Best Practices for Data-Driven Decision Making

It’s time to stop thinking like it’s 2005. Correlation may flatter your GTM story, but only causation proves impact. More than 80% of companies missed their sales forecast in at least one quarter over the last two years (Gong, 2024). In H1 2024, 49% of companies missed their revenue goals (GTM Partners Benchmark Report, 2024). At the same time, executives keep putting faith in attribution models that only tell a sliver of the story. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: too often, data is interpreted in ways that confirm existing assumptions rather than test them. Harvard Business Review found that sales leaders are frequently blindsided by overinflated forecasts driven by “all-too-human behavior” (Harvard Business Review, 2019). GTM Partners research shows that poor data quality can cost companies up to 25% of annual revenue, yet 60% don’t even measure these costs. That’s value leakage every CFO cares about. It’s time to fix this. Here are 5 ways to make GTM decisions actually data-driven: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗻𝘂𝗹𝗹 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀: Harvard Business Review notes that “consistently accurate sales forecasts are rare because many companies fail to align their sales and marketing departments.” Assume your campaign 𝘸𝘰𝘯’𝘵 work—then try to prove yourself wrong.     2. 𝗥𝘂𝗻 𝗽𝗿𝗼𝗽𝗲𝗿 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝘁𝗲𝘀𝘁𝘀: Compare your marketing results to a control group to see the actual lift your efforts create. MIT Sloan warns that confirmation bias leads us to “interpret ambiguous facts in light of preexisting attitudes.” Stop crediting natural growth to your LinkedIn ads.     3. 𝗕𝘂𝗶𝗹𝗱 𝗿𝗲𝗱 𝘁𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗺𝗮𝗷𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: MIT Sloan recommends bringing together “different perspectives on the same issue” because organizational biases cloud interpretation. Create space for contrarians—the risks of blind spots are too expensive to ignore.     4. 𝗧𝗿𝗮𝗰𝗸 𝗹𝗲𝗮𝗱��𝗻𝗴 𝙖𝙣𝙙 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Research shows the average B2B buyer has ~31 touchpoints with a brand before deciding (Dreamdata, 2024). Your last-touch attribution is missing most of the story.     5. 𝗣𝗿𝗲-𝗿𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀: Record in advance your testing methodology and success criteria. This prevents “analysis after the fact” bias and ensures accountability when results don’t fit expectations. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: If your data never challenges you, it’s not science; it’s storytelling. The companies that break through are the ones willing to let the data argue back. What’s the most obvious confirmation bias you’ve seen in GTM? #GTM #MarketingLeadership #causalinference  

Last week, I posted about data strategies’ tendency to focus on the data itself, overlooking the (data-driven) decisioning process itself. All it not lost. First, it is appropriate that the majority of the focus remains on the supply of high-quality #data relative to the perceived demand for it through the lenses of specific use cases. But there is an opportunity to complement this by addressing the decisioning process itself. 7 initiatives you can consider: 1) Create a structured decision-making framework that integrates data into the strategic decision-making process. This is a reusable framework that can be used to explain in a variety of scenarios how decisions can be made. Intuition is not immediately a bad thing, but the framework raises awareness about its limitations, and the role of data to overcome them. 2) Equip leaders with the skills to interpret and use data effectively in strategic contexts. This can include offering training programs focusing on data literacy, decision-making biases, hypothesis development, and data #analytics techniques tailored for strategic planning. A light version could be an on-demand training. 3) Improve your #MI systems and dashboards to provide real-time, relevant, and easily interpretable data for strategic decision-makers. If data is to play a supporting role to intuition in a number of important scenarios, then at least that data should be available and reliable. 4) Encourage a #dataculture, including in the top executive tier. This is the most important and all-encompassing recommendation, but at the same time the least tactical and tangible. Promote the use of data in strategic discussions, celebrate data-driven successes, and create forums for sharing best practices. 5) Integrate #datascientists within strategic planning teams. Explore options to assign them to work directly with executives on strategic initiatives, providing data analysis, modeling, and interpretation services as part of the decision-making process. 6) Make decisioning a formal pillar of your #datastrategy alongside common existing ones like data architecture, data quality, and metadata management. Develop initiatives and goals focused on improving decision-making processes, including training, tools, and metrics. 7) Conduct strategic data reviews to evaluate how effectively data was used. Avoid being overly critical of the decision-makers; the goal is to refine the process, not question the decisions themselves. Consider what data could have been sought at the time to validate or challenge the decision. Both data and intuition have roles to play in strategic decision-making. No leap in data or #AI will change that. The goal is to balance the two, which requires investment in the decision-making process to complement the existing focus on the data itself. Full POV ➡️ https://lnkd.in/e3F-R6V7

