Groundwork

Data aggregation is the process of gathering data and presenting it in a summarized format. The data can be gathered from multiple sources with the intent of combining the sources into a summary for data analysis. This is a crucial step because  data analysis accuracy depends heavily on the amount and quality of the data used. It is important to gather high-quality, accurate data and a large enough amount to create relevant results.

Associations can collect data on their members and website visitors. The aggregate data can include statistics on member demographics and behavior metrics, such as average age or number of inquiries. This aggregated data can be used by the marketing team to personalize messaging, offers and more in the user’s digital experience with the brand. It can also be used by the content team to learn which content has the highest engagement rates and by the board to help choose how to allocate budget toward marketing or content development strategies.

Data mining is the process of turning raw data into useful information. By using technology to look for patterns in large batches of data, associations can learn more about their members to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection, warehousing and computer processing.

Benefits of data mining

• Automated decision-making
  Data mining allows organizations to continually analyze data and automate both routine and critical decisions without the delay of human judgment, allowing streamlined decision making and enhancing the daily processes of an organization.
• Accurate prediction and forecasting
  Planning is a critical process within every organization. Data mining facilitates planning and provides leaders with reliable forecasts based on past trends and current conditions.
• Cost reduction
  Data mining allows for more efficient use and allocation of resources. Organizations can plan and make automated decisions with accurate forecasts that will result in maximum cost reduction.

Member insights
Firms deploy data mining models from member data to uncover key characteristics and differences among members. Data mining can be used to create avatars and personalize each touchpoint to improve overall member experience.

Data discovery is the collection and analysis of data from various sources to understand hidden patterns and trends. It is the first step in fully using an organization’s data to influence critical business decisions. Through the data discovery process, data is gathered, combined and analyzed in a sequence of steps. The goal is to make messy and scattered data clean, understandable and user-friendly.

1. Connect and blend data

   The first step in the data discovery process is to gather the right data in one place. Data, scattered across many sources, must be placed in a single area where analysis can take place.

2. Cleanse and prepare data

   Raw data imported from different sources can rarely be analyzed as-is. Data needs to be cleaned and formatted in ways that facilitate reliable and thorough analysis.

3. Share data

   With data constructed and free from redundant or unneeded information, it must be shared with others in the organization. Even though this data is the single version of the truth, it can be leveraged in different ways.

4. Analyze and generate insights

   Individuals can read, analyze, and create value from validated data when there is a single distributed version. Common tools include distributional analysis, predictive models and market basket analysis. It is important to understand the type of insights generated by different analytical tools.

5. Visualize insights

   Data visualization takes the information and insights drawn from your data and presents it in an interactive graph or chart.

Forecasting uses historical data to make informed estimates to predict future trends. Associations can use forecasting to predict fundraising revenue, budgets and cash flow.