Archive for Data Discovery

5 Data Discovery ProTips To Follow

There are some things to keep in mind when you infuse data discovery throughout your organization. But first, let’s take a quick look back.

Once upon a time, an organization could simply rely on instinct, politics, and tradition in order to make decisions. However, with the rapid pace of technological change, organizations today can’t afford to rely on just those to guide their strategy and decisions. You now need data to help guide you.

According to Harvard Business Review, top performing organizations are 5x more likely to use data to make decisions. Where does your organization fall on this decision making continuum? When you use  data to determine your next steps in reaching your organization’s goals, you enable data discovery to happen.

Data discovery is a practice that your organization needs to incorporate into your everyday work life because it helps you change how you view a problem because you’re using data to help you formulate a solution. However, there are some things to keep in mind when you infuse data discovery throughout your organization.

Here are 5 protips your organization needs to follow for effective data discovery…

 

 

1. Give your users access to the data.

This seems obvious, and you might be thinking “I’m already doing that,” but how easy is it for staff members to pull reports and look at data when they need it? Can they do it themselves, or do they rely on the IT team to pull those reports for them? If that’s the case, then it doesn’t count. Sometimes what IT provides might be too technical, or grouped in a different way, or it might even lack a key piece of data that you need. The request to revise the report is time-consuming not just for you, but for the IT team because it’s taking time away from other projects they’re doing.

Ideally, everyone should have the ability to pull reports and queries whenever they need them. Don’t think of it as another team is stepping on the IT department’s toes or they’re trying to “take away their job.”  You’re just making it accessible to everyone because data is valuable to the entire organization, not just one department. If everyone can benefit from using data in their jobs, then why not make it easy for everyone to obtain?

2. Use visualization to amplify your data.

Data isn’t valuable without having the proper tools to help you view data in a more enhanced light. You need visuals to convey your story in a powerful way, and data visualization does just that. Charts, bar graphs, and other types of visuals allow your team to spot trends and see correlations between key data points which can help you make decisions faster and pivot when needed to better align with the overall goals. It also helps you identify what’s working and what isn’t. Your data story needs both data and visual images. You can’t have one without the other or you risk losing the value that comes with data visualization. Just as a story isn’t compelling without these necessary elements (plot, characters, conflict, climax, and resolution), a data story won’t have a significant impact without both a visual and the data itself.

3. Choose the right tools.

Can you recall the last time you experienced technology troubles? When the tools and technology you rely on each day to do your job doesn’t work the way it should, it derails your entire day. Before you decide to invest in a data analytics platform, be sure to select one that best meets your organization’s needs and helps you attain your goals. Every company has different needs and goals so examine what your organization’s data needs are, and then research the data analytics platforms that are available to you.

4. Manage your data through data governance.

Data is only valuable if you know what type of data you have to work with and what it means for you. In order to “know your data,” do these 3 activities at your organization.

  1. Create a business glossary
  2. Create a data catalog
  3. Create a data dictionary

This helps you and your team get your data in an organized way that you can use more effectively. These documents establish transparency across the organization because everyone can refer to these pieces to have a deeper understanding of what data currently exists and how they can use it to guide their decisions and plans.

This also sets the stage for establishing standard operating procedures when it comes to data management. It’s imperative to keep these documents up to date though or overtime they’ll become less valuable to your organization. As your organization evolves, your data needs will too so it helps to communicate that understanding as well so everyone is on the same page to keep it clean.

5. Establish meaningful KPIs.

While having the right tools and following data governance procedures makes data discovery more efficient and useful, you also need to consider KPIs. KPIs give you something to measure your success against. Before you can identify what your KPIs are, you need to know what data you have and if it’s relevant to the organization’s goals. This varies with every company so once you know what they are, you can establish meaningful KPIs to work towards and eliminate the vanity metrics.

Data discovery allows you to find patterns, correlations, and insights that can transform your organization. The more you encourage data discovery across the organization, the more it becomes a natural extension of everyone’s thinking. It gets easier to drill down and filter out what you need and inspires you to ask questions and process information faster.

