It feels familiar: actual sales, last year, and budget side by side for every month. And of course, the same problem appears with many other business metrics: current performance compared with a previous period and a target or benchmark.

But this familiar choice quickly becomes noisy.

With 12 months and 3 series, the audience has to read 36 columns. The chart shows the data, but it does not clearly answer the business questions:

Are we above or below budget?
Are we better or worse than last year?
Which months need attention?

A vertical bullet graph answers these questions faster.

Instead of treating all values equally, it gives each one a clear role:

• Actual sales are the main column.
• Last year becomes a light background reference.
• Budget becomes a small target marker.

This visual hierarchy matches the business logic. Actual sales are the result. Last year is context. Budget is the target.

That is why the chart becomes easier to read.

For each month, the viewer can immediately see whether actual sales are above or below the budget marker, and whether they are higher or lower than last year. The comparison happens inside one compact visual unit, not across several neighboring bars.

This also makes exceptions easier to highlight.

In a classic clustered column chart, colors are usually used to distinguish between scenarios: actual, previous period, and target. In a bullet graph, those roles are already clear from the structure of the chart. That means color is freed up for a more strategic purpose: highlighting what matters.

In the example, the story is not that every month is equally important. The story is that November promotions pulled demand forward, while Christmas sales fell below expectations.

A vertical bullet graph supports this message directly. November can be highlighted in green, December in red, and the remaining months can stay neutral. The audience sees the pattern before they start decoding the numbers.

Budget also works better as a marker than as a column. It is not a result; it is a target. Showing it as a small line communicates that much more clearly than showing it as another bar.

The same applies to last year. It is not the main story. It is historical context. A light background column gives it presence without letting it compete with the actual result.

This approach works in many other business scenarios too:

• revenue vs forecast and last year
• costs vs budget and prior period
• headcount vs plan and last quarter
• margin vs target and moving average
• inventory vs required stock level and last week
• customer satisfaction vs benchmark and previous survey results

Dashboards do not have the luxury of space.

Every visual has to earn its place. It needs to be compact enough to save screen real estate, but also accurate and intuitive enough to be understood quickly.

That is exactly where a vertical bullet graph works so well.

It combines the actual result, historical context, and target in one small visual unit. It reduces clutter, keeps comparisons precise, and makes the main message easier to see.

For this reason, the bullet graph is one of the charts I use most often in my dashboard toolkit. It is not just a cleaner alternative to clustered columns. It is often a better match for the way dashboards are actually read: quickly, repeatedly, and under pressure.

And if you don’t know how to create this chart in Excel, here is a step-by-step guide.

Článek A Better Chart for Actuals vs Budget vs Last Year se nejdříve objevil na Olga Rudakova.

You put on your shoes, start your watch, and tell yourself: today is not about speed. Today is a slow run. Stay in heart rate zone 2. Keep it light. Build endurance.

And then, a few minutes later, the watch beeps.

Too fast.

You slow down.

For a while, everything is fine. Then you get distracted. Your legs naturally speed up. Maybe someone passes you. Maybe the song in your headphones pushes your rhythm. Maybe your ego quietly whispers: You can go faster than this.

The watch beeps again.

Too fast.

Three running modes, three kinds of effort

In my training, I usually run in three modes.

The first is the slow run. The goal is to stay in zone 2, where the body builds aerobic endurance. It does not look impressive. It does not feel heroic. But it is the foundation.

The second is interval training: fast running, then walking or recovery, repeated several times. This is obviously hard. You know when you are pushing. You know when you are recovering.

The third is a longer run at a solid pace, which helps me prepare for a half-marathon. It is not a sprint, but it requires focus, strength, and the ability to keep going.

And this is the strange thing I discovered: the slow run is often the hardest one.

Not because it hurts the most. Intervals hurt more. Long runs require more time and stamina. But the slow run is hard in a different way. It requires constant self-control. You have to resist the temptation to speed up.

