Warning: A non-numeric value encountered in /homepages/0/d741589923/htdocs/clickandbuilds/AssociationAnalytics/wp-content/themes/Builder/lib/builder-core/lib/layout-engine/modules/class-layout-module.php on line 499

Warning: A non-numeric value encountered in /homepages/0/d741589923/htdocs/clickandbuilds/AssociationAnalytics/wp-content/themes/Builder/lib/builder-core/lib/layout-engine/modules/class-layout-module.php on line 499

Archive for associations

Partnership Provides Powerful Member Engagement Analytics

(Washington, DC—August 13, 2018) Association Analytics, the leading data analytics software and services company for associations, and Higher Logic, the industry leader in cloud-based engagement platforms, have announced their formal partnership.

Sharing the goal of innovating associations through technology, this dynamic union originated on May 23, 2018. An unprecedented case study detailing the impact of this new integration was released at the ASAE Annual Conference, featuring the predictive membership engagement and revenue indications ASAE has captured.

Higher Logic’s Director of Partnerships, Bobby Kaighn, explained, “What may surprise association leaders is the potency of analytical trends when the Higher Logic community platform is integrated with Association Analytics’ Acumen data analytics platform. You can see the exact retention rate of members who engage in specific community activities, the relationship between community interactions and attendance at annual meetings, and the ratio of membership revenue to the frequency of online community participation.”

Associations dedicate their sources to storing information on everything from finances to event registrations, and most of these sources are continually upgrading their advanced reporting functions to include visual analytics. Now imagine if an association could lay these advanced data sets, like marketing campaign data (from one source), on top of member engagement statistics (from another source). This is where the synchronized connectivity of this partnership innovates the association industry.

Higher Logic’s data-driven approach helps organizations track and manage meaningful interactions along each stage of the member journey. Its expanded suite of engagement capabilities includes online communities and marketing automation, covering everything from the initial web visit to renewal and ongoing engagement.

Julie Sciullo, CEO of Association Analytics, said, “With Higher Logic as a standard Acumen platform integration, we’ve been able to quickly and easily import community data in real-time to interpret past and current data, bringing trends to light for strategic action plans.  The integration with Higher Logic empowers associations to overlay hundreds of data combinations to gauge performance and predict future outcomes.”

With the partnership between Association Analytics and Higher Logic already innovating associations, today’s announcement is shared in celebration of this ongoing and increasing benefit to the industry. For more, don’t miss the latest case study featuring ASAE’s actionable analytics on retention rates, the relationship between specific interactions and attendance at annual meetings, and the ratio of membership revenue to the frequency of online engagement.

Association Analytics (A2) is an industry leader in data analysis and management products, services, and training. We innovate associations by funneling your databases continuously into one powerful visual analytics dashboard for real-time, instant interpretation and decision-making power. The platform is called Acumen and it’s designed specifically for associations. This hassle-free, hosted analytics platform means you no longer need IT staff to run reports. Acumen is intuitive and easy-to-use with out-of-box visualizations & reports that encourage cross-staff adoption. The flexible design allows you to choose only the modules you need and includes seamless built-in integrations with your AMS, LMS, email, and finance platforms. Modules include Membership Engagement, Events, Community, Finance, Membership, Sales/Orders, Email Marketing/Automation, and the Executive Dashboard feature. It’s time you had a single source of truth with a 360 degree view for better, faster decisions, enhanced member experiences, improved staff efficiency, and increased revenue. Learn more at www.associationanalytics.com

Higher Logic is an industry leader in cloud-based engagement platforms. Our data-driven approach gives organizations an expanded suite of engagement capabilities, including online communities and marketing automation. From the initial web visit to renewal and ongoing engagement, we help you track and manage interactions along each stage of the digital customer experience. Organizations worldwide use Higher Logic to bring people all together, by giving their community a home where they can interact, share ideas, answer questions, and stay connected. Everything we do – the tools and features in our software, our services, partnerships, best practices – drives our ultimate goal of making your organization successful. Visit www.higherlogic.com.

