Newsletter #22: "Amateurs talk strategy. Professionals talk tactics." Let's get tactical about AI --> Data Infrastructure + AI Maturity.

“Amateurs talk strategy. Professionals talk tactics.”

Of course there's a time and place for both as you know what happens in hockey if you look at the puck too much... ;)

So here we go.

What can get lost in all of the justified enthusiasm and optimism around Artificial Intelligence is what a foundation looks like for a company seeking to become more data-driven.

Measured by the quality of the decisions they make in relation to the quality of the associated outcomes those decisions generate and of course, how they reflect on both to drive continuous improvement.

For those looking for strategic trends and big ideas, this week’s newsletter isn’t for you.?

For those looking for a more tangible way to think about AI and actionable tactics that can impact your business today and tomorrow, Newsletter #22 is written for you.

“I believe in the basics: attention to, and perfection of, tiny details that might be commonly overlooked. They may seem trivial, perhaps even laughable to those who don’t understand, but they aren’t. They are fundamental to your progress in basketball, business, and life. They are the difference between champions and near champions.

For example, at the first squad meeting each season, held two weeks before our first actual practice, I personally demonstrated how I wanted players to put on their socks each and every time: carefully roll the socks down over the toes, ball of the foot, arch and around the heel, then pull the sock up snug so there will be no wrinkles of any kind.”

- John Wooden

There is a flow that exists in every company where data is processed into insights, insights are used to make decisions which result in execution that is measured against their intended outcome and associated benefit to the company.

This flow is then reviewed and strengthened based on performance data.

The above is an abstraction of the nuanced reality you all face everyday but if you break your reality down to its essence, good chance the above is a big part of what’s going on.?

I thought it would be a helpful way to contextualize the following.

In periods of exponential change (shout out ChatGPT 11.30.22), the companies that figure out how to harness the tools driving the new paradigm faster than their competition are going to win more often and could create a sustainable competitive advantage along the way to separate themselves from their competition.

While we could talk tactically about a couple areas, the one that I’m going to focus on is the relationship between Data Infrastructure + AI Maturity.

Just as my man John Wooden focused his All-Americans on how to put on their socks and shoes, teams that focus on the relationship between their Data Infrastructure and AI Maturity are doing the work that Wooden would be stoked on.

Data Infrastructure are your foundational systems, tools, and processes used to collect, store, manage, process and analyze data.

Storage: Where your data is kept.?

Processing: Tools and platforms that handle your data.

Integration: Systems that ensure data flows smoothly between different parts of your systems.

Management: Tools for governance, quality and metadata management.

Explainability: Your team’s ability to successfully explain the above to non-technical stakeholders.

AI Maturity is a more abstract term that describes how well your company operationalizes AI technologies to deliver on priority goals and associated outcomes.?

It’s not just having your Data Infrastructure sorted out and AI models built but how well they’re integrated into your day-to-day workflow.?

It’s about the tactical and tangible value they generate today / tomorrow and how your company drives continuous improvement to get more out of them over time aka People, Process + Technology.?

Whereas Data Engineering is focused on building Data Infrastructure for data generation, collection and storage, Data Science is focused on extracting insights from complex and at times very big data that help realize the promise of AI within your organization, improving your AI Maturity along the way.

Easiest way to measure your AI Maturity is to come up with a basic AI Maturity Index + Assessment that your company, not just technical leadership, can understand that maps your current state, desired future state and the stages in between.

Done so in a way that is always thinking about the “so what?” aka “why are we ultimately doing this and how do we know we’re succeeding?”

Crawl, walk + run type stuff that you can break down into a matrix that has a numerical scoring grid with buckets based on ranges.

As you meaningfully improve workflows and associated outcomes, your maturity moves up and you can invest in increasingly sophisticated technology and generate more / better results.

Relationship Between Data Infrastructure + AI Maturity?

Your Data Infrastructure is the foundation upon which your AI Maturity is built. The breadth and depth of your Data Infrastructure determines how gnarly your AI models can be.

The better your Data Infrastructure, the better your data hygiene and therefore the more high-quality data your company can harness to execute increasingly sophisticated AI enabled initiatives, today.

The scalability quotient of your Data Infrastructure is also important as you crawl, walk + run your way towards more complex applications.

The more things change, the more they stay the same aka People, Process + Technology.?

Your company’s ability to use the Data Infrastructure tools available to them vs the delta between the capabilities of your tech stack and the skillset of the people / team’s the tools are made available to is something that requires calibration on an on-going basis.

Something to keep in mind is how technical a user must be to easily use the technology.

The more low code no code the tech stack, the more democratized access it will enable which generally translates to more people being able to benefit.

