As an Analytics Developer at Qlik, I create data assets, processes and training that enable more people across the company to access, understand and use data. This takes me beyond building tables of data in a database – instead, it requires me to present a data model to the organization as a product. As a result, a “consumer” (someone from Qlik) can find and use a “data model” on a particular use case to explore and to answer questions related to that topic.
My Masters in Mechanical Engineering and MBA from Brigham Young University (BYU) helped me develop a model-centric mindset with a view into both the commercial and people side of business. It wasn’t directly through this education that I started my career in IT at a retail company, but it set me on the path towards data analytics and data literacy.
I’ve always enjoyed the combination of technical data modeling with the visual and ‘personal’ aspects of creating reporting and analytics dashboards, so a career in analytics became obvious after I started it. Luckily, I had a colleague during my time at BYU who helped me see a future for my skills and interests in data.
Data is the main ingredient of my current role, and now I am trying to become a more proficient chef! Part of my day-to-day work involves helping my team to prepare data and tools in better ways so that they are more consumable by a broad group of users – enabling data literacy. A fascinating part of this work is reviewing the adoption data for the assets our team creates: how many people are using certain apps? How often are they creating their own analytics?
I work with a humanitarian organization that Qlik partners with, We See Hope, which teaches valuable life skills to children and youth in Africa. Helping this organization visualize and share its funding and impact data has been particularly rewarding. With this data at their fingertips, We See Hope can have an even greater impact on people and communities in Africa because it enables them to make better data-driven decisions and present to financial donors and organizations in a way that is more visually compelling. Elsewhere, I’m passionate about creating data-centric tools that streamline the daily routine of coworkers and allows them to shift from data gathering to more thoughtful analysis and decision-making. This efficiency through better data and processes is applicable to so many areas of life!
Data literacy is so multi-faceted that I’m still figuring this out. In some roles, it means in-depth knowledge of data itself through statistics and data science. In other settings, it means understanding data in the business context, such as KPIs for a particular business function. Common across all roles is the recognition of assumptions we make about data and analytics and the limitations of the data we have.
For me, improving data literacy here at Qlik means that individuals across our organization have access to tools and resources which allow them to take charge of their own learning and decision-making with data. In a data literate world, individuals regularly question their assumptions and interrogate the data they see or are preparing. Their data-related questions will also be carefully reviewed to ensure that each one points to a specific, meaningful action or outcome.
As one of the primary goals of my team, providing self-service data and analytics is influencing employee data literacy in several ways. As analytics developers like me focus more on self-service than just producing data and reports, we spend more energy creating documentation and training, while considering visual elements helps with our analytics – which promote business context and practical application of the data.
In companies where a self-service platform is prioritized and made available, the company culture can shift to emphasize the responsibility of individual employees to improve their own data literacy. In such a culture, excuses about not understanding data or business definitions and endless arguments about whose “number” is right can be replaced by thoughtful discussions and debate about data within a proper context.
Starting out my data journey with a more focused, action-oriented mindset would have been helpful. We are often quick to generate data, reports and apps which are aesthetically pleasing and informative, but which might lose sight of what meaningful actions and decisions these tools should enable. Looking back at an analytics project, it’s often clear how much time and effort was spent on data and reporting which was not ever actionable.
This is a mindset I’ve learned to adopt. Before beginning any data project, I take the time to thoughtfully elicit a list of specific decisions and actions we want to enable through data and ensure we are capturing the data that will support those outcomes.
Always ask ‘what meaningful decision or action will my efforts enable?’ If the answer is unclear, try to find clarity and resist the temptation to jump into the data analytics right away in the hope you will find something meaningful as you go.
For those already in the working world, I would suggest learning to methodically and respectfully explore a business leader’s request for data and analysis until you both have a clear vision of how it will enable specific decisions and actions.
Finally, have the courage to recognize that more data is not always better and that not all data is good or useful. The same is true for the slick reporting and analytics tools that we sometimes create. Learn to step back, reassess and reevaluate your work, and be willing to toss out stale or low-value analytics tools as needed.
I frequently have discussions with my children about things we read or hear in the media, particularly statistics and polls. My feeling is that the sooner they realize that data is everywhere and that not all data is good or useful, the better. Even if no numbers are present, most data in the public space has been gathered and shared by individuals or organizations with specific goals or motivations for sharing the data. They want to persuade others to align with their perspective on the topic. Given this reality, I try to instill in them a healthy level of skepticism and scrutiny about the data they see or hear and consider its source and the motivations behind it.
Further, I hope they will understand how much data can help them in learning about something, formulating an opinion on a topic, or making a decision. It encourages me to see them gather their own data, resist accepting “easy” assumptions and generalizations, and approach data and analytics with curiosity and confidence.