An Approach to Analytics both Hamilton and Jefferson Could Embrace

Happy 4th of July!  What a great time to think about data independence, democratization, and governance for your association.  In this post we’ll talk about the balance between the central management of data by IT and data directly managed by association staff.
Leading analytics tools provide great capabilities to empower people to make data-guided decisions. The ability to analyze diverse data from a breadth of sources in a usable way for association staff is a key feature of these tools. Examples include Power BI Content Packs and Tableau Data Connectors. These range from pre-built data sources based on specific applications such as Dynamics, Google Analytics, and Salesforce; to relatively rarer “NoSQL” sources such as JSON, MarkLogic, and Hadoop data. These tools rapidly make data from specific applications available in formats for easy reporting, but can still lead to data silos. Tools such as Power BI and Tableau provide dashboard and drill-through capabilities to help bring these difference sources together.

Downstream Data Integration

This method of downstream integration is commonly described as “data blending” and “late binding”. An application of this approach is a data lake that brings all data into the environment but only integrates specific parts of data for analysis when needed. This approach does present some risks, as the external data sources are not pre-processed to enhance data quality and ensure conformance. In addition, business staff can misinterpret the data relationships that can lead to incorrect decisions. This makes formal training, adoption, and governance processes even more vital to analytics success.

What about the Data Warehouse?

When should you consider using content packs and connectors and how does this relate to a data warehouse and your association? The key is understanding that they do not replace a data warehouse, but is actually an extension of it. Let’s look at a few scenarios and approaches.

  • Key factors to consider when combine data is how closely the data is linked to common data points from other sources, the complexity of the data, and the uniqueness of the audience. For example, people throughout the association want profitability measures based on detailed cost data from Dynamics, while the finance group has reporting needs unique to their group. An optimal approach is to bring cost data into the data warehouse while linking content pack data by GL codes and dates. This enables finance staff to visualize data from multiple sources while drilling into certain detail as part of their analysis.
  • Another consideration is the timeliness of data needed to guide decisions. While the data warehouse may be refreshed daily or every few hours, staff may need the immediate and real-time ability review data such as meeting registrations, this morning’s email campaign, or why web content has just gone viral. This is like the traditional “HOLAP”, or Hybrid Online Analytical Processing, approach where data is pre-aggregated while providing direct links to detailed source data. It is important to note that analytical reporting should not directly access source systems on a regular basis, but can be used for scenarios such as reviewing exceptions and individual transaction data.
  • In some cases, you might not be sure how business staff will use data and it is worthwhile for them to explore data prior to integration into the data warehouse. For example, marketing staff might want to compare basic web analytics measures from Google Analytics against other data sources over time. In the meantime, plans can be made to expand web analytics to capture individual engagement, align the information architecture with a taxonomy, and track external clicks through a sales funnel. As these features are completed, you can use a phased approach to better align web analytics and promote Google Analytics data into the data warehouse. This also helps with adoption as it rapidly provides business staff with priority data while introducing data discovery and visualizations based on actual association data.
  • Another important factor is preparing for advanced analytics. Most of what we’ve described involves interactive data discovery using visualizations. In the case of advanced analytics, the data must be in a tightly integrated environment such as a data warehouse to build predictive models and generate results to drive action.

It’s not about the Tools

The common element is that using data from sources internal and external to your association requires accurate relationships between these sources, a common understanding of data, and confidence in data to drive data-guided decisions. This makes developing an analytics strategy with processes and governance even more important. As we’ve said on many occasions: it’s not about the tools, it’s the people.
Your association’s approach to data democratization doesn’t need to rise to the level of a constitutional convention or lead to overly passionate disputes.

How to Determine What Data to Combine

There is a lot of value in combining data from one business area with data from another business area. Similar to a jigsaw puzzle, when we combine data sets and put the pieces together, we get a complete picture of customers, events, and activities. But how do you know what data to combine?

