Most executives do not trust their data. Without certainty that their data is accurate, business decisions become increasingly risky and it’s difficult for the company in question to compete. For the specific reasons executives do not trust their data, and solutions to fix them, check out this article.
In a nutshell: Making critical business decisions is hard enough. It's even more challenging when the organization does not trust its data.
So how does a company know if they have ‘good’ data? Data that can predict customer purchasing behavior, reduce churn, and gain market share? High-quality data can be defined by three traits: the completeness, number of data points, and data recency.
Real-world data will always have incomplete or missing values, especially if they gather it from several sources. Incomplete data can cause flawed reports and skewed conclusions for actuaries and underwriters. For businesses, it translates to poor customer insights, inaccurate business intelligence and losing ROI.
Data completeness, therefore, is an essential component of the data quality and has a high correlation to validity and accuracy. If the data is missing, the information cannot be validated and if it is not validated, it cannot be considered accurate.
It is easy to ignore missing values and proceed to compile reports or performing activities knowing that you have missing data. The result is tasks that deliver poor responses (such as an email marketing campaign that ignored missing last names and had to struggle with duplicates), reports that have misleading conclusions affecting policies and critical reforms, business plans that fail and errors that have legal implications.
All this said, the realistic goal of data completeness therefore, is not to have perfect, 100% data. It’s to ensure that the data essential to your purpose is valid, complete, accurate and usable. The tools you have at your disposal, such as the ReFocus Data Strength Calculator, is a technology that will help you get there.
Number of Data Points
How much data you have, and the numbers of years you have it for are an important part of data quality. Too few data points means they will make business decisions with little historical context or more heavily influenced by data outliers.
The quality of data points is also influenced by the average number of attributes that are collected. Attributes are specific information relating to a customer (e.g. age, gender, zip code, last purchase, etc.). Together, the quantity of data, and the number of attributes within the data make up the number of data points. They work symbiotically for data quality - the general rule is, the more of both you have, the stronger the data is.
One of the most important reasons to collect both is that certain business decisions require different information. Not having enough data or the right data can limit the ability to use the information a company has to make data-driven decisions.
On the ReFocus Data Strength calculator, these are separate to best generate a score. Data collection should be uniform so that the dataset aligns over different time periods. For new businesses, data will be useful after three quarters of collection, when patterns around seasonality emerge. Collecting the same data overtime allows companies to make direct comparison for customer buying behavior, churn, and pricing.
Data is like milk: give it enough time and it will spoil in how consumable it is. As data ages, it naturally becomes less specific to a company’s customers and their behavior. Making decisions on data that is too old is the same as deciding on data that is from the last decade - you just do not want to do it.
Data recency is an important part of a company’s overall data quality. When it is older than a year, it becomes less relevant because there is less certainty that it is accurate. Data that is older than a year is still important, and businesses should maintain it as part of a larger dataset. Older data provides important information on seasonality, and the more historical data there is, the higher confidence decisions makers can place on it for pattern identification.
Data recency is a force multiplier for the above two data quality indicators. That is why companies should try to verify that data approaching the year mark is still valid. For the ReFocus Data Strength Calculator, select the answer that best matches how often information is added or modified.
Trusting your data
When a company’s data is complete, contains a large number of data points, and is recent, it can be trusted. That trust means that executives can rely on their data to make important business decisions. With data becoming increasingly proprietary, and useful to maintain a competitive advantage, all businesses must seek to maintain a strong database of customer information.
The last decade has shown how data driven businesses decisions are the most important indicator of long-term revenue and viability for a company. We built the ReFocus Data Strength Calculator to help businesses discover how ready and viable their data is. Scoring in the top 80th percentile means that your company is off to a brilliant start - and better than 80% of other companies. To get truly accurate, and gain real competitive advantages, scoring in the 90th percentile should be the goal of every executive.
In the coming months, we will continue to make updates to the Data Strength Calculator. If you have any feedback or suggestions for improvements, please drop us a line, or send me an email at email@example.com.