I've spent 6+ years in BI & analytics. Here are 5 unexpected ways I've seen BI improve decision-making: 𝟭/ 𝗨𝗻𝗰𝗼𝘃𝗲𝗿𝘀 𝗵𝗶𝗱𝗱𝗲𝗻 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀 Business Intelligence can reveal unexpected correlations between seemingly unrelated data sets. For example, it might identify a link between weather patterns and product demand or between employee engagement scores and customer satisfaction. These insights allow business leaders to make decisions that factor in deeper, underlying dynamics. This often results in more innovative strategies. 𝟮/ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝘀 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗽𝗹𝗮𝗻𝗻𝗶𝗻�� BI tools allow leaders to model various scenarios based on historical data, external factors, and current trends. These "what-if" analyses help in visualizing multiple outcomes and their potential impacts. When you know the possible outcomes, you feel more confident in uncertain situations. The difference between this and following gut instinct is it quantifies risks and opportunities before they become realities. 𝟯/ 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝘀 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 BI is not just about looking in the past. Its predictive capabilities allow leaders to anticipate trends and changes before they happen. BI tools can detect early signals of shifts, which enables leaders to proactively adjust their strategies, rather than react after the fact. 𝟰. 𝗙𝗼𝘀𝘁𝗲𝗿𝘀 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗯𝘆 𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗱𝗼𝘄𝗻 𝗱𝗮𝘁𝗮 𝘀𝗶𝗹𝗼𝘀 BI integrates data from various sources into a unified platform. Providing a holistic view of the organization empowers cross-functional teams to make aligned, informed decisions. Leaders can then drive a data-driven culture where insights are shared, thus reducing departmental biases and blind spots. 𝟱/ 𝗥𝗲𝗱𝘂𝗰𝗲𝘀 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗯𝗶𝗮𝘀 𝗶𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 Daniel Kahneman showed us that human decision-making is often clouded by biases. BI helps mitigate these biases by presenting objective data that challenges assumptions and forces decision-makers to confront the reality of their business. Armed with clear, data-driven insights, leaders can make decisions rooted in facts, not assumptions.

Series #3 of 7: First Principles - Data-Driven Decision Making Are you truly data-driven in your organization? Being data-driven isn’t about having a lot of data; it’s about having the right data and analytics across the entire GTM engine, ensuring it’s clean and accurate, and knowing how to draw insights from it. Let's start by stating the obvious - you can only be data-driven if your data is clean and accurate. Flawed or incomplete data leads to misguided conclusions and ineffective strategies—sometimes serious enough to derail an entire organization. Most companies don’t prioritize data early enough. Building products and scaling revenue may feel more exciting, but the larger you get, the messier it becomes to clean up your data. Delay data priorities too long, and it becomes harder to manage your GTM system effectively. Right when the stakes are highest, you'll be lacking what you need for thoughtful, informed decisions. So once you have good, complete data, how do you interpret it? What metric matters most? As the graphic shows, not all metrics are equal. Companies at different maturity stages should focus on different metrics, as should different levels of leadership. Start by knowing what question you’re trying to answer. A CFO might need to know how adding sales headcount impacts unit economics. A frontline manager wants insights into rep performance. Marketing wants to ensure their ICP aligns with successful accounts. The Board might ask if launching an enterprise segment is the best way to scale. Each question matters, and each metric has value. Understand what you’re asking, why, and what data inputs guide you. And once you know what you’re looking for, look at it from multiple angles. In any system, actions in one part affect others, often in non-obvious ways. Look too narrowly, and you risk missing key insights. Systems are tricky like that. A drop in customer satisfaction might seem like a CS or Support issue, so a CEO may push OKRs for those teams. But maybe the root cause is signing an enterprise client outside your ICP, straining the development team, leading to lower quality releases and more bugs—ultimately impacting customer satisfaction. It’s easy to try to solve the wrong problem.Immediate connections aren’t always the only ones or the right ones. A last word for aspiring execs: never hesitate to ask questions about data and metrics if you don’t understand them. Fear of looking unqualified holds people back, but data comes with assumptions you need to align on. There are at least five ways to calculate LTV, for instance. Without a clear conversation, execs might read the same data very differently. In short, ask questions and stay curious. So make sure when you say you're data-driven, you can really mean it! #revenuearchitecture #data #revops #saas

