This week, we learn how data orchestration is the key to delivering great customer experiences and we take a look at the role of direct mail in nonprofit organizations. We receive a highly curated guide of useful articles about digital transformation. We get some thoughts on being a data science entrepreneur in a disruptive economy. And finally, we consider five factors that are shaping data science.

Data Orchestration Is Key to Delivering Great Customer Experiences 

by Naras Eechambadi, contributing writer for 

At most companies, there are several marketing tools and technology, each generating rich data about transactions, customers, and their behavior. Tapping into this valuable data will allow you to deliver highly personalized, relevant messaging, and offers to your customers. But harnessing this data to extract meaningful information and being able to act on it in a timely way remains a major challenge. This is where data orchestration comes in. Learn how data orchestration can be done right. 


What’s Your Job, Direct Mail?

by Christopher Foster, contributing writer for

Each piece of your marketing mix has a very specific role. And each channel has different jobs in the customer journey of awareness, interest, consideration, engagement, and advocacy. Direct mail has a very specific job to do for nonprofits in 2019. Its job is to stick around until the recipient delivers a strong donation. Learn how to create effective direct mail for your organization.


Enterprise Guide to Digital Transformation

by Cathleen Gagne, contributing writer for

IT teams are expected to modernize and transform their organization’s technologies. But with so many buzzwords and emerging ‘hot’ technologies, it’s difficult to know what that really means. It can be hard to get a clear picture of how to start your own IT modernization, or even highlight the specific areas to investigate. There’s just so much information to wade through. And often, you’re only seeing information in isolation. Here’s a curated list of articles, broken down into sections: how to start; IT and business roles; mistakes made along the way; transformative technologies; and who’s doing it now. 


Some Thoughts On Being a Data Science Entrepreneur in a Disruptive Economy

by Richard Boire, contributing writer for

When it comes to data science and starting a business, two routes can be followed. The first is to develop specific products. And the second is to develop services that solve specific business problems. Demand is the key to success. So how do data scientists create demand? This article examines this question and provides an interesting take on how to craft a business and product through data science.  


Five Factors Shaping Data Science

by Ryohei Fujimaki, contributing writer for

A survey conducted by Univa found that 96 percent of respondents expected an “explosion in machine learning projects” in production by 2020. Fraud detection, customer analysis, churn prediction, and other applications are driving this rapid growth of artificial intelligence (AI) and machine learning (ML). But another study conducted this year by Dimensional Research found that 80 percent of companies reported stalled AI and ML projects. This article outlines five factors that are causing these slowdowns. 



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