Data Storytelling: all you need to know

It has now been proven that data-driven companies perform better than other companies with a strong consensus culture. Why? Because data analysis tends to significantly improve decision-making, and therefore enhance operational efficiency. However, most companies will simply collect data and then proceed to provide it to their teams: this is a first step, but it is far from enough. Only Data Storytelling will help you make sure the data will be operated optimally… So here is why Data storytelling is essential, and how to implement it in your organization!

Why does Data Storytelling matter?

In order to make better data-driven decisions, you need to get your business team involved in data analysis and make sure they truly understand its value. 

This is precisely where Data Storytelling comes in quite handy.

While data visualization only provides decision-makers with charts, Data storytelling goes further: it gives them context, insights and interpretation, and wraps all this data in a more understandable narrative. 

The benefits of Data storytelling are many, such as making data more actionable, and therefore enhancing productivity as well as enabling agile decision-making.

Ad-hoc reports: the kind of story you need to tell your team

Ad-hoc reporting enables teams to meet a specific need at a specific time – as opposed to recurring reporting, where reports are generated on a regular schedule, to track specific metrics over time. 

Ad-hoc reports make decision-making easier by providing detailed data insights into specific events (top-selling products for a given period, sales performances in different regions, etc.). They allow Business Users – in other words, people who aren’t part of the Analytics team – to adapt their strategy and drive business outcomes more efficiently…and they happen to be perfectly suited for Data Storytelling!

Ad-hoc reports: the best way to support business decisions?

Contextual reporting

When a specific situation calls for quick action, such as a drastic change in client behavior or market conditions, contextual reporting proves extremely useful. And since ad-hoc reports can be generated on the spot and fairly quickly, they enable Business Users to gather the right information on the situation and take data-driven decisions accordingly. 

Sharing insights

Sharing insights with stakeholders – executives, managers or customers – is also a great way to optimize business decision-making. In this context, ad-hoc reports make this process much easier, because they are flexible and interactive. They empower stakeholders to understand and engage with the information they are provided with. 

Ad-hoc reporting improves communication and collaboration within the company, and supports better decision-making. However, keep in mind that an ad-hoc report is often a one-off: it is true in a specific context (the one it was created in), but cannot be used directly in another one.

How to create a powerful Data story

Business Users nowadays tend to feel overwhelmed by the huge amounts of data they are provided with, and they need to cut through all that noise.

In this context, a compelling narrative combined with clear visualization can prove very helpful for them.

Your Data story should include:

• an exposition, which will give the context; 
• one or several characters (customers, stakeholders, or the organization as a whole);
• a conflict – in other words, an issue or specific event that affects the characters and will be the main focus of your story;
• a clear resolution, in the form of a recommended course of action or a solution that will advance informed decision-making.

For your Data Storytelling to be effective, you must choose a precise business goal, one or two striking key figures, and create an actionable conclusion.

Data storytelling also needs a storyteller (the Data team) and an audience – and the storyteller must have a good understanding of this audience in order to organize and highlight the data accordingly.

Data culture: make sure your team is ready to hear your story

Data Storytelling focuses on what kind of decisions Business Users can make thanks to the data they are provided with – but you must also consider how they will receive and treat this information: if Data stories do not have a listening crowd, they won’t have much of a positive impact on decision-making.

This means that you need to get Business Users to trust and leverage data to guide their decisions: the stronger your Data culture and Data literacy are, the more impactful your Data Storytelling will be. 

Why Data Storytelling needs Data culture

Data culture is a set of principles, beliefs and practices shared by the people who trust data and use it in their decision-making processes. On the contrary, only a few years ago, companies used to rely on assumptions, intuitions and past trends when they wanted to determine the best course of action.

In order for your Data culture to be strong and sustainable, there are four important criteria to keep in mind.

• Availability: when is data available? In which form? 
• Literacy: are Business Users trained and skilled enough to understand the data at hand? Will they be able to leverage it properly? 
• Trust: is the data trustworthy? Where and when was the data extracted? Can Business Users self-serve while being confident that the data will be accurate?
• Storytelling: are Business Users qualified to include data points in their reasoning, all the while being able to challenge data when needed?

