Datalicious Case Study
Datalicious, based in Sydney, is a full-service data analytics agency. The business is part of the Veda-Equifax Company, the leading provider of credit information and analysis in Australia and New Zealand. Datalicious has expanded rapidly, evolving from a specialist consulting agency to become an innovative software development company. Its software solutions include SuperTag, DataCollector, DataExchange, and the OptimaHub advanced marketing analytics platform.
We can launch new projects for customers faster with AWS and start building purchase paths quicker now. In addition, we now have the ability to do analytics in near-real time."
Head of Data Engineering, Datalicious
The proliferation of digital platforms has brought marketers both opportunities and challenges. It has given marketers huge quantities of data on purchasing behavior, but left them with the problem of trying to make sense of it all. Datalicious has built a successful business on speeding up the process of analyzing the data from these digital channels. This has led to the development of OptimaHub, an advanced marketing and customer analytics platform built on Amazon Web Services (AWS) technology. Christian Bartens, chief executive officer and founder of Datalicious, says, “Essentially, we are trying to identify the purchase path of consumers. We have data on multiple touch points between brands and consumers, and we have to stitch that data together using technology to create each consumer’s purchase path.”
Often, the company analyzes data for more than a million consumers, with each one registering between 500 and 1,000 touch points over a period of 10 weeks. Bartens says, “We’re using this data to tell marketers how to optimize marketing spend.” The business has come a long way in a relatively short time. A couple of years ago, client data from consumer touch points was crunched on premises using servers. But with the evolution of cloud technology, the business made the switch to cloud-based processing. However, Datalicious still faced a number of challenges with the cloud services it was using at that time. The main problem was ensuring performance. Kanishka Mohaia, head of data engineering at Datalicious, says, “We ran into limitations with the previous service. You couldn’t guarantee whether your process were going to successfully complete within the given timeframe.”
Besides looking to tackle performance issues, Datalicious was keen to hear how moving to AWS could improve the cloud computing experience, and in particular serve their data processing vision for OptimaHub. Mohaia says, “Cost is an important issue and we are always looking at ways to improve efficiencies. Likewise, it is key for us to continue delivering more value to our customer relationships.”
Why Amazon Web Services
Datalicious considered its options. The company was already aware of the AWS suite of offerings, but it was only when the team at AWS began to explain the services in detail that the business became aware of their true value. In fact, after a couple of sessions testing solutions including Amazon Redshift, Amazon DynamoDB, and Amazon Kinesis, Datalicious decided to move to AWS.
Datalicious transitioned from its prior cloud provider to AWS in less than six weeks. Now data is streamed to Amazon Kinesis, where it is analyzed continuously. Mohaia says, “The data comes in on a raw stream and connectors clean the data, conduct some transformations, and push it to the next stream. The data receives further processing and gets pushed to another stream.”
The company has instances of Amazon Elastic Compute Cloud (Amazon EC2) for compute capacity and Amazon Simple Storage Service (Amazon S3) for storage. In addition, Datalicious uses Amazon DynamoDB for a fully managed NoSQL database, Amazon ElastiCache to accelerate the performance of the most heavily used applications, and Amazon Redshift as a data warehouse. Mohaia says, “We back up all the data on Amazon S3 in case it needs to be reprocessed. An Amazon EC2 instance is used in the event we need to reprocess the data in the stream.”
All the processed data is stored in Amazon Redshift, where it gets extracted by an internal Datalicious application to present via dashboards. “Our system connects to Amazon Redshift and updates the dashboard live,” says Mohaia.
Since transitioning to AWS, Datalicious has seen an improvement in the time it takes to deliver client data and the purchasing pathways of their consumers. “We can launch new projects for customers faster with AWS and start building purchasing pathways quicker. In addition, we now have the ability to do analytics in near-real time,” says Mohaia.
The company can also now identify individual consumer pathways in minutes when it once took days. To be able to identify each person’s purchasing pathway so quickly can give marketers an advantage, enabling them to develop marketing campaigns on-the-fly and maximize ROI.
The time savings on projects and near-real-time capabilities mean Datalicious can offer customers greater value. At a time when data analytics is an increasingly competitive industry, this will help the agency differentiate itself from competitors and help drive continued growth.
For Datalicious, the pricing of AWS makes it a cost-effective solution. For example, the company pays a fixed cost for Amazon Redshift regardless of the number of clients using their services. As such, when Datalicious adds more client projects to the AWS solution, the cost per client decreases.
In the time Datalicious has used AWS, service has been nearly continuous, helping the company achieve 99.999 percent availability. This has given Datalicious confidence to expand the number of AWS solutions it is using and the business is currently in the process of moving client applications to AWS. Looking ahead, Datalicious plans to evaluate the possibility of using Amazon QuickSight to provide low-cost, easy-to-use analytics and visualizations. Furthermore, the company is keen to deploy Amazon CloudWatch for monitoring AWS cloud resources; AWS CloudTrail, which records Amazon API Gateway calls for each account and delivers log files; and Amazon Glacier, to provide low-cost storage for data archiving and backups.
AWS Services Used
Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance.
Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud.
Amazon ElastiCache offers fully managed Redis and Memcached. Seamlessly deploy, run, and scale popular open source compatible in-memory data stores.
Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale.
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