It is based upon the fact that customer expectations have changed across businesses and sectors, with customers now expecting the same kind of engagement from financial institutions that they do with other products or services.

Working with data is always tricky because there's so much that can go wrong so quickly. Any CRM solution that claims to do its job well has to deal not just with data duplication or conflicts, but with third-party applications, back-office systems that don't talk to each other, external systems and a seemingly unlimited number of integration points. 

Here's an interesting piece of information that got lost in the hype surrounding Salesforce's biggest deal ever. Apparently, the acquisition of MuleSoft in March for $6.5 billion was met with skepticism by senior management, until they were gently informed by a financial services firm of the importance of connecting data that is stored in disparate systems.

http://dmradio.dataversity.net/julia-python-r-the-rise-of-jupyter-notebooks/

Click above to listen to Martin Sykora, on DM Radio. Martin joins the discussion at the ten-minute mark, and for the roundtable discussion at the 49-minute mark!

The flexibility and cost effectiveness of Apache Hadoop was quickly recognized by many organizations as an effective delivery vehicle to empower business users with operational self-service query and analytic capabilities.  Many organizations established, or are presently establishing, data lakes for operational intelligence query and analytics capabilities for the field personnel who need them most, best understand the data, and are the most capable of actioning insights gleaned. 

Many organizations established, or are presently establishing data lakes as a cost effective means of provisioning operational intelligence query and analytics capabilities directly to the field personnel who need them the most, understand the data the best, and are the most capable of actioning insights gleaned.  Sounds like an ideal arrangement.  

The Apache Open Source contributions to Hadoop are numerous and cover a broad portion of a reference architecture. It has been some time since we considered foundational low cost storage and in-place query capabilities. And as we saw in the "Data Lakes" blog posting, many organizations utilized this foundational offering.

In this blog series, we’ll explore the concepts that make up the Semantic Data Lake. We’ll begin with an introduction to Hadoop – what is it and why was it developed? Techniques traditionally applied when mastering complicated organizational reference data such as customers often require centralization to enforce standardization. 

For some, it’s about reducing costs by modernizing the back office.  For others, it’s about leveraging disruptors within their enterprise.  For NexJ, it’s all about the customer.In the age of the customer, the customer is in control of their journey.  They dictate how and when they will interact with the firm.