Take Inventory of What You Have

To get started, take inventory of the data you already have available in your business area. Let’s take members for example. Membership teams often require a high level of granularity. They also have years of membership data that can be leveraged. The data they have may be stored in their Association Management System, Customer Relationship Management system, and their Financial Management System.
After identifying the data sources, consider what data is stored in each data source. Identify the file type and how you extract or integrate the data with other systems.

Consider What’s Missing

To determine what data could augment your existing data source, think about the aspects of the customer or activity that you care about.
What information could help answer your business question? If you don’t have a business question, what information would provide additional insight on customer behavior?
The membership team typically has data on when a member joined, membership type, length of membership, contact information, and dues payments. What other information would help them understand members? It may be helpful to combine membership data with components from other areas such as the number of events attended in the past two years, the last meeting attended, age, member status, tenure in the industry, and total spending in the past year.

Combine and Analyze

Combine the data and analyze it. Look for trends ad relationships. Distill down the information so that each component of activity that is of interest is presented as attributes of that person.
The table below shows some combined information as it relates to the Top 10 and Bottom 10 thread topics from an association’s online community. Using the information, we can see what a correlation may exist between a person’s attributes and the most active threads. From the data below, it looks like younger individuals with less membership tenure and professional development are replying and posting to threads generated by younger authors than the bottom threads. Perhaps action can be taken to target the younger members with messaging encouraging them and providing the benefits of authoring and responding to community posts.
Once you combine data, you can determine if there is actually a relationship between two data sets. You can also see if you need additional data to augment your analysis. Using business intelligence tools, like Tableau, allows you to easily connect data sets and experiment.

Association Analytics: Begin with the End in Mind

Often association leaders ask me, “Where is the best place for us to begin our data analytics initiative?”  I like this question and am reminded of Steven Covey’s expression that before we undertake a new initiative, we should “begin with the end in mind”.  When it comes to data analytics for associations, this means starting with your strategic plan.
One association’s strategic plan, for example, specified that they would grow meeting attendance by 10% in 3 years.  This was their largest source of revenue.
In the past the association made decisions by looking at historical trends and was unable to confidently estimate expected attendance, revenue, venue amenities needed, onsite staffing levels and more.  In fact, the more events they held, the greater the risk of an incorrect estimate.
Yet because of the strategic initiative to increase attendance, the first thing they tried was to increase the number of events and market them by email campaigns and direct mail.
The visualization below is a simple bar chart showing the number events by year. The higher the bar, the more events. The color shading of the bars indicate the number of registrants. The darker the green, the more registrants. We can quickly see 2010 had the most events, but not the most registrants.  In other words, although the association increased the number of events, this did not increase the number of attendees. In fact, holding more events actually caused a decrease in meeting profitability as a whole, since there were more expenses associated with fewer registrants.
By reducing the number of events by combining smaller, similar events to a larger meetings, the organization was able to dramatically exceed the target set by the board.  In other words, by holding fewer events, they had more participants.

What is the Cost of Not using Data?

When making business decisions it’s important to weigh the costs of:

  1. Bad decisions
  2. Decision delay
  3. Lost opportunities
  4. Damage to image/reputation

Often these costs are much greater than the costs associated with analyzing data. Taking a “decision tree” approach can be a logical way to evaluate where to start:

  • Are there areas in your strategic plan where data can answer questions, provide insight and direction?
  • Which areas do our customers care about? Will it make a difference to them – add more
    value, improve their experience – advance our mission?
  • Do we have the data – or can we get it?
  • Will we be able to take action on the results of the analysis?


For Nonprofits and Associations, the Time is Now for BI

Business Intelligence can improve nonprofit and association decision making.  Most of us are familiar with the scenario where we extract information from a variety of different systems that each serve their own purpose, but are not integrated.  In recent years, there has been a push to integrate systems or to try to put everything into one multi-purpose system, like an AMS (Association Management System) or CRM (Customer Relationship Management).  There are certainly benefits to this especially for the front-end users and customer service representatives.  However, there are challenges as well.  For example, it often will slow down a transactional system to store every email that was ever sent to a member.  It may also be cost prohibitive to have a real-time integration between all of your systems.

