Talk Data to Me: The Art of Analytics Translation

From actionable insights to data-driven next steps, data professionals are asked to deliver their results in specific ways, yet the details often remain shrouded in mystery. Even stakeholders from the same team might disagree on the criteria—what exactly is actionable, and to whom? This uncertainty isn't anything new—it's a challenge that's stuck around since the first pen-and-paper roll-up. During my time as a business analyst, I saw time and again how this hampers decision-making and slows down progress. I'm Annie Phan, and I've learned that what many organizations are lacking is an insider who is fluent in the language of both boardrooms and spreadsheets: an analytics translator. The language of numbers "Data literacy is the problem," some might be quick to say. It's certainly part of the confusion. Data literacy is about competently reading and writing the language of data, but literacy alone isn't enough if people across the organization speak different dialects—team-specific, context-heavy interpretations of the analysis. For example, how do you get from sentiment analysis (from the data team) to a prospect roadmap (for the sales team)? This is where translation comes in, by ensuring a BI team's findings are communicated clearly to each corner of the business, in their respective dialects. Analytics translation is, simply put, the process of taking analytics results and transforming them into business value. It's the critical step between wrangling numbers and executing a plan—a necessity often entrusted to individuals with the most cross-organizational knowledge. This isn't by chance. To be an effective analytics translator, you need both technical expertise (to make sense of the data) and domain knowledge (to identify and prioritize business problems). The good news? This is a skill set that can be learned and cultivated, making it a valuable addition to any existing role. For organizations ready to upskill their talent, introducing their BI and business teams to analytics translation is a must. If the communication gap data professionals encounter isn't enough of a sign, consider: McKinsey estimates a job market of two to four million roles with an analytics translation focus by 2026. As big data gets bigger, so does the need for effective communicators within organizations. An O'Reilly survey indicated that 47% of respondents believed analytics translation to be the biggest hurdle to successfully adopting AI/ML. Training algorithms and educating coworkers on data is a 'tooth brushing problem'—it needs to be done regularly, and handled well, to keep organizations healthy. Why speak data? In my personal experience, analytics translation adds tremendous value to existing roles by helping businesses understand the impact of the solutions being built. During my time as a strategy consultant for IBM's Chief Analytics Office, I found myself between the Global Sales team and a group of data scientists trying to tackle low reply rates. For the sales team, the best approach was trial and error, but no two clients are the same. For the data scientists, the results were largely anecdotal; calculating a reply rate wasn't giving the team any new insights. Both teams needed more to work with. First, I focused on building a foundation for their shared language: Historical email and call logs were rich with data that was both easy to understand and quantifiable to both parties. Metrics like time sent and recipient response times were measurable, and a time series analysis identified the optimal times for contact, which was further refined by clustering analysis for prospects most likely to respond. Suddenly, the data team was providing those elusive actionable insights, and the sales team was performing targeted outreach. Next came subject lines and message content, which the sales team felt could use more personalization. For the data scientists, I recognized this meant sentiment analysis. We used a deep learning model to enhance our initial findings by focusing on messaging, resulting in a system that could suggest changes to match the prospect's communication preferences. Putting it all together meant mapping out various data sources—CRMs and logs from the sales team—and connecting them with effective ETL processes to support these new features—enabling ongoing analysis and optimization from the data team. Reply rates for both emails and phone calls improved, and the data science solution was scalable, iterative, and aligned with delivering data-driven next steps. Everyone's happy. Becoming fluent in analytics As with translating languages, there's an art to effective analytics translation. First, you need to be fluent the source language—the terminology and number-heavy output of a given analysis—and the target language—often, the business team's big picture, framed in dollars and deadlines. Key skills include: Technical knowledge: Come to the table with an appreciation for the technical aspects behind an analysis. If you're learning these skills from the business side, educate yourself on exploratory data analysis, and build a solid foundation in both classical and Bayesian statistics to start. It will allow you to ask the right questions and translate findings effectively, instead of relying on the vague idea of "actionability". Familiarity with AI/ML: Staying on top of common machine learning (ML) techniques, especially deep