AI

Why Is Data Analytics Literacy Key To AI Competency Development?

Read more at www.forbes.com

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.

Last week, I discussed the importance of User Centered Design Literacy and in this blog, I will discuss the importance of Data Analytics Literacy, one of the key technical literacy skills in building AI capabilities that are robust and operational focused.

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

This blog defines data analytics literacy, and frames the dimensions of data analytics to provide key insights and questions that Board Directors and CEO’s can use to guide their leadership in advancing their company’s journey into advanced AI and Data Science capabilities.

What does Data Analytics Literacy Mean?

According to Gartner Group, 50% of organizations today lack sufficient AI and data literacy skills to achieve business value. Based on my executive experiences over the past ten years advancing AI enablements into enterprise and mid-market companies, there is no question in my mind that one of the most significant gaps in board directors, CEOs and C-Suite is being able to speak a common language reg: Data to lead and champion their workforces to understand that data literacy must become a core competency not just for AI professionals, but for all professionals in a company.

Without being able to have foundational communication skills to speak about data no company striving to advance AI as a core business operating process will ever be world class.

What is data literacy?

According to Gartner Group, it 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.”

The majority of Fortune 500 talent management leaders have positioned data literacy as a critical skills for the 21st century for employee skill development. Gartner has recently predicted that 80% of larger enterprise are actively rolling out data literacy initiatives to up-skill their workforce throughout 2021 and beyond – given the imperative to modernize businesses.

The Government of Canada has released a comprehensive Data Strategy Roadmap for the Federal Public Service to accelerate the knowledge of data literacy skills among public citizens. Their strategy information provides an excellent review of diverse definitions of data literacy to help advance knowledge from diverse sources helpful to Chief Human Resource Officers (CHROs) that may be tasked in developing a data analytics skill development strategy.

OECD identifies data literacy as one of the six most critical skills for innovation in the public sector. They have a maturity scale for data literacy organized into four areas:

1.) Data being used in decision making,

2.) Data being used to manage public services,

3.) The ability for data non specialists to engage with data specialists, and

4.) The ability of data specialists to communicate effectively with non-specialists about data and the results of data analysis.

QLIK, a software analytics company, has been very active in developing content to democratize knowledge of data in their data literacy project and they offer a product agnostic data certification that provides an agile path for businesses to augment their talent knowledge sources.

In the helpful sources below, I highlight a number of other free data literacy sources from leaders: IBM, Salesforce (Tableau), SAP, etc. as there is a great deal of content being made available to advance digital analytics and digital literacy in general that there is no excuse for executives to not mobilize focus in all industries and in all company sizes. With so many people impacted by Covid-19, taking these free online courses on digital and data literacy may make the difference in getting back into the workforce or not.

As we say in my company, “We Love to Make Data Speak.

What questions should a Board Director or a CEO ask to advance their organization’s data driven mindset?

First, it is important to understand how deep data analytics must go to reshape /rebuild/or transform a corporate culture.

Many companies simply roll out data literacy training programs and assume the knowledge will stick and that the organizational behaviours will evolve to enable richer data analytics. This is so not true.

Advancing a robust data analytics journey roadmap requires an integrated and systemic approach to challenge current operating practices, systems, cultural values, beliefs and norms. It requires deep organizational reprogramming and continual inspection methods, with skilled ethnographers observing behavioural changes, training coaches to support the changes, and ensuring that C level leaders learn the new language of data digital literacy otherwise — culture remains rooted in the past.

The top questions I recommend leaders (Board Directors/CEOs) to ask to get a pulse check on their digital analytics literacy foundations are summarized below.

It is imperative that leaders understand that data is the fuel that feeds AI models and if data is not robust then all their companies are doing is potentially deeply engraining the past into their operating methods and also with higher odds of data bias risks. The last thing we need in the AI reemergence industry is giving false hopes that AI models will see futures that they cannot see, and then over time, value realization in AI outcomes is questioned and organizations are not learning and AI investments become more constrained. Maintaining curiosity to learn and evolve is key given what China is doing in their longer term strategy to ensure AI is pervasive in all things.

There are so many companies that I go into and it becomes so clear that their data management operating practices are severely broken, hence their AI journey has to be done in smaller and more tightly controlled centers of excellence versus worrying about the full spectrum of data challenges and broken operating systems, and avoiding the deep endless abyss.

So leaders, you must dig deep into data validity, data bias and audit controls before investing in AI model building or isolate data rich areas to demonstrate value and inch along so you are learning about AI.

Here are key questions to guide leaders foreword to ensure data analytics mindsets have stronger foundations, based on my executive experiences in both Fortune 500 companies, as well as mid-market enterprises.