Only if Business Users understand what data as a whole will it have an impact on decision-making – otherwise, reports will not be used enough (or at all). This makes Data culture a pillar of Data Storytelling.

All in all, if you want Business users to make relevant data-based decisions, you need to tell them a story with a strong structure and understandable narrative. Use expressions such as: “Here is the issue”, “Here is what data tells us”, “Here is the finding you can base your decision on”, etc.

Why Data culture needs Data Storytelling

Why you need to build a strong Data culture

Data Culture isn’t something you can buy off the shelf: you need to put effort into it, and build it just like you would build your corporate culture.

Depending on the size of your company, strengthening your Data culture will require different processes and tools, involve different people, and you will encounter different roadblocks (whether behavioral or logistical).

The process usually starts with the management team, but the Data team is the one responsible for developing and maintaining the company’s Data Culture – which also represents the sum of all the employees’ individual Data Culture.

Building a powerful data stack will give your employees access to high-quality data, but don’t forget that data on its own doesn’t hold much value. If you want it to help you drive business outcomes, it has to be available when and where it’s needed. All in all, creating a powerful data infrastructure won’t spark change if it doesn’t come with real cultural change. 

How Data Storytelling can help you

Even though technology is definitely helpful when it comes to creating a strong Data Culture, it cannot be the only driver: communication is also key. This means that you need to communicate on your goals, but also your expectations regarding Business Users themselves. 

And Data Storytelling is a powerful ally in this endeavor.

When provided with clear, understandable and actionable reports, Business Users are more likely to use them in their decision-making, and therefore include data in their work habits. This leads to the development of Data culture in the organization. 

It can be a virtuous circle: the more developed your Data culture is, the stronger your Data Storytelling becomes and the further you can go into achieving your goals.

Data ELT: no Data Storytelling without data quality

If you want to tell a Data story, you need data: this is one of the pillars of Data Storytelling. But it’s easier said than done: your data needs to be trustworthy, high-quality, up-to-date, and easily accessible.

This means that you need, before launching any Analytics project, what we call a “Single Source Of Truth” – in other words, a unified data warehouse – which goes hand in hand with the ELT process. 

ELT: a prerequisite for data Analytics

ELT stands for “Extract, Load, Transform”. This refers to the data integration process, where raw data is transferred from a data source to a target system (data warehouse, data lake, etc.), and then prepared for various uses. 

The ELT pipeline is made up of 3 operations: 

• Extracting data from one or more source systems
• Loading: adding the extracted data to the target database.
• Transforming: converting data from its source format to the format required for analysis.

Besides, ELT’s main purpose is consolidation: gathering data from multiple sources into a unified data warehouse – a.k.a. Single Source of Truth – provides Business Users with a consolidated outlook on data. This makes analysis and reporting easier for them.

On the other hand, transformation operations help improve data quality, in order to make it more trustworthy and actionable for Business Users!

How to assess data quality

In order to accurately determine how qualitative your data is, you need to take several parameters into account: 

• Accuracy: does the data represent real-world scenarios accurately, and can this accuracy be confirmed with a trustworthy source?
• Completeness: can the data deliver all the available values? 
• Consistency: does the data stay uniform, even when it moves across several networks and apps? Are there conflicts?
• Validity: does the data match the company’s predefined rules, parameters and formats?
• Uniqueness: are there duplicates? Do some values overlap across datasets?
• Timeliness: is the data available and accessible when needed? Is it up-to-date?

Now that your data is ready, clean and accessible, it is time to leverage it. The question is: which BI tool should you use? 

Analytics platform: covering the last mile of Data Storytelling

We know that Data Storytelling is a powerful way to communicate insights and data findings in a compelling and engaging way. An Analytics platform is a great ally in this process: it provides users with the tools and infrastructure they need to collect, store, analyze and/or visualize data, build reports – in order to gather new insights. 

