Archive for Association Analytics

Data Analytics Group Acquires Association Analytics

(Washington, DC—February 15, 2018) Data Analytics Group, a leader in comprehensive analytics solutions for companies and nonprofits, acquires Association Analytics.

Association Analytics, the leading data analytics company for associations and non-profits brings a uniquely positioned and proprietary suite of products and services to DAG’s product lines. Julie Sciullo, owner of Data Analytics Group and current Chief Operating Officer of Association Analytics becomes its new President and CEO.

“With our purchase of the company, we will bring new resources and ideas to Association Analytics to help our clients advance their mission and grow,” said Sciullo. “In today’s highly competitive market, associations are continually looking for ways to deliver value and support their members. Our innovative products make it easier for them to understand trends and glean insights from membership, marketing, fundraising, financial and other data so they can serve their audience more effectively.”

Association Analytics founder and CEO Debbie King will become an advisor to the company.

“I am extremely proud of the market position Association Analytics established during my ownership. I am confident in the company’s leadership, team and vision to influence and shape the future of analytics in the association and non-profit space,” said King, who founded the company in 1999. “Under Julie’s leadership, the company has doubled in size, and I expect continued growth under the ownership of Data Analytics Group.”

Association Analytics offers products and services designed for associations to get a 360 view of key data sources through an intuitive, easy-to-use visual interface. In March, the company plans the official release of the analytics product which has been under intense development during the past two years.

The product combines multiple data sources in order for association and non-profit leaders to quickly and easily identify trends and opportunities to retain members, refine marketing strategies, and find new audiences and revenue sources.

About Data Analytics Group

Data Analytics Group is a US-based company that delivers comprehensive analytics solutions for companies and nonprofits to provide visibility and insights that drive data guided decisions.

About Association Analytics

Founded in 1999, Association Analytics helps associations make decisions with confidence to advance their mission and improve the world. Our products and services bring together disparate data sources to provide a 360 view of trends and opportunities to retain and increase membership, discover new sources of revenue and deliver value. Our team of experts provide strategic consulting, data architecture, data quality management, data visualizations, analysis, and training.

How to Structure Your Association’s Data Analytics Team

Having a data analytics team is essential for your organization to move forward with an analytics strategy and to drive a data-guided culture. Before you build your team and identify what roles you should hire and/or fill, you need to determine what your organization’s goals are and how data analytics can help you achieve those goals. It’s also good to recognize what responsibilities need to be created for you to attain those goals.

Another way to assess what roles you need when forming your data analytics team is to consider where your organization is in how they use data. Once you know those, then you can assemble a team of people to assign the projects to.

Here are 5 roles to consider when structuring your association’s data analytics team.

Data Analyst

A data analyst role could be quite versatile depending on how your organization chooses to define this position. This individual will have a data-guided mindset and a curious nature for understanding what the data is trying to convey. If your organization is looking for an AMS, this person could play an important role in the RFP process. Other responsibilities could include pulling data reports based on requests, ensuring data accuracy, and conducting data integrity audits. Besides having an analytical skillset, it’s also best for this individual to have strong relationship skills because she or he will be working with various members on the leadership team to communicate what the data is saying and provide recommendations on what to do.

Data Warehouse Manager

Think of the Data Warehouse Manager as a “gatekeeper” for your data. This individual plays an instrumental role in maintaining the data integrity and ensuring everyone is following data management best practices. If your organization doesn’t have a central data repository, then a data warehouse manager will lead the project in creating a single place where all the data resides. This person will also monitor the database to ensure the data is accurate and consumable for other staff members. This person could also assist in the development of written internal procedures that help with data upkeep and work cross-functionally to communicate these policies.

Database Developer

This role will work closely with the data warehouse manager and will be responsible for the actual creation of the database. It’s up to them to design a centralized database that is user-friendly, reliable, and effective. She or he will continually optimize the database and its functionality as the organization continues to grow and needs change. Depending on how your organization wants to define this role, this person could also have a hand in developing a data dictionary and catalogue. As with the other roles, this person will possess excellent communication skills and is adaptable. When building the database, it’s likely that last-minute changes will get thrown at this individual so she or she needs to be flexible. 

