Data Storytelling: More than Just Business Analysis

As we predicted the increased importance of “data storytelling” this year, the ability to craft an effective story out of data is the X-factor that will enable the best BI architects to differentiate themselves and demonstrate the value proposition of their solutions.

Data Storytelling is about the process of developing a business narrative backed by insightful, cogent data analysis. The key difference between this newer concept and good, old-fashioned business analysis is the inclusion of visualization design in the delivery of that narrative. As BI vendors now offer powerful and flexible visualization tools and an increasingly sophisticated understanding of the way to display data clearly to highlight data-based conclusions is more prevalent in the workplace, BI architects enjoy a rich, new opportunity to highlight compelling business opportunities and to focus relevant attention on them. This is data storytelling.

Experts agree that effective data storytelling starts with an effective selection of business indices or KPI’s. These are the data streams whose inflection points indicate a past, current, or future change in the status of business. So far though, this is no different from our tried-and-true business analysis. In fact the first set of KPI’s a business will initially follow (prior to data discovery endeavors to mine for even more subtle indicators) will be KPI’s they have been using even without sophisticated visualisations.

While trailing and coincident indicators are moderately easier to identify by retroactively analyzing business activity in light of contemporary or subsequent data, determining which data sources can be used as leading indicators requires both the analysis of data to determine which indices precede a business change, coupled with a cogent narrative to explain why. The narrative is important because it differentiates coincidence from causality. Here’s a simple example: for company Z, a down-tick in sales in mid-March might be explainable as seasonality of business based on a changeover to summer product offerings. But a closer analysis of the customer data might reveal that a number of major clients have fiscal years ending in March and April, and their sensitivity to year-end/year-begin spending causes them to delay their purchases into the second calendar quarter.

As the example highlights, valuable insights come from a focus on a story to tell or a question to answer (in this case, the question likely was “what’s happening to our sales at the end of the first quarter?”), a connection between a deep knowledge of the data available, and an ability to filter the signal from the noise, that is, the data that affects the reality defined within the story’s narrative (the customers’ business cycles) versus the data not germane or even misleading to the question.

In the next article, we will explore how to develop the data narrative via visualisation once the key data insights have been uncovered. Come back soon to hear how!

DataHub Writer: Douglas R. Briggs
Mr. Briggs has been active in the fields of Data Warehousing and Business Intelligence for the entirety of his 17-year career. He was responsible for the early adoption and promulgation of BI at one of the world’s largest consumer product companies and developed their initial BI competency center. He has consulted with numerous other companies and is regard to effective BI practices. He holds a Master of Science degree in Computer Science from the University of Illinois at Urbana-Champaign and a Bachelor of Arts degree from Williams College (Mass).
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Data Storytelling: More than Just Business Analysis

As we predicted the increased importance of “data storytelling” this year, the ability to craft an effective story out of data is the X-factor that will enable the best BI architects to differentiate themselves and demonstrate the value proposition of their solutions.

Data Storytelling is about the process of developing a business narrative backed by insightful, cogent data analysis. The key difference between this newer concept and good, old-fashioned business analysis is the inclusion of visualization design in the delivery of that narrative. As BI vendors now offer powerful and flexible visualization tools and an increasingly sophisticated understanding of the way to display data clearly to highlight data-based conclusions is more prevalent in the workplace, BI architects enjoy a rich, new opportunity to highlight compelling business opportunities and to focus relevant attention on them. This is data storytelling.

Experts agree that effective data storytelling starts with an effective selection of business indices or KPI’s. These are the data streams whose inflection points indicate a past, current, or future change in the status of business. So far though, this is no different from our tried-and-true business analysis. In fact the first set of KPI’s a business will initially follow (prior to data discovery endeavors to mine for even more subtle indicators) will be KPI’s they have been using even without sophisticated visualisations.

While trailing and coincident indicators are moderately easier to identify by retroactively analyzing business activity in light of contemporary or subsequent data, determining which data sources can be used as leading indicators requires both the analysis of data to determine which indices precede a business change, coupled with a cogent narrative to explain why. The narrative is important because it differentiates coincidence from causality. Here’s a simple example: for company Z, a down-tick in sales in mid-March might be explainable as seasonality of business based on a changeover to summer product offerings. But a closer analysis of the customer data might reveal that a number of major clients have fiscal years ending in March and April, and their sensitivity to year-end/year-begin spending causes them to delay their purchases into the second calendar quarter.

As the example highlights, valuable insights come from a focus on a story to tell or a question to answer (in this case, the question likely was “what’s happening to our sales at the end of the first quarter?”), a connection between a deep knowledge of the data available, and an ability to filter the signal from the noise, that is, the data that affects the reality defined within the story’s narrative (the customers’ business cycles) versus the data not germane or even misleading to the question.

In the next article, we will explore how to develop the data narrative via visualisation once the key data insights have been uncovered. Come back soon to hear how!

DataHub Writer: Douglas R. Briggs
Mr. Briggs has been active in the fields of Data Warehousing and Business Intelligence for the entirety of his 17-year career. He was responsible for the early adoption and promulgation of BI at one of the world’s largest consumer product companies and developed their initial BI competency center. He has consulted with numerous other companies and is regard to effective BI practices. He holds a Master of Science degree in Computer Science from the University of Illinois at Urbana-Champaign and a Bachelor of Arts degree from Williams College (Mass).
View Linkedin Profile->
Other Articles by Douglas->

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