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Cisco DNA Spaces Data Cleansing Process

Cisco DNA Spaces Data Cleansing Process

Everyday, customers have the opportunity to leverage billions of data points through wireless. Cisco DNA Spaces helps process wireless data into meaningful business insights. Read on to see how we differentiate our location data processing engine.

Step 1:

Data reliability

Cisco DNA Spaces identifies
potential issues in input data
streams and triggers alerts

How is it processed?

We identify the following issues:

  • Data interruption: Data is not received from a location.
  • Data reconciliation: “Visits” data is not tallying with data seen from “connected” users on the network.
  • Data variance: Data values are abnormally higher or lower than average.
Step 2:

Data accuracy

Cisco DNA Spaces uses a machine learning
process to identify industry specific
insights based on visitor classification

How is it processed?
data accuracy

We isolate core groups across specific industries to allow for more relevant data.

  • Example: Isolate data by guests vs non-guests in hotels or tourists vs locals in malls.

We extract non-core groups who are likely to significantly skew data sets.

  • Example: Extract users identified as employees, transients, etc.

We avoid duplication of data when users have multiple devices.

  • Example: In workspaces where users have multiple devices, we group their devices by a unique user id.
Step 3:

Data normalization

Cisco DNA Spaces normalizes
metrics to ensure comparability
across locations

How is it processed?

We convert absolute data to relative data and normalize customers’ size factor. Customers are able to benchmark and compare property performance to gain valuable context.

  • Example: Normalize metrics by number of people, indices, proportions, percentages, ratios, square footage, number of rooms, etc.
Step 4:

Industry standard metrics

Cisco DNA Spaces
standardizes metrics to
increase business relevance

How is it processed?

Our location hierarchy feature helps customers manage and group their network based on region, brand, zones, etc. The level of context in reports is dependent on the hierarchy structure.

  • Example: A retailer with 500 stores might aggregate their hierarchy with a broader region view to a specific store to shopping zones.
  • Example: A single hospital might aggregate their hierarchy by buildings to floor level to departments.

We process reports differently across industries to increase data relevancy for customers.

  • Example: How we compute “time spent” and “visit” count can change across deployments. A singular “visit” in a stadium is when someone enters and leaves. This is not the same for hotels where a visit can be one week or one month.

Learn more about

Cisco DNA Spaces

Read how we ensure

Privacy and Data Protection