Chief Data Officer

Ideally, you want a data advocate to have a strong presence on the executive team. This role will spearhead the data management and analytics projects performed within their department or team. She or he will also play an integral role in making sure the entire organization understands that data helps drive growth and offers a competitive advantage. Following best practices for database management and governance falls into this role as well. And this person will advocate for that consistency, or it will be impossible to make the best use of the data.

This individual will be in a unique position to inspire a change in the organizational culture. He or she could encourage people to adopt a more data-guided mindset. Without this individual, it will be challenging to push for data to be treated as a strategic asset.

Big Data Visualizer

Think of this role as the “storyteller” of the team. This individual will take the data living in your warehouse and transform it into a visual and informative narrative that’s utilized by various departments. Since this person will be a “storyteller,” she or he could also be involved in writing proposals when wanting to get buy-in for a piece of technology.

Since this person will work with cross-functional teams to provide data visualizations, she or he must have strong communication skills and be able to explain data insights in different ways that resonate with their audiences. This will also involve developing and maintaining a collection of data visuals such as graphs, charts, and dashboards that other team members can access.

As with the previous roles, this person also ensures the data integrity of the warehouse. It’s a responsibility that can’t fall on solely on one team member. Everyone will play a part!

A Final Thought…

There you have it! These are the roles to consider filling when building your data analytics team.

Keep in mind that every organization is unique, and one way to approach it is by examining where your organization is in terms of analytics maturity. And if your association doesn’t have the budget for some of these roles, then that’s okay! Responsibilities can be merged into one role, and you can prioritize one responsibility over another. There’s no right or wrong way to structure your data analytics team. It’s ultimately up to your team to determine which roles are vital to the success of your organization. After all, it’s not the data that’s your most valuable asset. It’s the people at your organization.

Learn More About DAMM for Associations

To learn more about the DAMM for Associations and what a data analytics model can do for your organization contact us and sign up for our monthly newsletter.

6 Surprise Findings to Help Your Association Become Data-Guided

Our gut instincts play a role in our decisions – both in our personal and professional life. And more times than not, we can trust our intuition when making decisions. However, in our associations, relying on gut isn’t enough to achieve strategic goals and to stay relevant. We also have to be data-guided in our decisions. Both go hand in hand. This is because today we have more decisions to make than ever before, and the pace of change itself is increasing exponentially. It’s really just not possible for associations to rely on instinct, politics or tradition in today’s world.
A few weeks ago, Ric Camacho, Chief Technology & Digital Officer, Specialty Food Association Inc. and I presented on Business Analytics Projects and Initiatives at ASAE Annual. During this year’s ASAE Annual Meeting in Toronto, we conducted a live poll of our audience during our session about Business Analytics Projects and Initiatives and what we discovered was surprising. Although these findings are from polling the attendees of one session, based on over a decade of experience we find these statistics to be representative of what we find holds true at most associations.
Here are 6 surprise findings from the survey results:
1) Data isn’t easily available in one centralized location for easy reporting and analysis.
Only 8% agree that the data is available in one centralized location, while 72% disagree and feel that their data isn’t easily accessible in one location. When data is scattered in different systems and spreadsheets, this leads to data silos. The data that one group has in their system can be just as valuable for someone else at their association – especially when combined with other data. And while it’s tempting to put your data in a system that is convenient for you, that’s not helping in creating a more data-guided, transparent culture either. Everyone at your organization should have the ability to drill down into data visualizations to pull data at any time. Combining data in one easy-to-access environment creates transparency across the organization, which in turn helps staff members make better decisions about both strategy and execution.
2) Business staff don’t have the tools to easily access data with having to rely on IT.
72% indicate their staff have to rely on their IT team to access the data they need. Ideally, the data shouldn’t be only accessible via IT. If you can have access to and can quickly understand the meaning of data when you need it, think of the all the time you will save! You can spend more time examining it and making your next move. Data is an asset for everyone at your association, regardless of which department they’re in. By having easy access to data, you can better define business goals and develop strategies to attain them. You can also track KPIs on a regular cadence when they are available across the organization and updated automatically every day. Relying on another department to perform a task that you should be able to do yourself is a burden to both you and the department pulling the data, particularly if the other department has a lot on their plate. You feel more empowered in your roles when you have the ability to pull reports that allow you to do your job more effectively – which also makes you happier and more fulfilled!
3) We don’t use visualization tools to understand our data and make decisions quickly.
69% of attendees believe that their existing data analytics tools aren’t visually appealing. Believe it or not, you can showcase your data in an attractive and meaningful way. Most people are naturally drawn to attractive visuals. When presenting your data story to your executive team, you’re more likely to capture and hold attention if the data is presented in a visually appealing way. Telling your data story using a dashboard instead of a traditional spreadsheet is a great way to demonstrate patterns or trends which help you make more strategic decisions because you have the data to back up your ideas.
4) We don’t use data to effectively segment and target marketing.
One way to annoy and potentially lose your members is by spamming them with irrelevant content. Each member at your organization is unique and has different interests. And you can determine what content is valuable to them if you examine your member data and demographics. It’s up to your team to determine what segments make the most sense for your organization. By segmenting your audience, you show that you care about your members and their needs and want to offer them something of value.
5) Data is recognized as an asset and analytics is seen as critical to strategic success.
Another surprising revelation from this survey is how many people understand that data is an integral part of an organization’s success. However, they aren’t utilizing it in the best way possible for the reasons listed above. Getting organizational buy-in for an analytics platform can be an uphill battle if your CEO and board don’t understand the need. It can be a cultural realignment if that’s not how decisions have been made in the past. However, it’s not impossible to change the culture if their goal is to further the organization’s mission, and you can’t do that relying on instinct alone.
6) Data analytics has the support of the CEO and board.
68% of association staff have the support of the CEO and board that data analytics plays a key role in driving their association’s mission – and this is great news! Getting buy-in from the executives is half the battle so once you have their support for using data to make decisions, then it’s easy to explain the need for an analytics platform. Data analytics is something that everyone at your organization can benefit from – but remember the “tool” is not the solution. It’s a process, not a project. The important thing is to begin where you are and create a roadmap that will meet your needs both now and in the future.