And I think work is very similar.

Work has heart rate zones too

At work, we also have different zones.

There is zone 5 work: deadlines, urgent delivery, last-minute presentations, crisis mode, the final push before something important. This kind of work is sometimes necessary. It can even feel exciting. There is adrenaline. There is focus. There is a clear finish line.

But we cannot live there all the time.

Then there is zone 2 work.

This is the work of building the pipeline. Preparing before things become urgent. Thinking clearly. Prioritizing. Improving quality. Creating systems. Having conversations early. Making better decisions before we are forced to make fast ones.

Zone 2 work is less dramatic.

Nobody applauds you for preventing chaos. Nobody says, “Great job thinking ahead before this became a disaster.” But this is often the work that creates the best long-term results.

The temptation to speed up

The hardest part of slow running is that the body keeps wanting to go faster.

The hardest part of good work is that the mind does the same.

We want to answer one more email. Finish one more task. Say yes to one more request. Cross one more thing from the to-do list.

For employees, slowing down may feel like not doing enough.
For entrepreneurs, slowing down may feel dangerous — as if every pause means lost opportunity.

Fast feels productive.

Busy feels responsible.

Urgent feels important.

But speed can be deceptive. We may be moving quickly without moving in the right direction. We may be crossing off tasks while avoiding the deeper work that would actually improve the result.

In running, if I turn every session into a fast run, I may feel strong for a while. But eventually, I will get tired, stop improving, or get injured.

At work, if every day becomes a sprint, something similar happens. Quality drops. Prioritization disappears. We react instead of thinking. We deliver, but we do not build.

Slow is not weak

Slow running is not lazy running.

It is disciplined running.

It is running with a purpose that is bigger than today’s pace.

And zone 2 work is not lazy work. It is not a lack of ambition. It is the kind of work that builds capacity, quality, and long-term results.

The slow run keeps teaching me the same lesson: not every form of progress feels like speed.

Sometimes the real training happens when we resist the urge to accelerate.

And maybe the same is true at work.

Sometimes the most productive thing we can do is slow down enough to think, choose, prepare, and build something that lasts.

Článek Zone 2 Work: Why Slower Is Often Harder se nejdříve objevil na Olga Rudakova.

Sometimes a chart looks fine until you ask one simple question: what exactly am I looking at?

At first glance, the original chart looks perfectly reasonable.

It shows the price of a basic women’s T-shirt over time, and a line chart is usually a safe choice for time series. You could even argue that a column chart might work better for month-by-month comparison, but nothing feels obviously broken.

Then you pause for a second.

What is this chart actually showing?

If the x-axis is monthly, what is the metric behind each point? Is it the average price in that month? The end-of-month price? The price on a specific day? The 27th of each month, for example, since I’m writing this on April 27?

That is the first major issue.

The chart does not define the measure clearly enough. And if the measure is unclear, the chart is already on shaky ground. Before we discuss aesthetics, formatting, or labeling, we need to know what the data point represents.

That alone is a meaningful catch. If you spotted it, you were looking at the chart the right way.

But there is a second issue, and in my view it is even more important.

A line chart suggests continuous movement.

That is fine for many things: temperature, traffic, sales volume, or demand patterns. But does the price of a single retail item really behave like that?

Not usually.

A T-shirt price does not slowly glide upward from December into January and then continue a smooth climb through February. In reality, the price tends to stay fixed for a while, then change suddenly. It jumps up because of a price increase. It drops because of a promotion. It stays flat again. Then it changes again.

That is not a smooth process. It is a series of discrete steps.

Which means a line chart is telling the wrong story.

A step chart tells the right one.

The original line chart implies gradual movement where none exists. The step chart shows the actual pricing behavior: flat periods interrupted by specific events. And once you switch to that view, the story becomes much clearer.

In this case, the year started at 239 CZK, then moved to a promotional price point of 199 CZK in November. After that came a February price increase, later another promotion at 219 CZK in May, and eventually a current price of 255 CZK, which represents 6.7% growth over the year.

