This is a guest blog post contributed by our partner minubo.
Some of the great retail experts still think that omni-channel is a concept of the far future – an idea or vision that has still nothing to do with the present. But in our work with brands and retailers, we feel and see that omni-channel is becoming more and more relevant and taking its place in the minds of strategic and operational roles in commerce organizations. I would even take a step further and say: Omni-channel can become reality, right now!
The analysis of your customer’s journey can give you insights of how customers make decisions, what they like and obviously don’t like. It can literally show you how to become more successful.
Because we always like to share our knowledge with you, we will give you some tips and tricks on pushing your revenue to a new level. Just a tiny hint: Big data is the key!
This blog post will provide more information, how we revolutionize the BI infrastructure for businesses with simple to sophisticated data needs by creating our new product: DataVirtuality Pipes.
Launching a new product is an awesome feeling. Everybody worked very hard for the past weeks and months. And as the launch date comes closer and closer the whole team gets very excited. All the hard work, the process with so many sophisticated people makes us very sentimental, especially when we think of where we started.
It’s been said that information is the oil of the 21st century. Massive amounts of information are already being generated, captured, and parsed to eliminate uncertainty and improve business decisions. With the emergence of the Internet of Things (IoT), immense numbers of everyday objects are acquiring network connectivity, allowing them to send and receive data. In 2008, there were already more “things” connected to the Internet than people, and by 2020, the amount of Internet-connected things is expected to reach 50 billion¹.
The logical data warehouse works by intelligently marrying two distinct technologies to create an entirely new manner of integrating data. The first technology is data federation, which connects two or more disparate databases and makes them all appear as if they were a single database. The second is analytical database management providing semantic business-friendly data element naming and modeling allowing flexible ingestion and modeling options.
A modern data integration strategy employs what’s known as “best-fit engineering,” whereby each part of the data management infrastructure utilizes the most appropriate technology solution to perform its role, including storing data determined by business requirements and service-level agreements (SLAs). Unlike a data lake, this new architecture has a distributed approach, aligning information storage selection, with information use, and leveraging multiple data technologies that are fit for specific purposes. A hybrid approach can also significantly reduce costs and time to delivery when changes or additions in the warehouse are required.