1.) Do you have an end to end data value chain understanding of your entire enterprise’s data factory at work? How understandable and easily accessible is this knowledge? Has your data value chain been reviewed and inspected?

2.) Do you have a mature competency in Enterprise Process Management (EPM) that easily visualizes data logic flows into an online data dictionary so everything is easily codified, sourced, and maintained? Are owners clearly accountable and leaders held responsible for their data and process practices as part of their executive compensation?

3.) Do you have a core value for data defined clearly as an asset and /or sustainability resource guiding your company culture?

4.) Do you have incentives and recognition programs for data driven leadership behaviours?

5.) Do you know the quality of your data across your different data transaction assets (i.e.: CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), PRM (Partner Relationship Management), etc. and is their an integrated operating budget ear marking all data improvement programs across the enterprise?

6.) How balanced is your company in terms of operational focus on ensuring all data capture set up is valid, consistent, accurate/robust vs putting emphasis on report generation to make decisions against? Is it balanced or are you using reports that are flawed with inaccuracies?

7.) How is your culture valuing data in its full life cycle (creation, collection, reporting, etc.)? Are business owners in place relevant to diverse process and data repositories with clearly defined KPIs (key performance indicators)? Do you have metrics clearly defined to ensure data management and data analytics literacy is nurtured in all organizational functions and levels? How are you building data skills and are you following a consistent data management methodology?

8.) Are data champions and data stewards clearly identified for each of your core business operating areas? Does your company follow a data management methodology to ensure data literacy foundations are a priority for everyone in the company?

9.) Is there a data governance operating process to ensure an ongoing data strategy and roadmap is a core operating process reporting into the CEO of COO levels?

10.)                Does the organization have external benchmarking and knowledge sharing enablements to advance its data analytics literacy maturity practices?

The ability to understand and communicate in a common data language is a critical skill for board directors and CEOs to ensure is in place. Deriving value from data and analytics is a major competitive advantage lever, and some like to say data is all about digital dexterity, which means all employees have the ability and desire to use data and emerging analytics technology to derive better decisions and outcomes for the company.

Going a level deeper some other key leadership data analytics inspection questions, you could ask are questions like:

1.) How many employees are skilled in statistics, operations research, predictive analytics, prescriptive analytics, etc, and understand concepts like: correlations, confidence levels on false or positive signals, clustering analytics, etc. versus basic math concepts like averages or medians?

2.) Can your managers clearly explain the outputs of their business systems and operating processes? Do they understand the quality of their data and are they able to operate all the systems their employees are expected to operate so they appreciate each field that is capturing the data and what type of data is being captured and if its needed or not to run the business at a deep data validation /audit like mind set orientation?

3.) How many resources do you have that are skilled data scientists, or AI engineers and more importantly how many of them can explain what they do so managers and front line employees understand them?If people cannot understand the language from data scientists and AI leaders, and be able to take action to move a company foreward we have a major communication and comprehension gap.

Summary

There 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.

It is simply inevitable just like we built the air traffic systems to control the airplane navigation systems, we will be building AI machine learning operations (MLOps) and data analytics functions to control all the AI modelling systems and in time, we will get it right.

I predict that at least 50% of current businesses worldwide won’t survive the transformation and the carnage may be higher. Companies that are building up data and AI intelligence in their early roots will simply be more nimble than companies built twenty , or even ten years ago, so we may see giants like IBM have to acquire companies like SalesForce – or vice versa, but the titan consolidation is inevitable as the digital analytics literacy imperative is taking hold everywhere. I for one, look forward to the Titan’s acquisition of data analytics companies as Salesforce’s acquisition of Tableau was a clear signal of where they are heading, and so the next chess move by IBM and SAP will also be most interesting to see unfold. Perhaps a wise move would be combine General Dynamics (Robotics ) with and IBM and start to seriously think about intelligent robots and cobots in everything, as analytics flows deeper into AI and robotic operating systems.

In summary, this blog reinforced the importance of data analytics literacy and identified some key questions to evaluate your organization’s maturity in data? As the field of data analytics is so deep, my next blog will continue exploring it and will also define the different types of data analytics methods that enable richer insights, as its key Board Directors and CEO’s understand some of the major analytic categories to speak data analytics and “walk the talk.”

As discussed throughout my blog series, AI has many benefits for business, the economy, and for helping to solve many of our world’s deepest challenges. However to get to the other side, it is not enough to have a quality user centric design approach unless the data sources are robust and the culture is maturing in its digital analytics literacy skills and competencies.

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.

Helpful Learning Sources,

Certified Data Management Professional

Data Management Professional (DMBOK)

IBM Skills Ignite

Microsoft’s Digital Literacy Learning

Salesforce’s Tableau Digital Literacy Learning

SAP Open SAP Learning

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.

Read more at www.forbes.com

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