How to Develop a Data Analytics Process

You want to have more impact at work, so you’ve decided to learn how to analyze data. Congrats! Data skills are a powerful way to differentiate yourself professionally. If I may, I would offer a piece of advice. Please don’t do what I did when I first started. I approached learning data analysis backward. My mistake was leading with technology and data. The most efficient way to learn data analysis and have an impact is to lead with business questions. When you lead with technology and data, you fall into the classic trap of having a hammer and then starting to look for nails to pound with it. However, when you first start with business questions, you tend to focus on what truly matters. Take the following examples from various business domains: [Product Management] - What feature(s) are most important to our sticky customers? [Customer Service] - Can we handle more volume with a different mix of agents? [HR] - Is the bad attrition rate of Org A higher compared to Org B? [Marketing] - Are there synergies between digital ad channels? Questions like the above guide you in several ways: 1 – What analysis technique(s) you should learn. Not every technique is applicable in every situation. 2 – What data you need in your analysis efforts to answer the question. 3 – Who is the audience for the answer? Depending on the audience, you may need to choose a technique that provides more detailed explanations (e.g., logistic regression vs a random forest). NOTE – When considering analysis techniques, it is imperative to use the following in your evaluation: A – Can the analysis technique provide an acceptable answer based on the question and audience? B – Which of the shortlist of analysis techniques is the quickest/easiest for you to learn? You want to use the simplest technique that gets the job done. C – Of the shortlist of analysis techniques from A & B, can you use familiar tooling (e.g., Excel)? You want to avoid learning new tools until you absolutely need them. Over the years, I had to learn the above the hard way. My business stakeholders never cared about the underlying technology, only the results. I wasted much time learning “cool” tech that I never used in practice. BTW – Over the years, I’ve ended up using a small number of techniques 90+% of the time in my data analyses: 👉 Exploratory data analysis 👉 Process behavior charts 👉 Random forests 👉 K-means clustering 👉 Logistic regression 👉 Linear regression 👉 Market basket analysis 👉 Process mining Here’s the best part. The first two techniques are easily accomplished using out-of-the-box Excel features. The first six techniques are easily accomplished with Python in Excel. Planning on learning some data analysis skills this weekend? Please keep the above in mind. Your time is valuable. Maximize the ROI of your study efforts. Stay healthy and happy data sleuthing! #excel #microsoftexcel #pythoninexcel #analytics #businessanalytics

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.

If you're building a data career, mastering the art of measurement planning can be one of the most effective ways to differentiate yourself from your peers. Companies need people who are thinking about this every time they launch a new initiative. If you can develop strong skills here, it can be your ticket to getting involved earlier on, in more projects, and to becoming seen as a true strategic partner in your organization. Here's what you should focus on... 1. Think Business First -> Resist the urge to dive straight into the data. -> Understand how critical this project is to the business. -> Ask what the key goals for the initiative are. -> What are the most important questions you'll answer? 2. Know Your Audience -> Who is driving the project? Is this the primary audience? -> What are the goals and incentives of key stakeholders? -> What data can you provide that will help them? -> What types of info may inspire them to take action? 3. Define the Key Performance Indicators (KPIs) -> For the goals identified, translate them to metrics -> Prioritize metrics based on importance to stakeholders -> Go a layer deeper, and think about KPI driving levers -> How do you picture optimizing the businesses KPIs? 4. Identify the Data Sources You'll Need -> Where will you get each data point you need? -> Who owns or manages each existing data source? -> Are the data sources available real-time? -> Are there gaps in existing data? How do you fill them? -> How can you automate or streamline reporting? If you can follow this framework, you should be able to break down any project and build a measurement plan that will help your organization identify goals, understand outcomes, and optimize performance to drive the business to new heights. We've got a free guide that goes deeper on this, called 'How to Build a Measurement' plan. CHECK IT OUT: --> https://bit.ly/3eaXGmq @ Data Pros - what else would you add here? #data #analytics #businessintelligence #measurement #planningforsuccess