John Sanchez

John Sanchez is Managing Director of financial planning and training consultancy FPA Group, LLC . He has more than 20 years’ experience including working at a top ten accounting firm, mergers and acquisitions work for Fortune 500 companies, and leadership positions in consolidated business groups such as Royal Caribbean Cruises and AutoNation. John has substantial experience in capital and strategic planning with proven skills in large-scale budgeting, forecasting and financial planning.

Since his move into developing business training courses for financial professionals in the areas of communications, financial planning and analysis, budgeting and forecasting, John has established a clientele including solo start-ups and multi-billion dollar conglomerates in an eclectic array of industries. Among his many projects, he was hired by the Association of Financial Professionals (AFP) as the instructor for the inaugural exam preparation course for the Certified FP&A Professional certification.

John holds a bachelor’s degree in accounting and has served as a member of the board of directors for several organizations.

 

Author's Articles

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Improving Communication With Data Visualizations

By John Sanchez, Keynote Speaker, Corporate Trainer and Author

Research shows that the human retina can transmit data to the brain about 60,000 times faster than it can transmit simple text! On top of processing visuals faster, people retain visual information at more than three times the rate of text alone. The speed with which we process visual data is important, not just for expediency, but because we deal with increasingly large volumes of data we need to be able to communicate a lot with a little. These are just some of the reasons data visualizations are used more and more by organizations that want to make better decisions to drive business performance. 

A Simple Definition

Many view data visualization as just a recent term for what has long been known as visual communication. But, data visualization is not just a new buzzword, it is an entire field of study. There are universities all over the world that offer degrees, some even masters degrees, in data science with an emphasis in data visualization. It's not a new concept, just new terminology. For finance and accounting professionals who frequently communicate numeric information, data visualization is particularly relevant. For clarity, let's look at a simple definition so that we are all on the same page when we talk about data visualization. Data visualization is simply the graphic representation of quantitative information. 

Show, Don’t Tell

The old, “a picture is worth a thousand words” concept really comes in handy when you have to make a point that could take a lot longer without the benefit of visuals. Let's look at a simple example. Let's look at a very simple shape like a square. If we were to try to convey the idea of a square to someone using only text we might say, a square is a four-sided flat shape with straight sides, a regular quadrilateral, with four equal sides and four equal angles, where every interior angle is a right angle (90°). Whew! What a mouthful. While that might be a perfectly fine, accurate description of a square, wouldn't a visual image of a square be a much more efficient way to get the point across?

Not only is the visual image of a square quicker and easier to understand, but you could see that it is a square from across the room, which you could not do with the text. Think about the impact of that if you're developing a dashboard to communicate lots of information. How valuable is it for your end users to be able to understand something in an instant?

Spot Things We Otherwise Couldn't/Wouldn't

Visual imagery helps us identify relationships more easily. Whether it’s the parts of a whole, like in a puzzle...

...or a connection that is easy to see, but hard to discern using logic, visuals sometimes make relationships clearer.

        

Kurt Koffka said of visual imagery, “The whole is other than the sum of the parts.” Koffka is one of the psychologists that developed the Gestalt principles of visual perception, which include a handful of principles of visual perception. Simply put, data visualization helps us make more of data than merely the sum of its parts. 

Many organizations all over the world have jumped in with both feet using data visualization. Some of the biggest players in a wide variety of industries have been using data visualization tools for years, including Audi AG, Bank of America, Barclays, Citigroup, ConocoPhillips, and Exxon Mobil to name just a few. 

Visualizations Can Be Interactive

While finance and accounting professionals frequently communicate numeric data, they are not the only ones using data visualizations. Many professions use them. Some of the visualizations I've run across that were created by marketers illustrate some novel and dynamic uses of data visualization tools. Here are a couple you might like that make use of  interactive visualizations:

These visualizations were created to create engagement and they are good examples for anyone who might want to get users of their communications more involved and engaged. No matter your audience, getting them engaged with the data boosts understanding, retention and leads people to take more action.

If you communicate data at all, it is worth investing some time to explore data visualization. There is a wide variety of tools available to make creating visualizations easier than ever before. With a modest investment of time you can try different options, come up with your wish list of functionality and narrow the field. Before you know it, you'll be creating visuals that take how you communicate information to a whole new level.