𝐇𝐚𝐫𝐧𝐞𝐬𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐈 & 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐨 𝐃𝐫𝐢𝐯𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 In today’s rapidly evolving business environment, leveraging AI and data analytics has become critical to drive strategic decision-making. But true value comes not just from implementing these technologies but from how effectively they are integrated into business processes and culture. Here’s a deeper dive into maximizing their impact: 𝟏. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐅𝐮𝐭𝐮𝐫𝐞-𝐑𝐞𝐚𝐝𝐲 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: AI-powered predictive models go beyond historical analysis to forecast future trends, risks, and opportunities. Companies leveraging predictive analytics can anticipate shifts in market demands, customer behavior, and emerging industry patterns. For example, by analyzing millions of data points, AI algorithms can predict product demand, reducing inventory costs and minimizing waste. 𝟐. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐇𝐲𝐩𝐞𝐫-𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: AI-driven analytics enable organizations to segment their customer base with pinpoint accuracy and deliver hyper-personalized experiences. Consumer goods companies, for instance, have used AI to create tailored marketing campaigns and product offerings, resulting in a 20-30% increase in customer retention rates. This capability turns data into a competitive advantage by fostering deep customer loyalty. 𝟑. 𝐃𝐚𝐭𝐚-𝐁𝐚𝐜𝐤𝐞𝐝 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: Operational inefficiencies often drain resources and hinder growth. AI systems analyze complex datasets to uncover inefficiencies in supply chains, manufacturing processes, and service delivery. For example, machine learning models can identify patterns of equipment failure before they occur, enabling predictive maintenance that reduces downtime by up to 50%. This optimization ultimately leads to increased productivity and lower costs. 𝟒. 𝐀 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 Data-driven decision-making extends beyond technology; it demands a cultural shift. Companies must foster a mindset where data insights are valued and applied at every organizational level. This requires training teams, promoting data literacy, and breaking down silos. When data informs every decision, from boardroom strategy to daily operations, organizations are equipped to innovate faster and adapt to change. To drive meaningful outcomes with AI and analytics, leaders must focus not just on adoption but on embedding these tools into the organization's DNA. The real power lies in cultivating an environment where data-driven insights guide every move. 💡 How is your organization embedding AI and data-driven practices into its strategy? #DataDrivenLeadership #AIandAnalytics #StrategicPartnerships #DigitalInnovation #BusinessTransformation #TechLeadership #OperationalExcellence #ConsumerGoodsInnovation

Being data-driven isn't defined by past achievements, but by how an organization responds every day when the data speaks. Companies love showcasing their trophies—slick dashboards, AI models, and analytics projects—as proof of a "data-driven mindset." Yet these are often symbols rather than signs of true transformation. It may seem like an achievement to launch ambitious data initiatives, but the true challenge is to routinely challenge long-held assumptions with evidence. A data-driven organization is defined not by isolated successes, but by everyday decisions throughout its ranks. Do managers abandon projects when numbers suggest failure? Can employees successfully question superiors with evidence? These moments reveal true data-driven identity. Companies with genuine data-driven cultures embrace necessary transparency and discomfort. They subject decision-making to rigorous scrutiny. Amazon's "disagree and commit" approach encourages evidence-based debate of all ideas. Netflix shares critical metrics with employees, enabling informed decisions without waiting for top-down direction. Transparency demonstrates confidence. True data-driven culture also means shifting from reliance on a few data specialists to empowering every employee. When the sales manager tests strategies methodically, the designer eagerly reviews A/B tests, and HR analyzes retention weekly, insights emerge organically. The question becomes not "who has the data?" but "who doesn't?" Many leaders mistake occasional analytics wins for transformation. They point to expensive platforms or new chief data officers as evidence of change. But technology merely amplifies existing culture—good or bad. An insecure executive team remains resistant to transparency despite sophisticated dashboards. A confident team can drive substantial insights from simple spreadsheets. The best data platform is a receptive mind. To become truly data-driven, prioritize openness, humility, and curiosity. Focus on everyday rigor over flashy initiatives, and analytical thinking at every desk over analytics showcases. Success isn't an impressive project portfolio but an organization that instinctively seeks, respects, and acts on evidence at every turn. Being data-driven isn't what you do occasionally—it's who you are every day.

