Author Archive for associationanalytics – Page 2

Take the First Step on the Analytics Road – Join Us at PersoniFest

Serving up a concise yet comprehensive presentation on how to start shaping your organization’s analytics strategy, I will be taking the stage at PersoniFest in Austin, on Tuesday, April 10th at 9:30 a.m. CST. Co-presenters, sharing their valuable experiences, include Adam Rosenbaum, Director of Information Systems at the Council for Advancement and Support of Education (CASE) and Joel Loughman, Director of Association Management System Software at the Institute of Real Estate Management (IREM).

Association Analytics pros will be the first to tell you that analytics itself is about so much more than just data volume or data access. As the amount of available information surges, and as the ways to scrutinize that information grows, it’s easy to think that sheer availability of data analytics is sufficient. But you don’t have to settle for that one-size-fits-all mindset!

In this breakthrough session, we will illuminate the initial steps your organization needs to take in order to develop an analytics vision tailored to its operational objectives. We’ll also spell out approaches to getting the most out of your integrated data — creating interactive visualizations that replace static spreadsheets and reports. We’ll make sure you come away with an unshakeable understanding that data is not simply meant to be collected and stored; it is meant to be analyzed and acted upon.

Here, you’ll learn how to use data to enhance member and customer experiences, improve operational efficiency, and optimize financial performance. We hope you can join us! Show up knowing where you are; we’ll make sure you leave understanding how to best use data to get where you want to go.

Hope to see you there. Register today – https://goo.gl/iXEh96

 

Revolutionize the Way Your Organization Scrutinizes Data

The Analytic Mindset is a way of thinking poised to revolutionize the way organizations scrutinize their data. Volume of data is one thing, but how you extract value from that data is proving to be much more significant. It drives an organization’s ability to illuminate the context in which their data is acquired, which in turn can optimize the processes, strategies, and tactics.

In our upcoming presentation, we’re going to take a detailed look at the remarkable acceleration of data acquisition — and how our traditional ability to evaluate data is proving to be inadequate. By applying the Analytic Mindset, however, we’re closing the gap between informational volume and finding its actual value. It’s the key to making smart decisions that are not just data-driven, but driven by the most informed perspective on the right data.

We urge you to join us at the ASAE Great Ideas Conference, where we’ll cover the imperatives of the Analytical Mindset:

• Assumptions should be questioned
• A picture is worth 1,000 words (or numbers)
• Anything (and we mean ANYTHING) is measurable
• Data should be accessible and understandable
• Insights should be actionable
• Actions should be monitored

Ready to learn more about the Analytic Mindset? We’ll see you in Colorado Springs this Sunday. https://goo.gl/t8PGPj

Your Association Needs An Analytics Translator

If you have a data analytics team or you are in the process of forming one, the analytics translator is a role to consider hiring for. This individual plays a critical role on that team and at your organization. She or he serves as the link between the analytics team and the executive team because this individual takes the information from the data team and translates it on a broader scope, so stakeholders can see how analytics impacts the organization.

Think of the analytics translator akin to a marketing operations manager at a B2B or B2C company. A marketing operations person is a subject matter expert on marketing technology solutions and typically works very closely with the executive teams. That individual also oversees the implementation of a marketing automation system, the data, and the processes to support the organization. A person in this role also works with the demand generation team to support any campaigns they want to execute and provides the metrics and reporting. She or he has both qualitative and analytical skills and possesses superb communication and project management skills to be successful in their role.

So, what does an analytics translator do then? According to Mckinesy and Company, someone in this role “communicates data science features and capabilities to internal and external stakeholders in order to identify business needs and uncover areas in need of deeper data exploration.”

Ideally, an analytics translator possesses the technical fluency in analytics, strong project management and communications skills, and a keen interest in staying current with industry trends.

Now you might be thinking that your data analytics team already has many of these skills, but the analytics translator is truly a unique role that an organization benefits from having. Here are 3 reasons your association needs to bring an analytics translator on staff.

1. Help demonstrate how analytics solves business problems

Analytics continues to play an instrumental role at organizations because it helps staff members make data-guided decisions to help them better align with their overall organization’s goals. Sometimes one person (such as a data analyst) alone cannot convince their executive board that a data analytics platform is a worthwhile investment. In some cases, it takes someone such as an analytics translator to convey the need to purchase an analytics tool because this person has the expertise to communicate how it can truly solve your organization’s challenges in an easy-to-understand way.