That sequence tells a very different story from the original line.

Now we are no longer looking at a vague upward trend. We are looking at pricing decisions.

That is the real value of choosing the right chart type. Good data visualization makes the underlying business process visible.

And that is why I keep coming back to the idea that data visualization is a language.

If “step chart” is not yet part of your vocabulary, it probably should be.

Because once you learn this chart type, you start seeing many more places where it belongs.

Prices are one example, but far from the only one.

Think about inventory levels. Stock does not usually decline in a smooth curve. It stays the same, then drops when orders go out, then jumps when replenishment arrives.

Think about account balances. Think about interest rates. Tax bands. Subscription prices. Minimum wage requirements. Contracted tariffs. Credit limits. Service tiers. Any metric that stays fixed until a decision, event, or rule changes it is a candidate for a step chart.

And this matters because the wrong chart choice can quietly change the meaning of the whole communication.

A smooth line can make a process look organic, gradual, even predictable. A step chart can reveal that the same process is policy-driven, event-driven, or operationally discrete.

So the next time you are charting a metric over time, do not stop at the question, “Is this a time series?”

Go further:

Does this value move continuously, or does it change in steps?

That one decision can completely change the story your chart tells.

And once you see that, you start noticing step-chart problems everywhere.

If creating a step chart in Excel feels awkward, I have included a video link showing how to create one.

Článek Step Charts: When a Line Chart Lies se nejdříve objevil na Olga Rudakova.

And strangely enough, that taught me how data visualization should be learned.

My first foreign language was English. I learned it the traditional way: grammar books, exercises, gap-filling, memorizing rules, and lots of correction. It worked, eventually. But it was slow, effortful, and honestly quite boring.

My second foreign language was Czech. I learned it in a completely different way: by living in the language. I listened, observed, guessed, tried, made mistakes, adjusted, and kept speaking. It felt more natural. It was faster. And the result was better.

Over time, I realized that business chart making works the same way.

Many people try to learn charts the way I learned English: by memorizing chart choosers, studying complex decision trees, and searching for the “correct” chart type for every situation.

Of course, that can help a little.
But in my experience, it is not the best way to become fluent.

Because charts are not just technical objects.
They are a language. That is why we call it data communication.

You learn the language not by memorizing all the rules.
You learn it by speaking.

Data visualization as a language

When children learn to speak, they do not start with perfect grammar. They start with trial and error. They imitate. They simplify. They say funny things. They make “wrong” sentences that are actually signs of progress.

The same is true for charts.

You do not become a confident chart maker by waiting until you know every rule. You become one by building charts again and again. By experimenting. By noticing patterns. By comparing what works and what does not. By making small, and sometimes big mistakes. By solving real business communication problems one after another.

Eventually, you stop asking:

“Which chart does the rulebook say I should use?”

And you start asking:

“What am I trying to say?”
“What should my audience notice first?”
“What makes this clearer?”

That is the shift from knowing about charts to being able to use them.

Why I created Chart Anatomy

That is exactly why I started this newsletter.

There is no shortage of chart rules online.
Use bars for comparison.
Use lines for trends.
Avoid pie charts.
Start the axis at zero.
Remove clutter.

Some of this advice is useful. Some of it is oversimplified. And most of it is not enough.

Because what business professionals really need is not another giant chart chooser scheme.

They need practice:

• in seeing.
• in choosing.
• in simplifying.

Practice in shaping a chart so it says one thing clearly.

That is what Chart Anatomy will be about.

Not chart theory in the abstract.
Not overwhelming frameworks.
Not endless lists of dos and don’ts.

Instead, bite-sized lessons for clearer, more persuasive business charts: one element at a time. Practical examples only.

My advice if you want to get better at business charts

Treat chart making like language learning.

Do not wait until you feel ready.
Start speaking.