Ready to Plan?

Contact us at info@AssociationAnalytics.com or (800) 920-9739 to discuss your association’s analytics strategy and roadmap.

Analytics Guide to 2016 ASAE Technology Conference & Expo

There are a bounty of sessions on data and data analytics at the 2016 ASAE Technology Conference & Expo, being held December 13-14 in National Harbor, MD. For a data-centric conference experience, here’s our guide to the #TECH16:

Tuesday, December 13

10:15 AM – 11:15 AM

Demystifying Data Analytics
Association Analytics® CEO Debbie King joins Karen Blonde and Nikki Vann for this 101 session where they will provide a step-by-step guide for getting started with data analytics. They’ll also share a scorecard to help you judge the current state of analytics in your association and identify opportunities to grow.

1:45 PM – 2:45 PM

Advanced Web Analytics: Get A 360-Degree View of Your Users
Stephanie Yamkovenko, Juan Sanchez, and Jen Boland share how you can make the most of Google Analytics.

3:00-4:30 PM

Association Fail Fest
Association Analytics® Vice President Tori Liu helped organize this fun session that celebrates failure as an important part of innovation. Come here stories from other association professional about how they failed and more importantly what they learned from the failure.
The Lean Startup Changes Everything
The National Council of Architectural Registration Boards, an Association Analytics® client, has a mature, successful analytics program due in part to their commitment to the Lean Startup methodology. Elizabeth Engel and Guillermo Ortiz de Zarate provide a an overview of this helpful approach.

Wednesday, December 14

10:15 AM – 11:15 AM

The Evolving Digital Transformation
Data analytics goes hand-in-hand with digital transformation, which is why we are excited for this panel discussion featuring Ron McGrath, Prabash Shrestha, Jennifer Syer, Leslie Hauver, and Julie Huebsch.

1:45 PM – 2:45 PM

The Foresight of Advanced Analytics
Association Analytics® Chief Analytics Officer Kelly Baker and Galina Kozachenko, Director of Strategic Data Analytics with the Association for Financial Professionals, will share how AFP used predicitive analytics to determine members’ likelihood of renewal and how it’s sharpening the focus and improving the efficacy of their membership retention efforts.