The article was first published in Unit 4 Prevero Blog

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Using Analytics to Enhance Decision Making

By John Sanchez, Keynote Speaker, Corporate Trainer and Author

Analytics are nothing new, but what is new is how they are being used and how pervasive they are. Recent surveys show that more than 90% of organizations believe that using analytics more effectively will lead to important insights to inform decision making, and 49% of businesses said the biggest benefit of using analytics is improving decision making. The crux of the analytics movement is making better decisions and more organizations are moving towards a data-driven culture, but only about a third of organizations surveyed said they would describe their current culture as data-driven. So, where is the disconnect?

Data-Driven Decision-Making Culture

Maybe the best way to answer this question is to examine what it takes to have a data-driven culture with analytics at the heart of decision making. One of the key characteristics of a data-driven culture is transparency. The right people need access to data to perform the analysis they need to make better decisions and disjointed or locked down data does them no good. The dilemma this presents is adhering to data governance policies while still allowing access to the right information by the right people at the right time. 

Some of the analytics tools that are currently in use by organizations who are maximizing their analytics function make it easier to strike the proper balance between transparency and compliance with data governance policies with well-defined user access policies and audit trails to ensure people can use only data they need and any changes to data are well documented.

Establishing Data as the Decision Driver

Another key characteristic an organization needs to have to extract the most out of their analytics is to cultivate data as a key decision driver. There are many approaches to decision making and among them are a variety of non-data driven methods. Decision by consensus, for example, is a common decision-making methodology that does not have data as a core decision driver. This is not to say that there is no merit in this method, but it is not compatible with a strongly data-driven decision making culture, so this is a big consideration. Changing the culture is not without some significant challenges, so plan accordingly and get a handle on the resources that will be required if a major culture shift is in order.

Get the Right People on the Team

Culture, by nature, has to do with people and having the right people on your team is critical to successfully establishing analytics as that key decision driver. That means hiring, training and retaining talent with the right set of skills and attributes. You will need analysts who are curious, eager to learn and flexible. Curiosity doesn't just mean asking questions. It means asking good questions, critically evaluating answers, and asking good follow up questions when necessary. 

I recall being frustrated when I was managing financial analysts and I would see an explanation of a variance with phrases like "Variance due to volume increase". While it may be a factually correct answer, it does little to provide insight. Analysts must understand how to ask questions that lead to insights so they are empowering decision makers, not just reporting data.

An eagerness to learn is especially important in an environment that is constantly changing, as people need to learn new skills as they are required to adapt to new technology or circumstances. Whether you have a formal training program or knowledge and skills are shared informally, there must be a commitment to continual learning if you are to keep pace with top competitors. Not only is this important for your current employees to do their jobs well, but it is also important to keep them on your team at all as the business landscape at top companies gets more and more competitive. Demand for finance managers, for example, is expected to grow at nearly 20% per year, which is much faster than many other jobs.

If you recruit and retain top talent, establish a culture where data is a key decision driver and foster an environment of wisely planned transparency, you put yourself and your organization in a position to maximize your leverage on analytics to drive decisions that can improve business performance.

The article was first published in Unit 4 Prevero Blog

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Applying Analytics to Storytelling

By John Sanchez, Keynote Speaker, Corporate Trainer and Author

storytellingAccording to Google's Chief Economist, Dr. Hal Varian, "The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it— that’s going to be a hugely important skill in the next decades." LinkedIn confirmed this when they analyzed the skills most needed in 2018 and among the top skills they identified are data analysis, presentation and communication skills. Applying storytelling to analytics embodies all these skills and is critical to FP&A professionals who want to move the next level.

As FP&A professionals we are expected to not just report on data, but to add value to our organizations with analysis we produce. There are many reasons we should strive to incorporate storytelling into how we communicate the results of our work. In their best-selling book "Made to Stick", Chip and Dan Heath talk about how people's ability to recall information was more than ten times better when they learned information through a story, rather than being presented with data alone. Beyond just making information more memorable, research shows that stories increase people's propensity to act on what they have seen and heard in stories, so storytelling is clearly an invaluable tool we should use when communicating.

Don't just present numbers, help people understand the story behind the numbers

The key to using analytics is extracting insights that drive effective decision making and storytelling works as a lever to get the most out of those insights. The first step to developing stories based on our insights is to start with the end in mind. What decisions will your analysis support that drives the business forward? Answer this question first, rather than being pulled in whatever direction data takes you. Exploring data with an open mind is important, but it is once we have developed our insights that we must move to the communication piece of the puzzle, so decide first what your end goal is. Are you communicating to inform, to influence, to persuade, to educate? There are many reasons we analyze data, so get clarity on your goal first. Once you have decided on your desired outcome you must start to develop your story using a combination of visuals and narrative.

