It has been a milestone year for the digital analytics industry. For one, IDC predicted that the 'big data and analytics' market will reach US$125 billion in hardware, software and services revenue, while Forbes estimated that companies will spend an average of $7.4 million on data-driven initiatives.
Notably, Forbes also estimates that the “professional services-to-technology ratio will also increase 25 percent this year”, suggesting that the focus is shifting from technology to people. Optimal Ways CEO Nicolas Mal agrees; he predicted “the focus [of digital analytics] will shift to the management and communication of digital data within companies”.
This points to an important trend in the digital analytics industry. That is, many organisations today seem to be less constrained by the choice of which solution, platform or architecture best suits their needs as they are stifled by how to find and nurture the right talent to succeed with analytics. And this issue isn’t limited to big-data analytics. No matter what volume, velocity or structure of data you’re working with, the issue for many business leaders is the same, “How can I use the data I have to make impactful business decisions?”In my opinion, what seems to be missing from most conversations related to analytics talent today is the role of communication, or more specifically, storytelling.
Why does data need a storyteller?
The concept of telling a story based on data is not new. The proposed marriage of strong communication skills with deep technical and statistical understanding can be traced back to the emergence of the data scientist. In 2012, Thomas Davenport described this role as one that requires not only programming and statistical smarts, but also someone who can “communicate in language that all their stakeholders understand... [and who can] demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both.”
In Nancy Duarte’s book, Resonate, she explores what it takes to be a great communicator in business. She makes a great case for why facts or data alone are often not enough to convince decision makers to follow a particular course of action. In particular, she highlights that the information you present needs to incite an emotional response from the audience in order for it to resonate.
Google’s Analytics Advocate Daniel Waisberg also touched on the importance of storytelling when using data to drive strategic decision-making. In a 2013 article titled "Tell a Meaningful Story With Data", Waisberg says, “most organisations recognise that being a successful, data- driven company requires skilled developers and analysts, [but few] grasp how to use data to tell a meaningful story that resonates both intellectually and emotionally with an audience”.
Interestingly, both Waisberg and Duarte mention the importance of triggering an emotional response from your audience when communicating through data.
When it comes to data driven decision-making, organisations don’t need data. They need insight. Data is simply a means to an end. It’s the raw, unrefined material that you use to obtain insight which has business value. But insight alone often isn’t enough to convince a boardroom or stakeholder; you need to consider how to communicate the insight to your audience, and the best way to do this is through a great story. This is something I’ve witnessed time and time again, where a killer insight, which has the potential to transform the business, falls flat because of poor presentation.
In the past, industry practitioners have loosely referred to people who use data to communicate a point of view or course of action in business as data storytellers. I find this moniker somewhat lacking as it doesn’t quite capture the full range of skills needed to be an effective storyteller in the field of digital analytics. As such, I prefer to refer to these analytical superstars as 'empirical storytellers'. For me, empirical storytelling effectively captures the intersection at which the art of eliciting an emotional response meets the science of analytics.
What makes a great empirical storyteller?
Here are the key skillsets that make the empirical storyteller stand out from traditional number crunchers.
Multilingual: In the world of marketing, business and analytics, the empirical storyteller is someone who speaks multiple languages. They have the ability to traverse varying levels of abstraction, they are as comfortable in a boardroom environment discussing business objectives as they are in a campaign war room reviewing system architecture and tagging requirements.
Interpreters: Beyond understanding different business and technology languages, empirical storytellers also possess the ability to interpret and simplify concepts into a unifying language that stakeholders from different functions can understand.
Critical thinkers: In his book, Wrong, renowned statistician David Freedman introduces a concept known as the streetlight effect whereby individuals analysing data sometimes spend their time “looking for answers where the light is better rather than where the truth is more likely to lie”. This is an important concept that separates the empirical storytellers from the rest. Empirical storytellers always look to delve deep into the data and unpack layers of complexity as a means to discover truth.
Collaborators: In Information Week’s 10 Analytics Jobs Trends for 2015, Stacy Blanchard stated that the new generation of digital analyst talent craves collaboration. She believes that organisations will struggle to retain top analytics talent “if they’re not collaborating with like-minded colleagues and...don’t see how the insights they’re developing are actually having an impact on the business”. This is especially true of the empirical storyteller, as they can’t be stuck in a silo or chained to a desk. They thrive when they work across disciplines and want to see that the insights they deliver are creating an impact.
How do empirical storytellers turn data into a story that will create an impact?