A Better Way

time_to_goTo evaluate if this situation is optimal, ask yourself if right now you could accurately measure engagement across all channels and programs.  The goal is to not only enable you to increase that involvement, but also to clearly demonstrate the positive impact your association or nonprofit has on your sphere of influence.  I’ve often found that this broad level of analysis can be elusive.  When your board asks for this information, you may have to spend days pulling information from all of your systems and trying to come up with some form of analysis.  But don’t forget to save those complex spreadsheets and don’t forget how they were created so that they can once again be recreated next month!  This kind of activity is what makes me cringe and think, there has to be a better way!  The exciting news is that there is a better way and it’s not as hard as you might think.

Private Sector Paved the Way

The private and commercial sector has already set the stage.  It’s like having a big brother or sister that pushes the boundaries, makes mistakes, and figures out the best way to excel.  Now associations and nonprofits can learn from their mistakes, modify their path to meet our unique needs, and off we go.  The commercial sector has proven the value of Business Intelligence (BI).  Which is, simply stated, transforming data into meaningful information that can be used to make good decisions.  However, remember that the technology is just the enabler – the “tools” to get the job done – it’s people who are the differentiators.
BI can be used to bring together disparate data sets so that historical analysis and predictive modeling can guide decision-making.  It can also be used to supplement your standard customer data with social media content, including customer’s interests, purchasing behavior, and social circles.  What if you could find those customers who spend very little money with your association, yet enjoy being a part of it, and also have a huge circle of influence?  You could reach out to them to help promote your organization.  BI can also identify the key metrics which will help determine overall effectiveness in your space.

BI is Expected

I’m sure you’ve heard about Business Intelligence by now – it’s been a buzz word for a long time and in fact is almost overused.  But the time to act is now.  If your competitors aren’t using it, they will be soon and have you stopped to think that the private sector is a competitor these days?  Especially with respect to content, education and networking.  Chances are your customers are already using BI (or analytics), even if they don’t realize it.  Each time they purchase something at a large online retailer and see things like “other customers have bought this” or “you might also like”, they are reaping the benefits of BI.  It’s something they’ve come to expect.  They also want a custom, user-centric experience from you.  In order to provide this to them, you must first understand them.  You likely already have that information, you just need to harness it.

Get Started

To get started, take a look at my previous blog, Preflight Checklist for Association Business Intelligence. This blog outlines a few preliminary steps you can take now on your path to using BI and making data-driven decisions.

Top Trends in Business Intelligence for Associations for 2014

Top Trends in Business Intelligence for Associations for 2014
Tableau software recently compiled their set of Top Ten Business Intelligence Trends for 2014. Not all of these trends will be impacting the association space. Some of these trends are more relevant in the corporate space, but could become more relevant as more time passes and technologies are less expensive and accessible. In this blog we will review the trends compiled by Tableau as well go in depth on the trends that will directly affect you and what you should do about it.
Below is the Top Ten list as published by Tableau.