Let’s say your organization already has an analytics platform, but your organization’s stakeholders still don’t understand the value of it. That’s where an analytics translator can also help. An analytics translator is in a unique position where she or he can demonstrate the power of data analytics by identifying where it solves a business need. Once this individual discovers an area of opportunity, she or he works with the analytics team who then creates data models that show where the problem is and how best to resolve it. The translator might offer suggestions on adjusting the model so that it produces actionable insights that their executive team can easily interpret. Once the model is complete, this person ensures it’s the final product before delivering it over to the executive team to review.

2. Function as a Project Manager

One of the attributes an analytics translator must posses is excellent project manager skills. In the association world, this individual can operate as an “internal” project manager. She or he serves as a go-between the business leaders and the data analytics team ensuring that an analytics project is successfully executed from start to finish. This individual also can communicate the project’s progress in a digestible way to different team members at the company. If the stakeholders want changes to the project, then they work with the analytics translator who then relays those changes to the analytics team. The analytics translator helps to bridge that gap that exists between those who are a part of the data team and the executive team, so each side is better aligned with one another.

3. Assist in implementing solutions

An analytics translator serves as an advocate for adopting new solutions at your organization. One of the challenges your organization might face is some staff members hesitation in incorporating analytics into their roles. Shifting to a data-guided mindset is intimidating if your team isn’t accustomed to thinking in that manner. Also, sometimes company politics are a roadblock when attempting to implement a new tool or project across the organization. The analytics translator has the potential to champion the need to use analytics or needs to possess the grit to “fight” that battle because there will be people who are less enthusiastic to the notion.

If the resistance to adopt to new technology or start a new project exists in your organization, don’t feel as if you’re the only one experiencing that. It’s more common than you realize. That’s why it’s essential to have people such as an analytics translator to work as the driving force in implementing new solutions at your organization.

What if there’s budget constraints?

Even if an analytics translator isn’t a role you can fill at this time due to budget constraints, it’s a great position to keep in mind for the future. If hiring someone for that position isn’t possible though, then consider delegating the responsibilities to your data analytics team. It’s likely there’s someone on that team that possess some of these skills, so it’s just a matter of honing them and shifting their responsibilities around. Sometimes this is a better option because this person is already familiar with the company, processes, and data. And remember, an analytics translator doesn’t have to be a data scientist to be successful in this role.

Need help?

Contact us at info@AssociationAnalytics.com or (800) 920-9739 to explore if the Analytics Translator role is right for your association.

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.

The Analytical Mindset and Association Leaders

Cultivating a data-guided culture (with an analytical mindset) is a challenge that you need to readily take on if you want your organization to remain relevant in the minds of your members and stay ahead in your respective industries. Those who choose not to embrace an analytical mindset will get left behind.

Right now, there is no better time than for your association to use data as an opportunity to grow your organization. Gone are the days you can simply use instinct, politics, and tradition to make decisions. You need to leverage your wisdom along with the data that is available to you.

To instill an analytical mindset across your organization, you need to take a step back and “begin with the end in mind.” What’s the end goal or desired outcome? Once you determine that, then you develop a plan to get there.

We’ll be expanding on the topic of cultivating an analytical mindset at Association’s Forum SmartTech Conference on Thursday, February 1, 2018 at 1:10 CST. This will be a great opportunity to pick up some actionable steps on how you can be a champion for embracing an analytical mindset across your organization.

Here are 3 ways you can shape the analytical mindset at your association.

1. Identify your organization’s current analytical mindset IQ.

One of the first things you need to do before promoting a data-guided culture is to evaluate your organization’s data maturity. There are different assessments out there, however, consider using one that’s more tailored towards associations. The Data Analytics Maturity Model (DAMM) is designed by associations leaders for associations to assess where they land in the model and to offer ideas on how to develop their own action plan. There are 5 stages in the DAMM model: Learning, Planning, Building, Applying and Leading. Let’s say your organization falls into Stage 2, which is Planning. Your team is aware of the ramifications in not having an effective data analytics strategy. Your data is living in the AMS, but it’s possible that some team members don’t even know where to find the data or what they need to extract from it. Another key indicator in this stage is that there’s a lack of trust in the accuracy of the data. It’s possible there are several different data source points, but it’s hard to know what’s correct because there’s no data governance program in place. All hope isn’t lost though. If you recognize the costs of not having a data strategy and can get staff members who value data involved with your cultural initiative, then you’re already on the right track.

