The human brain can process entire images that the eye sees in as little as 13 milliseconds. 

Regardless if you are the CEO, technology director, or compliance officer within your organization, information in the form of graphs and charts is not only easier to digest, but also promotes data-driven decision-making.

But there is a problem: Most organizations generate massive amounts of organizational data every day that is left unused, taking up storage in its rawest form. Consequently, teams are unaware of the data available to them, and if they are aware of it, they are unsure how to access or interpret it. Scattered and disorganized data requires hours of manual consolidation, cleansing, and validation, and the output is ultimately prone to manual errors. 

If your teams are inundated with spreadsheets and spending an inordinate amount of time on gathering, cleansing,  and reconciling disparate data sources manually rather than providing value-add analysis, then it might be time for a change.

If your organization’s leadership is not leveraging organizational data as a valuable asset to drive proactive risk mitigation and decision-making, then the real question is, “How much am I spending by NOT investing in my data?” 

The cold, hard truth is that organizations can no longer afford to rely on spreadsheets and dirty data to make business decisions; however, data visualization can help to automate the consolidation and aggregation of data, equipping teams with the power to quickly interpret information to drive business results and increase overall team efficiency and satisfaction.

What is Data Visualization?

Before we cover how visualizing your data can help your organization, you may be wondering, what is data visualization? 

Data visualization is the transformation of unstructured or raw data into a visual form to communicate complex data relationships and data-driven insights in a way that is captivating and easy to understand. By succinctly summarizing copious amounts of organizational information into visually appealing reports, teams do not have to dissect and analyze underlying data to understand trends over time.

Data visualization bridges the gap between data and action by providing access to real-time metrics, allowing businesses to be better positioned when it comes to: 

• • • Planning and forecasting
    • Enabling consistent data across enterprises for increased speed, accessibility, ease of use, and ultimately lower costs
    • Increased operational efficiency

In addition, data visualizations provide leaders the opportunity to harness existing data and leverage it to learn from past mistakes, build on past successes, and anticipate developments that drive innovation and accurately predict future outcomes. 

Key Benefits of Data Visualization

The average organization collects data across 400 different sources. However, about 47% of that data goes completely unused because it is disorganized, unstructured, and dirty, which can cost your organization countless hours and dollars. 

In order to fully realize the value that your data can offer, good data visualization is imperative. However, investing in data visualization tools and technologies without organizational buy-in or foundational data practices can actually prevent companies from maximizing ROI in the long run. In order to ensure sustainable value realization, you must first establish core data governance practices, clean up data sources, and determine the data needs of your organization.

Once these foundational data practices are in place, data visualization can deliver the following key benefits:

1. Increased Comprehensibility of Data and the Break Down of Data Silos

Because visual data is processed much faster by the human brain, presenting data in an easily consumable format has the incredible ability to streamline organizational production. In contrast to text form, which has more historically been used as the preferred medium for exchanging information, humans can process visual images 60,000x faster. Furthermore, data visualization provides a much more interactive approach to displaying data, thus allowing users to quickly understand the story the data is telling without needing words to provide context. Presenting data to your executives or teams in a visual manner allows for far fewer gaps in communication throughout the enterprise, which can ultimately shorten business meetings by 24% and give you and your teams more time for other value-add initiatives.

Additionally, data visualization can break down data silos within your organization and reduce the amount of time spent on manual reporting. Sixty percent of employees believe they could save six or more hours if static reporting was automated. Business Intelligence tools bridge the gap between siloed data and reporting by utilizing centralized data to display accessible visual reports. Ultimately, implementing a centralized Business Intelligence solution can help prevent wasted efforts on non-value-add activities, while also acting as a catalyst for cross-functional collaboration. 

