Elevating Data Literacy in Small Businesses
This is also a talk in MDS Fest 2024. See talk here.
Why Data Literacy?
Over the course of writing 30 tips for data practitioners across various levels — from the individual to the community — I’ve gleaned insights from my experiences in two small businesses. These insights have not only resonated with others but have also led me to a profound realization prompted by a question from a respected friend: “What’s the one thing you really want to talk about?” This question challenged me to distill the essence of my message into something deeply valuable. And here it is: The most rewarding aspect for me has been seeing the transformative change in how people engage with data.
But the value of data literacy extends beyond personal fulfillment; it’s a wise investment for businesses. A comprehensive study has revealed that improved corporate data literacy is not just about operational efficiency. It translates into an increase in enterprise value — between $320 to $534 million — over competitors with lower levels of data literacy. Furthermore, 85% of C-suite executives now believe that being data-literate will soon be as vital as computer literacy is today. Just imagine the untapped potential in small businesses, where agility and innovation combine, ready to leverage these insights for a competitive edge.
In this article, I aim to equip you with both the mindset and practical methods that will set you up for success as one of the pioneering data practitioners in a small business.
Elevating data literacy within small businesses is not without its challenges.
Challenges in Elevating Data Literacy
Challenge #1: Persisting with the Old Ways
One significant hurdle stems from psychological barriers to change, which can manifest in various forms among team members. Recognizing these barriers is the first step towards crafting effective strategies to overcome them.
You might be so excited to introduce the modern data stacks. However, people only care if they can get a better experience, the modern data experience, for example, being able to answer why the revenue is slowing down without checking lots of raw data from scratch. They don’t care if the tool is fancy nor the latest technology.
This excitement for new technologies, moreover, can trigger defensive reactions among team members. Many individuals prefer the familiarity of existing processes, even if they are inefficient. This reluctance is magnified by the perceived effort required to learn these new tools. A prevailing lack of confidence in one’s ability to master unfamiliar technologies can deter individuals from even attempting to engage with these tools. For instance, despite the advanced capabilities of modern data analysis tools, such as interactive and visually appealing dashboards in Metabase, some individuals still prefer downloading data to spreadsheets for personal manipulation and analysis. This preference stems from a desire for control and a comfort with the familiar.
Some team members may fail to see how data literacy applies to their role or contributes to organizational goals, viewing it as irrelevant to their jobs. Some may view the push towards data literacy as merely following a trend, skeptical of its lasting value or applicability to their day-to-day responsibilities.
Adding to this complexity is the challenge of information overload. People need time to consume the new information. When introduced too rapidly or in too complex a manner, the influx of new concepts and terminology can overwhelm, leading to resistance. This is not just about the volume of information but also about its presentation and the perceived relevance to individual roles within the organization.
In navigating these challenges, it’s crucial to recognize the nuanced blend of psychological, cultural, and practical concerns that fuel resistance to adopting modern data practices. Recognizing these underlying reasons is crucial for developing targeted strategies that can gently guide teams towards embracing change.
Challenge #2: Misalignment and Misunderstanding
When you dive in how people read, understand, create, and communicate data as information, I’m sure you’ll find some misalignment and misunderstanding.
You might be the first person to notice that the WAU (weekly active users) numbers are different between Team A and Team B because you are the first to join and review such detailed numbers across both teams. The root cause might be as simple as differing definitions of the first day of the week or more complex due to the difference of what “active” means.
After you’ve aligned the WAU, you may discover differing user active rates. Let’s say, Mike and Christina use the same data points but apply different denominators. You don’t realize the difference until you see their formulas in their spreadsheets.
- Mike: weekly active users / weekly visitors
- Christina: weekly active users / weekly users who stay signed in
There is no right or wrong. Christina focuses on users who stay signed in, perhaps believing that staying signed in is a stronger indicator of continuous engagement or interest in the product. Indeed, there are many possible explanations for these differences.
When fundamental metrics like the user active rate are subject to varying interpretations, it not only complicates internal discussions but also impedes the organization’s ability to craft coherent strategies based on data-informed insights. The challenge lies not just in the numbers themselves but in forging a common understanding of what these numbers represent and how they should take actions.
Challenge #3: Your Data Doesn’t Match Mine.
This challenge is essentially a culmination of the first two. It arises when individuals cling to old habits, such as downloading data to manipulate in their spreadsheets, compounded by the variability in definitions and calculations. Beyond “active,” other fundamental metrics like “revenue” and “lead” also suffer from inconsistent definitions. The differences in calculation methods stem from varied understandings or objectives, further muddying the waters.