The recipe for telling great stories has been covered extensively in the past. Duarte, for example, explores the process and core elements of successful storytelling in her book. But storytelling as applied to digital analytics is a much less explored space. As such, here are some thought starters on the process for empirical storytelling when applied to data and analytics.
1. Start with purpose
In a blog post about being an effective analyst, analytics guru Avinash Kaushik stated that the number one mistake analysts make is “working without purpose”. Empirical storytellers always start with a clear objective, or purpose in mind. They ask questions. What is the business challenge I’m trying to solve? What is the key message I’m trying to communicate? Or what hypothesis am I attempting to prove? Defining a purpose will help develop a narrative that grounds the story and ensures it’s told in a structured and logical way.
Example: A B2C company that focuses on youth in Indonesia is losing sales online while at the same time there has been an increase in local smartphone penetration. You suspect, although the website is responsive, it is not fully optimised for a mobile-first experience. In this case, the purpose is to prove two things. First, that there is an upward trend among the target audience toward surfing the web on mobile devices. And your second to prove whether or not existing users are getting the best possible experience while visiting the site.
2. Create a storyline
After defining the objective, empirical storytellers need to map out a storyline. It’s generally accepted that a good story typically includes five core elements; a setting, plot, characters, conflict and a resolution. Interestingly, these five elements apply equally well to the thought process an analyst might go through when designing a story. Below is an example of how to create a storyline using the purpose defined above.
Setting: The context or scope of your analysis. Think about the setting and consider the geographic, demographic and psychographic boundaries of your story. For example, are you looking at an issue that affects your brand globally, regionally, or locally? Are you concerned with a specific demographic or segment? The setting for our purpose is Indonesia.
Plot: The narrative that grounds your story, which is derived from your purpose. The plot should outline how you will take the audience on a journey that intertwines the context, characters, conflict and solution. In our example, mobile is fast becoming the screen of choice for Indonesian youths.
Characters: Characters play out the plot and give your purpose context, and these are usually the sources of data and data points you leverage to prove your hypothesis or point of view. In this example, the characters will be financial data, website clickstream data and research data.
Conflict: Conflict deals with the business challenge or gap you are attempting to address or overcome. The website isn’t providing a good experience for our core audience and we are potentially losing business because of this.
Solution: Your recommended action to resolve conflict. For empirical storytellers, the solution is discovered through and supported by data. In our example, the solution is to develop a mobile strategy that prioritises mobile and build a mobile-responsive site.
3. Choose your medium
Next, the empirical storyteller chooses a presentation medium that is best suited to communicate the story. Whether this is an infographic, a standard Powerpoint deck, a video or an interactive presentation platform like Prezi, the medium should be carefully selected based on its ability to communicate the message and elicit an emotional response from the audience.
In our example, the Indonesian client is more familiar with traditional presentation methods, so a Powerpoint deck will be the best way to communicate the setting, plot, characters, conflict and solution that will make a compelling business case for a mobile-first strategy.
4. Know your audience
As mentioned above, empirical storytellers need to select the best medium to present the story based on their audience. This also speaks to a larger issue. That is, you need to understand the audience you are communicating to. Are they marketers, HR, PR, finance or a mix of business functions? What level is the audience (junior versus C-suite) and how comfortable or competent are they when it comes to data and data visualisation?
Once you know your audience, you’ll need to tailor the flow, language and vernacular you use to apply to the group. This is especially true when it comes to using data. Some individuals understand how to interpret charts and data visualisation, while others are less comfortable with this. Empirical storytellers not only understand and adhere to good standards when it comes to data visualisation, but also know how to represent data in the best possible way based on the audience's competence and comfort levels.
Stephen Tracy is data and insights lead with SapientNitro SEA
The stakeholders in our Indonesian example are senior members of the brand team who oversee marketing and technology within the business. So they will be comfortable with marketing terms and digital strategy. However, low-level language and specifications related to coding languages, technology and data architecture, or analytics implementation and tagging are likely to be too technical and not appropriate.
Why your next hire should be an empirical storyteller
In recent years the digital analytics industry has continued to mature and evolve. Unfortunately the importance of communication and storytelling as a function of digital analytics has yet to emerge as a critical issue. However, recent trends in this field suggest that progress in both technology and governance is quickly paving the way for a new focus on people and the communication of data and insight. Storytelling is at the root of this issue, and the business leaders who recognise and invest in empirical storytellers will succeed in nurturing data-driven culture within their organisation that enables impactful, strategic decision-making.