Ready to Plan?

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

What Would Your Members Write in a Postcard to You?

Dear Data

Have you ever heard of a book titled Dear Data? I first learned about it from an article in Wired magazine.  The book is not a story, but rather a collection of postcards written between two information designers who communicated in data.
Giorgia Lupi, a resident of Brooklyn whose native tongue is Italian, and Stefanie Posavec, a resident of London who speaks English, first met at a design conference in 2014. At that time, they wondered if they could get to know each other through data without speaking the same language. They proceeded to find out by mailing each other postcards for 52 straight weeks expressed as visualizations.
Each week was a new theme extracted from daily life, such as sleep habits, spending habits, checks of yourself in the mirror, and number of times saying thank you. The book compiles all 104 postcards and positions them side by side with an explanation of each.
You can preview several pages of the book here, and it is absolutely fascinating to see how the same topic can be communicated differently, the way everyday life turns into a visualization, and the detailed artwork on these postcards.
The complexity and mundanity of everyday life jumps off the pages and the legends for interpreting each could definitely get you thinking creatively about new ways to visualize your own data – or life.

Counting Something Means It Matters

The best summary of the book is a quote from Lupi, “counting something means it matters.” Think of all the things you could count about your members. What could you discover about how your members engage with your association?
Let’s take it one step further. If your members sent you a postcard every week for one year with a data visualization about their lives, what would the postcards reveal about them, their interests, and their relationship to your organization?
Would your association play a prominent role in the narrative? Would you discover untapped areas of their lives where your organization could add value?

Data Analytics

It is unrealistic to ask your members to send weekly postcards, but you can use data analytics to visualize and understand your members and their journeys.
Pulling from data sources like your website, your AMS, and social media, you can paint a complete picture of your members. When we understand our members and customers, we can guide them along a more personalized journey.
Whether through postcards or data analytics, you can get to know somebody through data and that insight can help you better serve your members.

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.
thread-stats
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.

The Power of Combining Data from Multiple Sources

Super Charge Your Data

Combining or blending data happens when you connect two or more different data sources. Combining sources from multiple data sources reminds me of one of my son’s favorite shows, Power Rangers. While each one is committed to fighting evil, each Ranger has a unique skill and weapon. When their enemy is too great to handle individually, they combine their unique powers to create a Megazord. A well designed data mart is the ultimate Megazord that can battle the evilness of fragmented information.

Message Activity Analysis

Let me tell you what I mean. Information from your marketing system can be measured and analyzed. You are probably familiar with some common marketing key performance indicators (KPIs) such as number of sent emails, delivery rate, bounce rate, open rate, etc. This is interesting in itself to analyze which messages have higher opens and clicks and which ones are below average.
You might get something that looks like this:
standard message stats

Combine Powers

What makes this information really pop is combining it with your other data sources. Combining the messaging activity data with demographics from your AMS helps you evaluate the influence that things like job level, member type, age and/or gender have on your key messaging metrics.
open rate by generation
In this example, when we look at Open Rates by generation, we can see that those in “The Greatest Generation” have dramatically lower Open Rates than the other generations. The “Baby Boomers” have the highest open rate. If we were to only look at the average open rates, we might miss this distinction. What actions could you take knowing this information? Perhaps sending an extra mailing to your older members for important communication?

How to Blend Data

Watch this advanced Tableau tutorial to learn how to blend data. For deep analysis and improved performance, we recommend investing in a data warehouses and data marts using a dimensional data model. Learn more about our approach to data blending.

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!