2. Save Costs & Drive ROI

How can visualizing your data really drive a return on investment? The answer to this question is unique to every organization and depends on the problem you are trying to solve; however, the competitive advantages to investing in Business Intelligence are as follows:

• • • • • Operational efficiencies (e.g., reducing manual time spent creating static reports and gathering disparate data, eliminating redundancies, streamlining access to visual representations of data)
        • Decreased reaction time in implementing risk mitigation efforts 
        • Process improvements 
        • Increased employee productivity and performance
        • Improved customer satisfaction
        • IT enhancements and efficiencies
        • Regulatory compliance

In addition to understanding the tangible benefits of implementing a Business Intelligence solution, it is equally important to note the true costs of not having one. How much will a lack of visibility, process inefficiencies, employee unproductivity, and outdated IT enhancements cost your organization over time? Some potential costs to consider include:

• • • • • Employee turnover: Outdated processes and manual labor can easily cause IT personnel to become overworked and burned out 
        • Missed opportunities: Leaders cannot swifty and proactively react to macroeconomic changes without real-time access to organizational data across departments
        • Wasted time and resources: Employees spend too much time manually organizing data and building reports which increases the likelihood of errors

While ROI looks different for every organization, statistics show that data visualization offers an average of $13.01 ROI on every dollar spent. Business Intelligence tools make your data centralized and easily accessible, so employees spend more time on business functions rather than compounding the problem large amounts of data can present.

Data Visualization Best Practices

The quantitative and qualitative benefits of implementing Business Intelligence tools are endless; however, to fully capitalize on your investment in data visualization, you will want to consider these five data visualization best practices:

1. Identify Your Most Critical Data

The first best practice is to establish a core set of data that is most relevant to the entire enterprise. By first defining the business impact you are striving to achieve from implementing data visualization, you can then identify your most critical data element. Are you hoping to:

• • • • • Explore ad hoc data quickly and roughly to find fast solutions?
        • Inform decision-making behind large projects? 
        • Use data to support ongoing decision-making? 

Once you identify your most critical data elements, you can begin to strategically and actively reduce the volume of data you do not need and acquire new data elements to paint a holistic picture of your organization.

2. Establish Data Governance

Establishing data governance to aggregate and organize by effectively managing data definitions and values is imperative to lay the foundation for sustainable value realization from an investment in data visualization. A few key first steps include:

• • • • • Implementing a data dictionary
        • Designating data stewards and a data governance council 
        • Choosing data governance solutions
        • Implementing ongoing training, processes, and policies that work for your enterprise 

But these steps are only the beginning. Keep in mind that data management requires ongoing evaluation of data quality to best promote accurate reporting.

3. Implement a Centralized Data Model 

In order to blend data sources into cohesive visualizations, it is best practice to create a centralized data repository. ​​Whether the aggregation occurs within a reporting tool itself or reporting database, it is imperative to blend data sources to provide cross-functional reporting. Benefits of having a centralized data model include:

• • • • • Having a single source of truth of record for your data
        • Providing cross-functional synergies between organizational business units
        • Building a path for improved data literacy across the organization 
        • Empowering data consumers across the company to make more informed decisions 

4. Create a Data-Driven Culture

A key consideration of any digital transformation is to ensure employees within the organization embrace the new technology. In order to increase adoption and combat any resistance to change, it is essential to develop a cultural framework that motivates your employees to leverage the Business Intelligence tools available to them. Of course, this is easier said than done. Today, only 24% of companies admit to having a truly data-driven culture. A few challenges to overcome include:

• • • • • Changing the narrative of data analysis from an IT task to the responsibility of everyone within the organization
        • Educating team members on how to leverage data for their specific roles
        • Ensuring a data initiatives are supported by upper-level management 
        • Following best security and compliance practices to avoid fines and loss of reputation 

In spite of these challenges, a data-driven culture is possible to achieve. You set yourself up for success when:

• • • • • Data quality, governance, and centralization have been completed
        • High data quality is encouraged by executives and key organizational leaders
        • Employees are empowered to use data with supported training, templates, and reporting metrics
        • Data is displayed through visualizations, making it easy for employees to interpret it and make impactful decisions
        • A cross-program communication strategy is in place to enable effective collaboration among the entire organization 

5. Know the Audience

Another data visualization best practice is to know your audience. When designing reports, it is important to understand who is the intended audience of the report and what information the end-user needs. For example, executive-level audiences will require a different level of granularity than employees completing day-to-day tasks. A few dashboards to consider to variate data visualization for different audiences include the following:

