Using Data Analytics in Consulting

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

Data complexity increases as volume, velocity, and variety expand. Today, most organizations measure more things than they did in the past and struggle to manage all their data. In my #analytics consulting career, I’ve seen data teams approach data complexity in two ways. 1️⃣ 'Pass along’ approach Essentially, analytics teams relay the data complexity to business teams and stakeholders. Over time, more data complexity means more data products and more complicated offerings. 👉 A basic dashboard becomes more detailed with multiple tabs and advanced filters. 👉 A simple 10-page report turns into a 60-page one. 👉 A single access point for customer information expands to five disparate systems. I remember talking to an analytics executive who bragged that his organization had over 20,000 Power BI reports or dashboards. While he might have been impressed by this number, I don’t think the business teams at his organization would have been as enthusiastic. The ‘pass along’ approach deters data adoption rather than encouraging more people to use data. End users become increasingly overwhelmed by the expanding number of increasingly complex data products. This approach is focused on production rather than business outcomes. 2️⃣ ‘Focused and streamlined’ approach These data teams realize a ‘pass along’ approach only transfers the data complexity to business users and doesn’t directly address it. While it may not be possible to offset the increasing data complexity completely, these analytics teams strive to mitigate it as much as possible. They understand data products can be enriched with more or better information, but that doesn’t mean business users should be burdened with excessive amounts of data and increasingly complicated tools. These analytics teams realize they can expand data adoption by offering focused, meaningful information and streamlining how it is delivered. Their goal is to make the data as accessible and useful as possible, not overwhelming or confusing.   Some #data professionals will push back on this optimized approach. They may feel business teams won’t appreciate their ‘behind the curtain’ contributions to making data easy to access and use. I disagree. When you streamline the ability for business teams to access relevant, useful data, the value your team delivers will be clearer and more tangible to them. Success in analytics is about driving business outcomes—what you accomplish with the data—not the quantity or wizardry of the data products you produce. As a final point, these two approaches will use AI very differently. The 'pass along' approach will use it to shovel more data at a faster pace, piling on to the information that is already being ignored. The other will use AI to simplify the data complexity and help more business users extract better insights, which will expand user adoption. Do you agree with my take? What approach is your analytics team using?

$12,900,000 That's how much the average organization loses yearly due to bad data (according to Gartner). Back in 2016, IBM estimated an even wilder number: $3,100,000,000 That's 3.1 trillion - *with a T* - dollars lost annually in the United States due to bad data. I know, these numbers are so absurd that they seem made up. Well... they aren't (you can check). They are as real as the importance of data integrity throughout the sales and customer lifecycle. But let’s drill down a bit. 🛠️ 💡 It’s not just about the staggering losses. It’s about understanding the cascading impact of data integrity – from quote to revenue. Think about it: 1️⃣ Accurate Pricing: Avoid losing revenue due to underquoting or damaging trust with overquoting. 2️⃣ Streamlined Sales Cycles: Quicker decisions, fewer delays. 3️⃣ Compliance: Stay ready for audits and regulatory checks. 4️⃣ Informed Decisions: Data integrity = better forecasting and strategic planning. 5️⃣ Enhanced Customer Relationships: Transparency builds trust and loyalty. 6️⃣ Accurate Revenue Recognition: Directly affects financial health and market perception. 7️⃣ Increased Operational Efficiency: Less cleanup, more automation. 8️⃣ Competitive Edge: In a data-driven world, accuracy is king. And, as a colleague who ran revenue at an enterprise-level SaaS company once put it, "Data integrity sits at the top of the list. It's everything. It’s not just about billing and earning; it’s about fostering long-term customer commitments." Imagine being able to: - Upsell effectively by monitoring customer usage. - Identify potential churn and engage proactively. - Harness data to create meaningful customer dialogues. *That’s* the power of data integrity. 🔍 So, next time you look at your data practices, ask yourself – are you just looking at numbers or seeing the stories they tell? #DataIntegrity #RevOps #CPQ

