Data Storytellers Wanted

Data-Storytelling2The Big Data Boom has been disruptive. Companies have made large investments in Big Data Architectures, with the goal of transforming Big Data into Actionable Insights. In order to obtain the desired results, special handling of these insights is needed. The Data Storyteller role was born to foster and steward insights, and deliver the story around what data means and how it provides value.

In a typical company, the Business Organization? Structure to support Big Data initiatives includes dedicated Data Management and Analytics Teams.

The Data Management Team often performs many of the ‘traditional’ data management functions.

  • Data Quality: Ensuring the data delivered is deemed to be fit for use.
  • Data Governance: The coordination of people, process, and technology to leverage information as a corporate asset.
  • Reporting: The delivery of information for stakeholder consumption.

However, many Data Analysts on the Data Management Team are inquisitive by nature, and are moving outside of traditional functions. Data Analysts are understanding business/industry challenges and seeking to address them by leveraging Big Data. They are generating hypotheses, then validating them in the data. In essence, they are performing ‘Descriptive Analytics’, or condensing big data into smaller, more useful nuggets of information.

In order for analysts to successfully leverage data to generate insights, they need context.   Without this context Big Data has minimal use, and that’s where you see lots of companies investing in Metadata Management Solutions. To be clear- you don’t just purchase a metadata solution, plug it in, and reap the benefits. Successfully implementing Metadata Management requires data stewardship. The metadata needs to be harvested, frameworks need to be established, and processes/procedures implemented to ensure the pertinent information is populated/maintained.

Metadata Management provides answers to the following questions.

  • What does the data mean?
  • Where does the data come from?
  • What business rules have been applied to the data?
  • Who is the Business Owner/SME for this data?
  • What Data Governance issues apply to the data?
  • What other facts should I know about this data to use it?

Companies are also investing heavily in Analytics, and this dedicated team is often focused on predictive analytics.

  • Techniques: They utilize a variety of statistics, modeling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future.
  • Nuggets: Analytics uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
  • Value Delivered: These learnings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.

Data and Analytics teams are generating lots of interesting insights, but these need to be properly communicated and sold in order to recognize the value provided. This is a critical component in the process and that’s where the ‘Data Storyteller’ role comes in. This hybrid role can effectively navigate the business, data, analytics, and IT, but most importantly has the ability to connect the dots in these efforts. The Data Storyteller can grab the nuggets of insights that are generated, interpret them, and effectively communicate/explain the findings across different levels of an organization.

Data Storytellers often leverage Business Intelligence (BI) Tools, as data visualization aids greatly in getting points across. Have you ever heard the saying “a picture is worth a thousand words”? This is very true, specifically when it comes to Data Storytelling. Data Visualization makes the story easier for the audience to consume and understand. Additionally, its interactive capabilities make for an enjoyable customer experience. Data storytellers often perform heavy segmentation of the data to formulate the story.   They focus on key dimensions of data and craft views that paint a picture for the user to articulate what the data means. A baseline is typically included so that an effective visual comparison can be had. For example, say I am trying to tell the story around how an Auto Manufacturer is doing in the Millennial Segment. I may pull in US Census data around Millennials which provides the baseline to better articulate the effectiveness of that specific Auto Manufacturer in reaching Millennials.

As with many things in life, communication and delivery is critical to the success of the Data Storyteller. When prepping for readouts, data storytellers should ask themselves these questions.

  • Can each view provided be easily explained?
    • Views should be intuitive, so if you are going to have trouble outlining one, it should be removed or reworked.
  • What does the view mean?
    • Interpret the data and call out the high level findings.
  • Is this interesting/actionable?
    • If the answer is no, you have more work to do. Think about the audience, the use cases, and what you are trying to deliver when crafting views.
  • How can this intelligence be used? What’s the action?
    • Take the next step in interpreting data. Be prepared to provide possible actions.
  • Is the data visualization slick?
    • The ‘Wow’ Factor cannot be underestimated here, dress up the output to further capture the audience.

Staffing a Data Storyteller role can be a challenge. No clear career path exists and the skills needed span across multiple areas (Data, Analytics, IT, and to a certain extent, Sales). The storyteller is interpreting data and translating it into actionable intelligence, crafting a story/presentation, and formally communicating/selling these insights. The Data Storyteller always keeps the target audience in mind when crafting the story/presentation, ensuring the interests and needs of the customer are covered. Further, the words used in the story are carefully selected, always looking to turn negatives into positives. As more companies progress in their Big Data journeys they will surely be looking for Data Storytellers to make insights actionable.

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About Kenneth Viciana

Kenneth Viciana has extensive Data Management experience in the Financial Industry. He has successfully led and delivered many strategic data initiatives at Fortune 500 Companies. Kenneth's primary management areas of focus include; Data Warehousing, Data Quality, Data Governance, Big Data, and Business Intelligence. He is driven to deliver powerful data management solutions in support of generating corporate revenue. In his current role at Equifax, Kenneth leads a team that is responsible for Enterprise Data Strategy, and transforming Big Data into valuable insights that are the catalyst for accelerating the delivery of solutions to real life problems.

  • http://www.decisionlab.net Daniel Upton

    I love this idea, and not just for BigData. As an Agile Data Warehouse data modeler, I begin with a data story before doing modeling. However, having a data story vetted more fully by a Data Story teller is a step forward towards more frequent iterations of delivered value. Also, as many of us are ready to move past the (I believe timeworn) principle of a ‘single version of the truth’, wherein the solution (thus, the data model for traditional DW solutions) is trying (often failing) to use data to provide an enterprise-wide answer that does not really exist on an enterprise-level. As such, the data story is the obvious practical alternative, and hence the data storyteller becomes an important role in a forward thinking organization.

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