OLAP on Big Data and its Business Impact
Big Data is an important asset for an organization today, and its impact on business is undeniable. However, very few can deal with the speed and scale at which data gets accumulated. Most organizations find it challenging to connect their BI tools to Big Data as response times are painfully slow. This is because every time a BI query is fired, a massive amount of data needs to be processed. As a result, queries that must return in seconds take minutes and hours, and users find it difficult to access and use their Big Data reserves for business impact.
Why do we need OLAP on Big Data?
Organizations have tried several ways to address this problem and provide their users with self-service, interactive access to Big Data with instant response times. A common approach is to pull data from the Big Data platform to an external data mart and perform analysis there. But this approach brings in latency issues and makes it difficult to scale for increasing data volumes.
Kyvos has successfully solved these problems by introducing OLAP to the world of Big Data, enabling business users to consume their data instantly without the need to work on the Big Data platform directly. Using OLAP technologies, Big Data can be stored in the form of multidimensional cubes, which are basically an aggregation of measures and dimensions. Multidimensional queries can be then run on these cubes, and the querying tools will get instant responses to those queries.
OLAP on Big Data facilitates easy access to tons of data, allowing users to get a speedy response to almost any question. Users can drill down into their data to get more details as well as conduct side-by-side comparative analysis on historical data to understand trends. It also provides quick answers to iterative queries enabling easy data discovery.
Thus, business users can get answers to their questions instantly, enabling them to create a difference with Big Data and its business impact.
Building an OLAP cube – Does size matter?
When we talk of Big Data, the size of the OLAP cube does matter. To enable multidimensional analytics on huge volumes of data coming in from a wide variety of sources, organizations need to build OLAP cubes that can handle any number of dimensions and measures. They should also be able to quickly ingest increasing flow of high-speed incoming data without any effect on response times.
The innovative OLAP on Big Data technology from Kyvos enables pre-aggregation of huge volumes of data across multiple dimensions, building highly optimized cubes that are stored in a distributed manner across the Big Data infrastructure, both on-premise or cloud. These multi-dimensional cubes can store data in the most granular form and support in-depth analysis across any dimension with interactive response times.
Several Fortune 500 companies have deployed OLAP on Big Data solution from Kyvos to create analytic windows on huge amounts of historical data. Kyvos has enabled them to create business impact by enabling advanced forecasting and trend analysis. Most customers have successfully achieved impressive ROI from their data investments by transforming their data insights directly into business benefits.
Verizon, a global digital media Fortune 14 company, used Kyvos to build an OLAP cube with 168 Billion fact rows and almost 10 terabytes of raw data. This cube enables instant analysis across 38 dimensions in less than a second. It helps them conduct interactive analytics on the massive amount of viewership data generated from their 6 million subscribers. Instant insights into their customer data have helped them perform month-over-month comparisons to understand media metrics and trends, programming successes and failures, viewer behaviors by demographics, and more. Today, business users at Verizon use these insights to power their marketing decisions and enhance customer experiences.
If you want to learn how Kyvos solves the problem of speed and scale with OLAP on Big Data and its business impact on Verizon, watch the recording of our webinar βInstant BI on Big Data at Verizon: Building an OLAP cube with 168B fact rowsβ by Arun Jinde, Technical Architect at Verizon and Ajay Anand, VP Products & Marketing at Kyvos Insights.