Nearly every data practitioner has encountered this frustrating situation: “Your data doesn’t match mine.” This phrase often marks the beginning of a the process to unravel where the discrepancies lie, whether in the data sources, the processing methods, or the interpretations. It underscores the critical need for standardized practices and clear communication to avoid such conflicts and ensure that everyone is working with the same, accurate set of data.
Beyond all these challenges, let’s dive in what you can do!
Introduce the Mindsets and the Practical Methods
Your Goal as a Data Practitioner
As data practitioners, our primary aim is to sift through the numerical noise to unearth insights that can significantly impact our business. Ultimately, the goal is to enhance business outcomes. We strive to empower our colleagues to make data-informed decisions, fostering a culture where better business practices are the norm. The underlying belief is that data-informed individuals are equipped to make superior decisions.
Hence, it’s essential to keep in mind that our role transcends building sophisticated ETL processes or discouraging the use of spreadsheets. Take, for example, a strategy we employed: displaying daily revenue from programmatic ads alongside target figures on a TV screen prominently placed within the office. Initially lagging by 50%, we embarked on a series of experiments — optimizing ad placements, experimenting with sticky ads, and introducing video formats — each step informed by data insights. This methodical approach allowed us to gradually close the gap, eventually meeting our revenue goals.
Throughout this journey, our discussions rarely centered on the technicalities of our data stack. Instead, we focused on deriving actionable insights, deciding on steps forward based on data revelations, and measuring their direct impact on our business — such as how viewable impressions influence programmatic ad revenue.
Data literacy, the competency to read, understand, create, and communicate data as actionable information, becomes a beacon for identifying data-informed individuals. For instance, moving from a vague “I think viewability may help” to the specific “I increased the viewable impressions by 20% on this page, resulting in a 3% uptick in our inventory” signifies a shift towards data-driven thinking. We aim to transform conversations from the subjective “I feel” or “the boss said” to the objective “the numbers suggest.”
Encouraging this shift means aiming to change people’s behavior — promoting an environment where data becomes a natural part of decision-making conversations. Changing behavior, especially others habits, is challenging. Much like how health apps strive to influence dietary habits or exercise routines, we seek to embed data literacy into the fabric of our daily work life, transforming intuition-based decisions into data-informed strategies.
Summarizing this section, our mission as data practitioners is to profoundly influence our business by transforming people’s behavior towards a more data-informed approach. Now, let’s explore how we can achieve this transformative goal.
Your Organization Wants You to Succeed
Keep this foremost in your mind: the people around you are rooting for your success. This understanding forms the bedrock of support as you navigate through the inevitable frustrations and challenges ahead.
Consider the reason you were brought on board, especially significant if you’re stepping in as the first data specialist. The team believes in the transformative power of data; they believe in your ability to harness this power. They’ve likely experimented with becoming more data-informed in the past, encountering obstacles or identifying areas seeking enhancement in how data impacts decisions. Your presence is a testament to their commitment to this journey.
With this in mind, embrace the support from your colleagues. Their belief in the value of data and in you is a powerful asset as you work together towards a common goal.
Take Advantage in a Small Business
The landscape of a small business typically presents a unique set of characteristics:
- Size and Simplicity: The scale of data operations is manageable, often involving millions of rows across fewer than 1,000 tables daily. With less than 30 data sources and fewer than 50 intermediate points in the data pipeline, the setup remains straightforward and manageable.
- Stable Data Sources: Once initial setups are complete, new data sources are seldom added, sometimes going months without changes.
- Limited Domain Knowledge Required: Mastery in a few key areas such as Sales, Finance, and Product knowledge is often sufficient to make significant impacts.
Spreadsheet Mastery: A notable asset in small businesses is the prevalence of ‘spreadsheet champions’ — individuals who wield spreadsheets with finesse, crafting complex formulas that facilitate data access and reporting. This familiarity with data and numbers lays a foundational culture of data engagement. This person will be your go-to-person!
However, there are inherent challenges:
- Limited Data Team Size: Often, you might find yourself as the sole data professional, tasked with a broad spectrum of responsibilities from data engineering to analysis.
- Knowledge Gaps: A common hurdle is the organizational blind spot towards the necessity of robust data pipelines, aligned metrics, or the distinction between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems.
