One of the most important elements of a successful data and analytics strategy is the need for a shared language (or common understanding of key terms) among your stakeholders and teams. However, with technology evolving every hour and each team interpreting or using tech in their own way, even basic terms can have vastly different meanings from one conversation to the next.<\/p>\n
Despite this variability, people often use business, data, and technology terms, assuming everyone around them is in the know.<\/strong> The rest of the room might nod and smile despite profound confusion, hesitant to ask questions that could expose their own lack of knowledge. When this happens, saying nothing might protect your ego in the short term but lead to bigger embarrassments over time.<\/p>\n We can share a dozen stories to illustrate the point: There was the startup that invested significant time and money into an annual report only to realize each department had defined \u201ccustomer\u201d differently, rendering their data useless. There was the exceptional salesperson who had sold $10 million worth of products only to learn she had, in fact, not been profitable year over year.<\/p>\n When this salesperson asked, \u201cHow could that be?\u201d She learned she had spent more than $9 million, inadvertently, due to inconsistent definitions of certain key \u201ccosts\u201d within her organization. In her case, we were able to implement shared terminology just in time for her to not only keep her job but to eventually thrive in it.<\/p>\n Not every story has a happy ending. When you embark on important projects, such as AI adoption, without a clear data and analytics strategy<\/a>, the results can be financially devastating. Asking \u201cdumb questions\u201d is one of our favorite ways to avoid catastrophe. (It can also be kind of fun when you realize everyone else was secretly feeling \u201cdumb\u201d too.) Here\u2019s how to ask the right questions \u2014 plus communication tips and tactics from high-stakes scenarios across industries.<\/strong><\/p>\n Often, when we recommend that clients assess whether their teams share an understanding of key business and technology terms, they say something like: \u201cWe already did that,\u201d or \u201cWe\u2019re already in agreement on that.\u201d<\/p>\n There tends to be an inherent belief that because certain concepts are so basic everyone must have the same knowledge about them.<\/strong> If a project is already underway, it may be especially difficult to convince a leader to check in with their teams about months- or years-old assumptions.<\/p>\n It is possible that precise communication is a cornerstone of your data and analytics strategy. At the same time, it is incredibly common for leaders to realize \u2014 long into major projects \u2014 that their direct collaborators do not share their assumptions about core business and technical terms.<\/p>\n Want to test this phenomenon? Screencast an anonymous survey at the start of strategic meetings and presentations.<\/p>\n Ask a basic question, like, \u201cWhat does AI adoption mean<\/a>?\u201d Give people five minutes to weigh in via their smartphones and then display the results on your screen. If people relate varied or incorrect responses, don\u2019t panic. Lean into the results and use them to spark a dialogue about the importance of communication in data strategy<\/a>.<\/strong><\/p>\n Pro-tip: At the start of any project, ask your stakeholders, \u201cWhat does success mean to you?\u201d You\u2019d be amazed how many multi-million-dollar projects have been derailed due to conflicting assumptions about this most basic question.<\/p>\n Like the technology world, the healthcare world has its own special language, and professionals sometimes forget they once had to learn that lingo too. It\u2019s so common for clinicians to speak in a way only they understand that up to 80 percent of information shared in healthcare visits is forgotten immediately. Roughly half of what people remember is incorrect<\/a>.<\/p>\n To account for this, many health educators have adopted a method called \u201cteach back,\u201d which is helpful in the data world too. Teach back involves asking a patient \u2014 or, in our case, a client \u2014 to repeat what you\u2019ve told them (or to demonstrate the steps in a process you\u2019ve shared). People often believe they\u2019ve retained 100 percent of the information.<\/strong> However, when quizzed, they can be surprised to realize how little they truly remember. Once you assess their knowledge gaps, you can fill in the blanks together.<\/p>\n This trick is equally powerful when used the other way to expose the confusing parts of something a collaborator has said to you. For instance, you might say, \u201cThis is what I heard you say, did I get it right?\u201d Make an honest attempt to repeat what you understood.<\/strong> You can include definitions of key terms they used, even if they didn\u2019t offer that level of specificity. Then, step back and allow them to correct you.<\/p>\n This humble but effective approach can shield their ego and yours. They aren\u2019t likely to see it as a challenge, and they aren\u2019t likely to think any less of you either. Instead, they will probably talk more and offer some much-needed clarity.<\/p>\n Not everyone enjoys a sports metaphor, but they exist for a reason: Translating complex concepts using a familiar metaphor can help people build new knowledge within a framework they already understand.<\/p>\n If sports aren\u2019t your thing, think about other hobbies or interests to borrow from.