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Best of 2020 – Top 4 Data Enrichment Blog Posts

Authors Photo Precisely Editor | December 11, 2020

Data enrichment is about seeing the world in a more nuanced way and adding context to existing business data.  The results can help leaders to identify patterns that reveal buyer behavior, discover new opportunities, and inform better decisions.

2020 brought some exciting content in the area of Data Enrichment. As 2020 comes to an end, let’s count down the Top 5 Data Enrichment blog posts of the year that brought us insight and knowledge.

#4 3 Benefits of Layering Data Sets for Complex Insight

Modern data analytics tools make it possible to apply context to data in a myriad of distinct ways. Historically, data analysts have aggregated consumers into broadly defined groups. Nielsen Media Research, for example, provides rating information for people from the ages of 13 to 17, 18 to 24, 25 to 34, and so on. This approach is useful, but it provides limited opportunities for insight. Data enrichment will add context to existing data. Read more >

#3 The Data Science Behind an Address

What is an address? Most people will respond that an address is where they live, where they work, or where they go in their spare time. An address is what you write on an envelope you’re mailing, or where your online orders are shipped to. An address is a geographic place and an identity.

Address data has different meanings to different organizations, depending on how they incorporate address data into their workflows.  I’ve been building address data for a long time, but whenever I think of an address, my brain immediately associates it with static, physical locations.  However, for many organizations, the concept of an address has changed greatly in the last decade. Read more >

Read the report

Changing the Rules of Data

Companies are rethinking data-driven practices to keep pace and win in today’s digital market. Learn what the leaders do differently and how they address challenges.

#2 Exploring the Role of Data in the Mortgage Industry

Big data and its corollaries – artificial intelligence (AI) and machine learning – are having a transformative effect on virtually every industry. As the volume of data collected by businesses, governments, and NGOs increases at a faster and faster rate, the accuracy of predictive insights produced by that data increases.

The mortgage industry is no exception. Consider a few use cases. Read more >

#1 Data-driven Real Estate

Data is at the heart of every real estate decision, whether you’re an investor or buyer. What makes a property unique is everything from the type of flooring to the township it sits in. According to a 2019 National Association of Realtors report, school quality is the fifth most important factor in the home buying process, following neighborhood quality, proximity to jobs, affordability, and convenience to friends or family.

Research suggests this is not isolated to American consumers; a 2016 research study in Australia found that the four types of data profiles most relevant to prospective home buyers were related to the property, region, educational system, and local transportation. Data visualization is key – another study found that visitors spend 40% more time on a real estate listing page when it contains a dynamic visualization of not only the property itself but also the area around it. Read more >

To learn how companies are rethinking data-driven practices to keep pace and win in today’s digital market, read Changing the Rules of Data, a research report from Harvard Business Review Analytic Services.