Building a Data-Driven Culture in Consulting Firms

Practice what you preach! How we leverage AI at IBM Consulting. Adopting AI successfully isn’t just about having the technology—it’s about making it part of the everyday flow of work. At IBM Consulting, we’ve embraced this philosophy by weaving AI deeply into our consultants’ work. The goal: Make AI essential, intuitive, and trusted. One year ago, we launched IBM Consulting Advantage, an AI-powered delivery platform that supports our global consulting workforce. Today, it has over 85,000 active users, more than 2,000 AI assistants, and over 60 industry-specific applications. The results have been remarkable, with up to 50% productivity gains on various tasks. This is what we learned along our own AI journey: 1. Embedding AI in Everyday Workflows To drive adoption, AI must feel natural and helpful. For example, we’ve embedded AI capabilities directly into repeatable consulting methods, like cloud migration processes, where they provide the most value. 2. Fostering a Growth Mindset AI’s potential grows with creative thinking. We encourage teams to innovate continuously, finding new applications for AI. For instance, we’re developing smaller, industry-specific foundation models, tailored for complex tasks like compliance or code modernization in regulated industries. Clients are part of this process, making innovation collaborative and relevant. 3. Building Trust in AI AI adoption thrives on trust. Our consultants receive targeted training to use AI confidently, and we provide open channels—like comment boards and Slack forums—for feedback. These insights directly shape future enhancements to our platform. Our consultants are empowered to question AI outputs and understand their source, ensuring confidence in what AI delivers. 4. Empowering Employees as Creators AI isn’t something that happens to people—it’s a tool that works for them. We’ve built a culture where consultants can create their own AI assistants to address specific challenges. These assistants can be shared, improved, and upvoted by peers, creating a collaborative ecosystem of innovation. By making AI easy, intuitive, and empowering, IBM Consulting Advantage is transforming how we work—and how we help our clients embrace AI. Organizations that truly want to leverage AI need to combine technology with human expertise and behavior change. At IBM Consulting, we’re not just preaching this message; we’re living it. #IBM #IBMiX #AI #genAI

When we talk about data strategy, we obsess over systems, governance, and business value. What we forget to obsess about is incentives. Here's a hard truth from many years spent in data-driven transformation: Data strategies don't fail because of technology. They fail because John in Sales cares about deals and not data quality, because Sarah in Operations has 20 more urgent tasks than data documentation, and because no one in the C-Suite is glancing at that fancy new dashboard for any of their decision making. Lasting change only happens when good data practices and data-driven thinking become personally valuable: When documenting data increases the annual bonus. When cleaning data fast-tracks a promotion. When data-driven decision making influences performance reviews. When managers earn respect for changing their mind based on data. We must therefore rethink how we approach the human side of data strategy. When it comes to people, it's not enough to talk about Data Literacy and Data Culture. We need a candid conversation about incentives. Often when I raise this point, the initial reaction is a little dismissive ("if it's good for the company, it will turn out to be good for the individual"), sometimes even slightly hostile ("if employees don't understand the importance of data, they're at the wrong place"). This is naive and lazy thinking. Understanding and communicating the value of data at a company level is a solvable challenge. If, however, data-driven behaviors aren't appreciated or rewarded in day-to-day work, who can fault employees and management for prioritizing urgent short-term tasks over long-term investments in data? There’s a difference between saying "this will save the company millions" and "this will save you hours every week and advance your career." Organizational researchers have long understood that organizations work at three levels: Company, team, and individual. True transformation happens at the intersection of these levels, when organizational needs and personal growth align. Miss the personal level, however, and you're building a digital castle in the air. So ask yourself this crucial question: "How do we align data culture with daily work experience?" If you can't answer that question with specific examples and convincing incentives, your data strategy needs to get personal. When good data practices become a path to personal success, cultural change will follow naturally.

