As massive amounts of data are stockpiled in databases and CRM platforms, executives are turning to data analysts to make sense of it all. But why are so many businesses still struggling to turn location data into profitable outcomes?
It’s not you, it’s your data.
Implementing data-driven decision making can only show a return on investment for businesses if analysts ask the right questions with the right data. But analysts often find themselves working with incomplete, inconsistent, or even incorrect data from which few, if any, actionable insights can be discovered.
Data enrichment helps solve this problem (and it happens to be the first step in our Location Intelligence methodology).
Data enrichment is a process that enhances, refines or otherwise augments existing data, typically with imported datasets.
By enriching raw data with supplemental datasets and advanced spatial analysis techniques, analysts generate location-specific information wherein data-driven solutions to challenging business problems can be discovered. Here are four ways you can get started enriching your data:
Geocoding is one of the first steps any business can take to enrich their data. If you have any type of address data, then geocoding can help render numerical and categorical data into latitude and longitude coordinates.
The added value here is the standardization geocoding provides analysts who can now conduct more in depth visualizations and analyses in a fraction of the time. (Plus, it’s a lot easier to visualize coordinates when you get to stage three of the Location Intelligence method).
Pro-tip: Don’t have physical address data? Try geocoding IP addresses instead!
If you’re looking to calculate route optimization or knowing the distance between two points would be helpful in your data analysis, then you’ll want to investigate enriching your data with routing. Talk to your LI provider about the best way to enrich with routing, or check out tools like OpenStreetMap (and CARTO).
Enriching your data with an area of influence analysis involves creating “isolines,” contoured lines that display equally calculated levels over a given surface area. This enables you to view different polygons calculating the travel time from one location to another within that polygon.
Imagine you have a variety of stores and want to see who lives within a 15-minute walk of those stores. Creating an area of influence will transform your point data into polygon data, which helps more precisely define your area of influence. Take a look at this demo using subway stops and demographic data.
Enriching data with spatial boundaries is important, but adding demographic measurements can also help analysts get a better picture of their customer or target audience.
Population demographics are often provided by importing census tract data from a curated catalog. Popular measures for customer discovery tend to include: age, race, gender, education level, occupation, average income, and political party affiliation to name a few.
Whether you’re discovering consumer behaviors and trends from past transactions, accelerating time to purchase within the buyer’s journey, or designing marketing campaigns tailored to specific customer types, data enrichment is an important first step in your overall Location Intelligence strategy.
Have data enrichment hacks of your own? Share them with us on Twitter, Facebook, or LinkedIn page!
Happy Data Enriching
Credit card transaction insights allow decision makers to equip themselves with a deeper understanding of consumers and trends. In partnering with Mastercard, and with the ...Location Intelligence
The unprecedented growth in Americans aged 65 and over is driving demand in the senior housing subsector. The Census Bureau expects the U.S. senior population to increase b...Location Intelligence
It’s no secret that the retail and real estate industries have been disrupted over the past decade. Traditional Retailers are learning to co-exist with ecommerce giants and...Location Intelligence
Please fill out the below form and we'll be in touch real soon.