3:00 PM – 4:00 PM

Kickstart Your Efforts Post-#Tech16: Automation. Personalization. Analytics
Have you ever picked up a new idea at a conference, but then struggled to implement when you return to the office? Debra Sutton, Laura Sparks, and Katherine Matthews with the Entomological Society of America will share how their association successfully launched three new initiatives, including an analytics program, by starting small.

Expo

Stop by booth #633 to see how data analytics can help you make decisions with confidence. Enter for a chance to win an Amazon gift card for your holiday shopping. We’ll be raffling off one of four $25 Amazon gift cards at 12:15 pm and 1:15 pm each day.

Columns Aren’t Just for Advice and Holding Up Buildings: They Can also Help Your Analytics

Traditional databases, like an Association Management Systems, are designed to handle frequent transactions and store data. This is very different from dimensional data models that are specifically designed for analysis while aligning with the analytical workflow.
columns_rows

Data, Files, and Blocks

“Cloud computing” is a bit of a misnomer. Data is still stored in files made up of blocks on a computer.
To increase efficiency, databases store entire table rows of data in the same block. For example, all of a customer’s attributes such as name, address, member type, and previous event attendance are stored in a single block for fast retrieval. In this scenario, each “row” represents an individual customer while each “column” represents their different attributes.
If you think about it, most analytics involves aggregating data such as sums, counts, and averages that span many rows. Your exploration might eventually lead you to detailed individual records, but it will likely take several steps to identify these records. This means that if you looking at say, average event revenue, the database will need to retrieve entire records from several blocks just to get the revenue field for the eventual calculation. Imagine having to individually navigate many shelves from left to right when you could just quickly create a stack of what you need!

Columns and Rows

Similar to the goal of a dimensional data model, database technologies can further optimize analytics by primarily storing data in columns instead of rows. For this scenario involving average event revenue, the database simply accesses a single block with all of the data for the revenue column across all rows.
These columnar databases significantly improve performance and storage while providing several other key benefits.

  • Data compression: Since columns are generally the same data type, compression methods best suited for the type of data can be applied to reduce needed storage. In addition, aggregations can sometimes be performed directly on compressed data.
  • Advanced analytics: Many of the algorithms underlying advanced analytics leverage vector and matrix structures that are easily populated by single columns of data.
  • Change tracking: Some technologies track changes at the column level, so you can maintain granular history without having to unnecessarily repeat other data.
  • Sparse data storage: For columns that maintain valuable data that is infrequently populated such as specify product purchases; traditional database technologies need to maintain “NULL” values while column-based databases avoid this storage.
  • Efficient distributed processing: Similar to managing file blocks, column-based technologies can distribute data across machines based on column to rapidly process data in parallel.

Potential Options

Examples of columnar database technologies include Apache HBase, Google BigQuery, and Amazon Redshift. HBase is part of the open-source Hadoop ecosystem, BigQuery is a cloud-based service based on technology that served as a precursor to Hadoop, and Amazon Redshift is a cloud-based service that is part of the popular Amazon Web Services (AWS) offering.
Speaking of holding up buildings, our friends at the National Council of Architectural Registration Boards created some great visualizations based on Amazon Redshift using Tableau Public. Analytics tools such as Tableau and Microsoft Power BI offer native connectors to Amazon Redshift and other big data technologies.  These technologies are another way that you can enhance your analytics using data and tools that you already have with cloud services to rapidly make data-guided decisions for your association.

How Your Association Can Implement Propensity Modeling

Last week, we introduced you to Propensity Modeling and how it can help your association make data-guided decisions while providing great value to your customers. We’ll now dig into some of the technical detail and steps to implement Propensity Modeling.

Step 1. Prepare Your Data

Consistent, complete, and accurate data is the foundation of predictive modeling. Your data should ultimately look like a very wide row with a dependent variable of 1 or 0 relating to the business action taken (or not) along with a variety of independent variables with values at the time of transaction.
Categorical data should be converted to “dummy” variables where values are transformed into individual columns as opposed to row-based data that is ideal for data exploration.  Fortunately, the ability to quickly access high-quality and timely data regardless of source from an environment such as a dimensional data model makes the process much easier.