learning, SVMs, adaptive boosting, and the life cycle of MLs, is increasingly necessary. You should be able to recognize which models to use and when, as well as spot potential errors like overfitting. Both the analyses and solutions you advocate will likely see massive returns, in time savings and effort, from smart AI/ML applications. Familiarity with your org: You need to understand your company's dependencies and tech stack, including programming languages, infrastructure, and deployment mechanisms. Being familiar with internal tools and frameworks, as well as having strong stakeholder management skills, will make cross-organizational work that much easier. Tailor your translations to fit the specific context and workflow of your audience. Business acumen: A strong grasp of your company's KPIs and metrics, and how they roll up into customer retention, revenue, and other key outcomes, is critical. Being able to place yourself in the broader economic and corporate environment will help identify the right analytics projects for delivering the most business value. This insight ensures that your translations aren't just accurate, but strategically relevant. Interpersonal skills: Not to be overlooked, understanding the business dynamics, communication style, and culture of your source and target language is a big part of identifying and articulating what they need. BI teams will have worked hard to produce the analysis; their business counterparts are being asked to place an incredible amount of trust when they commit to a course of action. Be prepared to support both parties in your translation. Navigating organizational politics and conflict resolutions can sometimes be a part of the translation process. Mastering these skills is akin to becoming fluent in a new language—one that allows you to bridge gaps, resolve misunderstandings (both technical and otherwise), and **ensure that those data-driven next steps are both understood and well-executed across your org. From actionable insights to data-driven next steps, data professionals are asked to deliver their results in specific ways, yet the details often remain shrouded in mystery. Even stakeholders from the same team might disagree on the criteria—what exactly is actionable, and to whom? This uncertainty isn't anything new—it's a challenge that's stuck around since the first pen-and-paper roll-up. During my time as a business analyst, I saw time and again how this hampers decision-making and slows down progress. I'm Annie Phan, I'm Annie Phan , Annie Phan and I've learned that what many organizations are lacking is an insider who is fluent in the language of both boardrooms and spreadsheets: an analytics translator. what many organizations are lacking an analytics translator. The language of numbers "Data literacy is the problem," some might be quick to say. It's certainly part of the confusion. Data literacy is about competently reading and writing the language of data, but literacy alone isn't enough if people across the organization speak different dialects —team-specific, context-heavy interpretations of the analysis. For example, how do you get from sentiment analysis (from the data team) to a prospect roadmap (for the sales team)? This is where translation comes in, by ensuring a BI team's findings are communicated clearly to each corner of the business, in their respective dialects. "Data literacy is the problem," l iteracy iteracy dialects dialects sentiment analysis (from the data team) prospect roadmap (for the sales team)? Analytics translation is, simply put, the process of taking analytics results and transforming them into business value. It's the critical step between wrangling numbers and executing a plan—a necessity often entrusted to individuals with the most cross-organizational knowledge. This isn't by chance. To be an effective analytics translator, you need both technical expertise (to make sense of the data) and domain knowledge (to identify and prioritize business problems). The good news? This is a skill set that can be learned and cultivated, making it a valuable addition to any existing role. For organizations ready to upskill their talent, introducing their BI and business teams to analytics translation is a must. Analytics effective analytics translator, technical expertise (to make sense of the data) and domain knowledge (to identify and prioritize business problems). If the communication gap data professionals encounter isn't enough of a sign , consider: communication gap data professionals encounter isn't enough of a sign McKinsey estimates a job market of two to four million roles with an analytics translation focus by 2026. As big data gets bigger, so does the need for effective communicators within organizations. An O'Reilly survey indicated that 47% of respondents believed analytics translation to be the biggest hurdle to successfully adopting AI/ML. Training algorithms and educating coworkers on data is a 'tooth brushing problem'—it needs to be done regularly, and handled well, to keep organizations healthy. McKinsey estimates a job market of two to four million roles with an analytics translation focus by 2026 . As big data gets bigger, so does the need for effective communicators within organizations. McKinsey McKinsey two to four million roles with an analytics translation focus by 2026 An O'Reilly survey indicated that 47% of respondents believed analytics translation to be the biggest hurdle to