2. Form a data analytics team.

A data analytics team is essential if you want to spread the idea of adopting an analytical mindset across the organization. This team will also play a fundamental role in driving your data analytics strategy and ensuring it aligns with your association’s overall strategic plan. Before you hire a team though, you will need to identify how this team will function and how they will proactively use analytics to transform the culture and business.

Your data analytics team can encompass some of the following positions: Data Analyst, Chief Data Officer, and a Database Developer. Of course, some roles can be a blending of different positions depending on your organization’s needs and budget for hiring additional team members. Ideally, there should already be a business executive who sees data as a corporate asset and possesses analytical qualities. That individual can be a champion for creating a data analytics team. With this individual and a data analytics team in place, you can demonstrate the value in investing in data analytics and the need to begin a cultural shift. And here’s another approach this team can take: Tie it back to the bottom line. That is a compelling way to get team members’ attentions since everyone contributes to that in some capacity.

 

 

3. Lead the change management process.

One of the biggest obstacles you’ll face when shaping an analytical mindset among your staff is the staff itself. Many people don’t instantly welcome change. It takes a significant amount of energy for people to alter the way they do things even if their current routine is inefficient. Change is a tough concept to embrace! There are ways to work around that though. You will need to integrate some change management principles into your organization. The three fundamentals of change management are educate, incent, and orchestrate. The other mantra you can follow is the head, heart, and herd approach as discussed in the book “Switch: How to Change Things When Change is Hard” by Chip and Dan Heath.

Here’s how you can handle each stage:

Educate: Rather than simply issuing a mandate that everyone needs to adopt an analytical mindset, it’s better to start with educating your staff as to why it’s in the best interest of everyone to take a data-guided approach. Explain the rationale behind it and demonstrate how their jobs will change for the better. Be transparent rather than secretive. Show how it affects the bottom line.

Incent: You will need to consider the “what’s in it for me” mindset that your team has. It’s perfectly normal to have that thought. Asking people to deviate from what’s familiar without offering up something that makes the change worthwhile will make them more resistant to the idea. What constitutes as a strong incentive varies, but keep in mind it’s not always about the money.

Orchestrate: You will need to establish processes and routines to change your team’s habits and overall company culture. When everyone follows the new routines and processes, then others will emulate that behavior. It takes time for new habits to develop and become ingrained, but eventually that’s how a new culture will form.

When you instill an analytical mindset across your organization, teams will be more inclined to use data to guide their strategies because it is the new normal. People will exhibit confidence when using it and combine that with their instinct to make decisions. Keep in mind though, data isn’t the actual driver. It’s there to support and assist your team, and it’s the people who are the real driving force.

Are you planning to attend the SmartTech Conference? Our discussion at this conference contains an in-depth look at adopting the analytical mindset. Register online!

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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.

Building Blocks for Creating an Analytics Strategy

An analytics strategy offers many benefits to your organization and now more than never, analytics is changing the way organizations run their day-to-day operations.

For your organization to stay relevant in the minds of your members, your team needs an analytics strategy to guide the decisions, so you can deliver an optimal customer experience. Without a proper analytics strategy, you’ll see a loss in productivity among your team members, incorrect decisions being made, and an incohesive customer experience.

With these costs in mind, it’s worthwhile for your team to invest the time and money into an analytics strategy.

Wondering how to even proceed with developing an analytics strategy? Consider breaking it up into these 4 focus areas.

1. People

 

An analytics strategy isn’t tangible without a team of people who see the value in having data analytics. When building an analytics strategy, one of the first things you need to do is assemble a team. This team is comprised of data champions in key business areas. These people also assist in creating a training plan that helps those who are not as data literate. This is also a good time to evaluate the need to staff additional people to get the analytics strategy off the ground.

Creating a data culture is not an easy task, but it’s by no means impossible. A strong emphasis needs to be placed on change management and even after your analytics strategy has been created, it’s essential to continue to empower your team to use data to guide their decisions. And for incoming hires, incorporate data literacy into their onboarding.

2. Process

 

Processes play an instrumental role in moving an analytics strategy forward even if there are bumps along the road. In some cases, organizations might not have established processes in place when it pertains to data governance. And when you’re accustomed to not having processes, this leads to poor data quality and redundancy because there’s no established way of entering in the data. Implement a repeatable process to get your team into the habit of inputting data the correct way to improve data quality in the long-run. And don’t hesitate to review and refine the process over time.

3. Technology

When creating an analytics strategy, you need to examine all the systems your data could be residing in and then determine which data is the most accurate, up-to-date, and relevant. Ideally, your data needs to reside in one central repository that’s accessible and viewable across your organization. And ensuring data accuracy is essential to maintaining the integrity of it so it’s seen as the “single source of truth.”