For Chief Data Officers, the key to unlocking data’s full potential is to make it a true business driver. Here’s how an outcome-driven approach can turn data into measurable results: 1. Think Beyond Metrics—Aim for Transformational KPIs- Traditional data metrics like accuracy and volume fall short of demonstrating true value. Instead, look for KPIs that are transformational—like “time-to-insight” or “decision acceleration.” These capture how fast data helps you pivot, innovate, and win market opportunities. 2. Create a 'Data-Centric Culture' with Cross-Functional Teams- Silos are a common pitfall, but a cross-functional approach can turn data insights into shared wins. For example, embedding data leads within business units fosters a culture where everyone has a stake in data-driven decisions. When every department feels ownership, data projects gain momentum and support across the board. 3. Invest in Scalable Governance from Day One- Governance isn’t just about compliance—it’s what allows your team to scale insights quickly and confidently. Automating quality checks and setting clear data ownership across departments is critical for reliable, enterprise-level data management. This approach builds a foundation that accelerates trust and innovation.

Ten Steps to Creating a Data-Driven Culture - Harvard Business Review 1. Start the data-driven culture from the top (C-suite) 2. Choose metrics with care (business outcome -focused) 3. Don’t pigeonhole your data scientists (embed across Firm) 4. Fix basic data-access issues quickly (bad data in, bad data out) 5. Quantify uncertainty (be explicit and quantitative about levels of uncertainty) 6. Make proofs of concept robust and straightforward, not fancy and brittle 7. Offer specialized training at the appropriate time 8. Use analytics to help employees as well as customers 9. Trade flexibility for consistency willingly - at least in the short term 10. Explain analytical choices as a matter of course "For many companies, a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making. Why is it so hard? The biggest obstacles to creating data-based businesses aren’t technical; they’re cultural. Companies - and the divisions and individuals that comprise them - often fall back on habit, because alternatives look too risky. Data can provide a form of evidence to back up hypotheses, giving managers the confidence to jump into new areas and processes without taking a leap in the dark. But simply aspiring to be data-driven is not enough. To be driven by data, companies need to develop cultures in which this mindset can flourish. Leaders can promote this shift through example, by practicing new habits and creating expectations for what it really means to root decisions in data." - David Waller #DataDriven #Culture #Transformation Olaf J Groth, PhD University of California, Berkeley, Haas School of Business Berkeley Chief Technology Officer Program https://lnkd.in/eNurB-kb

Data Analytics: 3 Techniques to Supercharge Business Decision-Making As a business leader, leveraging data analytics effectively can give you a major competitive edge. But with so much data available, it can be challenging to know where to focus time. Here are three key techniques that any business can use to harness data for better decision-making: 1. Focus on the Right Metrics  While it seems simple, start with defining what you want to know. The foundation of analytics success is measuring what matters most. Advice I provide to our business leaders and clients: zero in on key performance indicators (KPIs) that directly impact your goals and objectives. For example, an ecommerce company might focus on metrics like conversion rate, average order value, and customer lifetime value. A subscription business would prioritize churn rate and monthly recurring revenue. An internal business unit supporting a group of employees will be focusing on successful tickets closed and internal satisfaction. By aligning KPIs with strategy, you'll surface the insights that move the needle. 2. Make Data Visual While raw numbers have their place, data visualization is essential for uncovering insights at a glance. As humans we are drawn to conceptual and visual presentation, and often take more away in a few minutes scan than inspecting raw data for hours. Charts and dashboards make complex data intuitive, allowing visual exploration to spot trends and outliers. A regional sales dashboard could instantly reveal which territories are underperforming. A product heatmap could show which features drive retention. A risk assessment is better when you have color / conceptual driven outliers highlighted. Arm your team with visualization tools like #Tableau or #PowerBI to make data accessible. 3. Predict the Future with Machine Learning Data begs the question 'so what'. What next can be uncovered more often today through machine learning techniques which takes analytics to the next level by analyzing information at immense scale to predict likely outcomes. ML models can forecast demand to optimize inventory scenarios, predict and prevent customer churn, or dynamically set prices to maximize profit. Traditionally the domain of experts, new AutoML tools are found in leading products like #Alteryx and #DataRobot which are putting the power of predictive analytics into the hands of business users. Data analytics is ultimately about aligning insights with action. By focusing on core metrics, visualizing data effectively, and leveraging machine learning for predictive insights, business leaders can use data to make confident decisions quickly. Pick one area to get started, define clear objectives, and empower your team with analytics. You'll be well on your way to a data-driven competitive advantage. (image via Midjourney.ai) #data #analytics #businessintelligence #decisionmaking #leadership #newwaysofworking