It’s hard to find beautiful and compelling data visualizations. Even when using tools that provide the ability to create stunning visuals many product offerings fail. It’s certainly true that “big data” and proper analytics can deliver actionable insights leading to smarter decisions. Yet, the value of said insights depends greatly on the quality of the data. Larger data volumes and it’s variety make it more difficult to establish confidence. What’s to blame? Does it really matter?
The Blame Game
It’s not uncommon for analysts and other stakeholders to blame the tool. When executives are not able to make confident decisions with the data, reports, analysis provided the tool is often ‘thrown under the bus’. But is that really the issue? Analysts are expected to use available data and tools to provide insights. It hardly seems appropriate then to blame the tool for decision making shortcomings. Rather, the analyst provides additional information and expert advice when the tool (or the data) creates gaps in business intelligence.
This brings us to the data. We’ve all heard something similar to ‘garbage in, garbage out’ in reference to bad data. While that statement is, at it’s basic definition true, it does not need to be true in practice. There are many tools available for auditing and cleaning data. Examples in the data industry are R and DataWrangler (which eventually was spun off into Trifacta). Google has a tool called Google Refine. There are several products in the travel industry too such as Cornerstone’s DataCleanser application or Grasp Prepare from Grasp Technologies. I suppose the point is that you should be using a data auditing product.
Common Travel Data Audits
- Ensure data is in correct currency
- Identify and delete duplicate flights caused by split ticketing
- Identify missing hotel names, chains, and rates
- Delete duplicate hotel and car information caused by improper invoicing
Is it worth it?
If you like to make good decisions then it is well worth the effort. In a survey recently released by IBM it was reported that companies with <70% data accuracy were unhappy with their decisions 2/3 of the time, companies with >90% data accuracy gave executives reliable information 4 out of 5 times, and companies with high data quality saw >24% improved performance last fiscal year.
Beautiful Data Visualization
You’ve now taken the first step to great data visualizations. It’s time to start developing those visuals which will provide executives, managers, directors, and the like with the insights needed to make smart business decisions. How will you do it? Where do you start? We’ll discuss that in Beautiful Data Visualization – Part 2!