After completing this module, you will be able to:
1. Summarize the stages of the data maturity spectrum.
2. Describe the connection between data aggregation and data context.
3. Explain the consequences of data decay.
The tasks below will be automatically checked off once you complete taking the quiz.
Now that you have the groundwork for understanding BA, let’s dive deeper into the process for applying BA to your organization.
Data maturity indicates how advanced an organization’s data analysis is. Organizations reach a high level of data maturity when their data becomes incorporated in every decision the organization makes.
Data maturity can be defined in four stages. Where does your organization fall?
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Decision sciences and context
How and why do people choose to accept or reject ideas and opportunities? This is important to understand if you want to grow your organization. There’s actually a degree called decision sciences, and it's the study of how and why people make decisions. Some decisions are easy when cost vs. value is evaluated. If the cost is too high or too low for the value, the decision is easy. What about everything in the middle, when we are uncertain whether or not the cost is appropriate for the value? Consumers research and get data to determine the value to inform the decision making process. Are we using all the data available to us? We need data plus context, or the data will not be nearly as meaningful. For example, if you only have the price of an item and not the details around the product or service, knowing the price is not very useful; we need the whole picture.
Context provides relevant facts from the environment. This can include data from member interactions, social media, news and broader market changes. Context data improves member experiences by incorporating information from outside of your organization. By looking outward at the broader environment and the lives of your members, you can strengthen big-picture decision-making and improve customer interaction-level experiences.
5 ways to collect contextual data
- Personalization and marketing automation software can leverage artificial intelligence to personalize content.
- Social media monitoring software helps companies keep a stronger pulse on social conversations relevant to your industry.
- Data extraction software can scrape publicly available web page data that would otherwise be difficult to export, download, record or analyze.
- Data integration software allows separate applications to communicate, share and integrate data.
- Utilize association management systems and collect demographic, community platform, event, transactional and volunteerism data.
Data decay/data quality management
The most conservative estimates indicate that at least 30% of customer data decays (becomes outdated) every year.
Low-quality data is a real problem for most organizations because:
- 15% of email users change their email address one or more times a year
- 20% of all postal addresses change every year
- 18% of all telephone numbers change every year
- 21% of all CEOs change every year
- 25-33% of email addresses become outdated every year
- 60% of people change job titles within their organizations each year
The result of poor-quality data is an increased cost per member, fewer conversions, reduced profits and reduced revenue including loss in productivity, wasted communications and marketing spend and wasted resources due to wrong decisions.
The top three reasons for poor data quality include:
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Some key dimensions of data quality include:
- Completeness indicates whether the data gathered is sufficient to draw conclusions. This can be assessed by ensuring there is no missing information in any data set.
- Consistency ensures data across all the systems in an organization is synchronized and reflects the same information. An example of consistent data includes recording the enrollment date in the same date format as in a member’s information spreadsheet.
- Accuracy implies whether the data represents what it should. This can be measured against source data and validated against user-defined association rules/bi-laws.
- Timeliness means the data is available when expected to facilitate data-driven decision making.
- Uniqueness involves making sure there are no duplicates present in the data. For example, the lack of unique data can result in multiple emails being sent to a single member due to duplicate records.
Validity measures whether data meets the standards or criteria set by the business user.
Download your Determine Your Organization’s Data Maturity Stage Worksheet and the 5 Ways to Collect Contextual Data Worksheet to identify where your organization’s data analysis is currently and to improve member experiences by incorporating information from outside of your organization! Save and download or print.
CHECK FOR UNDERSTANDING
See if you understand the concepts by completing the quiz. Click Initiate Module Quiz to begin.
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