Derby Curse or Myth? Using Data to Challenge Instinct and Assumptions

The Major League Baseball All-Star festivities kick off tonight with the home run derby, where the leading power hitters face off to see who can hit the most home runs in a three round contest. Like most sports all-star events, the derby struggles to maintain relevance and TV ratings, but there is no shortage of interest in the so-called Home Run Derby CurseThe debate on whether the curse is fact or fiction is a fun example of the importance of using data to challenge assumptions.
According to the curse, participating in the derby causes a major decline in home run production in the second half of the season. The theory is that all those big swings rob players of their energy or mess up their swing mechanics causing big declines in performance over the second half of the season. There are many examples to support this conclusion. In a famous example from 2005, Bobby Abreu of the Phillies came into the derby hitting .307 with 18 HR.  He launched 41 homers in a record setting derby win, then slumped through the second half of the season with a .260 batting average and only 6HR, blaming the decline on residual effects from the derby.
Data analysts and baseball stats geeks have studied this subject exhaustively in recent years, proving the curse to be a myth using two main arguments:

  • Regression to the mean. Derby participants are very often enjoying extraordinary, career best, seasons hitting home runs at a much higher than usual rate.  It’s natural for them to regress in the second half to a rate that is closer to their normal production.
  • There is no abnormal drop in production. Looking past some high profile second half collapses, you find:
    • 9 of the previous 16 winners have actually increased HR production in the second half. (Yahoo Weekly Rotation).
    • The production of derby participants does decline but less than those who did not participate (thebiglead).
    • The second half of the derby season is low relative to their hot first half but exceeds their own career averages (SABR).

Watch the derby for fun and don’t be surprised if the curse is mentioned. In the office, use the same concepts to challenge your assumptions and beliefs about your business. Here are three tips help you distinguish fact from fiction in your association.

  1. Don’t rely on instinct or anecdotal evidence. Those who perpetuate this myth are disregarding the data. Your association’s data is an asset.  Don’t guess or assume when you can know.
  2. Compare results to baseline data. A trend in a particular segment is only significant to the extent it differs from the general population.
  3. Avoid Confirmation biasBelievers in in the derby myth can find a few cases every year to support their theory while ignoring the other data.  In business, errors like this can be costly. Even if a belief or assumption seems reasonable, be open to exploring alternatives.

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.

How to Harness the Power of Recommendation

Taking a customer-focused approach to data analytics helps provide optimal value, enhance engagement and understand the overall customer journey. Individuals’ actions provide valuable information that goes further than what is collected with surveys and online profiles. Additionally, actions uncover hidden patterns that can be used to build a recommendation system to guide customers toward other interests.
Here are the most common approaches to creating recommendation systems:

  • Collaborative filtering. This is based on data about similar users or similar items. It includes these techniques:
    • Item-based: Recommends items that are most similar to the user’s activity
    • User-based: Recommends items that are liked by similar users
  • Content-based filtering: Makes suggestions based on user profiles and similar item characteristics
  • Hybrid filtering: Combines different techniques

Recommendation systems results are similar to those on sites that suggest products and people, like Amazon and LinkedIn. Collaborative filtering leads to more of a self-learning process, since it is entirely based on actual activity and not data provided by users. There are scenarios where the others are more appropriate that we’ll address soon.
Similarity between users or items is measured by “distance” calculations from those long-ago geometry and trigonometry classes. You can use the results with a visualization tool such as Tableau, creating a similarity matrix and quickly identifying relationships.
correlation_matrix
It is sometimes helpful to group individuals and items into categories, which can be done by combining similarity scoring with data mining techniques like cluster analysis and decision trees.
Recommendation systems generally require data structured by columns instead of the row-based data that is best for interactive data discovery. Similar to text analytics, the items themselves — meetings, publications, donations, and content — represent large columns. It’s used by specialized R packages for the recommendation system features described in this book.
These algorithms generally need binary values, like a “yes” if someone purchased an item and “no” if he did not. But if users can rate items on a scale of 1-5, what does a score of 3 mean? Normalizing scores based on individual and overall ratings is a good way to answer this question.
The data requirements are really not as onerous as they may sound. Once data is in the right format for the R analysis tools, your imagination can take over to drive actionable association analytics. Content-based filtering works well for new users, and a hybrid approach can help prevent a “filter bubble” where some people get a too-narrow set of interests from similar recommendations.
Data from meeting registrations, membership history, donations, publication purchases, content interaction, web navigation, survey responses and profile characteristics can be used to guide association customers. Additionally recommendations can bring people with common interests together. This new insight can be used to enhance all customer interactions, ranging from email marketing to dynamic website presentation to event sessions.