  1. The end of data scientists. Data science moves from the specialist to the everyman. Familiarity with data analysis becomes part of the skill set of ordinary business users, not experts with “analyst” in their titles.
  2. Cloud business intelligence goes mainstream. Organizations that want to get up & running fast with analytics drive adoption of cloud-based business intelligence. The maturation of cloud services helps IT departments get comfortable with business intelligence in the cloud.
  3. Big data finally goes to the sky. Cloud data warehouses like Amazon Redshift and Google BigQuery transform the process of building out a data warehouse from a months-long process to a matter of days.  This enables rapid prototyping and a level of flexibility that previously was not possible.
  4. Agile business intelligence extends its lead. Self-service analytics becomes the norm at fast-moving companies. Business people begin to expect flexibility and usability from their dashboards.
  5. Predictive analytics. Once the realm of advanced and specialized systems, will move into the mainstream as businesses seek forward-looking rather than backward-looking insight from data.
  6. Embedded BI begins to emerge, in an attempt to put insight directly in the path of business activities.  Analytics start to live inside of transactional systems.  Ultimately, embedded BI will bring data to departments that have typically lagged: for example, on the shop floor and in retail environments.
  7. Storytelling becomes a priority, as people realize that a dashboard deluge without context is not helpful. Stories become a way to communicate ideas and insights using data. They also help people gain meaning from an overwhelming mass of big and disparate data.
  8. For leading-edge organizations, mobile business intelligence becomes the primary experience, not an occasional experience. Business users begin to demand access to information within the natural flow of their day, not back at their desks.
  9. Organizations begin to analyze social data in earnest, gaining insight beyond number of their likes and followers. Social data becomes a proxy for brand awareness and attitude, as well as fertile ground for competitive analysis.  Companies begin to use social data to understand how relevant they are to their customers.
  10. NoSQL is the new Hadoop. Organizations explore how to use unstructured data. NoSQL technologies become more popular as companies seek ways to assimilate this kind of data.  But in 2014, the intelligent use of unstructured data will still be the exception and not the norm.

Of these trends, only some will impact your association in 2014.  Below we have provided you information on the relevant trends to take to your executives to make sure you can stay ahead of the Business Intelligence curve.

  1. The end of data scientists. Most associations don’t have the resources for full time data scientists. Instead associations are putting the power of data-driven decisions in the hands of staff across departments. Staff will be trained on analytical skills that can be applied to their daily routine and need for information.
  2. Cloud business intelligence goes mainstream. Cloud-based solutions help associations to more quickly and often more economically begin using business intelligence. It also puts the data in the hands of everyone and anyone with internet access. Data belongs to the association business staff and the IT staff facilitates the management of this important asset.
  3. Agile business intelligence extends its lead. In the association space, DSK incorporates key aspects of agile processes to business intelligence initiatives because it dramatically increases the probability of a successful outcome. Typically, associations do require an estimate up front for budgeting purposes, but the scope and development are best performed in an agile, iterative fashion. See our blog post from last year about Agile Business Intelligence.
  4. Storytelling becomes a priority. We often believe data is a flat object, but viewed in context it has the power to tell a story about what is really happening.  We believe that through data discovery, association staff can learn to understand the story their data is telling. And once the story is understood the story, you have the power to can change the ending! Through the use of interactive data visualizations, your data can be analyzed in ways not thought of previously. 

Can you see your association following these business intelligence trends in 2014?

How to Choose the Right Visualization for your Association

Choosing the right data visualization is as important as choosing the right outfit to wear to an important meeting. Although your alma mater’s sweatshirt is perfect for the ball game, a suit and tie is more appropriate when trying to convince your board to increase your budget. Similarly, you are going to catch some flack for showing up to the game in a suit and tie! Choosing the right visualization for your audience is similar to choosing the right outfit for the function.
Did you know that the human brain is able to process images three times faster than text? From our primitive beginning, we’ve depended on our brain’s ability to detect subtle patterns and interpret meaning. So, how do you choose the right visualization? Let’s take a look at some common types of visualization and when they should be used to effectively communicate the story your data is telling.



  • Best used when exact quantities of numbers must be known.
  • Numbers are presented in rows and columns and may contain summary information, such as averages or totals.
  • This format is NOT favorable to finding trends and comparing sets of data because it is hard to analyze sets and numbers and the presentation is cumbersome with larger data sets. It is estimated that the visual working memory has a capacity of about seven items. This means that you can store up to 7 bits of information (like numbers) in your brain’s “RAM” simultaneously. If you build a table with financial information for each month of the year for different areas of your association, it becomes difficult to find outliers or even the most profitable month.
  • This kind of visualization is likely what many association staff are accustomed to (think of all those excel spreadsheets floating around your office) so you may need to use a tabular format in conjunction with one of the other types listed below to convey the information.
  • A variation of the tabular chart is a highlight table. A highlight table applies color to the cell based on its value. The use of color can make outliers stand out more.