successfully adopting AI/ML. Training algorithms and educating coworkers on data is a 'tooth brushing problem'—it needs to be done regularly, and handled well, to keep organizations healthy. O'Reilly survey O'Reilly survey 47% of respondents believed analytics translation biggest hurdle to successfully adopting AI/ML. Why speak data? In my personal experience, analytics translation adds tremendous value to existing roles by helping businesses understand the impact of the solutions being built. During my time as a strategy consultant for IBM's Chief Analytics Office, I found myself between the Global Sales team and a group of data scientists trying to tackle low reply rates. strategy consultant for IBM's Chief Analytics Office, For the sales team, the best approach was trial and error, but no two clients are the same. For the data scientists, the results were largely anecdotal; calculating a reply rate wasn't giving the team any new insights. Both teams needed more to work with. For the sales team, trial and error, but no two clients are the same. For the data scientists, largely anecdotal; First, I focused on building a foundation for their shared language: Historical email and call logs were rich with data that was both easy to understand and quantifiable to both parties. Metrics like time sent and recipient response times were measurable, and a time series analysis identified the optimal times for contact, which was further refined by clustering analysis for prospects most likely to respond. Suddenly, the data team was providing those elusive actionable insights , and the sales team was performing targeted outreach. building a foundation for their shared language: actionable insights actionable insights Next came subject lines and message content, which the sales team felt could use more personalization . For the data scientists, I recognized this meant sentiment analysis. We used a deep learning model to enhance our initial findings by focusing on messaging, resulting in a system that could suggest changes to match the prospect's communication preferences. came subject lines and message content, personalization personalization sentiment analysis . sentiment analysis deep learning model Putting it all together meant mapping out various data sources—CRMs and logs from the sales team—and connecting them with effective ETL processes to support these new features —enabling ongoing analysis and optimization from the data team. Reply rates for both emails and phone calls improved, and the data science solution was scalable, iterative, and aligned with delivering data-driven next steps. Everyone's happy. meant mapping out various data sources—CRMs and logs from the sales team—and connecting them with effective ETL processes to support these new features data-driven next steps . data-driven next steps Becoming fluent in analytics As with translating languages, there's an art to effective analytics translation. First, you need to be fluent the source language—the terminology and number-heavy output of a given analysis—and the target language—often, the business team's big picture, framed in dollars and deadlines. Key skills include: Technical knowledge: Come to the table with an appreciation for the technical aspects behind an analysis. If you're learning these skills from the business side, educate yourself on exploratory data analysis, and build a solid foundation in both classical and Bayesian statistics to start. It will allow you to ask the right questions and translate findings effectively, instead of relying on the vague idea of "actionability". Familiarity with AI/ML: Staying on top of common machine learning (ML) techniques, especially deep learning, SVMs, adaptive boosting, and the life cycle of MLs, is increasingly necessary. You should be able to recognize which models to use and when, as well as spot potential errors like overfitting. Both the analyses and solutions you advocate will likely see massive returns, in time savings and effort, from smart AI/ML applications. Familiarity with your org: You need to understand your company's dependencies and tech stack, including programming languages, infrastructure, and deployment mechanisms. Being familiar with internal tools and frameworks, as well as having strong stakeholder management skills, will make cross-organizational work that much easier. Tailor your translations to fit the specific context and workflow of your audience. Business acumen: A strong grasp of your company's KPIs and metrics, and how they roll up into customer retention, revenue, and other key outcomes, is critical. Being able to place yourself in the broader economic and corporate environment will help identify the right analytics projects for delivering the most business value. This insight ensures that your translations aren't just accurate, but strategically relevant. Interpersonal skills: Not to be overlooked, understanding the business dynamics, communication style, and culture of your source and target language is a big part of identifying and articulating what they need. BI teams will have worked hard to produce the analysis; their business counterparts are being asked to place an incredible amount of trust when they commit to a course of action. Be prepared to support both parties in your translation. Navigating organizational politics and conflict resolutions can sometimes be a part of the translation process. Technical knowledge: Come to the table with