Many amazing presenters fall into the trap of believing their data will speak for itself. But it never does… Our brains aren't spreadsheets, they're story processors. You may understand the importance of your data, but don't assume others do too. The truth is, data alone doesn't persuade…but the impact it has on your audience's lives does. Your job is to tell that story in your presentation. Here are a few steps to help transform your data into a story: 1. Formulate your Data Point of View. Your "DataPOV" is the big idea that all your data supports. It's not a finding; it's a clear recommendation based on what the data is telling you. Instead of "Our turnover rate increased 15% this quarter," your DataPOV might be "We need to invest $200K in management training because exit interviews show poor leadership is causing $1.2M in turnover costs." This becomes the north star for every slide, chart, and talking point. 2. Turn your DataPOV into a narrative arc. Build a complete story structure that moves from "what is" to "what could be." Open with current reality (supported by your data), build tension by showing what's at stake if nothing changes, then resolve with your recommended action. Every data point should advance this narrative, not just exist as isolated information. 3. Know your audience's decision-making role. Tailor your story based on whether your audience is a decision-maker, influencer, or implementer. Executives want clear implications and next steps. Match your storytelling pattern to their role and what you need from them. 4. Humanize your data. Behind every data point is a person with hopes, challenges, and aspirations. Instead of saying "60% of users requested this feature," share how specific individuals are struggling without it. The difference between being heard and being remembered comes down to this simple shift from stats to stories. Next time you're preparing to present data, ask yourself: "Is this just a data dump, or am I guiding my audience toward a new way of thinking?" #DataStorytelling #LeadershipCommunication #CommunicationSkills

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

As a data professional one thing you may be tasked with is helping drive a culture that embraces data. Culture and the people in an organization can hinder data work if they aren't wanting to utilize data, lack understanding around data, or are fearful. How can we help to excite and ignite the data work in an organization? One key thing you can do is to help generate curiosity in your organization and allow that to ignite innovation and use of data. Remember, the majority of the employees in an organization aren't data professionals by trade or title. Helping employees to embrace data can be difficult but you have the opportunity to help them succeed with data. Curiosity is a catalyst to data and AI work. How can we help to drive more curiosity in your organization? First, we need to foster a culture where we question things. As a data professional, help people to develop a pattern and habit of asking questions. What else can be done to help foster curiosity? ✅ Teach that experimentation is welcome. Allow people to share ideas and experiment on these ideas. ✅ Teach that you don't fail but you learn. As Nelson Mandela said: I never lose. I either win or learn. ✅ Free up people's time to do deep work. Allow or encourage people to block off 30 minutes a day to explore and learn. Teach that this time should be free from email, Teams or Slack, and text. Create an environment where critical thinking is encouraged. ✅ Provide access to resources. If we want non-data people to get excited about data we should provide resources for learning. ✅ Celebrate discovery and innovation. Celebrate questions, ideas, wins. ✅ Ask open-ended questions and encourage people to go and find answers. Overall, curiosity can ignite data work so allow it to do so. As a data professional lead the way and don't allow your own thoughts or ideas to block others. Foster the right culture. Stay nerdy, my friends #dataliteracy #AIliteracy #datastrategy #AIstrategy #data #AI #curiosity

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization

Everyone’s racing to build AI tools in Consulting. But few are asking the harder question: who do clients actually want to pay? It’s not the tool. It’s the expert who knows how to use it. Clients aren’t buying dashboards or data pipelines, they’re buying outcomes, clarity, and conviction. AI can synthesize information, but it can’t persuade a CFO, navigate a boardroom, or drive organizational change. That still takes someone with credibility and judgment. What clients are really paying for is permission. Confidence. The assurance that the person sitting across from them understands their world and can move it forward. AI can churn out insights, but it can’t deliver advice that sticks or change the trajectory of a business. You see this in the market dynamic, salaries for Senior Partners are exploding, whilst junior talent floods the landscape. The findings of our most recent study are clear: AI isn’t replacing consultants, it’s exposing them. The middle layer is getting squeezed. Firms built on large delivery teams and low-value workstreams are struggling to justify their fees. Meanwhile, elite, high-impact senior advisors using AI to sharpen their delivery are winning faster and more often. Everyone has access to the same tools. The difference is how effectively you apply them. The firms pulling ahead aren’t those with the flashiest proprietary tech, they’re the ones with the clearest communication style, the strongest operators, and the discipline to package their IP around real outcomes. The future of consulting won’t be AI-led or human-led. It’ll be judgment-led with AI as the accelerator. The real differentiator isn’t who builds the best tool. It’s who clients trust to use it and deliver results that matter. AI will make the best consultants 10x better. Everyone else? Exposed.