- Statistical Significance: The relatively small scale of data can delay achieving the volume needed for statistically significant insights.
Despite these challenges, small businesses offer fertile ground for innovative data literacy and management strategies.
Leveraging Existing Resources: The ubiquity of spreadsheets, despite their potential for causing data silos, can be turned into an advantage. By integrating a centralized data source, like Google BigQuery, into these spreadsheets, you can mitigate misalignment and enhance data uniformity. This approach, while not perfect, improves upon the disconnected alternatives.
Agile Decision-Making: The compact structure of small businesses facilitates a quicker turnaround in decision-making. This agility allows for the direct observation of how data-informed decisions translate to real-world outcomes. For instance, an analysis leading to a minor adjustment in product pricing can swiftly reflect in sales figures, offering immediate validation of the data’s value. Such experiences underscore the importance of data literacy, encouraging a culture of experimentation and feedback. This environment, where results from data initiatives are quickly evident, prompts a proactive stance towards data exploration and application, fostering a mentality of continuous improvement.
Recognizing these distinctive features and challenges of small businesses, let’s explore practical methods designed to leverage these attributes to our advantage.
1. Observe and Identify Opportunities
Engaging with your colleagues before deciding on the adoption of modern data stacks is crucial. They are the primary users of these data systems, and understanding their needs is key. Explore the decisions they need support with and how they currently use data to answer questions. This could involve understanding their methods for data retrieval, calculation, and report generation.
This approach is akin to Product Discovery in the product management realm. The goal is to align solutions with user needs rather than imposing predetermined technologies that may not address their challenges effectively. It’s about offering assistance in a manner that they find genuinely beneficial.
The advantage of working within the close-knit environments of small businesses is the direct and easy access to all data users. “Can we have a chat later today?” This accessibility allows for a comprehensive overview of the current data culture and each individual’s data literacy and familiarity, providing a clear picture of the landscape without needing to rely on assumptions.
As you discuss with your colleagues, you’ll likely uncover areas of misunderstanding, misalignment, and resistance to change. Remember, You Are Here to Assist, Not to Blame. Recognizing these challenges offers the opportunity for growth and improvement. It’s crucial to approach these discoveries not as faults but as avenues for development.
In these discussions, keep an eye out for the ‘spreadsheet champion’ — a person who not only has technical skills but also a profound understanding of the business’s operational requirements. This individual stands out for their keen interest in data and often serves as a bridge between raw data and actionable insights. They are invaluable for sharing domain knowledge and insights into the decision-making processes within the business. They will become your seed, their role is pivotal; they can inspire and educate others, enhancing the communal aspect of data literacy. They will also become the alpha user, offering testimonials and active engagement with your solutions, furthering the culture of data-informed decision-making.
2. Share Objectives
In the unique ecosystem of a small business, where product lines are often simpler and teams are smaller, the opportunity to align and share objectives becomes even more critical. Engage directly with data users and communicate your role as a supporter and enabler of their success. This direct engagement is not just about assistance; it’s about partnership. By aligning your goals with theirs, you bridge any gap between data management and operational needs, making their challenges your own.
This alignment is particularly effective in small business settings where there’s a natural to have shared goals at the company level. The smaller scale makes it easier to rally everyone around a common objective. Here, your mission to elevate data literacy and enable data-informed decisions resonates more deeply because it directly supports the overarching aims of the business.
Earn Trust. Trust is the cornerstone of this relationship. Without trust, even the most accurate data narratives and insights risk being undervalued. Sharing objectives isn’t just about stating your intentions; it’s about consistently demonstrating through your actions that the data you provide is reliable, accurate, and relevant.
Ensure that the data you work with and provide upholds the highest standards of quality. A single error can disproportionately affect trust levels in smaller settings, where the ripple effects of mistakes are more notable. Your commitment to accuracy and reliability not only builds trust but also reinforces the shared goal of leveraging data to drive the business forward.
After establishing a shared goal, the next step is to strategize on collaborative efforts towards achieving it. Tightly aligned. Loosely coupled. Taking the programmatic ads example further, once it’s understood that increasing viewable impressions is key to revenue growth, various team members can contribute in specialized ways. Engineers might focus on enhancing page speed to improve the overall user experience, while designers concentrate on optimizing ad placement for maximum visibility. Both roles aim to boost viewability but from different angles. This distinction highlights the importance of clarity in shared objectives, allowing each professional to leverage their expertise toward the collective aim.