 Line Charts

line chart

  • Best used when trying to visualize continuous data over time.
  • Line charts use a common scale and are ideal for showing trends in data over time.
  • Example: membership or registrant counts throughout the year compared to previous years.
  • Trend lines and goal lines can also be added to compare actual counts with certain benchmarks.

Bar Charts

bar chart

  • Best used when showing comparisons between categories.
  • The bars are proportional to the values they represent and can be shown either horizontally or vertically. One axis of the chart shows the specific categories being compared, and the other axis represents discrete values.
  • Example: Bar charts can be helpful when looking at certain segments of your customers, registrants or members.
  • Goal lines can also be added to compare the actual counts with your benchmarks.
  • A variation of the bar chart is the stacked bar chart. This incorporates the use of color to visually show how certain segments add up to the total. In the example above, it’s easy to see that while 2010 Conference attendance counts are higher, the number of Paid attendees actually decreased from the previous year.
  • Another variation of the bar chart is called a bullet chart. This chart allows you to take a single measure (for example, revenue) and compare it to another measure (for example, revenue goal). It also can display percentiles.

bullet chart

Pie Charts

pie chart

  • Best used to compare parts to the whole.
  • Pie charts make it easy for an audience to understand the relative importance of values.
  • Using this format for more than 5 sections is not recommended as it can become difficult to compare the results. Too many sections make interpretation difficult because the difference between the sections can become too narrow to effectively interpret.
  • Often, even when wanting to compare parts to the whole, a bar chart can be more effective.

In addition to difference chart types, the use of filters and sorting is important to increase the association staff person’s ability to explore the data in more detail.
The goal of any visualization should be to communicate the information in the most concise and impactful way by using the appropriate visualizations for your data.  Effective visualizations enable your audience to quickly understand the story in the data and speeds the ability for association staff to reach key insights.

7 Business Intelligence Mistakes Associations Should Avoid

failure_successThe private sector and government organizations have been using Business Intelligence (BI) for over 15 years. The good news for those of us in the association space is that we can learn from their mistakes and avoid them when starting BI programs for our associations. Below is a summary of common mistakes and how they can be avoided.

1. Not Solving a Real Business Problem

It’s easy to get caught up in the hype of business intelligence. It’s cool, it’s great for your resume and you may be getting pressure to just get it done. However, too many organizations fail to focus on the real business problems and opportunities they should address. Even if IT is ready and willing to take on a BI project, the initiative will fail deliver a return on your investment if it is not directly connected to addressing the problems and opportunities the association needs to address.
First identify and prioritize the business situations that need attention. We advise our clients to begin small.  Start with an area that will yield measurable benefits (such as Finance or Membership) but initially select an aspect of the area that can be analyzed in a relatively short time-frame.  A skilled analyst can collect requirements, conduct interviews, and facilitate group sessions focused on identifying and describing the challenges and opportunities facing the association and how they tie to the association’s strategic plan. Working as a team the analyst, business users, and technical resources locate and document the available data sources as well as identify the gaps in which essential data is not being collected. Once this information is gathered, requirements are grouped into logical areas.

2. Focusing on the Dashboard

You may have seen impressive examples of flashy dashboards from BI vendors, media outlets, or personal websites. It is easier than ever before to grab data from any source and present it in a visually pleasing matter. However, to make that visualization meaningful and actionable for your association, you will need to determine what will help your specific organization. Even in the association space, what makes sense for one group can be completely meaningless for another. Spend your time and resources to find and prepare data that will provide meaningful information to allow business users to make better decisions.  You can use the flashy visualizations and dashboards as inspiration once you have the underlying data and business questions formalized.