Having the data in a central repository isn’t enough to utilize to its full potential though. You need a tool that allows you to view it in a visual manner to effectively tell your data story. Charts and graphs present data in a way that’s easy to convey to your team. Think about it – what’s more likely to carry credibility among your team? A bar graph or a spreadsheet full of numbers? It’s easier for the mind to absorb a data visual than a page full of numbers that may or may not be relevant to your audience.

4. Data

Data is what ultimately guides your organization’s strategy because it reveals what your members want from you – be it better products or services, an alternative way to communicate with them, or even membership renewal opportunities. However, data is only as valuable as what you already have so it’s imperative that it’s accurate and consistent. If you have multiple data sources, then it’s hard to pinpoint which data is ultimately the “single source of truth” your team relies on. Let’s take a simple example of an email blast for membership renewal. What happens when half of those emails bounce back to you because the email addresses were incorrect?

Inaccurate data leads to distrust in it and a loss in productivity trying to track down the correct information. In order for your analytics strategy to come to fruition, a data cleanse needs to take place before it moves to a single repository. Consider performing a data cleanse every quarter to maintain the upkeep. Some other best practices to keep your data in good shape are to create a data dictionary and to conduct a data augmentation.

At the end of the day, analytics is all about outcomes. When you have an analytics strategy in place, you’re emboldened to make decisions faster because the data is consistent and accurate. This in turn impacts other areas of the business, the team’s productivity, and your members. A data analytics strategy creates an enhanced member experience by allowing you to deliver targeted, relevant member communications and product and service offerings aligned with member preferences. And when your messaging and offerings resonate with your members, you’ll see more conversions which helps drive revenue.

Learn More About DAMM for Associations

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

5 Areas To Assess Using the DAMM—Data Analytics Maturity Model

At ASAE Tech earlier this month, we shared our Data Analytics Maturity Model (DAMM) which is built for associations by association leaders. Designed as an assessment and high-level action plan, DAMM helps staff members identify where their organization is when it comes to taking a data-guided approach to overall strategy.

Keep in mind though each association is unique and the need for data analytics differs so answers to this assessment vary.

While there are several indicators in each stage of DAMM worth examining, here are 5 areas to hone in on because it impacts where your organization lands in the model.

 

 

1. Make your data accessible to everyone.

At this year’s ASAE Annual Meeting, we conducted a live poll of our audience and discovered some interesting statistics that hold true at many associations. 68% of association staff members recognize the potential value of data, but they lack the processes and tools to make it useful to everyone.

Stage 1 of the Data Analytics Maturity Model focuses on “learning.” Associations need to evaluate where their data resides and how easy it is for everyone at the organization to access it. Or is the reporting centralized in the IT department? In the past, it might have made sense for IT to pull the reports and to own the data. However, for your organization to flourish, everyone needs to be able to see the data when it’s convenient for them. A sign of an organization’s maturity is when the data silos have broken down. The ability to use data is essential for competitive advantage and should be a goal for the entire company.

2. Instill an analytics-guided culture.

Chances are you do have business leaders in your organization who possess analytical mindsets and understand the value of data analytics. Those are the champions driving your organization to be more data-guided. Those same staff members can form the core analytics team and begin to develop an effective data strategy that be used throughout the company.

Stage 2 of DAMM is the “planning” phase. At this point, there’s the recognition that there’s a lack of trust in the accuracy of the data. Your team is unsure what data they need or even where to find it. The core analytics team is essential in helping to overcome this challenge. 

This same team can also be the ones who experiment with data visualization and create standard operating procedures around data quality management.

All it takes is a small team of interested and motivated staff members to instill a more analytics-guided culture. It won’t happen overnight, of course, but they can be an influential force in encouraging people to embrace analytics in their day-to-day roles.

 

 

3. Develop clearly defined KPIs.

Stage 3 is the next stage of maturity for an organization, and it’s all about “building.” By this time, your association has a data strategy in mind and action plan for analytics that has been given the green light from your executive team. Once you’ve gained the executive team’s buy-in, you’ll discover that KPIs need to be established to track your success along the way. How you measure success varies with every department so it’s up to your team to determine what is most suitable to measure your performance. Ideally, these KPIs should go beyond “vanity metrics” and really drill down to what will directly impact the member experience, and these should track back to what your organization’s overall goals are. These take time to refine, but it’s a sign that your organization is maturing.

