Four Storytelling Techniques to Bring Your Data to Life

Nancy Duarte

The adage that “our world runs on data” means that decisions are being based on vast amounts of statistics. Data-derived insights drive what time trains stop running, when Starbucks introduces holiday cups, and the temperature of the building you might be sitting in right now.

Even though most corporate roles now work with data, it’s shockingly easy to forget that people generate most of it. When a user clicks a link, gets blood taken at the lab, or sets up a smartwatch, that person generates data. As people move, buy, sell, use, work, and live, their actions nudge numbers up or down and drive organizational decisions, big and small.

If it’s your role to communicate data insights and persuade people to change their behavior, you’ll have more influence and promote better decision-making if you emphasize the people behind the numbers. In a story, we root for the hero as he or she maneuvers through roadblocks. To use data to steer your organization in the right direction, you need to tap into the human tale your data can tell.

By leveraging four techniques from storytelling, leaders can bring a richer, more human understanding to the problem that the data reveals and better understand the opportunities it presents. Those techniques are identifying the hero and the hero’s adversary, speaking with people generating the data, identifying and addressing conflict, and sharing context.

Search for the Hero and the Adversary in the Data

Because most organizational data is generated by humans, the first step toward insight is to empathetically understand the people whose actions generate that data and who can turn slumps around.

These data-generating people are the characters in your story. In any story, myth, or movie, we get to know various characters and grow to love some and revile others. Some become heroes who overcome the obstacles in their paths and eventually defeat their adversaries.

In a data story, the hero is whoever can play a role in moving the data in the desirable direction. For businesses, the heroes could be employees, customers, or partners.

Consider the scenario of a CEO at a midsize software company where sales dropped 30% in the previous quarter. As she’s faced with the task of course-correcting this sharp decline, she needs to dig into the question, “What happened?” To find out, she’ll have to understand the people behind the company’s numbers and who the hero is who can reverse the sales slump.

In this scenario, the CEO’s determination is that the sales team is the likely hero in the turnaround. The data shows that they have been working harder than ever, and the decline isn’t due to their lack of effort. But perhaps the team has an adversary, something or someone causing the numbers to go down. Or perhaps new inefficiencies have been introduced by a change in process or added bureaucracy.

Speak With the People Generating the Data

Data tells you what has happened in the past, but it doesn’t always tell you why. Talking to the people generating the numbers can help.

To help a hero get unstuck, a leader has to go straight to the source. Reading forums, conducting surveys, analyzing customer comments, and hiring consultants are all tools to help learn what is in the hero’s way. But the best way to really understand people’s issues is to speak with them directly.

To do so, identify a random sampling of data heroes. Speak with them, asking about their concerns, opinions, and motivations. Empathetically listen. You’ll hear things that surveys and Salesforce data simply can’t tell you.

By talking directly with a senior manager, the software CEO from our sample scenario might learn that her sales team has been struggling to fully adapt to new sales software that was designed to streamline a previously sticky process. While half of the sales team has wholeheartedly embraced the change, more senior team members are feeling frustrated. They’re fumbling to learn the new tool and still leaning on a legacy process.

This is a human element of the story that may not have been revealed through data alone. But after speaking with the people behind the numbers, our CEO knows who to work with in order to reverse the trajectory.

Identify and Address Conflict

All heroes in a story face conflict. Having a hero to root for makes a tale engaging. Heroes typically face some classic encounters: discord with another character, clashes with nature, tension with a social group, war within themselves, and struggles with change.

In a business context, heroes can be in conflict with a system (as some of the sales team has been with the new software in our scenario), conflict with another person (a change in leadership could be causing issues within the organization), or conflict with themselves (maybe they’re struggling with burnout or didn’t take the training they were asked to).

By identifying the type of conflict people are facing, a leader gets a clearer view of how to communicate information that will help the hero get unstuck.

Share Context

Current data points, though significant, don’t exist in a vacuum. Data collected over time creates a bigger picture of victories and defeats. Sharing context can help leaders motivate their organizations and move their heroes forward and on to victory, especially after a defeat.

