How Data Literacy and Business Intelligence Drive AI/ML

As artificial intelligence (AI) and machine learning (ML) solutions become the wave of the future for insight-based decision making, it's important to understand the true roadmap that’s required to make this type of transformational commitment. Like other functions of a business, organizational success will be rooted at the individuals, the software tools they use, and their symbiotic relationship. 

In the world of data analytics, the “individuals” are each employee’s level of data literacy while the “software tools” are specifically business intelligence (BI) tools. One without the other minimizes an organization's ability to collect data, use it for analytics, and find useful insights — ultimately restricting you from making sound decisions. 

As Dr. Megan Brown, Director of Knowledge Management and Data Literacy at Starbucks put it in chapter 5 of Make AI & BI Work at Scale, “If you have a data-literate organization and few BI tools, you likely have a frustrated workforce….If you have BI tools but lack data literacy, you have limited adoption of your data, analytics, research insights, and visualizations.” 

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Once the right learning journeys are in place for data literacy enhancement, you’ve invested in an easily-adoptable BI tool, and you begin seeing top-down commitment from leadership, those pieces of the puzzle will come together for the development of organizational data literacy. This puts you in a prime position to make a move into AI/ML as a resource for data analytics, major decisions, and a significant competitive advantage.

Starting with the Individual 

While collecting data might be easy, incorporating it in your organization through evaluation,  analysis, and communication is a slightly different story. These components are what make up “data literacy” and unfortunately, very few employees (21%) are actually confident in their data literacy skills.     

That said, you’ll want to develop educational programs for your employees consisting of video tutorials, live courses, and other methods to create learning journeys. These programs should be tailored to the needs of each individual depending on their job function and be supplemented with executive analytics coaching that can help them translate their data into information, to knowledge, to wisdom, and finally — to decisions. 

Without individual data literacy capabilities, employees will struggle to adopt all (if any) of the modular features your BI tools have to offer and lack the ability to provide meaningful insights because they won’t actually know what the data is telling them.   

Scaling to the Organizational Level 

It goes without saying, organizational data literacy requires data literate employees. Once you have that part covered, it's the culture and systems around the individuals that dictate full-scale organizational adoption. 

Culturally, leadership advocacy is a key ingredient to organizational data literacy. On top of that, creating pressure by the respective managers and team leaders for new employees to become data literate quickly is a great way to organically create a data-centric culture. 

Systematically, organizations committed to data literacy will have both well-governed and highly-adopted BI tools that can help search through centralized metadata and do automated analysis. Included within the individualized education programs should be how-to find, review, and summarize organizational data using these BI tools.   

The more that leadership advocates for data literacy education, invests financial resources to the initiative, and commits to technology adoption to BI platforms —  the faster the progression will be to organizational data literacy.  

The Leap Into More Advanced (AI) Solutions 

Some of the baseline benefits of organizational data literacy include making data analytics become a natural part of your business language, more confident and reliable employees through data-based pitches, and better insights used for decisions. However, one additional underlying advantage of this new and improved data-centric organization is the foundation that's built for more advanced analytics methods such as AI and machine learning.    

Through trust in the organization and in themselves, employees can now have some assurance that your commitment is real and let you begin easing your way into more sophisticated tools. It's important that your employees have an understanding of advanced analytics, AI, and ML as a way to advocate for specific AI/ML products, identify strategic opportunities, and speak intelligently about the solutions to the point they can collaborate with the development teams putting the tools in place. 

Aside from the knowledge element of AI/ML, there’s an ethical component to putting these types of solutions in place. For instance, there may be a fear that employees are essentially “being replaced by robots” that needs to be addressed. Transparency by informing them of the true benefits of AI/ML and setting boundaries for how it will be used is a good way to build trust. You’ll also want to continue to push the educational-culture aspect of data literacy and extend the courses to AI/ML algorithms, technology ethics, and existing AI/ML solutions.  

Taking these holistic approaches to make your organization data literate will simplify your process into that next step to utilizing advanced analytics methods. After knowledge and trust of AI/ML solutions is established by your employees, the business as a whole will see nothing but limitless potential in what information can be collected and how it can be used.       

Time to Make AI & BI Work AtScale 

Want to learn more about how you can enhance your organizational decision-making and analytics through collaborative AI and BI systems? Download this complimentary book which contains thought-leadership contributions from 15 industry experts from the data and analytics world. Also, be sure to watch this data leader panel webinar as the panelists dive deeper into the process of scaling data literacy programs at their Enterprise organizations.

This article is a chapter review of author Megan Brown, PhD Director, Knowledge Management & Data Literacy, Starbucks chapter from the comprehensive book, “Make AI & BI Work At Scale”. This book is a Body of Knowledge (BoK) on how the Semantic Layer can help organizations achieve AI and BI at scale. This book gives a holistic perspective to the Data and Analytics community covering data management, data engineering, data science and decision science to improve the odds of delivering Data and Analytics solutions successfully. The authors in the 16 chapters who have contributed to this book include industry practitioners, subject matter experts (SMEs), and thought leaders who have a stellar track record in leveraging Data and Analytics solutions for improved business performance.

1. Brown, Megan, Make AI & BI Work at Scale, AtScale.com. 2022

2. The Human Impact of Data Literacy. Accenture. 2020