• • • • • Strategic dashboards: Provide holistic views of the overall health of the organization. Helpful for C-level decision making
        • Analytical dashboards: Provide analyses of trending data within specific time frames. Excellent for management teams and key decision-makers
        • Operation dashboards: Best for tracking KPIs and managing organizational operations. Good for management to track team performance and day-to-day team members who need to track individual performance

Rather than just displaying information that was previously in PowerPoint into a Business Intelligence tool, you can fully harness the power of data visualization by asking questions such as:

• • • • • What information does our business need to know? 
        • What metrics indicate performance? 
        • What types of decisions do my employees need to make?
        • What new data sources do I have available to better tell a story with my data? 

From there, you can design the right reports by leveraging data to surface actionable insights and improve business performance.

Success Stories: Implementing Data Visualization Into Your Organization

While there is no one-size-fits-all solution when it comes to visual analytics, at Kenway Consulting, our expertise and obsession with all things data have helped us paint the picture of transformative business opportunities for organizations just like yours. Here are a few examples of how Kenway has used data visualization to help small businesses to large, global enterprises alike.

Gain Insights From Untapped Data

A mobile application company developed an app focused on virtual engagement to provide cultural institutions with enhanced experiences for their visitors. The application collected large amounts of data from its users but had no way to make that data insightful for clients. The organization was looking for an analytics platform that could:

• • • Collect, analyze, and visualize millions of rows of user data, exhibiting engagement patterns, high traffic areas, and visitor demographics
    • Support strategic decision-making 
    • Serve as a net new product offering to be sold to cultural institutions 
    • Automate the processing of source data to support dashboards
    • Develop interactive dashboards that allow users to easily click into varying levels of information
    • Provide intuitive visualizations that are easy to use
    • Deploy rich dashboards that can hold as much information as possible while still being informative

Kenway developed a fully automated, end-to-end process to support the visualizations needed to help the client’s customers understand the value of their data and make informed decisions. The reports created provided insight into who was visiting their institutions, where visitors were spending most of their time, the most-visited areas of the property, and more. 

Read the full case study here.

Establish a Single Source of Truth 

A leading asset management firm had the goal of harnessing massive amounts of data to become more strategic and intentional in targeting its wealth advisor clients. However, due to numerous data inefficiencies and process gaps across the organization, it struggled to support its sales teams in understanding the full breadth of their relationships with current and potential clients. The company lacked real clarity around advisor profiles such as:

• • • The universe of products being purchased
    • Percentage of sales versus competitors
    • Engagement with the organization’s sales and marketing representatives

The main problems faced by the organization included:

1. 1. 1. Siloed data sources
      2. Disconnects in organizational communication
      3. Slow and ineffective processes

Kenway collaborated with the business to understand its needs, analyze the current state, and work with technology teams to build a design that would deliver results. To ensure the organization was set up for success and continued growth, we took the unique approach of working together with the asset management company, as opposed to helicoptering in and leaving them with a design and recommendations that were not tailored to their needs. 

This partnership also allowed the asset management company to cultivate institutional knowledge and build in-house capabilities and data visuals needed to support and adapt the modern data platform over time. This focus on enabling critical business outcomes built upon a solid baseline of governance and architectural capabilities helped to ensure sustainability and long-term success.

Read the full case study here.

Visualize Forecasting Data

When it comes to your business, it is better to be proactive rather than reactive. While we cannot predict the future, business forecasting can help you prepare for potential outcomes. Data visualization can be especially helpful in the development of forecasting charts. 

Forecasting charts analyze your descriptive analytics (historical data) over a specific period of time and provide predictive analytics, or trend lines, that extend past the current date to help you predict future business outcomes. Predictable forecasting can be beneficial when trying to:

• • • Predict future sales performance
    • Analyze product performance
    • Anticipate hiring trends and predict future workload 
    • Make business decisions such as implementing a new technology 

At Kenway, we offer Business Intelligence solutions like Power Business Intelligence to bring your business-critical insights to life through customized reports and dashboards. Using applications such as What-If Analysis, your organization can plan for best-case and worst-case scenarios over the next 6-12 months. 

