3 Simple Steps to Mapify your Big Data KPIs

Making location matter for Internet of Things

As your enterprise conforms to an “internet of things” world, your customers, employees, and seemingly unlimited connected devices are generating tons of location data. Machine and device generated data is a breeding ground for new big data opportunities, and over 80% of this data has a location attribute.

Why Location Matters?

Your customers and employees are location aware beacons thanks to smartphones and wearables. The physical world we live and work in is digitalized more than ever before. Harnessing this location data and effectively mapifying your KPIs can expose problems and opportunities that may be hidden in plain sight.

What is “Mapifying a KPI”?

Key performance indicators (KPI) stem from real business problems, align to a strategic objective, and should have measurable progress.

Mapifying KPIs, is the practice of identifying existing business problems where location, proximity, or distance play a vital role in creating new leading indicators and supporting analytics. 

Here are three important steps to mapify your KPIs.

Step 1: Organize your KPIs and supporting Analytics 

Mapifying your KPIs should support your existing strategic goals. Starting with your existing business KPIs and metrics for sales, operations, marketing, or HR, you can quickly uncover problems where location-based metrics can help support your lines of business. How to visualize and communicate progress using maps is not important during this first step.

Examples:

  • How does customer foot traffic (footfall) and engagement impact sales?
  • How do you maximize employee retention during relocation, due to increased commute times?

Step 2: Explore new ways to measure success

Spatial analytics opens up new ways to measure success. Density, distance, proximity, and other measurements are more common to GIS analysts than business leadership. Those organizations that master these measures within their lines of business have a competitive advantage. I recently covered some examples in my My Data is Bigger than Yours blog post.

Example:

Foot traffic and engagement are two entirely different leading indicators of sales and are measured in many different ways. While most retailers track basic footfall metrics, newer wi-fi, and sensor-based technologies relay detailed information where customers are coming from, how long they dwell, and potentially what products they are viewing.

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Step 3:  Hypothesize what is good or bad

For new “big data” powered leading indicators and analytics, it is critical to hypothesize your optimal measures for success. Then, using the analytics or GIS tools on hand, you can explore and standardize before holding your organization accountable for increasing performance.

Example:

When relocating employees or assets, the relative distance is interesting, but not important. In this scenario understanding the difference in commute times and what threshold employees are comfortable increasing their commute, is important. Understanding these thresholds through user interviews and/or statistical analysis  is the last step to having an effective leading indicator.

Want help with this top-down approach to Location Intelligence?

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Meet Ryan Goodman

Ryan Goodman is CMaps Analytics CEO, author, and expert in Business Intelligence. Ryan applies over 13 years designing enterprise analytics apps to his leadership role shaping CMaps Analytics' Location Intelligence platform strategy. Today, Ryan is focused on pushing wider adoption of analytics with an emphasis on geospatial analysis and Location based services.

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