'Everybody needs data literacy because data is everywhere. It’s the new currency, it's the language of the business. We need to be able to speak that' - Piyanka Jain, MIT Sloan.
The one aspect I enjoy about any discussion about adopting AI is how it logically leads to a conversation about business models and data. One of our leading services is helping business owners with funding, financial forecasting, planning, and decision-making. Engaging in "financial numbers" has never been an issue. However, bring up the subject business model and attention wanes immediately. Or, any talk about data is limited primarily to website and social media analytics. Finally, no more!
It's common knowledge that AI effectiveness depends entirely on data. Given that AI adoption is already becoming a foundational business component, as a business owner, you have two choices: i.e., will you rely on open-source, publically available data or build out your own proprietary (in-house and private) data library and "lab"? The first option is the easiest to do - but will deliver questionable or harmful value to your business. The second option depends on what digital tools you are already using.
Your data strategy - simplified
The success of any strategy is measured by implementation and results delivered—not by what it promises. This is especially true when developing or refining your data strategy. Data is not only an outcome of processes and tasks; it's, first and foremost, an enabler of effective strategic execution.
Get your house in order
Suppose your current data sources are primarily financial, web and social media analytics, and most of your analyses are done manually when needed. In that case, you must first step back toward your strategic objectives. In building a foundation for data generation and usage that leads to effective AI adoption, we recommend the following process:
- Start with Business Goals, Not Data: Start by reviewing your strategic goals and your most pressing questions or problems from a data generation, management, and analysis perspective, e.g., increasing sales, reducing costs, and improving customer satisfaction.
- Don't overcomplicate things: Don't follow into the trap of chasing "big data." Start by analyzing the data you're already collecting (e.g., sales, customer feedback, website metrics). Use spreadsheets or simple dashboards to organize and visualize your data.
- Identify data gaps and practical tools for bridging these gaps: What data gaps are there between what you like to benchmark or measure and what you currently have? Find practical tools or develop solutions to fill these data gaps.
- Invest in Basic Tools and Skills: Establish management discipline in using accessible tools like Google Analytics and Excel (including Pivot Tables) or low-cost CRM and ERP systems to manage data. Train your team on how to use these tools effectively, focusing on practical and actionable insight and skills.
- Keep Learning: Review your results regularly to see what's working and adjust your strategy. Gradually expand your data use—including piloting AI solutions—and, as you see success, add more advanced tools.
Data Discipline and Structure
If you are already using tools such as Salesforce and Hubspot (to name a few), your challenge is more than likely implementing or improving data discipline and structure. For example, one of my clients uses Salesforce and has no shortage of data. However, they lacked any analysis or reporting discipline and were making most of their decisions on case-by-case analysis. We know that breaking such habits isn't easy. In utilizing your data to more significant effect and impact, our advice would be the following:
- Start by establishing data tiers: Generally, data tiers consist of Strategic Management and Insights (quarterly reviews), Performance and Trends Data (monthly reporting and decision-making), and Operational Data (daily decisions and actions).
- Identify must-haves versus interesting data points: Each business has a certain "optimum data level" at which any additional data is more than likely to add to distraction or "analysis paralysis." You can either try to identify such data points in advance or simply start keeping a record of what data points come up the most regularly.
- Be careful of "what is known": As a business owner and your team, over time, certain numbers and data points start following an assumed or expected trend. Be very careful—in many of our engagements, such "known trends" mask gradual but very specific trends or patterns. Relying on dashboards can be problematic in missing such key decision points.
- Challenge and augment AI outputs: As a business, you would already be using AI in some form or another. We encourage you to review, challenge, and augment AI outputs with your and your teams' knowledge, insight, and intuition.
Foundation for Growth
Many business owners wish they could step back from the daily grind of running their business and instead focus on the bigger picture. An essential requirement for such a transition is developing a proprietary data strategy. By creating a solid data foundation that can grow with your business and encouraging a culture of data literacy among your team, you can empower everyone to participate in shaping the future of your business. Taking these steps will help you and your business thrive and create a healthier balance between your work and personal life.