• Build charts often.
• Try different versions.
• Iterate.
• Notice what feels clear and what feels confusing.
• Learn from good examples.
• Get peer feedback.

And allow yourself to be imperfect.

An imperfect chart you iterate and improve is more valuable than a perfect chart you never make.

What to expect next

In each issue of Chart Anatomy, I will take business charts apart piece by piece.
One issue, one practical lesson.

My goal is to help you build charts that don’t make people work so hard.
Charts that tell them where to look and what to see.

If you’d like to subscribe to the newsletter, you can find it on LinkedIn under the name Data Anatomy.

Článek Chart Anatomy: Why I’m starting a newsletter se nejdříve objevil na Olga Rudakova.

In this article, I’ll spill the beans on the most common data storytelling mistakes made by business professionals and help you steer clear of them. Let’s dive right in, shall we?

🚩 Mistake 1: Drill Down Instead of a Story

Now, don’t get me wrong, drill-down is a pretty nifty analytics technique. It helps us pinpoint the problem’s hiding spot and uncover its roots. It’s like hunting down a mystery, starting with the big picture and zooming in on individual business departments, brands, or even products. Totally normal, right?

But here’s where it gets tricky. Instead of explaining what we found during our data exploration journey, we end up dragging our audience through the entire process. That’s a big no-no. Remember, a data story needs to have all the elements of a captivating tale: a hero, an initiating event, a conflict, and a climax. So, let’s save the boredom and drill-down presentation structure for something less important, like long meetings.

🚩 Mistake 2: Analyst’s Story Instead of a Data Story

We all have a natural urge to spin stories. It’s in our DNA. And let’s be honest, data analysis can be quite the adventure. We navigate through raw data, play with hypotheses, stumble upon dead ends, and sometimes discover things we never even imagined. Exciting stuff, right? So, naturally, we want to share our experiences.

But hold your horses! There’s a fine line between telling your own analyst story and telling a compelling data story about your customers, employees, and brands. Analyst stories are perfect for those casual coffee machine chats, where you can impress your colleagues with your analytical prowess. However, when it comes to decision-making meetings and boardrooms, it’s time to put on your storytelling cape and be the guide, not the hero.

🚩 Mistake 3: Good Story Told Against a Bad Chart

Ah, the classic case of a good story gone wrong, tangled up with a lousy chart. It’s like a mismatched outfit that leaves everyone scratching their heads. Even if your story is data-based and narrated with finesse, your audience should be able to connect the dots between your words and what they see on the chart. Sadly, that’s not always the case.

To save ourselves from this visual disaster, we need to make sure that the key data insights and the main message of the story are crystal clear in the chart itself. It’s all about using those decluttering techniques, Gestalt principles, preattentive attributes, and other visualization tricks to avoid ending up with a half-baked data storytelling product. Otherwise, we risk ending up with a Frankenstein-like creation that barely resembles its intended purpose. Let’s avoid that, shall we?

🚩 Mistake 4: Good Chart with No Story in It

Picture this: a stunning chart that could win a beauty contest, but it’s missing something crucial—a captivating story. It’s like serving a sumptuous meal without any flavor!

Sadly, this mistake happens more often than a Monday morning coffee spill. Business analysts proudly showcase their beautifully crafted charts, but forget to infuse them with a compelling narrative. They reduce data storytelling to mere data visualization. Formatting data nicely and creating eye-catching charts is all well and good, but let’s not confuse that with being a data storyteller. Data storytelling goes beyond fancy visuals; it’s about connecting the dots, revealing insights, and telling a tale that captivates your audience. So don’t get too caught up in the aesthetics; make sure your chart has a juicy story to tell!

🚩 Mistake 5: Story Created Before Data Analysis

Last but not least, the classic blunder of putting the cart before the horse. If you craft a story before diving into data analysis, you’re treading dangerous waters. It’s like desperately searching for evidence to support your preconceived notions, falling into the confirmation bias trap.

