Do You Need OLAP on Big Data?

Author
Coco Li
Product Marketing Manager
Jul. 20, 2022
   

As digital enterprises grow in size and sophistication, the importance of analytics also grows, helping them to solve business problems, gain insight into trends, and better manage their operations. Analytics requires data–and lots of it–to be effective, but for organizations lacking the right tools, data can be both a blessing and a curse. Big Data is defined by the Four Vs of volume, variety, velocity, and veracity–qualities that  can bog down traditional approaches to analytics.

 

While analytics results improve with more data, those traditional approaches can become overwhelmed by too much data, resulting in slow or broken processes. That forces organizations to run their models on smaller data sets, or to use data samples, compromising results. Online analytical processing, or OLAP, allows organizations to efficiently operationalize Big Data assets at scale, giving them the option to not only use all of their own data, but to enrich that data with third-party resources as well. OLAP and Big Data opens more possibilities for taking full advantage of their data assets through collaboration and automation.

 

What is OLAP?

 

OLAP, is a powerful means of analyzing multidimensional data to quickly solve business problems or generate valuable business intelligence (BI). Typical OLAP processes include data discovery, reporting and visualization, forecasting, and running complex calculations, and although OLAP itself is not new, its application to the challenges specific to using Big Data are a novel approach and one that is helping organizations maximize the value of their data stores.

 

Should your organization consider OLAP for Big Data to more efficiently analyze data, extract more precise insights, and inform operations with better business intelligence? Let’s take a closer look at OLAP for Big Data and weigh the benefits. 

 

Benefits of applying OLAP to Big Data

One of the characteristics of Big Data—variety—means that it lacks a consistent structure, if it is structured at all. It can be transactional, operational, or textual, and in any and all formats. With Big Data, the more data there is, the more diversity of data you get. Not only are you dealing with data on a big scale, but to put the data to work you need to be able to access and operationalize it as is. Without the right tools, the potential value of Big Data is undermined by slow performance, time spent on preparation, and sub-optimal results. That is why Ventana Research found that only 23% of enterprises are satisfied with their technology’s support for big data.

 

OLAP on Big Data addresses the challenges many organizations face in operationalizing their data at scale by delivering four key benefits:

 
  • Speed - Multidimensional (cube) analysis accesses and aggregates data, and executes a chosen data model faster than traditional two-dimensional approaches, and without overwhelming computational resources.
  • Efficiency - Using precomputation, optimized model selection, and other streamlined processes, redundant effort and bottlenecks are minimized or eliminated.
  • Simplicity - With a dashboard-based interface and AI-enhanced decision engine, operations are point-and-click easy, and optimal processes and models are employed automatically. 
  • Lower Cost - By minimizing human effort and reducing the need for compute power, total cost of ownership is reduced  and time to value accelerated.
 

Typical Use Cases for OLAP on Big Data

 

An organization’s data assets are most valuable when they are democratized and available to as many individuals and business units as possible, then put to use for running reports or generating useful business intelligence. OLAP on Big Data enables organizations to do that without requiring specific preparation for each user’s needs. Instead, the data can be quickly accessed and used to execute historical performance analyses, generate predictive intelligence, and fully operationalize their data. Kylience enables organizations to realize the benefits of applying OLAP on Big Data and turn it into accurate and valuable business intelligence. Real-world use cases illustrate the value of using Kyligence to leverage OLAP on Big Data.

 

Case Study One: Fast Food, Fast Analytics – One of the world’s largest fast-food brands uses OLAP on Big Data to identify opportunities for improving menu efficiency and for creating precision marketing campaigns for specific regions—and even individual locations—based on insights extracted from millions of lines of data associated with orders. Using legacy systems, the chain was unable to access and run the data at certain times of the day, and even when they could, the results were inconsistent. Turning to Kylience to use OLAP on Big Data, their data analysts can now access the data at any time, run their models quickly and efficiently, and have complete confidence in the results.

 

Case Study Two: Meeting the Expectations of Millions –  The innovative self-service website and ecommerce builder Strikingly, with millions of customers worldwide, needed a way to provide site performance results via a user dashboard, and to meet customer expectations to return those results within seconds while supporting high data concurrency across all services. With Kyligence’s help, the company was able to create an OLAP on Big Data analytics process to support customer needs, thereby improving retention rates–all while enjoying a 35% reduction in operating costs compared to traditional approaches.