Step 2. Select Your Variables
checklist-1402461_960_720

Incorporating the right mix of features is vital to the success of any predictive model. While it’s great to have many variables available as candidates, having too many can actually harm model accuracy.
Several automated stepwise techniques are available to propose variables by iterating through different combinations while considering measures such as significance and model error. Simply relying on automated processes is not recommended as statistics should be tempered with business expertise to identify variables that are not meaningful or pick between highly correlated variables. Another challenge is the potential for over fitting, meaning the selected variables based on the sample data are not best for unseen data.

Step 3. Select Your Modeling Technique

Next, you will want to select a modeling technique. You will likely be deciding between a linear regression model and a logistic regression model.
Linear Regression models have outcomes based on nearly infinite continuous variables, such as time, money, or large counts. Propensity Modeling generally leverages Logistic Regression models to derive probability-based scores between a fixed range of 0 and 1. The underlying algorithms used to create models are very different as well.
Logistic Regression is often perceived as an approach to estimate binary outcomes by rounding to 0 or 1, but a score of .51 is very different from a score of .99.  A common approach is to assign records to categories using deciles, or 10 bins with equal ranges.

Step 4. Determine If You Need to Use Any Other Analytic Techniques

You can use several other advanced analytic techniques to accomplish goals similar to Propensity Modeling.

  • Clustering is a form of unsupervised learning as the model is not based on a specific outcome or dependent variable, but simply groups records such as individuals.  The groups can result in customer segments that are ideal for certain products or marketing approaches.
  • Collaborative Filtering is based solely on the actions of groups of users as opposed to individual characteristics.  This is a common approach for recommendation systems based on actions such as purchases, product ratings, or web activity.
  • Decision Trees traverse a path of variables with branch “splits” based on the contributions of variables to ultimate outcomes.  This technique can be effective when a very small set of variables lead to outcomes influenced by downstream groups of variables.

You can also combine models, where the results of one model are the input to another to create a ensemble models.
The decile scores generally represent a range from “sure thing” to “lost cause”. You can use the different decile groups to guide approaches such as the effort to retain individuals, pricing strategies, and marketing messages.

Step 5. Determine Measurement Approach

The Lift of a Propensity Model represents the ratio of the rate based on applying a model to the rate based on “random” individuals. An ideal way to derive this measure is to maintain a control group for comparison to a similar group leveraging the Propensity Model. If can be a difficult decision to risk potential revenue, so a common approach is to simply compare before-and-after results.

Step 6. Consider How You Will Take Action

Before using any analytics model, it’s a good idea to consider how you can take action on the information. What decisions will you make as a result of the information? Similarly, how will you measure the results of the action and use it to inform your model?
For example, you can use a propensity model to reduce expenses. Targeting individuals differently based on their propensity to take action can optimize costs in different ways. Costs might be direct costs, such as actual print mailings or list rentals, or costs can be indirect, such as many non-personalized emails that contribute to information overload. You will want to establish a baseline and a goal for cost reduction to measure success of the model.

Step 7. Identify Your Tool

A range of different options are available to implement Propensity Modeling.

  • R Programming: A popular open-source statistical programming language with many mature packages to perform the techniques underlying Propensity Modeling.
  • Alteryx Software: A software platform offering pre-built tools for different modeling techniques and business scenarios.
  • Amazon Machine Learning: A cloud-based service that is part of the comprehensive Amazon Web Services environment that provides visual wizards for tools to perform Propensity Modeling

This may seem like a lot of steps, but once you have all of your comprehensive data easily accessible along with an available user-friendly tool, all you will need is your imagination to better understand your association’s customer journeys to make valuable data-guided decisions.

How to Hire for an Analytical Mindset

What is the Analytical Mindsest?

In today’s fast-paced, data-driven world, high-performing organizations seek individuals with an analytical mindset. Individuals with an analytical mindset are able to analyze information, identify problems and trends, and solve complex problems. They are also curious. They ask “why” and they want to learn how to do things better, which improves the whole organization.

How to Hire for an Analytical Mindset

Many of our association clients ask us, “How do I hire someone with an analytical mindset?”  Here are five tips your association can follow during the interview process to make sure your next hire has an analytical mindset.
curious

Ask what they have learned recently.

