Why Data Modeling Is Essential
Do you find the whole process of data analytics and reporting confounding? You are not alone. Many businesses employ teams of trained data experts to tackle these tasks. But did you know there is a quick and easy way to pull data findings without relying on data scientists? It’s true!
ThoughtSpot’s Relational Search Engine is designed to accept questions and deliver insights to any business person — no formal training required. But this is only one part of a much large strategy. Below we will cover data modeling and why it is important for businesses like yours.
What’s the Deal with Dimensional Data Modeling?
Generally speaking, data models start out pretty simple, but as companies evolve and new requirements arise, things can get out of hand rather quickly. This is why it is so important that businesses adopt a data modeling solution with simpler data navigation, faster database performance and adaptability.
Let’s say you are trying to track customer service phone calls. There are any number of insights contained within your data including the total number of calls, the percentage of issues resolved, the average duration of the call, etc. You can only imagine how convoluted this can become as you grow your customer base and add new customer service reps. Querying this data could create a bottleneck effect in reporting, leading to hours of downtime until your findings are returned.
The key is to develop an analytics-ready data pipeline. Here are some fundamental data modeling principles to follow:
- Identify the specific business process you hope to track
Interested in tracking the number of customer service phone calls that resulted in high consumer satisfaction? Then focus on this for the time being. You can always revisit your data for additional insights later.
- Select the granularity of the data you seek
ThoughtSpot recommends starting with the finest granularity you can think of and moving from there.
- Strip out the dimensions
Identify the key attributes involved in your search and create separate dimension tables for each one. This will ensure that every dimension table is unique and reliable.
- Consolidate all the facts
Round up any remaining metrics (such as the number of unresolved customer calls or average minutes spent per call) and present them in a fact table. Alongside each entry, you ought to include foreign keys that reference the dimensions involved.
Luckily, ThoughtSpot simplifies this whole process. Our Relational Search Engine dynamically translates simple queries into complex aggregations complete with data visualizations, at-a-glance reporting and answers to questions you have yet to ask.
Want to see ThoughtSpot in action? Request a free demo today!WATCH DEMO
TECHNOLOGY WHITE PAPER
Relational Search: A New Paradigm for Data Analytics
More About Data Reporting
Blending Automatic Reporting with Ad Hoc AnalysisRead more
It’s Time to Kill the Static Business Analyst ReportRead more
Taking Data Visualizations Further with Artificial IntelligenceRead more
Reporting Analytics: Delivering Powerful Reports QuicklyRead more
A Better Way to Conduct & Share Retail KPI AnalysisRead more