We saw the importance of data in an enterprise. What enterprises face today is how to mine this data for information that can be used in favor of the business.
Even if we are able to bring this data into one place somehow, it's quite difficult to deal with this huge quantity of data and that too in a reasonable time. This is when the significance of Data lake comes into the picture. The next chapter details, in a holistic fashion, what Data lake is. Before getting there, let's detail the use case that we are trying to achieve throughout this book, with Data lake taking the center stage.
Data lake implementation using modern technologies would bring in many benefits, some of which are given as follows:
- Ability for business users, using various analyzes, to find various important aspects in the business with regard to people, processes, and also a good insight into various customers
- Allowing the business to do these analytics in a modest time frame rather than waiting for weeks or months
- Performance and quickness of data analysis in the hands of business users to quickly tweak business processes
The use case that we will be covering throughout this book is called Single Customer View. Single Customer View (SCV) is a well-known term in the industry, and so it has quite a few definitions, one of which is as follows:
A Single Customer View is an aggregated, consistent and holistic representation of the data known by an organisation about its customers.
- Wikipedia
Enterprises keeps customer data in varying degrees siloed in different business applications. The use case aims at collating these varying degrees of data from these business applications into one and helping the analysts looking at this data create a single customer view with all the distinct data collected. This single view brings in the capability of segmenting customers and helping the business to target the right customers with the right content.
The significance of this use case for the enterprise can be narrowed down to points as listed next:
- Customer segmentation
- Collating information
- Improving customer relations and, in turn, bringing is retention
- Deeper analytics/insight, and so on
Conceptually, the following figure (Figure 05) summarizes the use case that we plan to implement throughout this book. Structured, semi-structured, and unstructured data is fed into the Data lake. From the Data lake, the Single Customer View (SCV) is derived in a holistic fashion. The various data examples are also depicted in each category, which we will implement in this book. Doing so gives a full use of a Data lake in an enterprise and is more realistic:
Figure 05: Conceptual view of Data lake use case for SCV
Figure 05 shows that our Data lake acquires data from various sources (variety), has different velocities and volumes. This is more a conceptual high-level view of what we will be achieving after going through the whole book.
We are really excited, and we hope you are, too!