There are many data visualization tools with a wide variety of functionality, but even the most rudimentary visuals that you can create in spreadsheets are helpful to telling a story with data. If you have more sophisticated visualization tools, use them, but don't let the tool drive your story. Let the data tell the tale, with you as the narrator steering the story in the right direction. There are many great advantages of visualizing data, which is the subject of an upcoming article, but suffice to say we should always incorporate visuals whenever they make data quicker or easier to understand. 

Use narrative to add context

Use narrative to expand on what your visualizations show. You may have a visualization that clearly shows a trend, but the trend alone usually doesn't tell the entire story. Adding narrative provides context that helps people understand the bigger picture. Is your trend the result of some other data you can discuss or show with another visual? How is the data you present related? Maybe the trend in your visualization of your company's revenue is the result of several new marketing efforts that are illustrated by another visualization. Tie them together to create a complete picture of what is going on in the mind of your audience. 

By performing analysis with the end in mind, being intentional and focused in choosing what data to communicate and combining visualizations and narrative to craft your storytelling, you will take how you communicate with analytics to a new level.

 

The article was first published in Unit 4 Prevero Blog

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Making the Complex Easy to Understand: Analytics & Visualization

By John Sanchez, Keynote Speaker, Corporate Trainer and Author

A recent Deloitte survey showed that more than 80% of companies surveyed said that analytics are currently being used to support their corporate strategies. Other research by SAP showed that those using data visualizations estimated it would take more than nine hours longer to identify patterns, trends, and correlations in data without using visualizations.

There are many more research findings that point to the same thing: analytics and data visualizations are important tools that enhance decision making. But, how can we use them to make complex things easy to understand. There are a few things you can do to better use both tools to simplify the complex. 

To paraphrase the concept of Occam's razor, simpler explanations are generally better than more complex explanations. In a sense, the answer is in the question, "How do we make the complex easier to understand?" Keep the explanations simple.

Start With the End in Mind

Start by focusing on the purpose for communicating whatever analytic data or visualization you want to use. Are you trying to illustrate a trend? Identify exceptions? Show relationships? What conclusion do you want your audience to draw from what you communicate? This is one of the most important things you can do to frame your communication properly. As we discussed in an earlier article about the value of developing insights from data, knowing what your end user wants and needs is a critical piece to making the complex simple.

Remember the 80/20 Rule

Clear out some of the mental clutter by focusing on the small number of things that make the biggest impact. Not all data is important and the inclusion of irrelevant data serves only to clutter and confuse things. Your role is not simply to report on data. Your experience and skills are necessary to narrow down what’s important. 

Clarity of language

Be clear in the language you use when you communicate your analysis and visualizations. Use precise language and avoid vague or ambiguous terms whenever possible. Don't describe a trend as good when you can describe it as increasing by 20%. The first is subjective and the latter is concrete and not subject to interpretation. 

Choose the right type of visualizations

There are many visualization types to choose from, so choose carefully. Not all visualizations fit your data best. Be wary of the pitfalls of pie charts, for example. These are a very overused type of visualization and are sometimes the wrong choice. Too many pie slices (i.e. categories) is a common and classic mistake in the use of pie charts. If you have more than 6 categories to display, don’t use a pie chart. Learn some basic rules of visualizations, like which types work best in different situations. Illustrating a trend? Consider a line graph. Bar charts work well for comparisons, especially illustrating changes from one period or category to another.

Be careful with the scale of your visualizations to you do not distort the perception of differences between data items. This is a common mistake often made in interpreting visualizations that have been created specifically to distort perception. There are many other rules and guidelines, but these are a good start. 

Be Concise 

More is not always better, especially when communicating with executives. Say as much as possible with as few words and images as possible. This takes extra time and effort, but it pays big dividends. 

Use Chunking

Break down complex concepts into small, easy to digest parts. Chunk your communications into small, bite-size portions that are easy to understand on their own and build the story your insights reveal as a larger canvas that tells a story about your insights.

There are so many things you can do to make complex things easier to understand, it’s hard to fit them into one article, but these are a good starting point to get you exploring additional ways to serve your audience more effectively. There is plenty you can read about this topic, but also let feedback be a teacher. Listening effectively to feedback from those who consume your analytics and visualizations will tell you much of what you need to know to simplify them and make them more effective.