A true data story should be the result of thorough data analysis, not the other way around. Let the data guide you on this adventure, and you’ll uncover insights you never knew existed. It’s time to break free from the shackles of preconceived stories and embrace the awe-inspiring world of data-driven narratives!

Remember, data storytelling is a skill that requires time and practice to master. So don’t fret if you’ve made these mistakes before. We’ve all been there! With practice, dedication, and humbleness, you’ll soon be a data storytelling maestro. So go forth, my fellow data enthusiasts, and spin tales that captivate hearts and minds!

Článek The Five Most Common Mistakes in Data Storytelling se nejdříve objevil na Olga Rudakova.

Stephen Few, author of “Information Dashboard Design,” knows what’s up. He originally, back in 2006, classified dashboards into three categories: Strategic, Analytical, and Operational. But by the time the second edition of his book came out in 2013, he’d changed his mind. He realized that dashboards were excellent for monitoring performance, but not for in-depth analysis. And guess what? I’m with him on that.

So, let’s explore when dashboards are essential and when you need to bring out the big guns. I love dashboards – don’t get me wrong – and there are plenty of business cases for them. But you need to know their limitations if you want to make the most of them.

The Dashboard Zone

Picture a chart with four quadrants. The x-axis represents Strategic vs. Operational, while the y-axis represents Exploratory vs. Explanatory. Dashboards are only suitable for the explanatory analysis of operational performance. That’s what I call the “dashboard zone.”

Operational Explanatory

First things first: dashboards are amazing at monitoring and explaining operational (not strategic) performance. Most business problems fall into this category, and that’s where dashboards shine. The dashboard zone is huge and critical for timely decision-making across every business function.

Dashboards can answer questions like: Why did our profit margins decrease despite an increase in revenue? Why are our marketing campaigns not generating the expected ROI? How many customers canceled their subscriptions and why?

Well-designed driver-based dashboards should combine key performance metrics with their influencers – business drivers that help you understand why an unexpected change occurred. For example, business drivers affecting a company’s margin rate could include a shift in product mix towards low-margin products, volume discounts, early payment discounts, changes in purchase prices due to raw materials or energy prices, or changes in transportation and logistics costs due to fuel prices.

But let’s be real: when you’re interacting with a dashboard or adjusting filters and timeframes, you’re not performing real analytics. All the analytics has already been done at the dashboard design stage or even earlier during exploration.

Operational Exploratory

Exploring operational performance is where analytics happens. Operations managers, data scientists, and business analysts are searching for answers to questions like: What are our key sales drivers? How can we optimize our supply chain to reduce costs? How can we reduce customer churn?

This is where statistical and machine learning tools come in – not dashboards. The data used in this exploration (both internal and external) should come from the company’s data warehouse. The same data we use to understand relationships, causality, and influence between key metrics and business drivers will later be useful in our dashboards for regular monitoring, exploration, and contribution analysis.

Strategic Explanatory

You might be thinking, “If I use dashboards to monitor and explain operational performance, why can’t I do the same with strategic performance?” The short answer is that you can, but only for the monitoring part. It’s unlikely that questions like “Why are customers choosing our competitors over us?” or “Why is our employee turnover rate higher than industry standards?” can be answered using only internal data.

Sure, you can and should gather not only internal but also external market and competition data. Keep an eye on metrics like competitor prices and promotional activities; they serve as crucial explanatory factors in operational dashboards. However, let’s face it: the sheer volume and diversity of data required for strategic explanations are unlikely to be readily available. When it comes to strategic explorations, business analysts, with the support of senior managers, must seek plausible explanations beyond the confines of company dashboards.

Strategic Exploratory

Here, dashboards find themselves out of their depth, unable to provide satisfactory answers to questions like: What are the emerging trends in our industry? What is the market potential for a new product? What are our competitors up to? To conquer this land of uncertainties, market research, strategic analysis, and the aid of advisors and consultants should be used.

