How To Translate Data Into Actionable Business Insights

Your Data is Only Half the Story

In the rapidly expanding world of today, there is ample demand for professionals that can translate data for the business — analyze data and create recommendations for the business to take action from. It is all about taking that extra step to make data palatable for the business.

Data is everywhere. The business wants to leverage that data for better decision-making and to be agile and competitive in the market. Now, when we say business, many of the C-suite executives who make the decisions do not necessarily understand the raw data and how to analyze that. It is on the data professionals to do the job and pass on actionable insights.

Even lower on the hierarchy, your peers may not exactly understand what the accuracy of a predictive model means. I’ve heard people say “This is great information but what should I do next with it?” Therefore, it is quintessential that you not only analyze the data, but you present it in a way that the business too can make sense of.

In this blog, I want to highlight why data transformation and storytelling go together and a six-step process to translate data into actionable business insights

How Is Data Different From Insights?

People use data and insights interchangeably whereas there is a huge difference in what they mean

Data is raw and unprocessed. It could be numbers, text, images, audio or video files, etc., that may or may not make sense on its own nor does it provide any valuable inputs to the business

Insights are a result of analyzing information and drawing conclusions by transforming the raw data. Businesses today leverage insights, not data to maximize profitability, be cost-effective, and create value.

The Six Steps To Translating Data For The Business

1. Learn About The Business & Develop Acumen

The first step to solving any business problem is understanding the business itself. Unless you understand the challenges, needs, demands, and opportunities for a business, how can you optimize the present for a better future?

Learning about your organization and its book of business is a continued process. The way organizations do business is so dynamic and developing at a rapid rate that it is quintessential for data professionals to tune their data models and comprehend what the business needs.

In terms of storytelling, understanding the business context is similar to understanding the city and culture your story is based on — without that, you’d be missing the soul of your story.

2. Understand The Problem First

Your first instinct as a data professional might be to propose creating a model as a solution but without a clear, concise problem statement, it gets challenging for data professionals to translate the words into lines of code or excel sheets.

When you have a good understanding of the problem statement and you look at the data for the task, the acceptance or rejection of that data becomes extremely easy. You can think of the next steps in the data lifecycle.

I usually follow the below five-step process in working through the problem —

Read the problem statement or that email a couple of times
Break down your problem into smaller steps
Identify what parts of the problem you can solve
Explore every possible supporting information (read the docs!)
Decide on an approach (your data model, visualization, etc.)

3. Work Through Funnel Analysis To Find Your KPIs

Each department may have KPIs more critical than others because they are responsible for driving particular business impacts. Every time you are tasked with solving a business problem, start with all the relevant KPIs and metrics that matter to your modeling and analysis.

An effective funnel analysis requires coherent data analysis. Start with hypothesizing the problem (or creating your funnel) and test those using A/B or multivariate testing to start filtering out KPIs that may not add value to the intent of your problem statement. Based on the final KPIs, now is when you start creating your data analyses and models.

Example of funnel analysis — say you work with a beverage company and you’re tasked to predict the market share of one brand under the company catalog in the overall beverage market. Creating a funnel analysis in this case can help you reveal your top-selling products. You ask how? Here’s how —

Map the ideal end-to-end business process
Identify problems at each funnel stage
Collect as much relevant data as possible for your analysis
Monitor KPIs
Create an Action Priority Matrix to map efforts and impacts high and low across four quadrants
Ranking tasks from the above matrix according to importance will leave you with a to-do list and that is the outcome of your funnel analysis

4. Use the rule of three

Humans process information in an interesting way. We are proficient at pattern recognition and three being the smallest number of elements required to create a pattern, it is easy to digest information when it’s packaged in threes.

The core form of storytelling: movies, plays, and stories often follow the Three Act Structure where act one, act two, and act three capture the story by establishing the plot, contrasting, and climax. Similarly, the Rule of Three can make your content more engaging where you summarize your words into three action items or key takeaways. For example, you can summarize your presentation or key takeaways and action items as —

Takeaway 1 —
Takeaway 2—
Takeaway 3—

That makes it easy for your audience to remember your content and take action as needed

5. Make it simple to understand

I’ll keep this one very easy to read and understand —

Use less or no jargon in your presentation (or any communication)
Do not use a lot of colors in your decks and keep it easy on the eye
Use visuals that are easy to understand and sit well with the context
Keep your formatting simple and consistent across the slides in a deck
Label your axes in a chart, add color legends, and add subtexts where needed

6. Create a successful data presentation

Most people do not understand data.

Most people understand stories.

Sell stories, not data.

With the five points I talked about above, now is the perfect time to bring that all together to create an effective, concise, and successful data presentation —

Understand your audience — map the knowledge level of your audience and prepare your content depending on if they are your peers, stakeholders, or executives and leaders
Structure your content flow —a good story should have a seamless flow from introducing the agenda to addressing the key findings, provide supporting data, talk about the methodology, revisit key findings and this is what the entire blog is about: provide recommendations or next steps
Narrate it like a story — like every good movie has a plot, conflict, and climax, your data story should have a beginning, middle, and end to be a comprehensive narrative
Decide your story type — based on the problem statement, methodology used, and insights, your story can be a presentation, visualizations in a dashboard, or simply numbers in Excel
Keep it short and simple— spend a maximum of 5 minutes per slide and always leave 10 mins for Q&A

In conclusion, simply talking through your data is never enough. Storytelling with data is an extremely valued skill (almost like an art) and that is what we must strive for — translating data for the business.

That’s it from my end on this blog post. Thank you for reading! I hope you found it an interesting read. Let me know in the comments about your experience with storytelling, your journey in data, and what you are looking for in 2023!

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Happy Data Tenting!

Rashi is a data wiz from Chicago who loves to analyze data and create data stories to communicate insights. She’s a full-time healthcare data analytics consultant and blogs about data on weekends with a cup of coffee…