That being said, it’s important to always consider the evolution of team roles, learning curves, an incremental vs a more advanced approach and the fact that there is and always will be humans in the loop aka Humans + Machines not Humans vs Machines.

Another aspect to consider is the feedback loops you’re using to drive continuous improvement.

How easy it is to see the direct connection between the impact the tools are having on prioritized workflows and how often they’re generating the desired outcomes vs not so much is a critical part of determining which workflows to focus on in the first place.

What Does the Org Chart of Rockstars Driving Data Infrastructure + AI Maturity Look Like?

This is heavily dependent on the size of the organization, the data-driven decision making quotient of the organization etc but here’s a straightforward way to think about what the org could look like:

Chief Data Officer (CDO) or Chief Analytics Officer (CAO)

End to end responsibilities for the overall strategy for data management, governance and analytics across the organization. Reports into the CEO or COO to ensure alignment with business goals and objectives.

Head of Data Science

Leads the data science team, oversees model development and ensures alignment with the prioritized business use cases. Reports into the CDO or CAO.

Head of Data Engineering

Manages the data infrastructure to ensure that data is accessible, reliable and is optimized for analysis. Reports into the CDO or CAO.

Head of Data Governance + Quality

Establishes and enforces data governance policies, ensures data quality, compliance and security. Reports into CDO or CAO.

Head of Business Intelligence (BI)

Leads the BI team, focuses on reporting, dashboards and descriptive analytics to drive better business decisions. Reports into the CDO or CAO.

Data Science Team

Data Scientists focus on deploying, scaling and maintaining AI models in production environments. Reports into the Head of Data Science team.

Data Engineering Team

Data Engineers design, construct, install and maintain large-scale processing systems and data infrastructure to ensure databases are optimized, secure and available. Reports into the Head of Data Engineering.

Data Governance + Quality Team

Data Stewards oversee the quality, lifecycle and usability of specific data assets while Data Analysts ensure data quality, perform routine checks and assist in data-related tasks. Report into Head of Data Governance + Quality.

Business Intelligence Team

BI Analysts develop reports, dashboards and data visualizations. BI Developers handle the technical aspects of BI tools, integrations and performance. Report to Head of Business Intelligence.

Supporting Roles

Data Privacy Officer ensures compliance with data protection regulations. Domain Experts provide industry or function-specific insights to guide data projects. IT Support collaborates with data teams to ensure hardware and software needs are met.

Tangible and Actionable Tactics that Can Impact Your Business Immediately?

Develop a simple way to evaluate your data infrastructure, create a directional AI Maturity Index + Assessment and pick 3 opportunity paths and get after it.

To build the directional index + assessment, you could consider questions including the following:

Strategy + Vision

Do you have a clearly defined AI strategy aligned with your business objectives?

How would you describe the primary goal of your AI initiatives (i.e. innovation, cost reduction, increasing decision making speed, increasing revenue)?

Who is the point person and key stakeholders for AI within your organization?

ROI + Performance Metrics

How do you measure the ROI of your AI projects?

What key performance metrics do you use to evaluate the near-term and long-term success?

Data Infrastructure + Management

How centralized and organized is your data storage?

How would you rate the quality, cleanliness and accessibility of your data?

What role does data play in your top 5 most important decisions and associated workflows?

Talent + Expertise

Do you have a dedicated data science team and what are your expectations of them?

How would you rate the AI expertise of your team and what benchmark are you using?

How are you helping your Data Science team and others in the organization train and upskill?

Implementation + Use Cases

Are AI initiatives typically driven by business needs or technological exploration?

Which business functions or areas have benefited the most from AI in your organization and why?

How many AI projects has your company successfully implemented to date and how many do you project implementing in the next 90 days?

Mindset / Culture + Adoption

How would you rate the AI literacy of non-technical staff in your organization?

On a scale of 1-10, how would you rate the culture of data-driven decision-making across the organization and what is your rationale for the rating?

On a scale of 1-10, how open is your organization to adopting new AI-driven solutions or processes and what is your rationale for the rating?

Future Readiness

How do you stay updated with the latest advancements in AI and incorporate them into your strategy, focus / priorities and approach?

Where do you see your company’s AI capabilities in the next 3-5 years and why / how?

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While these questions represent a broad spectrum of areas, based on what you have going on, you could pick and choose or go deeper on a specific category or two.?

Either way, the answers will help you figure out what to tackle first and from there, you’re on your way.

That’s it for this week. I hope you?enjoyed it and have a great Sunday!

Also, look for a very exciting announcement from me tomorrow. about what's next.

It's the combination of my deep curiosity / passion for AI and returning to my roots of Sports, Media + Entertainment aka home.

So grateful, so excited + so ready. ??????????????

- Alec