Drive Revenue by Increasing Timeliness and Accessibility of Customer Data

Data visualization can help your organization have better insights from customer data to quickly identify and capitalize on new market opportunities.

In order to identify new customers in new markets, you first need to have a strong understanding of your current customer base. Aggregating and cleansing customer data that is spread over a range of disparate sources, such as sales, accounting, and marketing, can be extremely time-consuming and near impossible through conventional methods and excel spreadsheets. Even if you manage to combine various data sources, surfacing meaningful insights based on criteria such as product line, region, demographic, or sales territory can prove to be even more difficult.

Blending disparate customer data within a Business Intelligence tool allows you to create standardized KPIs, metrics, and visuals to better analyze the characteristics of your current customer base in real-time and become more intentional and strategic with your go-to-market strategy. 

Kenway has vast experience in leveraging analytics and data visualization to reveal a 360-degree view of your customers. To make this information even more powerful, Kenway can also blend external and internal datasets to present a macroeconomic view of your internal data trends.

No matter how savvy your sales organization and business leaders are, their innate ability to identify new opportunities does not compete with a tool that can quickly analyze and consolidate terabytes of data.

Selecting the Best Data Visualizations For Your Organization

There is no denying it: Enterprise data collection is not slowing down. In fact, over the next two years, it is expected to increase at a 42.2% annual growth rate. As the volume and complexity of data caches continue to proliferate, Business Intelligence and data visualization tools will enable your entire organization to consume the information being collected and make proactive business decisions.

Not sure how to navigate the future of your data? Kenway can help. From surveys and polls to decision support tools for the C-suite, our Power Business Intelligence portfolio highlights how our Business Intelligence engagements have helped transform data into consumable, interactive dashboards and reports that drive business-impacting decisions. Request a free data strategy consultation today.

 

Unless you have been living off the grid for the past few weeks, you have likely heard of the unfortunate situation on a United flight from Chicago to Louisville where a paying customer was forcibly “re-accommodated” to another flight to make room for United staff who needed to be transported to Louisville.  While some of the details around the events that transpired may be in dispute, the outcome was indisputably gruesome.  Thus, at one point the following day, United’s market cap had dropped over one billion dollars.  Beyond the cost of the pending lawsuit by the customer who was forcibly removed from the plane, United is facing the cost of the public relations nightmare, and the cost of lost customers who may never fly with them again.  I’m not here to debate United’s policies or procedures.  Rather, I am interested in understanding the $1B+ breakdown that occurred at the intersection of United’s risk management, decision trees, and predictive analytical models.

First: Why do airlines overbook flights?

It is a common industry practice for airlines to overbook some of their flights because events often arise that would result in a fare paying customer failing to board.  This includes things such as late arrivals, missed connections, customer cancellations, and rescheduling, among others.  In some of these cases, the airline may still recover some revenue in the form of cancellation or rescheduling fees.  However, in the case of missed connections, they miss out on revenue if the seat goes unfilled.  Further, as the laws of supply and demand would dictate, airlines often make their greatest margin on the last few seats sold on each flight.  Rather than increasing the cost of tickets across the board, and potentially being less competitive on pricing versus their competition, the airlines have built predictive models to help determine on which flights they should oversell and the quantity to oversell.  These predictive models are heavily based on historical data (i.e. there are certain airports where connections are more likely to be missed), seasonality, and weather, among other factors.  These models are (or at least should be) continually fine-tuned.

Second: Predictive analytics aren’t designed to predict exactly what will happen, they are designed to demonstrate what is most probable to happen. 

Despite the sophisticated predictive models that airlines have created, when flights are oversold, there are times when passengers are denied boarding.  In many cases, it is voluntary (a passenger voluntarily surrenders their ticket in exchange for compensation), but in other less fortunate cases, it is involuntary.  As the chart below demonstrates, most airlines do it--some just do it better than others.

 

Source: fivethirtyeight.com

Third: Where do risk management and decision trees come into play with predictive modeling? 

The predictive model needs to be informed by risk management and decision trees that leverage historical data to determine the acceptable number of seats to oversell to maximize profit on a given flight.  If the model is too aggressive, significant compensation is required for passengers denied boarding, and the flight is less profitable.  If the model isn’t aggressive enough, the opportunity for additional profitability will be missed.

