CASE STUDY
Can you give us an example of some of the data-driven decisions you make at Dreams and how Exasol ’ s technology helps with such decisions ?
Kasperova : As previously mentioned , our analytics aims to support all decisions in everything we do . Our reporting is aimed at understanding performance , identifying risk and opportunity early across all business KPIs .
In the post-implementation of Exasol , we have focused on data ingestion of large-volume externally-generated data sources aimed at better understanding our customer ’ s experience , be it data from consumer surveys , call-centre telephony system , product quality data , or data generated behind Dreams Sleepmatch ( proprietary mattress fit technology ), as well as drive efficiency through all that we do ( delivery vehicle telemetry , online Speed data , to name a few ).
We have also implemented a web speed monitoring tool which stores speed test data several times a day and enables monitoring of key speed KPIs trends , and results in additional monitoring provided for our online development teams to review the impact of code releases .
We are in the process of developing consumer survey dashboards following an ingestion of these external data sources ; aimed at providing end-users with a unified view of all key insights from all the relevant platforms where feedback and surveys are completed by our customers , to unify all reporting within one platform and make the end-user journey simpler . This will also benefit from further insights on customer experiences , improved accessibility among teams and providing further context to already developed analytics .
Stewart : Exasol has been a key component on the journey to automation and data ingestion from varied platforms , enabling easy access for our analytical resource , improving efficiency and releasing time to analyse rather than collate , prepare and ingest data .
As an example , we were able to provide the end-user with automated Sleepmatch analytics . Sleepmatch , a proprietary in-store mattress fit technology , is located in all Dreams stores and carries out live calculations to recommend the ideal product for each customer based on individual needs . Prior to ingestion , only limited high-level store usage stats were available , but with the ability to ingest into Exasol and analyse the large volumes of data , we can now understand the product recommendations made by Sleepmatch as well as better understand the typical customer preferences and requirements . This in turn will help us with product innovation and optimisation going forward . The added benefit of accessibility of data among the insights team results in data being easily blended and shared between projects , providing further context to all our reporting with minimal further effort .
What benefits have you seen since implementing the solution and how does this impact the end-user ?
Stewart : The consolidation of the data within the highly available Exasol Cluster has resulted in the data just being available . Previously , where data was distributed across technologies and source systems , we had numerous single points of failure which often disrupted data availability .
Exasol has enabled more relevant analytics , with Insight and Analytics teams more agile and self-sufficient without being heavily reliant on IT development and code . This enables them to spend more effective time on analytics and visualisations to meeting the business needs and requirements .
Also , Exasol has been a key component of the data lake build , which provides the end-user with more relevant reporting as data is shared easily from project to project .
How has the solution allowed you to scale and future-proof operations ?
Stewart : As mentioned , the cost-effective entry point enabled us to spec a cluster with significant head room , and Exasol ’ s licensing model enables a costeffective path for growth . Exasol will enable us to move away from a model of Kill and Fill ( aka Full Refresh ) to an incremental model for refreshing our collected data marts / summaries reducing our processing and data refresh load times and making us more efficient . p
EXASOL HAS BEEN A KEY
COMPONENT OF THE DATA LAKE BUILD , WHICH PROVIDES THE END-USER WITH MORE RELEVANT REPORTING AS DATA IS SHARED EASILY FROM PROJECT TO PROJECT .
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