<\/p>\n Cooking, movies, home repairs, childcare \u2014 depending on your audience, these can all be highly relatable areas of inspiration. A data and analytics strategy, for instance, could be a lot like a recipe if you and a colleague both like to cook.<\/strong><\/p>\n We don\u2019t all absorb information the same way. People have different learning styles and needs, and they can also have important accessibility needs like hearing and vision conditions. These are all factors to consider when working to increase alignment across people and teams<\/a>.<\/p>\n When sharing information with someone one-on-one, it can be incredibly helpful and respectful to ask how they like to learn or absorb information.<\/p>\n Assess whether they are a visual learner (someone who learns by seeing images or graphics), a reading learner (someone who learns by reading), an auditory learner (someone who learns by hearing information), a tactile learner (someone who learns through hands-on practice), or if they learn best in some other way. Make a genuine effort to share information with them according to those preferences.<\/p>\n Once you are done sharing information, don\u2019t ask the yes-or-no question, \u201cDo you have any questions for me?\u201d<\/strong><\/p>\n Instead, offer the open-ended, \u201cWhat questions do you have for me?\u201d This framing will welcome the person to speak up about any points of confusion.<\/p>\n If the conversation is not time-sensitive, refrain from asking people to weigh in on or make decisions about new information right away. Offer them a chance to study the information and schedule a time to regroup. The same goes for you \u2014 if you are absorbing complex news for the first time, avoid an immediate reaction. Take a break to reflect if time allows.<\/p>\n \u201cWalking the brain\u201d is a communication tactic we use often. Walking the brain is based on the premise that, in any large group, people will have various learning styles, personalities and needs. Developed by Hermann International as part of the Whole Brain Thinking Methodology, walking the brain can be especially helpful in cross-sector or cross-team presentations.<\/p>\n That is, in mixed-group settings, it may not be feasible to learn about everyone in your audience. Instead, it is wise to share broad-capture information using accessible terms and formats. Walking the brain can help you to respect the cognitive diversity of your audience, or appeal to people who might think differently about the same issue.<\/strong><\/p>\n For instance, certain audience members might look for high-level, executive summary-type information. Others will want to see raw data \u2014 they\u2019d prefer to do the math and arrive at the big \u201cso what\u201d themselves. Some might want to peek into the future and imagine all that is possible, while others want a concrete plan for right now. Hermann International offers a library of walking-the-brain resources<\/a> to help you respect cognitive diversity.<\/p>\n In addition, take care to ensure presentations and materials meet baseline accessibility standards. The Americans with Disabilities Act (ADA) offers an excellent guide, called The ADA Standards<\/a>, which offers instructions on how to create inclusive materials. One approach is to turn on closed captioning in virtual meetings. Likewise, the ADA explains how to make presentations screen-reader accessible for people with visual impairments.<\/p>\n \u201cMarchitecture\u201d refers to marketing gimmicks designed to sell data architecture, regardless of whether it aligns with business needs. We find that marchitecture is a common source of communication breakdowns between business and IT teams.<\/strong><\/p>\n For instance, business leaders might attend conferences, where vendors tell them they need a new solution, even if it isn\u2019t appropriate for their organizational context. The leader might return to their organization and instruct IT to adopt the new technology. The IT professionals are then placed in the difficult position of having to either explain why it is impractical to do so \u2014 or blindly follow top-down orders.<\/p>\n For example, we often hear business leaders tell IT professionals, \u201cWe need AI. Get us ready to do that.\u201d Their IT departments will say, \u201cWhat do you mean by AI?\u201d and leaders won\u2019t necessarily know. There are so many types of AI, and this may not be their area of expertise.<\/p>\n AI adoption isn\u2019t about fitting a company’s needs to any particular technology. It\u2019s about matching technical capabilities to strategic business needs<\/a>. That means every conversation about business and technology has to start with a common understanding of the business plan. Any conversation that follows should be about how AI solutions can support that plan.<\/p>\n Clear communication is essential to a successful data and analytics strategy.<\/strong> By establishing a common language, you can help everyone to feel comfortable and confident \u2014 and help your team to avoid costly mistakes. Simple strategies like asking \u201cdumb\u201d questions, using \u201csay back\u201d and defining key terms can prove pivotal to a project\u2019s success. It might not always seem necessary to simplify the conversation. However, your efforts to foster open and clear communication are likely to pay off not just in ROI, but in a warmer and more caring team environment.<\/p>\n \n 1: Don\u2019t Assume You\u2019re Already Doing It<\/h2>\n
2: Practice \u201cTeach Back\u201d<\/h2>\n
3: Use a Metaphor<\/h2>\n
4: Respect People\u2019s Learning Needs<\/h2>\n
5: \u201cWalk the Brain\u201d<\/h2>\n
6: Beware of \u201cMarchitecture\u201d<\/h2>\n
Conclusion<\/h2>\n