To test for intellectual curiosity, ask candidates to tell you about something they learned in the last few months. Why did you choose that topic to learn about? What was their approach to learning about the topic? How did they use what they learned?
Remember, a truly curious person often learns things outside of their core area of expertise because they value learning for its own sake.

Give them a short assignment.

Look for individuals who can not only identify problems, but quickly develop quality solutions. Before your second interview (after your initial screening), assign a short project and see how they respond. They should be eager to do it and you can tell by the quality of the result whether they were curious or just did surface treatment.
For example at Association Analytics®, we ask applicants to create a dashboard with Tableau. They can use any data set and create any type of visualization. Then, they walk us through their work and explain their findings. We listen carefully to understand their thought process – “I noticed x and wanted to find out why so I then created z”. We also look for self-awareness and critical thinking. Can they look at their work and identify ways it could be improved?

Listen for quality questions during the interview. 

Curious candidates will have lots of quality questions and they shouldn’t just be about the company benefits. We listen for questions about training and learning opportunities, questions about other team member’s skills, and questions about the nature of the analytical problems we solve for our clients.

Look for the intersection of business and technology.

Many people think that analytics is a purely technical field. But it’s not. A data analyst must understand the context – the business environment and its culture, processes, strategies and tactics – in order to truly succeed. The analyst must understand why the findings matter in order to clearly communicate the meaning of the data to the business staff who make decisions. They have to be curious about cause and effect. Probe carefully for this understanding by asking questions about the meaning.
Conversely, look for the capacity for data analysis in non-technical staff. They don’t need a background in statistics, but they should be able to review and understand data and be able to ask questions to learn more.

Validate your findings.

To find individuals with an analytical mindset and intellectual curiosity, employers must also be curious. Strive to learn as much as you can about a candidate. Validate your findings with outside assessments.
We ask candidates to complete the StrengthFinders Survey. According to Gallop, people who use their strengths every day are six times more likely to be engaged on the job. What we found with StrengthFinders is that people with the strengths of “Learner”, “Achiever”, “Analytical” or “Input” are all innately curious so we look for and select for those strengths.

Don’t Be Afraid to Ask ‘What If?’

The oft-cited Gartner image depicting an analytics maturity model shows different forms of analytics that associations can use to understand customers and make decisions with confidence. We’ve previously discussed how Predictive Analytics can provide valuable insight into your association business, but how can you move towards Prescriptive Analytics to answer ‘how can we make it happen?’ One way to get there is through “What-if” Analysis.
analytic-maturity

What is “What-if” Analysis?

“What-if” Analysis is the process of changing the scenarios or variables to see how those changes will affect the outcome. Associations might use this when they have limited data for making a decision or they’re considering launching a major new program. This type of analysis can help you make decisions with confidence.
With “What-if” Analysis, you begin with the end in mind while exploring a world of possibilities in your association’s data. It is a great way for your association to apply models developed for Predictive Analytics to move towards prescriptive analytics. “What-if Analysis” incorporates predictive and other models demonstrating data relationships and allows you to measure the potential impact of different strategies. Here are potential questions that What-if Analysis can help answer:

  • How will different levels of membership dues impact overall revenue?
  • Will changing the location of a conference increase attendance?
  • What marketing channel allocation will maximize conversion rates?

Potential Challenges of “What-if” Analysis

Implementing multiple models and making data assumptions present certain challenges, such as:

  • Data relationships might not be linear – customers eventually encounter diminishing returns as their activity increases
  • Other data relationships may emerge – increasing meeting attendance could decrease training course attendance
  • Price elasticity is not uniform at untested levels – the impact of price on customer decisions may not be easily estimated

Another key consideration is understanding when fundamental changes over time change previously discovered data relationships. Although the past is often the best predictor of the future, this is not always the case.  You can identify instances of data changing over time by consistently monitoring and exploring Descriptive Analytics based on historical data.
These challenges demonstrate why you need analysis beyond basic spreadsheet features.

