Building AI Leadership Brain Trust: Why Is Data Analytics Key To AI? Part Two Of Three

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.

In this blog series, I have identified forty skill domains in an AI Leadership Brain Trust Framework to guide board directors and CEO’s to ensure they can develop and accelerate their investments in successful AI initiatives. You can see the full roster of the forty leadership Brain Trust skills in my first blog. 

Each of the blogs in this series explores either a group of skills or does a deeper dive into one of the skill areas. I have come to the conclusion that to unlock the last mile of AI value realization that board directors and CEOs must accelerate building a unified brain trust (a unified set of leadership skills that are hardwired in relevant digital and AI skills) to modernize their organizations more rapidly.

Knowledge is key and if you locked up a room of board directors and CEOs in a board room and asked them (1) What steps are required to build a successful AI strategic plan and journey roadmap - what do you think would be the outcome? or (2) Where are your AI Investments and have you inventoried them or audited them? or (3) What is the difference between a computing scientist, a data scientist, and an AI scientist - would their digital literacy skills be sufficient enough to lead and guide their organizations forward? (4) What has been your Return on Investment (ROI) and value realization in your AI programs and/or AI products/solutions?

Sadly, I think we would find some very serious operational execution gaps in realizing the last mile in AI.

A great deal of R&D exploration and AI modelling exploration is underway but moving to sustaining operating practices and ensuring the ongoing knowledge of AI modelling outcomes, and value realization practices remain a major gap in the strategic deployments of AI programs.

In my last blog, I discussed the importance of Data Analytics Literacy as one of the key technical literacy skills in building AI capabilities that are robust and operational focused. This area is so critical that it is a three part series blog, 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. This second blog discusses the strategic leadership behaviours needed to advance data analytics literacy.

Technical Skills:

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

10.Sustainability Literacy

Data Analytics Literacy (Part Two of Three Blogs)

As a quick recap, According to Gartner Group, data analytics literacy means “ the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods, and techniques applied – and the ability to describe the use case, application and resulting value.”

What leadership behaviours must a Board Director a CEO exhibit to demonstrate a data analytics mindset?

Earlier in my career, I was fortunate to work at Xerox where business process orientation and fact oriented leadership were critical skills cultivated across the corporate culture and in General Management (GM) roles. General Electric and IBM were very similar to Xerox, in terms of their leadership skill and employee on-boarding programs. These earlier management training programs were all deeply rooted in Edward Deming’s Total Quality Management (TQM) and Business Process Re-engineering (BPM) leadership development programs which embedded data analytics leadership proficiency “fact-gathering” skills. There are a number of key skills, in particular, that supported these strong cultures which ensured data analytics literacy leadership approaches to business problem solving were foundational.

This blog addresses three of the leadership skills:

1. Recruiting the Right Talent to Enable your Change Strategy - In Peter Drucker’s famous HBR Article, he reinforced the importance of managing people and making people decisions was more important than anything less. Former CEOs, now deceased, like David McCamus, former Xerox Canada CEO, and Jack Welsh, former General Electric CEO, understood that every hire paves an organization’s road to success and has long lasting consequences if you don’t get this right. In the context of striving to become a data analytics literate organization, it is critical that the skills of Board Directors, C-suite, directors and managers be data analytics iterate. Speed to Proficiency is also critical that board directors ensure that their CEO leaders are digitally and data analytically savvy now. If not, board directors must initiate leadership changes as no company can afford to ignore the importance of digital and data analytics literacy. Ensuring all future hires have a foundation in digital literacy is less of a risk given the next generation are born with a technology pacifier and are very comfortable with everything digital. However, new young recruits often do not have data analytics skills hence organizations will need to develop robust employee on-boarding and general management learning and development programs to develop and retain these skills. Carrying a vision in all talent management practices - from attracting, developing and retaining - to embedding data analytics proficiency is a good place for board directors and CEO’s to evaluate their organization’s level of maturity, and then they can further appreciate how deep their change programs are needed to transform their business operations.
2. Speak with Authenticity and Candor - In Jack Welsh’s book, Winning he discussed that lack of candor is leadership is a killer as it, “ bocks smart ideas, fast action, and good people contributing all the stuff they’ve got.” Jack was criticized often for his frank candor, although he liked to say - it was kindness. There is likely no question that Jack could have finessed his communication delivery as a leader’s voice tone, pitch and volume can easily elicit the wrong emotions for humans to genuinely listen and embrace the feedback. In the context of data analytics literacy, executives need to be very clear that data is a strategic asset, and rather than echo these words, they must get into the details to demonstrate they actually know how their business processes and data flows actually operate. Sitting on the front lines and having to fill in the forms that their employees or their customers are being asked to complete will help give leaders with more insight, build more empathy and develop more precision to speak authentically and with candor to modernize their businesses in data analytics literacy.
3. Be Curious and Brave — I have talked about the importance of curiosity as a leadership behavior in my prior blogs as well. Learning to be confident to ask the hard tough questions and to challenge an organization or an individual’s thinking is an important skill for leaders to cultivate. Being open to new ideas is imperative, given the velocity that digital is advancing in our business and personal lives. World-class leaders thrive on asking questions, as they want to genuinely hear what other people’s perspectives are and consider them as valued inputs prior to making decisions. As humans, we are all innately born with being curious and have a desire to explore. Our eagerness to learn is a rich ingredient for successful human development. Our ability to be agile, to adapt to problem solve is strengthened by leaders that promote curiosity. Furthermore, core cultures where curiosity is valued, and also where it is balanced with more formal, repetitive and efficient operating processes bring the best of both worlds - creativity and structure. Two key reasons, one is that that the majority of today’s data is unstructured, living in the world-wide web, in individual’s laptops, emails or in word documents, vs in structured data bases, formalized technology systems of record. Analytics responsibility needs to weaved into every aspect of an organizations data production centers, and this architectural programmatic thinking requires expertise in systems thinking, and organizational learning methods. One training course is not building data analytics literacy - nor are more courses, the key is shifting the communication language to be infused with data analytics thinking.

Summary

As discussed in the prior blog, here is no question in my mind that if companies do not super-charge data driven leadership competency development, they will not grow, and will cease to exist as AI enablements are rapidly underpinning all operating processes, as businesses are under massive modernization for survival in an increasingly more intelligent data smart world.

In summary, this blog reinforced the importance of data analytics literacy in terms of three leadership behaviours 1). Recruit the Right Talent 2.) Speak with Authenticity and Candor and 3.) Be Curious and Brave. Board Directors and CEO’s must “ walk the analytics literacy talk” to guide their organizations forward.

Board directors and CEOs need to step up more and ensure that their digital business models that are leveraging AI have strong foundations where data analytics literacy is recognized as a critical skill competency to build trusted AI centers of excellence.

This being said their own leadership and employee engagement practices will determine their odds of success. Like in most transformational areas, the vision must start from the top, and this means - board directors and CEO’s must reflect on their own depth of digital and data analytics literacy and make investments for not only their own relevancy, but also for ensuring they are in fact - leading with analytics confidence.

More Information:

To see the full AI Brain Trust Framework introduced in the first blog, reference here. 

Note:

If you have any ideas, please do advise as I welcome your thoughts and perspectives.