 

Case Study Three: Data as a Product – Nearly every software company today operates on the SaaS model, with data analytics working behind the scenes to deliver a high level of value. One developer of a popular financial support application worked with Kyligence to build out a scalable analytics architecture using OLAP on Big Data to enable capabilities like rapid customer on-boarding, increase audit process efficiency by 90%, and provide a flexible back-end data service that supports fast, accurate queries on processes like running risk reports and performing expense line analysis.   

 

How to Tell if you Need OLAP on Big Data

 

Organizations that collect and generate data at scale can benefit from the adoption of OLAP on Big Data. This includes enterprises operating in accommodation and food service, financial services, government, high tech, hospitality and travel, manufacturing, retail, telecommunications, transportation and logistics, and more. 

 

In this era of remote work, necessitating collaboration with colleagues and partners that may be geographically distributed anywhere in the world, the benefits may be even greater. Of course, you’ll want to evaluate your current needs and systems, as well as determine future plans for integrating new or adding to existing analytics resources. But for any organization that places a premium on generating and applying powerful business intelligence, OLAP on Big Data may be a way to improve results while operating at a lower total cost of ownership (TCO). OLAP on Big Data can also be used to transform current analytics programs, including:

 

Scale-Up Capacity – If your enterprise’s business and technology transformation plans include more data collection and storage, OLAP for Big Data can help support turning that data into useful business intelligence.

 

Accelerate Processing – If your current data analytics programs are hindered by bottlenecks, or if your data science teams are constantly repeating processes, OLAP for Big Data can streamline analytics workflows and deliver faster time to value.

 

Increase Modeling Sophistication – If your current analytics programs are producing inconsistent or unreliable outputs, OLAP for Big Data can improve results by ensuring the right models are used, and that complete data sets are used regardless of scale.

 

OLAP for Big Data Maximizes Data Team Efficiency 

 

In the past, improvements in enterprise data analytics were supported by adding more people to an organization’s data teams to execute tasks like cleansing and preparation. But this is a costly, inefficient approach as data teams cannot scale in proportion to the volume of data most enterprises now collect. There is simply too much data for humans to manage. Investing in tools to supplement human effort is a far superior strategy. 

 

OLAP for Big Data acts as a “force multiplier” for data analytics teams because it eliminates common data bottlenecks, automates associated processes, and ensures easy access to all relevant data. OLAP for Big Data then makes teams more efficient with easy-to-use dashboards, leveraging precomputation, and supporting collaboration to ensure maximal productivity and delivering consistent, high-quality results.

 

Kyligence can Help 

 

As the strategic value of business intelligence increases, the importance of investing in the right technologies to support sophisticated analytics programs rises as well. Kyligence is trusted by global leaders in financial services, manufacturing and retail to provide an intelligent OLAP platform that simplifies their analytics processes with an AI-augmented engine that detects patterns from most frequently asked business queries, builds governed data marts automatically, and brings metrics accountability on the data lake to optimize their data pipelines.

 

Kyligence offers a number of products that can maximize the value of an organization’s big data assets through the application of OLAP on Big Data, while simplifying the underlying systems needed to support an effective analytics program. 

 

The Kyligence approach to OLAP on Big Data includes:

 

Smart Tiered Storage™ to enable cold start and run queries without pre-calculating data, giving data teams more options for analysis and exploration using data at scale.

 

Ultra-Multidimensional and Flexible Queries to support flexible analysis, allowing enterprises to streamline operations and make better decisions.

 

Agile Business Model simplifying data distribution and import, along with AI-augmented intelligent model optimization, reducing time and effort, and ensuring users build models based on needs, shortening release cycles, and making your business more agile.

 

Security Assurance streamlining enterprise-grade security management capabilities and customizable security policies that include end-to-end data encryption, controls for row and column-level data permissions, data backup, and recovery.

 

Extensive Integrations with leading BI tools from vendors like Cognos, MicroStrategy, Oracle, Power BI, Tableau, and others, ensuring the widest possible range of options for building your organization’s best possible analytics program.

 

Learn More About Kyligence OLAP on Big Data 

 

If you want to learn more about how Kyligence can support your organization’s data analytics needs with OLAP on Big Data, you can find more about the Kyligence approach to all-inclusive OLAP, the Case for OLAP in the Cloud, or take a free test drive online and experience the possibilities for yourself. Or contact Kyligence directly to talk about your organization’s specific needs.