In the sample scenario, if the CEO were to share just the most recent data, her team might not feel that they can recover the lost sales to date. Seeing a 30% decrease could demoralize them. But if the CEO zooms out a little and looks at a longer time frame, she might discover that sales bounced back after a similar decline five years earlier.

Sharing the details about how the sales team recovered in the past demonstrates to them that if they could make a turnaround then, they can certainly do it again.

We should never let our data speak for itself. With big data as pervasive as it is today, it is easily classified as noise, and that’s especially true when there is no real context to support it. Productive people help data move in a desirable direction. In every shocking statistic, hockey-stick growth curve, or line chart that hits the x-axis like a lead balloon, there’s a heroic story waiting to be revealed.

Learning to curate and tell stories within an organization can become a kind of superpower for a leader. By humanizing the data, leaders bring a greater understanding to the problems that data initially reveals. When they take the time to speak with the data story’s characters, get to know the hero-in-waiting’s fears and motivations, address the conflict that the hero is facing, and put the data challenge into an appropriate context, leaders develop a deeper, more human connection to their opportunities for moving forward.

ABOUT THE AUTHOR

Nancy Duarte (@nancyduarte) is CEO of Duarte Inc., a communications firm in Silicon Valley and New York. She’s the author of DataStory: Explain Data and Inspire Action Through Story (Ideapress Publishing, 2019).

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How to Establish Effective Global Transparency Reporting

Posted by Joseph D

When it comes to exchanges of value with health care professionals and organizations, life science companies are faced with a myriad of transparency laws and industry codes. This complicated regulatory environment is predicted to become more extensive in 2014 and beyond, making it important for these companies to understand their reporting requirements and optimize their systems. Many life science companies are taking a global approach to compliance, which is prompting them to improve the efficiency of their data management systems.

Overview of Transparency Regulations

As of 2013, Deloitte notes that countries with laws regarding HCP/O transparency reporting included the U.S., France and Slovakia, but that number is expected to grow in the future. The scope of these laws and the types of transactions they apply to differ between nations. For instance, the scope of transparency laws in the U.S. applies to physicians and teaching hospitals, while in France the scope is larger and also applies to software developers and media design companies. In the U.S., applicable transactions include all payments of value with some exceptions, while in France all advantages are under the jurisdiction of transparency laws, with no exceptions. Also, a number of countries have industry codes for transparency reporting.

Deloitte reports that countries with industry codes or are planning to implement them include:

• U.K.
• Netherlands
• Japan
• Europe
• Australia

Sunshine Act & HCP Reporting Regulations

There are some similarities between the Physician Payment Sunshine Act and HCP/O transparency reporting requirements. Financial exchanges between by health care providers, life science companies and other applicable entities are required to be reported to the government. The agencies involved may differ depending on the types of organizations conducting business, but the principle of reporting financial exchanges is the same. There are also similarities regarding the protection of identifiable information, which health care organizations are bound to do. Safeguarding information is a priority for all healthcare organizations, given the prevalence of data theft and the move to digital medical records.  

Ensure Consistent Data Collection

Data collection is critical for meeting transparency reporting requirements, meaning that efficient data management and storage are a priority. To ensure consistent data collection, life science companies can integrate their computer applications and move to cloud-based IT solutions. By tracking invoices, expense reports, customer records and payment processing with integrated systems, companies can aggregate data quickly and more accurately. Having information stored on multiple servers in a decentralized system only makes it harder to collect data, analyze it and create useful reports. Strengthening IT infrastructure, including the way data is maintained and evaluated, is perhaps the most important step in ensuring consistent data collection.

Streamline Processes for Global Spending Reporting

Streamlining service-oriented processes, including global spending reporting, is possible through quality management systems. Standard operating instructions, process flowcharts and SIPOC Diagrams can be used to identify waste within reporting tasks, leading to process improvement. The fewer delays and defects in the global spending reporting process, the less manpower it will take to meet reporting requirements. This translates to greater efficiency and bottom-line profit, because less direct labor and rework will go into administrative tasks. Also, stakeholders in the reporting process, such as regulatory agencies, will get better information to make decisions with.