3. Not Including Business Users when Data Modeling

Data modeling is a task performed by the technical team. However, how quickly business users embrace and adopt BI solutions often comes down to how the data is organized. The data needs to be organized in a way that makes sense to the business so that they can interact with it by having “conversations” directly with the data.   The technical complexities of a highly normalized transactional data model should be abstracted so that the business user interacts with familiar objects and attributes with friendly names. Discussions with business users should be conceptual, not technical. In addition to technical documentation, documentation should also be created to capture the concepts and components of the data model in a way that business users can understand.

4. Lack of Communication

Too often business intelligence projects start off strong. Requirements are gathered, discussions with business users are held and then IT goes off into their private corner to design and build a solution. They may be working hard and making progress, but the business is left in the dark. Regular communication with the core team, including in-person meetings, frequent demos and status reports are vital. When IT and the business users communicate regularly about the progress, adjustments can be made which improve the outcome.  BI is a business initiative and technology is the enabler.  It is not a technical project that should be foisted on the business.  Our experience has proven to us that collaboration and communication is the most essential ingredient in a successful BI program.

5. Not Providing Education and Documentation

Although many BI visual interfaces are built to be intuitive and use features users are already familiar with, training is absolutely necessary. At a minimum, the staff need to know what data is available and how it is organized. They also need to know how the data and the analysis applies to their day-to-day work. Staff members also must be provided with technical training on how to use the tool. Just like any software or technology, ongoing training is needed to provide continuity in the association as people move to new positions or are new to the association.
Documentation should also be provided. First, documentation about the published dashboards should be created. This includes topics like the name, location, business description and the business owner. Documentation about the data is also important. This includes the business name for each attribute and measure, business description, and the calculations used to create the measures. Develop documentation as you go. If you leave it until the end of the project, it has a way of falling to the wayside as deadlines near.

6. Not Providing Support after Development

Everyone is clamoring for more self-service. This term means different things to different people, but in the BI world this often means that the business users can go get the information they need without asking technical staff for help. This could involve the ability to drill-down for more details and/or change the filters or even create new visualizations. However, self-service does not mean that once the datamart is built and the BI tool is installed, IT is finished. IT must continue to be involved to ensure that the offerings continue to meet the needs of the business. Often, more data can be brought in and more analysis can be performed after the initial offering is a success. The needs of the association will evolve and the BI solution must also evolve.

7. Not Measuring Results

After the investment of time and energy to create a BI environment, sometimes organizations neglect to track the results. What you want to determine is if the initial business questions have been answered and if the business situation is improving. For example, if your initial business question was “How can I increase my membership retention?”, then evaluate if the BI solution added value by determining the improvement in the retention rate after BI enabled you to identify and connect with “at-risk” members. It is essential to track successes by quantifying and documenting when BI has resulted in an improvement in a key result or key performance indicator (KPI). This should be shared on a regular basis during staff meetings. Success stories justify the original investment and support future phases.


If you’ve made these mistakes before or see them happening now in your association, you are in good company. Some of these mistakes have been made by the best run and smartest companies in the world. The great news for associations and  nonprofits is that because we know what these common mistakes are, we can take deliberate measures to avoid them.
“A smart man makes a mistake, learns from it, and never makes that mistake again. But a wise man finds a smart man and learns from him how to avoid the mistake altogether.” –Roy H. Williams

Data Discovery – a “Bicycle for the Mind”

Steve Jobs said that computers are like a “bicycle for the mind” allowing us to go further faster with less effort.  Within the field of business intelligence, I believe data discovery should be the same, amplifying our intelligence and creativity, allowing us to see patterns and insights which we can use to create the future of our organizations.

Data Visualization is Key

A key feature of data discovery is the ability to present data visually in order to quickly convey the information to our brains.  The goal is to stimulate our minds and then to allow us to interact and have a direct “conversation” with the data.  We “ask” follow-on questions by clicking through and moving elements on the screen, “re-presenting” the data many ways.
We are inspired when we see data visually and notice how it changes based upon the questions we ask.  We can see the power as we interact with the data and the analysis process becomes a natural extension of the activity of thought, enabling us to drill down, drill up, filter, bring in more data sources, or create multiple visual interpretations.  Interactivity supports visual thinking.  We can work with the data visually at the speed of thought, rather than writing queries to the databases.  We can learn and reach insights faster.  When we interact with the data visually, we are participating in the data discovery process and data becomes our partner in gleaning the information that becomes business intelligence.