an appreciation for the technical aspects behind an analysis. If you're learning these skills from the business side, educate yourself on exploratory data analysis, and build a solid foundation in both classical and Bayesian statistics to start. It will allow you to ask the right questions and translate findings effectively, instead of relying on the vague idea of "actionability". Technical knowledge: Come to the table with an appreciation for the technical aspects behind an analysis. If you're learning these skills from the business side, educate yourself on exploratory data analysis, and build a solid foundation in both classical and Bayesian statistics to start. It will allow you to ask the right questions and translate findings effectively, instead of relying on the vague idea of "actionability". Technical knowledge: both classical and Bayesian statistics to start. "actionability". Familiarity with AI/ML: Staying on top of common machine learning (ML) techniques, especially deep learning, SVMs, adaptive boosting, and the life cycle of MLs, is increasingly necessary. You should be able to recognize which models to use and when, as well as spot potential errors like overfitting. Both the analyses and solutions you advocate will likely see massive returns, in time savings and effort, from smart AI/ML applications. Familiarity with AI/ML: Staying on top of common machine learning (ML) techniques , especially deep learning, SVMs, adaptive boosting, and the life cycle of MLs , is increasingly necessary. You should be able to recognize which models to use and when, as well as spot potential errors like overfitting. Both the analyses and solutions you advocate will likely see massive returns, in time savings and effort, from smart AI/ML applications. Familiarity with AI/ML: (ML) techniques deep learning, SVMs, adaptive boosting, and the life cycle of MLs smart AI/ML applications. Familiarity with your org: You need to understand your company's dependencies and tech stack, including programming languages, infrastructure, and deployment mechanisms. Being familiar with internal tools and frameworks, as well as having strong stakeholder management skills, will make cross-organizational work that much easier. Tailor your translations to fit the specific context and workflow of your audience. Familiarity with your org: You need to understand your company's dependencies and tech stack, including programming languages, infrastructure, and deployment mechanisms. Being familiar with internal tools and frameworks, as well as having strong stakeholder management skills, will make cross-organizational work that much easier. Tailor your translations to fit the specific context and workflow of your audience. Familiarity with your org: programming languages, infrastructure, and deployment mechanisms. Business acumen: A strong grasp of your company's KPIs and metrics, and how they roll up into customer retention, revenue, and other key outcomes, is critical. Being able to place yourself in the broader economic and corporate environment will help identify the right analytics projects for delivering the most business value. This insight ensures that your translations aren't just accurate, but strategically relevant. Business acumen: A strong grasp of your company's KPIs and metrics, and how they roll up into customer retention, revenue, and other key outcomes, is critical. Being able to place yourself in the broader economic and corporate environment will help identify the right analytics projects for delivering the most business value. This insight ensures that your translations aren't just accurate, but strategically relevant. Business acumen: customer retention, revenue, and other key outcomes, Interpersonal skills: Not to be overlooked, understanding the business dynamics, communication style, and culture of your source and target language is a big part of identifying and articulating what they need. BI teams will have worked hard to produce the analysis; their business counterparts are being asked to place an incredible amount of trust when they commit to a course of action. Be prepared to support both parties in your translation. Navigating organizational politics and conflict resolutions can sometimes be a part of the translation process. Interpersonal skills: Not to be overlooked, understanding the business dynamics, communication style, and culture of your source and target language is a big part of identifying and articulating what they need. BI teams will have worked hard to produce the analysis; their business counterparts are being asked to place an incredible amount of trust when they commit to a course of action. Be prepared to support both parties in your translation. Navigating organizational politics and conflict resolutions can sometimes be a part of the translation process. Interpersonal skills: business dynamics, communication style, and culture of your source and target language support both parties in your translation. organizational politics and conflict resolutions Mastering these skills is akin to becoming fluent in a new language—one that allows you to bridge gaps, resolve misunderstandings (both technical and otherwise), and **ensure that those data-driven next steps are both understood and well-executed across your org. akin to becoming fluent in a new language—one that allows you to bridge gaps, resolve misunderstandings (both technical and otherwise), data-driven next steps