Today, the concept of having a data ecosystem in place to improve personalised customer engagements and support customer retention should not be a foreign one. And yet, many companies still struggle to understand how to practically do so. With data providing significant benefits to create such a business advantage, it has become critical to understand how technology infrastructure, analytics, and applications can work together and be effectively used to capture and analyse data.
This TechTarget article provides an interesting read centring on the ecosystem components required to make the value-adds of personalisation and customer retention possible. The author writes that new and evolving technologies have made harvesting data related to customer activity more efficient with a greater ability to target responses. The key, as we all know, is to capture and analyse that data.
Today, we have progressed beyond the traditional reliance on a single, central data warehouse and business intelligence platform. To cope with the much larger and faster data volumes, and the wider variety of insights required (in much less time) from that increased data source, companies are looking at implementing more encompassing data ecosystems.
Improved insights
Of course, the preferred source for some of corporate reporting and analytics requirements will still be a centralised, productionised, and governed data warehouse. For corporate reports, regulatory reporting, and other closely monitored forms of reporting, there must still be a responsibly managed and governed information source as part of the data ecosystem.
But when it comes to the ad hoc insights and spur-of-the-moment analyses often required for the dynamic nature of customer expectations, alternative technologies such as data hubs and lakes are needed. This would also deal with the larger data volumes in circulation that often has sparser data knowledge points.
Analysis in a new world
In a way, these additional technologies replace the old staging database, making it more open to accept a variety of data sources faster. This gives data scientists and other analysts quicker access to the data before the small core percentage thereof is curated into the data warehouse for conventional corporate reporting.
Applying these insights for customer analysis will empower companies to extract more value from digital and mobile solutions. The author cites Amazon as an example of this data analysis when it comes to product recommendations and other engagement tactics. It also enables the company to conduct specialised short-term sales events, driven by predictive analytics.
The article concludes by stating that data is the fuel to create a brighter future for business. If that is the case, then the engine that drives all this is a data ecosystem developed for modern, digitally-led organisations.
