Predicting future sales opportunities can dramatically improve a company’s ability to maximize sales. We worked with a customer to combine historical sales activity with third party data to anticipate potential future sales. This enables:
Disparate data is merged from various sources providing a unified source for detailed analysis.
Using the customers motivation to identify opportunities which are underserved, we let the data do the talking with filters for critical products.
With thousands of transactions and customers, smart visualizations enable clear insights.
Using available tools and stock algorithms, our data science team was able to gather, analyze document and advise within a matter of weeks with near-zero impact to existing team activities.
Visitor activity on the retail client’s busy website generated an enormous amount of data that could not be managed by its relational database (DB) due to its complexity (semi-structured/unstructured), sheer volume, and high costs of licensing multiple nodes of the DB to manage that volume.
To better understand user behavior, product reach, and ad performance, the client needed data that relied on differing source formats, technologies, and tools.
Replace legacy systems to support scalability in terms of volume and complexity, simultaneously reducing the cost of implementation/license/hardware and increasing ROI.
As a retailer capturing user data at the click level, our client wanted a platform in which they could visualize and analyze data. MPP and similar database platforms were unable to complete multiple batches for geos.
Opia's engineers developed the end-to-end solution for the client, further enabling huge improvements in performance with memory tuning for the Hive processes. Our experts could spin up and run the entire platform and proof of concept in less than 100 hours.
The client had a core business process that involved funds, bonds, and securities data. They were using an SQL server database and created multiple databases (schemas; stage area, data mart) on the same server. The client had implemented Kimball Architecture for creating a reporting hub in SQL server DB. As the volume of data increased over time, the batch time also was increasing progressively.
Business users wanted the investment data to automatically update by 6:00 a.m. EST with the latest information from the previous business day, so they could receive insights on real-time sticker prices to make educated decisions on marginal trades for that day.
Improve the performance of the batch that was running for more than 12-13 hours during the night. We presented the client with multiple solutions and let them choose the best based on their timeframe, budget, resource needs, and skills.
We presented the client with a solution to split the STAGE and EDM databases on different servers and hardware so there is no I/O or CPU contention, which was showing up in the analysis. This project would require 6-12 months to recode and rewire all the existing processes to point to a different DB connection.
A second solution we presented was to move all the processes and data to Big Data technologies (proprietary or cloud) and use Hadoop Hortonworks platform (open source) to create data-lake and handle scalability through low-cost implementation. This would be the best-performing and most economical solution.