3. Show and Tell
Be Patient. Transforming the way people interact with data — to read, understand, create, and communicate information effectively — is a gradual process. This is especially true in small businesses where personal interactions and existing workflows are deeply ingrained. Aligning goals and identifying opportunities for improvement will take time, as will encouraging a culture where data-informed conversation becomes the norm.
Focus on Relevant Benefits. In a small business setting, highlighting the direct impact of data insights on individual roles and the broader business objectives is crucial. Begin not by showcasing the data team’s achievements, but by demonstrating a deep understanding of your colleagues’ challenges and how data can offer solutions. Tailored examples that resonate with their daily tasks and goals will speak volumes.
The close-knit nature of small businesses means word of mouth plays a pivotal role in fostering change. Let others speak for you. Encourage the success stories and positive experiences of influential figures, like the spreadsheet champion, serve as your advocates. This could take place during a bi-weekly all-hands meeting or through a dedicated sharing session. These influencers can significantly accelerate the acceptance and enthusiasm for data-informed approaches by sharing their firsthand experiences of improvement and success.
Sharing a single success story isn’t enough in the interconnected environment of a small business. Continuous dialogue, led by example, is essential. Engage in discussions that move beyond subjective feelings to data-backed insights. Whether it’s viewing messages in Slack or notes in meetings, always inquire about metrics, delve into calculations, and foster a shared understanding. This effort to lead by example, particularly in settings where teams are closely connected, ensures that the appreciation for data literacy spreads more effectively.
While the tight-knit structure of small businesses can facilitate faster spread of information and adoption of new practices, expect that this transformation won’t happen overnight. It changes bit by bit. The very connectedness that allows for rapid dissemination also means that trust and proven value are prerequisites for widespread acceptance. Cultivating this environment where data literacy can flourish is an ongoing process, reliant on patience, relevance, and the strategic leveraging of influencers within your organization.
4. Celebrate Small Wins
In our journey toward enhancing data literacy within small businesses, it’s essential to initiate change with small, manageable steps. The goal is not to abandon familiar tools like spreadsheets abruptly but to incrementally improve upon them, showcasing immediate benefits. One practical example is offering training on GA4(Google Analytics 4), clarifying concepts such as “Users vs. New Users vs. Active Users” and “Session Starts vs. Page Views,” and guiding users on creating custom reports. By helping people use existing platforms more effectively and efficiently, we lower the barrier to entry and highlight the tangible advantages of more aligned and transformed data practices.
This approach not only marks the technical progress but also assures your colleagues that these enhancements are both achievable and deeply beneficial. The visibility of such progress creates a supportive atmosphere where the sentiment, “If they can do it, so can I,” flourishes. Particularly in small businesses, where operations might revolve around a singular product line or shared domain, one person’s success becomes a catalyst for collective growth, making the adoption of data practices more relatable and accessible to all.
Celebrating these achievements is particularly meaningful in the small business context, where successes, no matter their size, are more visible and impactful thanks to tight-knit teams and direct communication channels. Simple wins, from reducing time spent on weekly reporting to enhancing the clarity and reliability of metrics, warrant significant celebration 🎉.
By actively celebrating small wins and ensuring these moments are seen, you tap into the inherent strengths of small businesses: a sense of community, shared goals, and the ability to directly witness and contribute to each other’s successes. This approach does more than just promote data literacy; it cultivates a culture of trust, motivation, and continuous innovation in leveraging data for business growth.
Any Other Thoughts?
In sharing these mindsets and methods, I’ve aimed to illuminate the path I’ve navigated through the complexities of data literacy in small businesses. Born from a blend of challenges and victories, these strategies are tailored to the nuances of working within compact teams.
Elevating data literacy is indeed a marathon, not a sprint — a journey defined by steady progress, adaptability, and collective effort. Each success, no matter how minor, and every obstacle overcome enriches the data-informed culture of your organization. This journey demands patience, perseverance, and an openness to new approaches.
I invite you to ponder how these strategies resonate with your experiences and to share your own stories of navigating data literacy. What tactics have propelled you forward? What valuable lessons have shaped your path? Let’s embrace the road ahead, fostering an environment ripe with opportunities for growth, learning, and significant breakthroughs.
🤩 I’m happy to hear how you do data or products. Feel free to reach out to me on LinkedIn Karen Hsieh or Twitter @ijac_wei.
🙋🙋♀️ Welcome to Ask Me Anything.
