Data Analytics – Enough Talk, How do I Get Started?
By Danzel Pinto and Eric Au
Associations have always had the challenge of doing more with less, and while COVID-19 has altered the way they’re operating, the age-old question remains: With less dollars, resources and time, how can associations be agile and plan for continuity? What you may not know is you have a great resource already at your disposal, and its name is data analytics.
Data analytics is the ability to draw insights from data, and most organizations are already performing it in some form or another – whether that be analyzing finances or examining their membership base. Understanding how to get started with data analytics can be overwhelming, but we’ve found associations have benefited most when looking to determine key traits of members, create targeted campaigns, understand new revenue options or determine retention strategies. Data analysis might give you comfort in a notion, or it might challenge your approach. Either way – data analytics can be scalable to your time, resources and objectives such that you can get some benefit for your association. So how can you get started?
- Determine your objective.
Most organizations focus on the data first instead of the objective. Setting a clear objective to guide your journey will help you understand when you’ve reached your goal, as well as the time and cost it may take to reach it. Aim to understand what you’re looking for and how it can tie back to your business objectives. Are you looking to:
Better understand your members? By determining key traits of highly involved members you can create campaigns that target new members with these traits.
Determine trends in fees? How can this information be utilized to target individuals for retention?
Proactively understand your financials? Gaining clear insight into where your funds are going will give light to areas becoming increasingly costly and identify areas where your organization could cut back.
If you happen to not have the data to complete your objective, that may be an indicator that you should consider collecting that data, which can be completed by adding a question to required forms or sending out a survey.
- Determine your data set.
Internal data is data that can be found throughout your organization, and may include membership databases, financial reporting information, or registration data for events. When selecting your data, try and think outside the box. What other internal data do you have access to that could support your objective?
Beyond your internal data, you can also incorporate external data, such as from Statistics Canada or other associations collecting data for use. Combined with your internal data, this can provide you new insights into your data.
- Clean your data.
Very often, the data collected by organizations is filled with mistakes, errors and incomplete values. This “dirty data” needs to be scrubbed – and while this is a very tedious and meticulous task – it is necessary to ensure accuracy in the analysis. Be sure to:
Complete data normalization: Ensure each item is described and spelt the same way. Devote some time to removing acronyms and correcting spelling errors.
Focus on format: Ensure your dates, subtotals, blank rows and columns are correctly displayed.
Fill in missing fields: Source missing information like email, job title, address, etc., if possible so that the analysis captures all the members information.
Merge data sets: If you determined you’ll require both internal and external data, you’ll need to merge these data sets together.
While it is ideal to have perfectly clean data, remember that using a moderately clean data set can still yield insight, potentially with more outliers than ideal.
- Analyze your data
Although there are numerous tools available to mine data, Microsoft Excel is a great tool to start as it is at your disposal and has a plethora of functionality, most of which can be learnt through quick YouTube tutorials. Once you have your tool selected, begin to mine your data. This includes presenting your data in numerous ways such as pie charts, bar graphs, pivot tables etc. For instance, if you were looking to better understand your members, you may want to create charts that:
- Show members by geography. Are members residing in a particular region? Is there a reason for that?
- Show members by age. What age group has the highest involvement? What reasons could that be?
Now, if you were to plot these two factors on the same graph, you could begin to understand if age or geography was influencing membership. Similarly, you could perform this study using wealth, gender, marital status, education, or any other data you may feel is relevant, most of which is at your disposal from your internal data collected or external data available.
As you build out your analysis, you’re creating descriptive analytics – which is a backwards lens that tells you what has happened. To move forward, you’ll need to create diagnostic analytics. Diagnostic analytics looks to understand why something has happened. For instance, if age is influencing membership, why is that the case? What are all the possible explanations for age influencing your membership base?
Since you now understand what and why something has happened, you can use that information to create analysis or models that can give you the likelihood of it occurring again. For instance, what is the likelihood that an age group will continue to have a high involvement rate? This is predictive analysis, and although challenging, it’s one of the most valuable pieces of data analytics.
Predictive analysis will provide you the forecast of potential outcomes, but what can you do to support or challenge these outcomes? You’ll need to create prescriptive analytics by putting this information into action. What plan can you execute in order to maximize your desired outcome? To follow our example, this could be figuring out which members you want to engage with more, and in which way, to support your prediction.
Although data analytics can feel daunting, we hope you’re able to use the steps we’ve outlined as you look to strengthen your strategy and gain insight into your operations. You may walk away with an insight you never knew or perhaps a confirmation that you are on the right track. As associations continue to look for ways to remain agile, data analytics can be a tool to get you there.
If you have any questions about how to get started with data analytics in your organization, feel free to reach out to us:
Danzel PintoEric Au
Principal, Charity-and-Not-for-profit Sector
[email protected]
Eric Au
National Data Analytics Leader
[email protected]