An Analytics platform is the “last mile” of the Data Storytelling process.

Once your database is ready to be explored, you need an Analytics tool that will enable you to actually delve into it and make it accessible to Business Users: by creating cohorts or charts (to name a few), you will empower them to analyze and share data whatever the format. 

What for? To foster collaborative, innovative and data-driven decision-making. This is what an Analytics platform is all about: enabling Business Users to self-serve and gather useful insights, as well as integrating data all across the organization to speed up the data-driven decision-making process. 

How to choose the right Analytics platform? 

Thinking about your teams 

In order to make this choice, you need to think about all your teams: 

• Analytics teams: is this platform suitable for Advanced Analytics? Will they be able to leverage it properly?
• Business Users: is the platform accessible and easy to use?

But the best platform for your Analysts won’t necessarily be the best platform for your Business Users. For instance, a solution such as a Metabase is clearly more appropriate for technical teams, whereas Looker is more adapted for people who don’t possess any data hard skills. 

Knowing your Business Users

As a result, you need to strike the right balance between both teams. And in order to do this, you’ll have to know more about your Business Users:

• Who are they? How many of them are there? 
• Which department do they work in? 
• Should the whole company get access to the Analytics platform?
• Are they skilled and data-literate enough to explore data autonomously? 

You will also need to determine their specific needs: 

• Do they need a lot of reporting? Do they want to share their reports to the whole company, or to specific users?
• How do they rely on data for their daily tasks?
• What kind of Advanced Analytics processes and tools do they use?
• Do they have access to a unified data warehouse? If not, do they rely on several data sources? 

Don’t take the choice of your Analytics platform lightly: it is a key tool for your Data Storytelling projects and processes!

A Data notebook: the best tool for Data Storytelling 

Analytics platforms vs. Data notebooks

Classic Analytics platforms are a great tool if you only want to display KPIs. But if you want to go further, you will soon find out that dashboards do not provide the necessary context for Business Users to fully understand your analysis. Besides, Data Analysts can only copy/paste data from their BI tool to a Google doc or Notion, which turns out to be time-consuming and can end up damaging your data’s quality. 

Data notebooks, on the other hand, display the Analysts’ work combined with relevant data documentation, which considerably helps Business Users make informed decisions. This is precisely why Data notebooks are the best tool for Data Storytelling.

What’s a Data notebook? 

Let’s dive deeper into what Data notebooks really are. 

They are a form of interactive computing. They consist of flexible and interactive environments where users can explore data and carry out analysis through several Big Data processing technologies. They make exploration and iteration much easier: users can – among other things – run queries or use filters, while visualizing data in real time. 

They also enable users to structure their findings by displaying the code along with textual explanations and outputs, and therefore avoid breaking the data lineage. They make reports easier to share and more accessible, through built-in visualization modules and output mechanisms. 

Plus, once a Data notebook has been written, it can be easily reproduced and reused for a similar analysis!

Bring your Data Storytelling to another level

Here is how to make the most of a Data notebook and improve your Data Storytelling:

• Data quality: make sure that your notebook is directly querying the data warehouse, and that there is no risk in copy/pasting.
• Good narrative: write – directly in the document – a text which gives relevant elements of context, explains the analysis and indicates how the data must be interpreted.
• Visualization: design and create charts and tables in order to make the analysis more compelling and easier to comprehend. 

A Data notebook will enable you to combine, in a single document: the data involved in the analysis (up-to-date and qualitative), its context, as well as the conclusions you drew from it. This makes it the most fitting solution for Data Storytelling!

Data Storytelling is a great tool for any data-driven organization: it enables all stakeholders to gather significant insights from data, even if they aren’t formally trained. How? By framing and presenting the relevant information in a clear and compelling way – and also by creating a story that incites everyone to understand and engage with the data involved. 

However, analysts often try to tell their data stories with unadapted tools (tables, charts, slides…), which represents a significant time loss and can damage the data’s quality. This is why Data notebooks are a game-changing tool: they are the most suitable format for all those who want to tell stories with data mixing analysis with documentation.