Getting Started

You can perform basic “What-if” Analysis in Microsoft Excel. However, you can take your “What-If” Analysis even farther, with these tips:

  • Get a Data Visualization Tool. You will want the power of interactive data visualization using tools, such as Tableau, to rapidly adjust data inputs and understand resulting changes.
  • Validate Data. You need to continuously re-validate models and measure the effectiveness of models to ensure the ongoing effectiveness of your models. Be sure to include this when you are considering resources. Also, not all data is created equal. You can use sensitivity analysis to identify the impact of individual variables on different outcomes.
  • Encourage “What-if” Questions. “What-if” Analysis works best in an innovative culture where intellectual curiosity is encouraged. Reward staff for experimenting and questioning long-held beliefs.

You can move your association towards Prescriptive Analytics to truly have conversations with data and create the future. So, now think about your own “what-if” questions!

Association Leaders and the Analytical Mindset

In April, I had the pleasure of speaking with a group of aspiring association executive leaders as part of the “Through the CEO Lens” series about how leaders of the future will increasingly need an analytical mindset. I was joined by David DeLorenzo with Delcor. You can view the recording below.
What is an analytical mindset and why is it important for leaders to have?
Association leaders today have more decisions to make and less time to make them. Having an analytical mindset – the ability to understand, visualize, and communicate data – will be the most important leadership skill in the next decade. When leaders understand the story their data is telling, they can leverage that knowledge to make decisions with confidence and shape the future for their organizations and their career.

Our Favorite Public Data Sources

US Census MapWe’ve demonstrated the importance of both leveraging the data that your association already has along with extending beyond the walls of your organizations to understand customer journeys.  Incorporating publicly available data provides many creative opportunities to further create association analytics to drive data-guided decisions.
1. U.S. Census Bureau
The most commonly requested data is probably that provided by the U.S. Census Bureau.  Census data is often associated with basic population counts required for Congressional Apportionment, but it is much more than just counting people.  The American Community Survey is updated annually and details changes in local communities.  Along with a trove of economic data, other valuable Census data includes American FactFinder (AFF) with diverse areas such as E-Commerce sales, home-based business, and purchased services.
Here is a quick tip concerning Census data.  The common geographic information in your data is probably zip code, which is really intended for United States Postal Service logistics. Fortunately, the Census Bureau provides ZIP Code Tabulation Areas (ZCTAs) that are generalized representations zip code areas along with data describing geographic relationships.
2. Data.gov
Another great source is data.gov, which aggregates information from nearly 500 publishers driven by the 2013 Federal Open Data Policy requiring “newly-generated government data is required to be made available in open, machine-readable formats, while continuing to ensure privacy and security.”  The data.gov website includes a range of data as broad as analytics born from your imagination:

3. National Oceanic and Atmospheric Administration
Eager to learn how weather impacts event registration?  The National Oceanic and Atmospheric Administration (NOAA) provides weather data including temperature and precipitation along with normal levels by the hour.
4. Centers for Medicare & Medicaid Services
How about the prevalence of health care topics and related data such as settings-of-care and insurance payments?  The Centers for Medicare & Medicaid Services (CMS) provides a range of health-related data.
5. Wikipedia
The most collective data source of all is provided by crowd-sourced Wikipedia that includes project and page view trends.
6. 990 Data
No discussion of association data sources can be complete without mentioning available data about associations themselves.  Annual IRS “990 data” provides details of organizations exempt under Sections 501(c)(3) through 501(c)(9) of the Internal Revenue Code.
Honorable Mentions
Various non-profit organizations and other NGOs contribute their valuable data to the public, including the National Opinion Research Center (NORC), the Pew Research Center, and the Sunlight Foundation that promotes making government and politics more accountable and transparent.
A growing source of public data compiled by data scientists is provided by Kaggle, a young company mainly known for holding data science competitions.  Fascinating data can come from unexpected sources, such as private organizations that generate unique data as a result of their core business.  For example, the ADP National Employment Report has evolved into an eagerly awaited economic indicator.  Another example is the widely cited U-Haul National Migration Trend Report detailing population changes that occasionally surprise people.
Now What?
If you’re anything like me, you might not have managed to read this far as exploring these data sources can rival the most addictive websites and video games.  A great feature of Tableau Desktop is to quickly visualize diverse data.  Once you decide data should be consistently available through the organization, creating a sustainable data architecture ensures your association can flexibly explore all available data together while providing the foundation for even more opportunities using advanced analytics.