<rant> During an interview with a #business leader from a large company, we discussed his view on data and its governance. He saw it as more of an impediment than an enabler. He had a rich agenda of use cases and processes he wanted to enhance, but data was consistently a bottleneck. He was uncertain about which data to obtain and from where, and existing processes suffered from unclear data sources and responsibilities for addressing gaps. The following day, I attended a session for another project in the same company. We needed data from several systems for a marketing analytics use case, including the CRM and ERP systems, both undergoing transformations. The consensus was that the best solution would be to manage the upstream data as an asset, appoint an owner, stabilize it, ensure minimally required quality control, and catalog it as part of the future state. However, the same leader interrupted, saying, "I don't have time for this. We need to resolve this quickly. Just use the #data we already have and make local transformations." I’ve seen the exact same thing happen so many times that my response to it sometimes now is just a shrug. And this does not just occur in client organizations – I’ve seen it just as often in the consulting organizations. So many times, when I discuss the importance of minimally required data governance considerations, I see frowns appear across the room or the video conference call. “We have to get the business what they want,” the credo runs. My frustration is twofold. Firstly, there's the hypocrisy. The same people who complain about historic data issues often refuse to prevent future ones when they have the chance. Because it is exactly when new systems and solutions are being created that the cost to implement governance and quality controls is the lowest and when you have the best opportunity to drive good data management. Secondly, there's the misconception that intelligent #datagovernance is expensive and time-consuming. It doesn't have to be. Ensuring correct data modeling, auto-discoverable data flows, and capturing requirements (that already exist in BRD documents anyway) in data governance catalogs and dictionaries are relatively low-cost. However, it does require commitment and a thoughtful approach. </rant>

As customer expectations change, we need to evolve our technical capabilities. The need for real-time data integration is here. IBM recently acquired StreamSets to provide financial services companies a path to realize consistent access and delivery of data across multiple data sources and formats while facilitating the design of smart data pipelines. Why is this important? Here are a few reasons: ✦ 87% of organizations require data to be ingested and analyzed within one day or faster ✦ 82% are making decisions based on state information ✦ 85% state stale data is leading to incorrect decisions and lost revenue With data continuously integrated as it becomes available, streaming data pipelines provide fresh data for various use cases in a time-sensitive manner, such as: ✦ Enhanced customer experiences, with real-time data ✦ Intelligent data pipelines, to reduce data drift ✦ Fraud detection, enabling swift responses to suspicious activities ✦ Real-time reporting and analytics, for immediate actionable insights ✦ Predictive maintenance, with real-time sensor data ✦ Cybersecurity, for enhanced situational awareness This capability is not just impressive, it's a game-changer. It not only addresses current data challenges but also paves the way for managing smart streaming data pipelines to deliver high-quality data needed to drive digital transformation. As Luv Aggarwal explains in his video (https://lnkd.in/e7WEiXfD), by having real-time data pipelines, companies can benefit from continuous, real-time processing, integration, and transfer of data when it is available, reducing latency and data staleness. This provides for better customer experiences and improved insights for agents, partners, and employees when making sales and servicing decisions, as listed in the use cases above. Data is not just a driving force behind innovation and growth, it's the fuel. As described in the IBM Technology Atlas (https://lnkd.in/eQMHn6Dy), data integration is expected to increase in sophistication every year. Real-time data pipelines provide capabilities that enable growth and innovation to realize success. Learn more: https://lnkd.in/eq62r5dk Dima Spivak Scott Brokaw IBM Data, AI & Automation #ibm #ibmtechnology #datapipeline