Data Quality Tools are Key for your Data Analytics Strategy
The availability and quantity of data is ever increasing, meaning that data quality tools are increasingly important.
As much as this can be a benefit to the travel industry, it can also be a burden. This information is only beneficial if it is useful and secure. If a company relies on a data bank to conduct their business, it has to be sure that data bank is of high quality.
If a company uses inaccurate data for forecasting, their entire business could be in jeopardy. In fact, over 45% of the travel industry has cited inaccurate data as an obstacle to productivity.
Data Quality Tools and AI
Artificial intelligence, or AI, has led to more data than every being accessible. However, if companies cannot identify what data is useful, the value of AI is lessened. The quality, nature and storage of data presents issues for the travel industry. And though advancements are constantly being made in data quality, they are not happening quickly enough to keep up with the technology.
A primary advantage to data analytics is the ability to track patterns in client behavior. But analysis is really only possible when data is structured. When data starts its life unstructured, it can require huge investments of time and money to extract use and value from this information. It can also be costly to correct inaccuracies with data. Clean up, reconciliation, and corrections all add up to major headaches and price tags.
Below, we outline four ways that companies can hedge against chaos and protect their data quality.
1. Assess current data
The first thing a company needs to do is audit existing data. A manager needs to make sure all data fits into industry standards. An audit should ensure sure that all data is categorized properly so that it can be incorporated into existing branding and marketing strategies effectively. It is recommended to streamline these categories across departments so all players are on the same page.
2. Clean the data set
The next thing a manager should do is get rid of all irrelevant and duplicate data. If it’s not useful, there is no need to store it. Storage is not only expensive but storing extra, unwanted data makes reading database tables more confusing. It slows down the extraction of relevant data as well. Once this scrub is complete, it should be ran through a management program such as Neustar. This will allow an analytics professional to compare this information with data provided by reliable and verified sources.
3. Implement a system that continuously authenticates data
It’s not efficient to constantly scrub all data. It is better, instead, to rely on an automated process for doing this. Since customer data becomes obsolete quickly, you want to make sure your information is up to date. The only surefire way to do this is to continuously authenticate data. There are multiple vendors companies can work with to have their database monitored and authenticated on an automated, regular basis.
4. Enrich data
Data can be further enhanced by integrating offline info such as demographic, psychographic and geographic data. This allows a firm to accurately predict and interpret customer behavior. It lets companies create a more personalized customer experience and refine targeting, which is key in the travel industry especially.