Reflection on healthy data culture in business: sometimes executives think success comes from controlling the narrative with their metrics. They tightly restrict access, shape what information is shared up the chain, and spend more energy on positioning than improving results. The best data driven organizations work differently: they democratize access to the data (with responsible governance), openly discuss failures and successes, and encourage a culture of experimentation and learning. High performance leadership isn't about secrecy. It's about owning the results and showing a commitment to keep improving. Better, never best. Trust is built through transparency and accountability to the results. In professionals sports, everyone can see the score. Everyone can see the shots you make and the shots you miss. The best players miss all the time. What makes them great is that they learn from the shots they miss, practice, and get back in the game again to take it to the next level. It's the same in business: winners are never afraid to be seen missing a shot. What they fear is complacency. Victory comes to the brave leaders willing to take measured risks, learn from the results, and collaborate with their teams and peers to make those results even better next time. In my experience, the more your culture embraces open discussion on data and metrics, the faster you will create success for your customers. Get uncomfortable, embrace experimentation and learning, and invest in making your data better, more visible, and more discussed if you want to win in the age of AI.

Stop saying you're data-driven. Start acting like it. Most teams who say they're "data-driven" are really just dashboard-dependent. They collect numbers, track everything, But still struggle to make better decisions. Instead of more dashboards, You likely just need more focused thinking. Here's how great go-to-market teams use data: 1. Be Data-Literate Know what you're looking at and what you're not. Learn to separate vanity from value → Ask: "What is this really telling me?" → Avoid metrics that live without context or comparison. 2. Be Curious Numbers are clues, not conclusions. Instead of accepting them at face value, explore the "why" behind the "what". → Ask: "What’s driving this change?" → Use data to uncover stories, not to validate assumptions. 3. Be Skeptical Good data begins with good process. Always question how it was collected, cleaned and defined. → Ask: "Where did this come from? How reliable is it?" → A beautiful dashboard can still hide broken foundations. 4. Be Action-Oriented Insight without movement is just information. It has to change what you do next. → Ask: "So what? Now what?" → Kill reports that don’t lead to clarity or action. 5. Be Focused More tracking rarely brings more truth. Choose a few metrics that genuinely move the business and master those. → Ask: "What are the few numbers that truly matter?" → Simplify. Data clutter clouds judgment. 6. Be Communicative Data only matters when people understand it. Translate insights into simple language, shared context and clear ownership. → Share what the numbers mean, not just what they are. → Make data a part of daily rhythm, not just slide decks. Being data-driven is about discipline. The habit of using information to decide faster and act with precision. If your team can do that, you'll waste less energy, And move with far more intent. Have you seen a data mistake being made a lot lately? ♻️ Share this to help teams leverage their data properly. Follow me, Francesco Gatti, for more on ecommerce growth.

Want a surefire way to alienate your data and analytics team? Ask them to build the analysis but not invite them to the meeting where it gets discussed. Works every time. Yesterday I posted about data culture. This is the follow-up nobody asked for: what actually changes it? Not a new platform. Not a reorg. Not a "data-driven transformation initiative" with a steering committee and a Confluence page nobody reads. Three things. All free. All uncomfortable. 𝐏𝐮𝐭 𝐚𝐧 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐞𝐚𝐦 𝐦𝐞𝐦𝐛𝐞𝐫 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐨𝐨𝐦 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐬 𝐦𝐚𝐝𝐞. Not after, when someone needs a chart to support a conclusion that's already been reached. Before. When the question is still open. When their input can actually change something. The difference between "support my decision" and "help me make a better one" is the entire ballgame. 𝐖𝐡𝐞𝐧 𝐝𝐚𝐭𝐚 𝐜𝐨𝐧𝐭𝐫𝐚𝐝𝐢𝐜𝐭𝐬 𝐭𝐡𝐞 𝐩𝐥𝐚𝐧, 𝐬𝐚𝐲 𝐬𝐨 𝐨𝐮𝐭 𝐥𝐨𝐮𝐝. Not in a footnote. Not buried in an appendix. If leadership can't handle being told the numbers disagree with their strategy, you don't have a data culture. You have a reporting culture wearing a data culture's clothes. 𝐒𝐭𝐨𝐩 𝐚𝐬𝐤𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐬𝐦𝐚𝐫𝐭𝐞𝐬𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐭𝐨 𝐬𝐩𝐞𝐧𝐝 80% 𝐨𝐟 𝐭𝐡𝐞𝐢𝐫 𝐭𝐢𝐦𝐞 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐚𝐧𝐝 𝐚𝐧𝐬𝐰𝐞𝐫𝐢𝐧𝐠 𝐨𝐧𝐞-𝐨𝐟𝐟 𝐫𝐞𝐪𝐮𝐞𝐬𝐭𝐬. Then wondering why they leave after 18 months. You hired a strategic thinker who gets S&*t done and turned them into a short-order cook. They noticed. Believe me. None of this requires a vendor. None of it requires budget approval. It requires someone senior enough to change how things work and who is brave enough to actually do it. That's the fix. It's just not the one anyone wants to buy.