Compliance with transparency regulation is a priority for life science companies, because they must adapt to the changing regulatory environment if they are going to survive. A global approach to meeting reporting requirements is something that can help life science companies grow their clientele and revenue streams in the future.

5 Best Practices to Secure Enterprise-Wide Buy-In for Master Data Management

Posted by Stephanie V

 

The benefits of master data management in the life sciences industry cannot be understated; the enterprise-wide MDM approach allows for better communication across departments, ultimately leading to a reduction in overhead cost and significant improvements in overall business functioning. Unfortunately, in spite of the ample evidence suggesting that MDM is the way of the future in the world of life sciences, stakeholders may not be in favor of this approach. Convincing reluctant stakeholders is possible, but it requires thorough research and a compelling presentation littered with case studies and industry-wide analytics. The securing of an enterprise-wide buy-in may be a guaranteed challenge, but the following five best practices should make reluctant stakeholders more amenable to the idea of MDM.

1. Offer a Simplified Explanation of Master Data Management

Master data management may not be a familiar concept among stakeholders. Those unable to understand the basics of this approach are far less likely to approve of an enterprise-wide buy-in. Thus, a simplified explanation may be required before launching into case studies, industry surveys and the like. In addition to explaining the basic concept of master data management, be sure to address the difference between functional and enterprise solutions while highlighting the benefits of the latter option.

2. Address Concerns Related to Data Loss and Redundancy

After learning the basics surrounding enterprise-wide commercial data solutions, stakeholders may voice concerns related to everything from data security to duplicate information. These are all valid concerns and must be addressed candidly so as to appease stakeholders' fears. If a thorough, detailed plan for data security is presented, stakeholders will be far easier to convince of the approach's superior return on investment.

3. Provide Tangible Evidence of ROI for MDM

A general overview of the master data management concept may be necessary at the outset of the presentation, but eventually, shallow coverage of MDM attributes will fail to convince investors of the necessity of a buy-in. Instead, tangible evidence should be used to demonstrate MDM's significant return on investment. This is particularly true if the presentation includes an argument in favor of enterprise-wide master data management. Functional master data solutions tend to be less expensive in terms of front-end implementation; due to the higher expense of enterprise-wide commercial data systems, departments may require incredibly convincing arguments. Instead of rambling on about the benefits of an enterprise-wide approach, let the numbers speak for themselves.

Case studies typically prove most effective in the midst of MDM presentations, particularly if said case studies focus on similar life science organizations. Look for a case study that most closely mimics current objectives, and use it to demonstrate how enterprise-wide buy-in for MDM could deliver impressive results. Consider complementing any selected case study with industry-wide surveys or polls to demonstrate the overall efficacy of enterprise MDM.

4. Target ROI Arguments to Each Department

Although evidence of ROI is absolutely vital to enterprise-wide buy-in success, it is not prudent to replicate the same information for each department. The facts and figures that prove most compelling to one group of individuals may completely fail to capture the attention of other buy-in prospects. Instead, all business analytics should be examined carefully to determine whether they are actually capable of convincing specific departments of the viability of MDM. Although there may be some overlap for certain figures, it is more likely that the presentation will differ slightly for each targeted department. After all, each department is likely to have a different idea of what exactly constitutes an impressive return on investment.

5. Secure Investment From Senior Management

Though the cooperation of every department is necessary for the successful implementation of enterprise-wide master data management; if a particular department proves difficult to convince, it may be prudent to spend more time targeting senior management who could potentially override any objections from other departments. Likewise, all others could approve of a enterprise-wide master data management buy-in, but without the consent of senior management, the opportunity is lost.

Getting company-wide buy-in for your master data management strategy is imperative in order to streamline communicaiton and effectively make sense of patient data for commercial positioning.