In the case of United, I suspect they grossly underestimated the risk management when assessing the decision tree to create the expected value based on the number of seats to oversell.  For purely illustrative purposes, let’s play out a few scenarios of the risk management and decision tree.  Note, this doesn’t account for the cash value differences in value between flight vouchers and cash or other costs.

Probability of a passenger on a United flight:

• Sitting in paid seat: 99.93%
• Voluntarily or involuntarily denied boarding: 0.07%

Risk probability when passenger is denied boarding (in hypothetical one seat overbooking):

• Risk: We overbook the flight by one seat; customer accepts first offer:
  • Probability: 50%
  • Cost: $400 airline voucher
  • Probability Adjusted Cost: $0.14
• Risk: We overbook the flight by one seat and no one voluntarily accepts the first offer, but accepts the second offer
  • Probability: 45%
  • Cost: $800 airline voucher
  • Probability Adjusted Cost: $0.25
• Risk: We overbook the flight by one seat and no one voluntarily accepts the $800 airline voucher; customer is randomly selected and involuntarily denied boarding; customer exits plane with little complaint
  • Probability: 3%
  • Cost: $1,350 cash
  • Probability Adjusted Cost: $0.03
• Risk: We overbook the flight by one seat and no one voluntarily accepts the $800 airline voucher; customer is randomly selected and involuntarily denied boarding; customer exits begrudgingly, vowing to never fly United again, and takes outrage to social media to 100+ followers
  • Probability: 1.5%
  • Cost: $1,350 cash + $5000 (cost of customer acquisition; possible additional customer loss based on social media following)
  • Probability Adjusted Cost: $0.88
• Risk: We overbook the flight by one seat and no one voluntarily accepts the $800 airline voucher; customer is randomly selected and involuntarily denied board after already boarding the plane; customer refuses to leave; airport security is called to remove the customer from the plane; customer is assaulted and sustains a concussion, broken nose, scrapes, and loses two teeth; Entire episode is posted to social resulting in $1B loss in market cap the following day; Customer wins seven-figure lawsuit
  • Probability: 0.5%
  • Cost: $1B+
  • Probability Adjusted Cost: $3,750

In this model, United would need to adjust its pricing strategy to account for a probable cost of a denied boarding at $3,751.30 per passenger.  If that were true, United would probably sell very few tickets.  I’m of the suspicion that United neither considered the probability nor the adjusted cost of the final risk listed above, and only accounted for $1.30 per passenger in probable costs related to a denied boarding.

Had United accounted properly for the risk management in their model, they might have done a few things differently.  First, they may have tweaked their predictive model to lower the probability that a customer is denied boarding (see Hawaiian Airlines).  Second, they may have changed their process for dealing with overbookings to reduce the likelihood of a customer being involuntarily denied boarding.  News sources indicated that United used random selection to determine the customers that were involuntarily removed from the plane.  While random may equate to “fair”, random also equates to dumb.  Given the information that is available or that could potentially be acquired, it would be borderline stupid for United to randomly select a customer.  Even if they narrowed it down to randomly selecting passengers based upon the lowest flight status, it would still be dumb.  Information can be utilized to make better decisions than random.  Consider the case of Delta (further details here) which allows customers to define in advance what they would be willing to accept to be bumped from a flight.  The decision then becomes far less than random, and more about informed economics.

So, let me ask you: Have you properly accounted for all risks in your predictive models?

Kenway is here to help. If you’d like to learn more about how we help companies build comprehensive predictive models, or if you’d just like to find out more about who we are, please reach out at [email protected].

 

Kenway just wrapped up a project where we had the opportunity to work towards improving a client’s Net Promoter Score (NPS).  On the surface, the concept is simple:  Customers are asked to provide a ranking based on their experience with a company.  Specifically, on a scale of 1-10, how likely are customers to recommend <Insert Company> to your family and friends?  Customers who give a 9 or 10 are a ”Promoter”, while customers who give a 7 or 8 are “Passive” and those who give a 6 or less are a “Detractor”.  To calculate NPS, simply take the % of Promoters less the % of Detractors.  Anything above 0 means you have more Promoters than Detractors, and you are well on your way to happy customers.  Anything above 50%, and you can consider yourself best in class!