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

Leaders, here’s a reality check! A data-driven future isn’t just about systems and strategies—it’s about people. Your success depends on: → Connecting people to your vision → Empowering them with the tools and skills to succeed → Leading with a focus on collaboration and inclusivity Data may drive decisions, but it’s the people that unlock its full potential. As you scale your organization, don’t overlook the human connections that turn data into meaningful impact. When your people grow, your organization thrives.     Want to harness the full potential of data? Want to drive smarter decisions and stronger organizations?   Start by building an inclusive data infrastructure where everyone can:   • Access data • Act on data • Align with data   Here's how:   1. Engage Individuals Show the value of data in decision-making.   2. Educate Teams Teach them how to leverage data to meet their goals.   3. Enable Infrastructure Connect systems, drive governance, foster literacy.   4. Promote Transparency Ensure data is open and accessible.   5. Encourage Collaboration Create a culture where data is shared and used collectively.   6. Support Continuous Learning Offer training and resources to build data skills.   7. Lead by Example Use data-driven insights in your leadership.   With these steps, you can transform your organization. Or enhance the data culture you already have.   It's not just good for your people. It's good for your community, too.   Data matters. Make it count.   P.S. Want to chat about keynotes? DM me “KEYNOTE”

At LS3P, the need for reliable project data was clear. The firm wanted to be able to answer straightforward questions—how large was this project? What was the construction cost? Was it LEED certified? Who was the client, and how many projects had LS3P completed for them before? These are basic details, but in practice, they were surprisingly difficult to track down. The original solution was to ask project managers to enter this information themselves. It made sense in theory. They were closest to the work, after all. But it didn’t work. Project managers were already stretched thin. Data entry, however important, simply wasn’t urgent enough to compete with the rest of their responsibilities. Progress was slow, and inconsistent. That changed in 2013. Rather than overhaul the system or invest in a new tool, Katie Robinson and her team tried something simpler. They invited emerging professionals—early-career team members who were working toward licensure—to take on a new role within project teams. They called them Data Managers. These weren’t data analysts or administrative staff. They were future architects and designers, embedded in active projects, working alongside project managers to help collect and verify key data at project milestones. Over time, a structure emerged. Each Data Manager would handle five to ten projects per year, typically spending no more than 20 to 30 hours total on the role. The benefits were immediate. Data improved. Reports became easier to generate. Project information became something the firm could rely on. But the larger impact was cultural. By placing early-career professionals in a position of trust and responsibility—where they were talking about cost per square foot, owner agreements, and consultant contracts with project managers—LS3P created an unexpected mentorship pipeline. Emerging professionals gained insights they might not have encountered for years otherwise. And project managers gained a collaborator who helped them stay accountable to the broader needs of the firm. More than a decade later, the program is still part of how LS3P works. It’s not complicated. But it works—because it was designed with empathy, clarity, and intention. 💡 This clip is an excerpt from episode 4, "Knowledge Management is Everyone's Job," part of our Welcome to KM 3.0 series in collaboration with the TRXL Podcast. 📺 🎧 You can watch or listen to the full conversation with Katie Robinson of LS3P ASSOCIATES LTD. here: https://lnkd.in/giZiiS8k #AEC #KnowledgeManagment #LearningOrganizations #SmarterByDesign

