from Women’s March with an estimated 4 million marchers nationwide. She can query who was at the start location of the Washington D.C. March (38.88° N, 77.01° W), at the starting time (1:15 PM EST), and Tweeted or Liked positively. This is the profile of a enthusiastic, conscientious person. The analyst can also query who was at the end location of the March (38.89° N, -77.03° W), but at the starting time of the March— perhaps a supporter or reporter.  Acting on the speed of thought, the analyst wants access to billions of rows of data, to draw a perimeter of the map to localize around the start of the March, focus on the start time, and filter by contextual data. And after that, try again with another set of criteria so that she can constantly refine her hypothesis to reach a conclusion.  But currently, each click will cause minutes or even hours of calculations before results are seen. This is due to the nature of CPUs – limited number of cores, memory speed, and the types of instructions it excels at.

Augmenting IT Data with  OT Data

Querying a database requires processing cores and fast memory. CPU based servers is limited up to only 22 processing cores and fairly fast memory. CPUs need to be clustered together to be able to serve the queries of billions of rows of data. Another type of processor, called GPU, has thousands of cores and very fast memory. The cores in the GPU process data in parallel and pass data extremely fast to memory. GPUs are so powerful that a single GPU server can sometimes replace multiple clusters of CPU servers. GPU can save money, reduce labor, lower energy consumption, and reduce space over CPU.

Whilst GPU is a great match for looking through billions of records in milliseconds, a database optimized for GPU is needed. That’s where G-DB comes in. G-DB offers two synergistic products – G-DB Server and G-DB Visual. G-DB Query is a GPU optimized database. It is an in-memory, columnar data highly optimized to harness the power of thousands of cores in the GPU.  Every SQL query that you submitted is broken down and re-targeted to run in parallel on thousands of GPU cores. That’s how we are able to return queries on billions of rows in milliseconds.  But the magic doesn’t stop there. Synergistically, GPU is also ultrafast at drawing the output of the query results. This is where G-DB Visual comes in. It renders the results of your queries immediately – so that you can use your eyes to help you brains to discover insights immediately.

Conclusion

Transaction, geo-data, and social media combined will enable insights into people not possible before.  Processing billions of rows of this type of data will be slow and/or expensive on a CPU based system, making this valuable data inaccessible. But GPU based systems, like G-DB, can handle this type and size of data with ease. With G-DB, not only can you gain insights at the speed of thought, you have ultrafast high fidelity visuals to match.