Conclusion

Though dashboards may have limitations in the grand scheme of analytics, their indispensable role in monitoring and explaining operational performance cannot be undermined. They unlock a multitude of use cases and opportunities within their realm. However, let us not burden them with the weight of true exploratory analysis. Instead, harness their strengths, leaving the task of in-depth exploration in the capable hands of other analytical tools.

Článek The Truth About Dashboards: They're Not Analytical Tools se nejdříve objevil na Olga Rudakova.

Článek Fishing for Insights: Reeling in Data Stories se nejdříve objevil na Olga Rudakova.

Dashboards have the potential to communicate insights effectively and enable users to make decisions. That’s the essence of data storytelling. In this article, we’ll discuss why data storytelling with dashboards is important and how to achieve it.

Dashboards as Storytellers:

Now, some people believe that only a skilled storyteller can transform information from a dashboard into a story and properly present it to users. While that may be true to some extent, what’s the point of a dashboard if it needs a translator between it and the final user?

Dashboards should be designed in a user-centric way that allows for direct interaction. They should be able to answer questions like “Why?” for diagnostic analytics and “What if?” for predictive analytics. The role of a storyteller in this process is not to interpret the dashboard but to design it in a way that tells a clear and engaging story.

Content: Don’t Settle for KPIs – Bring in Relevant Context and Business Drivers

When designing a dashboard, it’s important to bring in relevant internal and external context. Don’t limit yourself to actual vs. budget comparisons – look at trends, extrapolations, and scenarios. Make sure you have the right time period for comparison, industry or competitor benchmarks, and relative targets.

Identify and monitor your internal and external business drivers within the same dashboard. Talk to your business stakeholders at the design stage, not at the data interpretation stage. By bringing in relevant metrics into your dashboard at the design stage, you can cover 90% of possible explanations and find the answer to the question “why” within the dashboard.

If the relevant data needed to answer the “why” question cannot be found in your data warehouse, it’s time to pull in relevant operational, marketing, customer, and competition metrics. Chances are you’re already collecting all this information, and some other department is using it in isolation from you. Don’t let data silos limit your insights. By combining this information into driver-based dashboards, you can create a more complete and compelling story.

Form: Craft Your Tale through Smart Design

When it comes to dashboard design, space is at a premium. On the one hand you want to fit as much information as possible onto a single screen, on the other hand you want only pieces relevant to your story to be visible. Here are some tips how to achieve that, without overwhelming the end user:

• Use compact visuals such as four dashboard space savers to fit the whole dashboard into a single screen to make your story visible at a glance.
• Utilize conditional formatting and variance tolerance intervals to highlight everything related to the story, while everything irrelevant is greyed out.
• Use an alert color scheme instead of a red-green color scheme to speed up understanding and focus on what’s relevant.

Storytelling in Dashboards is not a Question of Whether, But of How

So, there you have it folks – dashboards can and should tell stories. By designing them in a user-centric way and focusing on the right content and form, we can create compelling data stories that enable decision-making. Remember, storytelling with dashboards is not a question of whether, but of how. So go ahead, take your dashboards to the next level and let them tell the story behind the numbers.

Článek Don't Translate Your Dashboards: Teach Them Tell Stories se nejdříve objevil na Olga Rudakova.

This short article explains the strategic role of dashboard design and helps you set the right key performance indicators (KPIs) and benchmarks for your business.

What we measure we become: the importance of choosing the right KPIs

“Dashboards are for performance monitoring,” – one would argue. “What does it have to do with strategic management?”

Srinivas Venkatram, founder and CEO of an ideas-in-action lab, called one of his books: “What we measure, we become.” These wise words aptly describe the impact dashboards have on our business.

Have you heard of the cobra effect? The term was invented by the economist Horst Siebert. During British rule in India, the British government worried about the number of cobras in Delhi and offered a reward for every dead cobra. It was originally a successful strategy – a large number of snakes were killed for the reward. However, over time, inventive people began to breed cobras for the sake of earning money.

