This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO’s to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results.
One of the most critical skills in AI leadership enablement is ensuring an organization has Data Analytics Literacy. This is the third blog discussing data analytics literacy. The first blog set the stage on the definition of data analytics literacy, and provided a list of questions relevant for CEOs and Board Directors to ask to advance their Data Analytics Literacy enablements to support a broader strategic foundation for AI Enablements. The second blog discussed three key leadership behaviours to advance data analytics literacy. The third blog explores other leadership behaviours demonstrating data analytics literacy to conclude this blog before advancing to Digital Literacy as another key technical skill for building out a world-class AI Enterprise Brain Trust.
1. Research Methods Literacy
2. Agile Methods Literacy
3. User Centered Design Literacy
4. Data Analytics Literacy
5. Digital Literacy (Cloud, SaaS, Computers, etc.)
6. Mathematics Literacy
7. Statistics Literacy
8. Sciences (Computing Science, Complexity Science, Physics) Literacy
9. Artificial Intelligence (AI) and Machine Learning (ML) Literacy
What leadership behaviours must a Board Director a CEO exhibit to demonstrate a data analytics mindset?
In all industries, organizations can only compete with more insights to advance customer needs, design and build new products to bring to market, improve their core operating operations, services, and most importantly attract, develop and retain the right talent.
Many experts like to refer to the accelerated importance of data analytics as the golden era of data.
This golden area is being driven by realities that now – every company has the opportunity to be 100% cloud based in all their operations, and can achieve a server-less architecture, as cloud systems are now affordable to every company.
A study completed by the USA National Center for Education Statistics, surveyed over 23 countries to evaluate the proficiency of data analytics and problem solving skills, and their findings reinforced the data literacy gap and imperative to prioritize critical applied subjects like: statistics, operations research, and probabilities. Canadian math tests show the declining levels of math proficiency in its schools — so countries need to worry that math skills competency is required to achieve a data driven mindset. Countries like China, India are doing an excellent job in higher math skills – resulting in their volumes of engineers graduating and engineering skills are essential for AI Brain Trust.
Many leading companies like: Adobe, IBM, SalesForce are creating digital academies to help their employees and customers improve their skills in data analytics.
Obvious starting points to ensure that you train your employees in a few key areas, like:
1.) Using Excel Effectively — all employees should understand the basics, but provide deeper proficiency training and skill development, as this is one easy way to appreciate the interpretation of data and analytics and build basic skills to ensure the data results are useful and meaningful.
2.) Understanding the importance of A/B Testing to test two different hypothesis with the same data set to see what results are more optimal
3.) Building Visualization Graphical Skills – investing in PowerPoint graphical skills so leaders can understand the results
4.) Building Story Telling Skills – Story telling is one of the most effective leadership skills to help people to see the big picture and be able to communicate for operational context and help on next step best action – execution is everything.
These are four basic skills not to lose sight of.
In addition to the skills, it is important to think of the organizational center of excellence to achieve data centric skills, so I always recommend that a company establish a Data Analytics or Data Skills Learning Environment to provide a foundation for growth.
A core learning center can help build a brand around the importance of data and analytics proficiency for employees. Having different levels of training and skill development to support building strong data analytics literacy is a critical anchor to weave into a company’s overall journey roadmap.
The positive outcome of building stronger data analytics literacy is that these skills improve employee confidence and better decisions are made when their are more facts to support a go forward motion decision.
Other key questions CEOs can ask that are very practical and easy for employees at all levels to build a leadership communication cadence around are:
1.) What data do you have to support your decision?
2.) What is the quality of the data you are using to advance your business case or challenge your business case? (clear pros and cons facts). Is the data trusted (Sources valid)?
3.) What decisions do you want to be made based on the data to enable not just a decision, but a sustaining behavior?
4.) How important are the decisions and what are the different types of decisions that can support your data analytics?
5.) How have your validated your analysis to ensure it is accurate, unbiased, and brings value to the organization?
6.) Has your research on your data involved any third-party or bench-making sources?
Data Analytics Literacy is a 21st skill imperative and companies need employee confidence to work with messy data, interpret data and derive insights to advance decision making. Data driven decision making improves leadership credibility and also improves business performance outcomes. A culture that is data analytics intensive will outfox a competitor any day, as relevant insights challenges are essential to compete in a smarter and increasingly – advanced Analytics AI intelligent world.
How mature is your organization in demonstrating data analytics literacy? This question is worth a deep reflection.
Board directors and CEOs need to ensure that their business models have strong foundations – those that have robust data analytics literacy as a critical skill competency will build AI centers of excellence that flourish.
To see the full AI Brain Trust Framework introduced in the first blog, reference here.
If you have any ideas, please do advise as I welcome your thoughts and perspectives.