4. Create a central repository that encompasses all key data sources.

It’s not uncommon to have data living in different systems because there was probably a time when this was a common practice at your association. When your association uses data and analytics to solve business problems, one of the immediate projects to tackle is keeping the data in one repository for everyone. Within this repository, you can find all the key data you need to have a deeper understanding of your members and the services you’re providing them.

Once your organization has a central place for data storage, you’re at Stage 4 which involves “applying” treating the data as an organizational asset and eventually becoming proficient in data analysis because it’s becoming a go-to source for understanding your organization and members. 

5. Implement a data governance program.

According to DAMM, the final stage is when your staff members are actively taking a data-guided approach to run their business. Data is routinely used by staff in their respective roles, and everyone has a 360-view of their members. One way to aid data-guided decision making is to establish a data governance program. Data governance keeps data updated and accurate for everyone who uses it. In order for this program to be successful and ongoing, you need to have a team take ownership of it. Whoever this group of staff members are, it’s up to them to communicate, follow, and continually improve the program so it’s relevant to the association as it continues to grow.

The DAMM for associations and nonprofits is an eye-opening way for your organization’s team to identify where it is in the model and what action steps you can take to move your association forward.

Learn More About DAMM for Associations

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

2017 ASAE Tech Conference Recap

The 2017 ASAE Tech Conference was yet another stellar event. Thank you to the entire ASAE team who managed the show—making it a wonderful experience for us to both participate and contribute. Some were there to learn, others to show and some just for fun! We were there for all three! 

Aside from exhibiting, we offered attendees the opportunity to get their DAMM (Data Analytics Maturity Model) Score at our booth,presented with clients at three different sessions and held a dinner at Voltaggio Brothers Steak House.

 

Speaking at the 2018 ASAE Technology Conference

Digital Convergence: Data Strategy

Our friends at Community Brands asked us to co-present a segment with Sigmund VanDamme during the Digital Convergence pre-conference workshop they hosted in conjunction with ASAE. We shared 5 areas that associations can assess as part of the Data Analytics Maturity Model (DAMM).

Keep It Clean: Harness the Power of Gold Standard Data Hygiene

Another pre-conference session was co-presented by our own Julie Sciullo and Sean Hewitt along with Adam Rosenbaum from CASE. This session was about developing meaningful KPIs to get more from data analytics.

Turbo Charge your Analytics with Agile Data Warehousing

Lastly, I co-presented a session with Ric Camacho from Specialty Food Association Inc. where we took a look at how the Specialty Food Association went from 0 to 60 in record time using a practical approach to bring useful data to their team in order to make decisions.

How ASAE Uses Data Analytics & Visualizations

During the opening keynote to the conference ASAE CIO Reggie Henry revealed a look at a real-time dashboard we helped create. This tool is incredibly powerful with data being pulled from multiple data sources including much of it from ASAE’s Collaborate community powered by Higher Logic.

 

 

Why is this so powerful? Simply put, it helps ASAE know and understand what members are talking about.

For example, the word cloud you see here is dynamic and has a drill-down capability that changes with the most relevant topics for the community.

On the top right, you can see a certain member type. On the bottom right, you can see member years. This is helpful, for example, if you’d like to filter all the “new” members to find out what they’re interested in topic wise based on community discussions.

 

 

This next image of the dashboard provides an overview, so you can see what different users have been doing. You’re able to see what prospects or former members have been doing in order to get a sense for the engagement level of these two different groups. Then you can drill down to see exactly why on is more engaged over another—pinpointing which ones are at risk and if something can be done to prevent decreased engagement.

These examples just scratch the surface regarding how the data captured in key systems can be visualized, analyzed, and then inform key discussions within an association.

Thank you to Reggie Henry for sharing the dashboard with attendees and also Andy Steggles at Higher Logic for sharing the dashboard during his session titled 5 Keys to Disrupting Member Engagement, Improving Satisfaction and Growing Retention. We’re proud of the work that’s been down and even more excited about the impact it can have on associations who begin to harness data in this manner.

Dinner at Voltaggio Brothers Steak House

Rounding out the conference, we had a intimate dinner with some clients at the wonderful Voltaggio Brothers Steak House inside MGM National Harbor. It was a treat to spend quality time with great people! The conversation was an enjoyable mix of personal and business oriented topics—something we look forward to at events such as these.

 

Learn More

To learn more about DAMM (Data Analytics Maturity Model) for Associations or dashboards driven from multiple data sources—contact us . You can also stay up-to-date on the latest content we’re sharing each month by signing up for our monthly newsletter.

 

 

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.