If you start paying glass makers for a kilogram of glass they produce, they will end up with a very thick glass (so thick that you can hardly see through it). If you pay them per square meter, they will produce very thin (and therefore fragile) glass.

The KPIs you choose to measure the performance of your business, the KPIs you use in traffic lights, and the gauges in your dashboards drive your teams’ efforts and shape your business. Think twice before including anything in your dashboard.

There are several reasons why you should ditch the old FP&A reports

However, in my practice, Controlling and FP&A teams don’t think twice about the structure of their reports. We are not in the habit of constantly reviewing and improving them. We just keep releasing the same reports every month, every week, every day.

Unfortunately, I hear quite often: “We’ll start by copying our old reports into our new business intelligence tools” from companies undergoing digital transformation. Thus, they immediately reject all the benefits that technology has to offer.

High quality, complete real-time data readily available from various sources requires a paradigm shift. If we are to reap the fruits of the new technology, we must start thinking differently, and our management reports must look different.

The “oldest” part of our old reports are plans and budgets. We carry this legacy of the 1950s and 1970s into the twenty-first century. Management by Objectives (MBO) is obsolete. We started to challenge it in the 1990s when the Beyond Budgeting movement emerged. But just know, with the fast spread of business intelligence tools and a powerful push by the COVID-19 pandemic, we see a viable alternative available to virtually every business.

Rather than comparing your company’s performance against a fixed target, say 5% sales growth, set a relative target of + 1% growth over the industry average or most important competitor. More objective performance metrics than plans and budgets are becoming available. Why stick with old ones?

At this point, we have established that not only the KPIs themselves but the benchmarks against which we compare them are strategically important. But we don’t stop there.

Drilling down is not a panacea: look for the meaning behind numbers

I’m pretty sure each of you has heard a similar dialogue between company management and your fellow financial controller or financial business partner.

– Our sales are declining this month.
– Why?
– Because our fresh food sales are underperforming.
– And why is that?
– It is driven primarily by dairy products.
– Why is this happening?
– As butter and yoghurts are significantly down.
– Why???
– That I do not know…

It might be frustrating to hear “I do not know”, but are controllers really to blame? In most of the companies I’ve worked for, reports and dashboards are nothing more than systems of breakdowns and drill-downs. Financial analysts are very good at drilling down to the smallest detail, but do they really understand performance drivers?

One of the tremendous benefits of modern business intelligence technology and tools is the ability to integrate data from multiple sources. You can have operational and financial data from different internal systems and external sources in one report. Why don’t you?

Instead of just slicing sales data across all possible dimensions, you can collect in one place data on product availability, data on queue lengths, data on prices, data on advertising activity of competitors. With this at their disposal, your controllers will answer you not only with a description of the problem (which categories are declining) but also with a plausible explanation (50% discount on competitor’s butter and delays in yoghurt delivery from a major supplier to our warehouse).

In summary

• Take dashboard design seriously. Gain the skills you need or bring in experts.
• Use a transition to Business Intelligence tools as an opportunity to reset your FP&A and Operational reporting completely.
• Choose the KPIs carefully for your dashboards, they have more strategic value than it seems.
• Choose objective relative benchmarks (e.g., industry averages) rather than fixed subjective benchmarks (e.g., budget).
• Move towards integrated operational and financial reporting. Bring important operational drivers into financial reports.
• Help your financial analysts go beyond your department’s KPIs to overall business understanding.

I believe that by following these steps, you will create dashboards that not only detect problems but also help fix them. A dashboard is not just a monitoring tool. Unless you make it one.

This article was first published at FP&A Trends

Článek Why Dashboards are a Powerful Tool in FP&A se nejdříve objevil na Olga Rudakova.

Článek Four Must-use Dashboard Space Savers se nejdříve objevil na Olga Rudakova.