Culture. It’s that confusing, nebulous thing that you can’t see, can’t touch, but can either make your organization wildly successful or stuck. It’s the extremes of oxygen 🔥versus carbon monoxide. ☠️ It is also the main reason why organizations have failed to become truly data driven and ready to settle for status quo - leaving data only in the hands of experts, using data to lie, or relying more on gut feel. As one data leader said to me, “Cindi, culture is really code for leadership - whether bad or good.” There is a partial truth in this, but there also are things you can do to create a data-driven culture. Here is my formula: ➡️Leadership - Do leaders showcase using data in their decisions. I applaud those willing to drill live into KPI charts in meetings, willing to confront the brutal truths when something is not performing well. They are more focused on course correction and learning from the data, rather than a fear of placing blame. ➡️Incentives: Incentives are perhaps the biggest area overlooked in encouraging behavior change. If you reward the bravado of a “shoot from the hip” decision, you will never create a data driven organization. Incentives can be financial or recognition. Gamification of insights and giving someone swag or bragging rights can go a long way. Career progression also matters. ➡️People: The type of people you recruit, their mindsets, their ways of working, adaptability and their diversity of thought both reflect your team’s culture and shape your team’s culture. To borrow a Jim Collins point - get the right people on the bus. ➡️Rituals: Rituals are part of culture. I recently got to participate in a customer’s ritual - they have a weekly happy hour where the executives were the bar tenders. It reflected both a team first and servant mindset. I chuckle when I think about wearing jeans to a meeting in Silicon Valley versus my suit and skirt attire as a Northeast girl. At ThoughtSpot we have a ritual of celebrating acts of selfless excellence and managers make the coffee. Habits that turn into rituals like creating a “blessed” version of the truth in the form of a $40K slide deck certainly undermine a data driven culture. What do you see as most enabling your data driven culture or undermining it?

Creating a data-driven culture doesn’t happen overnight — it’s something you have to build 𝐢𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧𝐚𝐥𝐥𝐲. After my last post, I got a lot of questions about practical tips we can take to create that culture within our organizations. So here's 4 actionable steps you can take starting today 👇 🔑 Provide easy access to data This is the simplest one. People need to be able to interact with something to see its value. At the very least, have a dashboard for important KPIs that is accessible to everyone in the company. Take the time to design it so it's intuitive and easy to understand (more on data UX later). I've also seen companies use Slackbots as an effective way to push weekly updates to relevant channels. 📚 Encourage data literacy Data without any context is just numbers. Make it easy for everyone to understand what each chart or value means. When in doubt over-communicate and explain exactly the definition behind everything in detail. This can be tooltips, a text FAQ at the bottom of your dashboard, or even a full-blown wiki. Just make sure it's easy to consume and not buried. When you get more advanced, you can offer internal training sessions or office hours. These venues can enable people to ask more specific questions relevant to their job, and even get some hands-on training with how to manipulate data. 🧑🔬 Make data core to the decision-making process As your team is deciding on the next initiative to focus on, bring data to help make your case. And push others to back up their ideas with data. Approach it by discussing a trend or unique segment that might indicate an opportunity. Create a hypothesis for why this data looks this way and what it means. If you can then project how these numbers would change based on your initiative, that's even better. 🎊 Celebrate data-driven wins After you're using data to inform your decisions, use it to help tell a story about new initiatives. Show the broader organization how data-driven decisions lead to success. The more people see data being used successfully, the more value they will see in it and want to join in themselves. When data becomes part of your company’s DNA, it empowers every team to make smarter decisions, innovate faster, and drive growth. What things have you tried to evangelize the importance of data within your organizations? Let me know in the comments!