In the life of a solopreneur or entrepreneur, there's often a need to wade through vast amounts of data or, sometimes, just a pile of raw information. This isn't about advanced data science. I'm aiming to give you a method, tailored for those who aren't tech-savvy, to handle a good chunk of data or raw information. Because of space constraints, I won't delve into detailed examples, but I'll lay out the framework and mindset. I might provide examples in future posts.
In the startup world, we're constantly faced with data analysis and the challenge of processing a ton of information. Decision-making is an everyday occurrence, as I've discussed in my previous post Quality Decisions Require Time: An Entrepreneur's Insight. Effective decisions rely heavily on thorough data analysis and information processing. Hence, having a straightforward, efficient method for this is crucial. Our method consists of two main parts: divide-and-conquer and accuracy validation.
Divide-and-Conquer:
1. Understand Your Objective: Before plunging into the data abyss, clarify your goals. Understand your specific needs. In the startup environment, data analysis typically falls into two categories:
- Validating a hypothesis.
- Gathering the data needed to form a new hypothesis.
The recurring decision-making pattern in startups is: create a hypothesis based on available information, prove it right or wrong, and then shape a new hypothesis for the next step. Whatever the nature of your task, understand your requirements. Remember, your "hypothesis" isn't a given fact; always scrutinize it. It's easy to implicitly trust our assumptions and forget to verify them. Blind faith can be fatal for a business, which is precisely why information gathering and rigorous data analysis are essential.
2. Break Down Your Goals and Draft a TODO List: A well-planned "divide" phase is pivotal to the "divide-and-conquer" strategy. If you're out to prove a hypothesis, gather both supporting and opposing data. Avoid cherry-picking data that only confirms your beliefs. To ensure a comprehensive data collection process, your TODO list should have actions addressing both perspectives. If your aim is to gather data for understanding, the start might seem daunting due to an unclear bigger picture. In this case, use an incremental improvement approach, akin to agile software development. Begin with what you know, and as you gather more, you'll uncover new aspects. Regularly revisit your roadmap and update your TODO list based on new insights. Be wary of data that might funnel you down a narrow viewpoint. Always aim to see the full picture.
3. Initiate Data Collection: Once you've set the stage, proceed with data collection and complete the tasks on your TODO list. This is relatively straightforward.
Validation:
The essence of validation lies in the "cross-validation" technique. Examine two pathways, A and B, and ascertain if they align. If not, one or both paths might be flawed. Here are four ways I commonly employ in my workflow:
1. Cross-Validate Using Collected Data: The trick here is recognizing the underlying patterns in your data, a skill that's honed over time. Even if you're adept at it, the right mindset is crucial. If your data collection was thorough, numerous inherent patterns should emerge. Identifying these patterns can guide your cross-validation. Compare datasets and see if they align. Discrepancies can either prompt further data collection or lead you to insightful conclusions.
2. Reference Your Knowledge Base: Over time, running a business gives you a repository of insights, either deliberately noted or instinctively learned. The key here is to sidestep emotional biases. Ensure your cross-referenced data lacks emotional undertones and is based on tested facts. This is why I advocate maintaining a written knowledge base and updating it regularly. Our memory can sometimes fill gaps with inaccuracies, so documented knowledge is more reliable.
3. Use Existing Business Data: If you're data-savvy or employ modern data management tools, you likely have up-to-date business data at hand. This real-time, high-quality data is invaluable for cross-validation.
4. Experiment for Further Validation: If the data you've collected, your established knowledge base, or your live business data doesn't provide clear validation, consider executing an experiment for further clarification. Carrying out an experiment is similar in mindset to the divide-and-conquer phase, and I'll be dedicating a future post to effectively conducting experiments. However, for now, understand that experiments have both time and cost implications. Assess the necessity before proceeding.
In Closing, embrace some level of uncertainty. (For a deeper dive into embracing uncertainty in real-world startup scenarios, check my post The Ability to Incorporate Imprecision: Essential in Startups & Solo Ventures.) In the unpredictable landscape of business, procuring high precision data can be a challenge. This challenge is even greater when you're on the right track and doing something genuinely groundbreaking. Most won't openly share their winning strategies or unique business mechanisms. If the results from paths A and B are in the same ballpark, consider your data validated. However, if there's a stark discrepancy in magnitude between the two, there's likely an error in one of them. But remember, encountering "incorrect" data isn't a setback. It's a stepping stone that clears away ambiguity. From here, you can decide your next step: initiating another data collection phase, discarding the results from paths A and B, or making an informed decision based on what you've learned.
This method is streamlined and practical, designed to make extensive data analysis both quick and reliable. In the startup realm, many of us don't have the resources for a full-blown, formal data science analysis. And often, it isn't even necessary. Worse yet, in-depth and prolonged data science analyses might mean missed opportunities. Sometimes, swift action, driven by an educated estimate (a decision bolstered by efficient and speedy data analysis), holds more value. I wholeheartedly vouch for this approach. Test it out, and I sincerely hope it benefits you.