How Analyzing Social Media is Like Walking Across Bridges

How do your customers connect with one another? Social media mixed with a historic mathematical theory can help you find those patterns and bridge gaps.
Combining social media with other external data can help your association use a range of personal interaction and engagement to move toward a more customer-focused approach. Analyzing social networks is often done through the mathematical concepts of graph theory and network theory, showing relationships between individuals. Richard Brath and David Jonker described using these concepts for business in their book.
socialnetwork1
Using technical analysis helps you identify people who are “connectors” and link to several groups, “influencers” who help groups form, and cliques that would otherwise be difficult to detect. Setting people up as a simple shape and line graph can be understood by:

  • Counting incoming and outgoing links between people.
  • Looking at the density of direct connections.
  • Examining the shortest and longest paths between people.
  • Considering how far the shapes are from one another.
  • Seeing how people tend to cluster together.

Graph analysis comes just from activities and does not make use of other attributes, like demographics. You can supplement this analysis with other data that you have. You can also use this kind of analysis to identify customers similar to the ones you found through social network analysis. It may be interesting to study things like how members’ interaction levels differ from those of non-members.
To bring social media data together with your other customer information, it should all come together in a data mart. In addition, you can also introduce text analytics to provide additional context. Different social media platforms make data available through application programming interfaces (APIs), which all have their own technical integration options and data scopes.
socialnetwork2
While social media analysis has been getting more popular recently, the graph theory that is used to break it down is centuries old. Mathematician Leonhard Euler famously used this theory in the 16th century to solve the “Seven Bridges of Königsberg” problem, devising a way to walk through a city while crossing each of its seven bridges only once.
While crossing physical bridges may not be on your list of priorities, social network and graph analysis can help cross several metaphoric bridges in your association, including:

  • Segmenting customers: Coming up with similar people, based on links and attributes.
  • Analyzing influence: Finding people with large numbers of connections and activity.
  • Analyzing the market basket: Figuring out items that are commonly purchased.
  • Finding general correlations: Seeing relationships between people, products, events and other things.
  • Website visit analysis: Determine which webpages are the most popular.

Visualization techniques can communicate the message in social network data through node size, node color, link weight, link colors, and labels. You can use a combination of visualization choices in Tableau to tell social network stories.

Find the Business in Your Data

Recently I read the phrase, “find the business in your data.”  For years I have been saying, “your data is telling a story, and once you understand the story, you can change the ending.” Both ideas are similar and powerful: hidden within your data are the stories about what business your association should be in!  So often association business models are based on what we think our members and customers want, or what they said they wanted on a survey.  But we know that what people say is not the same as what they do.
The best way to understand and serve our customers is to combine what they say they are interested in (explicit interests) with what they actually demonstrate interest in (implicit interests).  For example, if an individual indicates on their profile or a survey that they are interested in governance and board effectiveness (explicit interests), but an analysis of their web activity shows they read articles on digital media and innovation (implicit interests), then we know that we want to engage with them about all of these topics.  The way to do this is to combine behavioral and social data with transactional data from the AMS or CRM in order to truly get a 360 degree view of a customer, their interests, and their engagement.  So how do associations find the business in their data?  Over the years, we have found four primary ways that analytics can do just that:
Performance Management/KPI’s: What happened?
Data Discovery: Why did it happen?
Predictive Modeling: What will happen?
Social/Behavioral Data (Big Data): How can we make happen what matters most?
The worst thing in the world for an association is to experience a slow decline in their relevancy to their audience but to not understand the reasons why.  This “boiling frog” syndrome is worse than a dramatic decline because it is easy to ignore it or think it is not important, especially if only certain areas of the association’s business are declining but overall the organization is doing well.  The best way to understand the business in our data is to start to understand the stories that are hidden there.  Progressive associations are making 2016 the year they invest in analytics as the best way to remain vibrant and grow.