Salesforce, for example, always manages to make us smile. Last year, the question posed by its co-CEO after his company acquired MuleSoft for $6.5 billion was: “…this is integration software, what does that have to do with CRM?”

I know the first question that pops into many people’s heads when they hear about programs such as these is: ‘Why not wait until we find out more before signing up?’ That’s a valid question but, when it involves a CRM product from an 

We saw the complaints online, read the messages on Twitter, checked out memes that circulated all weekend, and thought long and hard about the massive outage that affected a whole lot of Salesforce users. There were conflicting reports at first but, apparently, the deployment of a database script inadvertently gave users broader access than intended. This is a polite way of saying there were serious issues related to security that affected a lot of paying customers.

Data migration involves a change in storage and database or application, which is what makes it a potentially complicated process. This is why we, at NexJ Systems, adopt industry best practices while managing migrations from legacy systems, using our extensive tooling and significant experience with client data encompassing a varying degree of size and scope.

Take Machine Learning and Deep Learning, both of which make an appearance whenever a discussion of AI begins. What defines Machine Learning? How does it work? Isn't Deep Learning just another form of Machine Learning? We thought it made sense to try and simplify answers to those knotty questions. So, here goes.

Today, firms have access to information at an unprecedented level and must contend with a highly regulated industry as well as the commodification of products and services. For a CRM solution like NexJ, this represents a challenge as well as a great deal of opportunity, because more information about a customer is a powerful tool when used effectively.

It doesn't take a genius to figure out that Artificial Intelligence (AI) has changed all kinds of industries and workplaces in a number of significant ways. Attitudes towards the use of AI have also shifted, along with the ways in which it has been approached. One of the biggest shifts has been the emphasis on top-down reasoning rather than bottom-up big data. 

When users spend a great deal of time collecting onboarding data, the drain on a user’s time means they have less time to spend on revenue-generating activities. When data is housed solely in an onboarding solution, the information that can’t be shared between systems is lost for processes performed outside of the onboarding software.

This is more true for the fields of Artificial Intelligence, Machine Learning, and Deep Learning than others, which is why our developers have put together a list of more commonly used terms to help you tell your Algorithms from your Active Learning, and Selection Bias from Sentiment Analysis.

Financial services organizations are accepting the advantages of cloud deployment because they are seeing that unified ecosystems, more agility, and better management of investments are all great for business. The cloud can be daunting though, for organizations that aren't clear about their priorities or don't have access to the expertise required to maintain or secure data effectively.

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.