How Does your Association Make Decisions Today?

An MIT study shows that most organizations still rely on instinct, intuition, politics and tradition.  But according to Harvard Business Review, top performing organizations are 5x more likely to use data to make decisions.  If we know that decisions based on data tend to be better decisions, why don’t we use it all the time?  Research says that only 15-20% of organizations believe they have access to the data they need in order to make good decisions.  Why is this?
IVeniceMonet once heard it explained this way – imagine you are Monet and you are explaining to a friend how to create a painting of Venice at twilight – like this one.  You might say, start out with a church steeple on the left sort of a brownish color and surround it by a golden light, with deep blue in the sky.  Oh yes, and maybe add some little purple/violet touches in the water to the right.  Do you think your friend would create a painting that looks like this one?  Probably not.  And yet that is what is happening daily between business and IT.  Business tries to describe in words the data that they want to see.  And IT tries their best to represent in reports and dashboards what they heard the business say.
The way it typically works is you have a question and ask IT to create a query or report and then when they deliver it, usually you have another question, or want another piece of data or have it grouped a different way.  Traditionally this change request has been considered a BAD thing!  And then you have to wait for IT to revise it.  At least that’s what used to happen to me when I worked for an association.  This process doesn’t really work well for either the IT department or the business users in the association trying to make decisions.

Change the Ending

Association staff want access to their data!  They don’t know their questions until they start to see the data.  Data discovery enables them to have a conversation with the data directly.  Data discovery allows all of us to shift perspective quickly and change the way we look at a problem.  We can cycle through different views deliberately trying to get the insight to pop out!
This is how we learn to understand the story our data is telling.  And once we understand the story, we can change the ending.

How to Create a Data Inventory for Associations and Nonprofits

Locating Your Data

Do you know where all your data is? Associations, like all businesses, have so many working parts that it can be difficult and time consuming to keep information both centralized and easily assessable. However, in the age of data analytics, it has become even more critical to know the location and importance of your data in order to use it to fulfill your mission.
After many years of working with clients, DSK Solutions has found that the first step to a successful BI strategy is to scope the project: outline what you already know and document your business questions.

Steps to Take

Begin by identifying your data sources:

  1. Data source name
  2. Location and server name
  3. Database/data mart/warehouse name
  4. Application name
  5. Format
  6. Size
  7. Update frequency
  8. Primary tables/cubes/lists
  9. Approximate number of rows/columns for primary tables
  10. Internal/external keys for primary tables, dimensions for primary cubes
  11. Names of external systems that provide data to data source:  Frequency of external system input and method of integration
  12. Names of external systems that this data source provides data to:  Frequency of system output to external systems and method of integration

Then, identify the more qualitative features and value:

  1. Purpose
  2. Importance
  3. Data sensitivity, security/compliance requirements
  4. Known data quality issues/current cleansing process (e.g., duplicates, missing information, etc.)
  5. Data owner or name of person/dept. responsible for data source
  6. Reports/exports produced from this data, departments using these outputs

Finally, connect your data with your important business questions:

  1. What role can data play in achieving your mission?
  2. How can you map your data to your strategic plan?
  3. What metrics reflect your most important data?
  4. How can you best collect and integrate your data to get a 360 degree view of your members and prospects?

We’ve helped many associations and nonprofits create a data inventory like the one depicted visually below.  One of the first steps in the process of analyzing data starts by creating a data inventory.
Data Inventory

Warning: A non-numeric value encountered in /homepages/0/d741589923/htdocs/clickandbuilds/AssociationAnalytics/wp-content/themes/Builder/lib/builder-core/lib/layout-engine/modules/class-layout-module.php on line 499