Data Management Best Practices from Master Data Experts

Former Marketing Director, Partnerships & Alliances
Dun & Bradstreet

Chief Data Experts Discuss data quality & Modern Data Strategy Implementation

You’re in for a treat! It isn’t every day you get to watch two well-versed data gurus sit down together and chat about the importance of data quality in the advancement of today’s global commerce.

Think about it as a data storm. If you harvest the power of the storm, you can propel your organization into the future. But if not, you can be crushed.
Olga Lagunova, Chief Data and Analytics Officer, Pitney Bowes
 

In this first of a four-part video series, Olga Lagunova, Pitney Bowes’ Chief Data and Analytics Officer, and Anthony Scriffignano, PhD, Dun & Bradstreet’s Chief Data Scientist, get right to the heart of the matter – the explosion of data in our rapidly changing world and how businesses need to adapt to make the most of it.

 

We are, as a human race, creating data at a rate which is arguably no longer even measurable. And everyone just wants the answer.
Anthony Scriffignano, PhD, Chief Data Scientist, Dun & Bradstreet
 

The current digital landscape of connected devices is wreaking havoc on previous ways organizations developed and executed their data management operations. Today’s methods need to take into account new sources of unstructured data, consider emerging regulators, and leverage prevailing technologies to fully tap into the power of a modern enterprise data strategy.

 

Data Quality - The Importance of Contextual Meaning (Part 2)

Today’s fast-paced, connected world has changed the playing field for conducting business as more of us turn to master data for better ways to grow and protect our organizations.

But how should B2B professionals think about the best data to use to advance our priorities? Olga Lagunova, Pitney Bowes’ Chief Data and Analytics Officer, and Anthony Scriffignano, PhD, Dun & Bradstreet’s Chief Data Scientist, both in the business of data for their organizations, are uniquely qualified to share their thinking.

The definition of data quality needs to be done upfront, before you embark on the journey of collecting or acquiring data from third parties.
Olga Lagunova, Chief Data and Analytics Officer, Pitney Bowes
 

In this second of a four-part video series, our master data experts focus on the fundamental question, “What is data quality?” While for some who live and breathe data management on a daily basis this may seem like a basic question, Anthony and Olga offer some insightful context that you should consider.

 

Here are a few data management highlights:

  • Data quality can be highly dependent on its intended purpose. It should therefore be defined upfront before determining what data is essential to manage a specific business need.
  • A good framework when defining data quality includes the dimensions of data accuracy, timeliness, completeness, and consistency – all of which require their own definitions based on a given business use case.
  • Bias in data can never technically be completely removed, so data practitioners need to find ways to deal with it. One way is to consider how wrong the data can be before leading to a different answer.

 

Data Strategy- Key Pillars to Master Data Implementation

A modern Master Data strategy is necessary for any data-driven organization to deliver excellent customer experiences, mitigate risk exposure, and grow.

Olga Lagunova, Pitney Bowes’ Chief Data and Analytics Officer, and Anthony Scriffignano, PhD, Dun & Bradstreet’s Chief Data Scientist, live on the cutting edge of dynamic data and understand what a nimble data management strategy needs to be for their respective organizations in pursuit of customer value.

Consider these recommendations when building your data management strategy and selecting the best data partners:

  • Be clear on the desired business outcome. Knowing what you want to achieve with your business strategy is a fundamental driver of your data strategy.
  • Define the key building blocks including discovery, curation, synthesis, fabrication, and delivery as part of your data strategy. Each of these blocks needs quality assurance and governance rigor.
  • Think of your data as an asset in the pursuit of serving your customers as you choose the best data partners and investments.

Data Innovation - Using Market Disruption To Your Advantage

All good things must come to an end. In this case, we are wrapping our skyline chat series with Olga Lagunova, Pitney Bowes’ Chief Data and Analytics Officer, and Anthony Scriffignano, PhD, Dun & Bradstreet’s Chief Data Scientist. The focus of this series has been on how an organization’s data strategy can be one of its biggest differentiators.

In our final episode, Olga and Anthony open their minds to the data innovations that organizations can take advantage of in what may feel like disruptive yet exciting times.

Here are a few of the gold nuggets these data visionaries shared:

  • The explosion of emerging technologies like Artificial Intelligence and IoT sit center stage for innovative uses of data. First, go back and look at your base assumptions. People who can do that are going to be powerful data innovators.
  • Find the intersection of innovation and disruption for some of the best data opportunities. Leaders must lead differently in the face of disruptive innovation.
  • Be curious. Be curious about what's happening in academia, in industry, in the best companies. It's all very important for innovation.