Critics of NPS say it’s too simple, it’s not statistically relevant and there is lack of sufficient evidence to indicate that tracking NPS drives growth.  The critics have a point; however, it’s hard to argue against any process with such a high focus on customer satisfaction.  We are constantly looking to our family and friends for recommendations.  If I were to look at any of my social media accounts right now, I could almost guarantee there is a post asking for recommendations about a company / product, providing reasons why I should never use a company / product, or promoting a company / product I absolutely have to try.

As Kenway pointed out in an earlier blog, ‘Your Information is Worthless’, information is not valuable unless the data is captured and interpreted in the right way.  Calculating your NPS is a valuable way to capture customer feedback as long as companies go beyond simply calculating the metric and actually develop a process to analyze, understand and drive necessary change.  Companies who keep the following best practices in mind will likely see value in capturing their NPS score:

1. Plan & Identify

Identify which customers you want to include in the survey.  Some companies may decide to target their entire customer base.  However, for starters, a more focused survey may be better.  The goal of any NPS process is to drive business value through customer service excellence.  If you have a specific territory that is preforming poorly or a call center that is having significant issues, then you may want to target customers in those areas.  Think about the key opportunities within your business and start by focusing in those areas.

2. Design & Develop

Create a design and develop your survey.  Your survey will include the standard NPS question, but how should it be worded?   Again, this will depend on how targeted you are trying to be.  Some companies decide to keep it very generic (e.g. “On a scale of 1-10, 10 being the highest score, how likely are you to recommend <insert company> to a friend or a family member?  If you had to name one thing that we could improve upon, what could that be?”)  Others may choose to target how satisfied customers were with their call center reps and ask a more targeted question (e.g.  “During your last interaction with us, you spoke with one of our Call Center Representatives. On a scale of 1-10, if you had your own company that was focused on service, how likely would you be to hire this person to work for you?  What exactly stood out as being good or bad about this service?” )  Regardless of the approach you decide to take, the key is to follow up with a question that helps you understand what drives your Promoters and Detractors. Understanding these drivers is just as important as the score itself.

3. Data Integration

In addition to analyzing the results of the survey question, it is very important to integrate your survey data with other key metrics to better understand factors that may or may not be impacting the customer’s satisfaction.  Integrating the survey data with other key metrics such as account information, product mix, eligibility and support metrics will help identify the key drivers behind customer satisfaction.  For example, looking at the product mix of your customers may help determine whether customers with complex product mixes are more likely to be Detractors than Promoters.  Or, looking at support metrics (e.g. first call resolution, time to resolve a ticket, etc.) may help you identify key processes that need improvement.

4. Measure & React

Now that you have the customer survey data at your finger tips, you have an invaluable opportunity to measure and react.  While companies may not have the ability to reach out to all Detractors, you will find that customers react very favorably when a company reaches out to let their customers know they were heard and have a plan to make sure the same mistakes are not repeated.  Additionally, you will likely start to uncover some quick wins (e.g. a quick change to a call center script or a simple tweak to your website) that will help you see an increase in your NPS score.

5. Get Proactive

In addition to measuring and reacting, understanding what drives your customers’ satisfaction or dissatisfaction provides a unique opportunity to get proactive in driving increased satisfaction.  Proactive communications are a definite convenience for customers.  For example, Airlines proactively contact customers to let them know about flight delays and severe weather warnings.  Billing departments proactively alert customers when an invoice is coming due to keep them from becoming past due.  Some wireless companies proactively identify customers who are experiencing dropped calls or poor service within their home and send them boosters to improve the service in the home to retain their business.  Using the survey data from the NPS survey can give you insight into the proactive touch points that can be done to not only improve first call resolution, but eliminate contacts altogether.

Implementing a NPS process may not be the right approach for all companies, but customer satisfaction is a key component to sustaining a healthy company.  If you are looking for help to measure and improve customer satisfaction, consider letting Kenway be your guide.  We are here to help.