 

The article was first published in Prevero Blog.
 

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Communicating Insights, Not Just Data

By John Sanchez, Keynote Speaker, Corporate Trainer and Author

According to a 2017 IBM report, "Every day, we create 2.5 quintillion bytes of data. To put that into perspective, 90 percent of the data in the world today has been created in the last two years alone." Those are some pretty staggering figures and businesses see the volume of their internally generated data growing all the time.

What we do with all that data can have varying degrees of impact, depending on a variety of factors. By analyzing data and developing insights that inform decision making we get a big bang for our data buck. Insights are they key to wringing the value out of data.

Data vs Insight: What's the Difference?

The contrast of data and insights reminds me of the phrase "Knowlege is power." The truth is, knowledge is only potential power and while many people say that data is valuable, it is only potentially valuable until we apply insights to it to unleash its power. Just what is this thing called insight and how can we use it to improve business results?

Data is simply raw numbers. Insights are the results of understanding the underlying nature of things and/or relationships between data. Insights typically allow us to make better decisions, which is why they are much more valuable than data. As defined in "The Art of Insight", by Charles Kiefer and Malcolm Constable, "Insights are really high-quality fresh thoughts. They result in a dramatically improved understanding of a situation or problem such that we see things more deeply and more accurately than before."

Another definition says, “Insight is the understanding of a specific cause and effect in a specific context.” Some people describe an insight as experiencing an “a-ha” moment, but don't let that make you think insights are serendipitous events out of our control. The specific wording of a definition or insight is less important than the common denominator, which is insights allow us to improve decision making. That's the lever that unleashes raw data's power.

Going from data to insights can take many forms, but there are some steps we can take to make it a systematic and repeatable process.

What is Your Goal?

Define what your end goal is. In other words, what issue are you trying to resolve? What question are you trying to answer? What problem are you trying to solve? If you are just running calculations or iterating models aimlessly, your chances of gleaning meaningful insights from your data are pretty slim. Don't misunderstand this to mean there is no value in adjusting as you go. If you start to see patterns that lead you somewhere, don't ignore them. Just as a scientists starts with a hypothesis, but if the evidence is clear that the data says otherwise, they continue to test their hypothesis, but they pay attention to what the data says. 

Analyze It

Analyze your data. Do some modeling, and I don't mean the type that requires a runway and designer clothes. Use what you do know to elicit things you don't know from your data. If you're trying to develop insights from financial information and you know there are certain patterns, that is a good starting point for modeling future financial performance. You may be familiar with disclaimers like the one the United States Securities and Exchange Commission requires for mutual funds which says, "past performance does not necessarily predict future results." Models based on known data don't necessarily predict future results, but they are a good starting point for discovering insights about your data and what it means.

There are tools beyond simple spreadsheets that make this type of modeling quicker, easier, and more effective than ever before. These tools are more affordable and easier to use than ever before, so you should explore them and figure out which tools might be a fit to give you leverage and move your analysis and insights to the next level.

Questions, Questions, Questions

Once you have done some analysis, review and analyze the output with an eye towards you end goal. What does the analysis mean in the context of the question to be answered or the problem to be solved? If the question you are trying to answer is "What will our sales forecast be for the coming 12 months?", what does your analysis tell you about the answer to that question. If you're working with high-quality data and you built a model wisely, with the end goal in mind your analysis should help you develop some quality insights rather quickly. That being said, don't rush the process. Take some time to think about your results and what they mean. Allow yourself to think outside the box. That's where some people think their best.

By approaching data systematically, you can gain insights you can use to make better decisions and that is when you get the real value from your data.

The article was first published in Prevero Blog

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Author's Articles

June 18, 2019

Research shows that the human retina can transmit data to the brain about 60,000 times faster than it can transmit simple text! On top of processing visuals faster, people retain visual information at more than three times the rate of text alone.

April 30, 2019

The crux of the analytics movement is making better decisions and more organizations are moving towards a data-driven culture, but only about a third of organizations surveyed said they would describe their current culture as data-driven. So, where is the disconnect?

February 11, 2019

According to Google's Chief Economist, Dr. Hal Varian, "The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it— that’s going to be a hugely important skill in the next decades."

January 15, 2019

To paraphrase the concept of Occam's razor, simpler explanations are generally better than more complex explanations. In a sense, the answer is in the question, "How do we make the complex easier to understand?" Keep the explanations simple.

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