05.01.2023 Executive Data Bytes – The Ultimate Guide To Crafting An AI and Data Strategy

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we'll try to answer the questions: "Why do you need a Data and AI strategy? What exactly is a Data and AI Strategy? And how do you develop a Data and AI Strategy?" The first part explains WHY, the second section explains WHAT, and the third and final portion explains HOW. This is a do not miss issue. Let's begin on the journey to clarity!

Focus piece: “Data Strategy: What It Is and Why AI Is Critical to Success and Scalability ”

Executive Summary

"Start With Why" is a phrase connected with Simon Sinek, an author who believes that starting with why promotes clarity, concentration, and motivation for individuals and organizations. And executives who understand why they need a Data and AI strategy, get to understand what it is and is not, and knowing your “why” brings? passion to see your efforts through to completion, or at the very least make something helpful to the business out of it. This section is dedicated to explaining why every organization requires an AI and data strategy.

Key Takeaways

• The first reason why businesses require a data strategy is that it connects corporate objectives and goals, which constitute the north star that provides direction to the what, why, and how of data. Having one allows you to choose the best course of action in any given event inside the firm as relating to data.
• AI strategy, on the other hand, lends meaning and purpose to every AI project that your organization intends to undertake. It outlines the steps that every AI project should take from inception to conclusion. This decreases the likelihood that you'll merely design or incorporate AI technologies into your goods to stand out as an AI-first firm or to simply keep up with the pack. Rather, it ensures that every AI project is designed in accordance with the company's goals and objectives, adding value and providing a profitable return on investment.
• Overall, data and AI strategy are critical for any firm, and one should not exist without the other. Whereas data strategy helps to lay a solid foundation, AI strategy builds on that foundation to ensure that all AI products and services developed within the organization deliver the required value.

Focus piece: “Developing a Data Strategy Template ”

Executive Summary

In order to catch up with firms that are Data and AI driven, many organizations try to move too quickly, hiring people and purchasing AI technologies without first determining how Data and AI fit into their business model, and some without understanding how it works. This article by Dataversity defines data strategy and goes on to discuss how it may be constructed, which we will look at in the following section.

Key Takeaways

• Data strategy is a set of strategies and procedures that specify what data is captured/collected, how it is captured/collected (without violating privacy standards), and for what purpose it will be utilized. "It represents a set of choices and decisions that together chart a high-level course of action to achieve high-level goals," according to Dataversity.
• Before explaining what an AI strategy is, it’s best to clarify that many people do not understand the distinction between AI and other related terms such as machine learning. These two, for example, are not synonymous. While machine learning makes predictions, AI takes those predictions and turns them into actionable steps that can be taken to achieve the desired result. For example, advising a route from work to home using Google Maps. On AI strategy, It now gives a framework for how an organization wants to employ AI technologies to meet its business objectives and ambitions. It provides a clear roadmap for establishing AI projects and calculating the ROI of your company's AI activities.
• To summarize, your data strategy should come before your AI strategy, as data is a critical component of AI systems, and using quality data to carry out your AI projects will raise the likelihood of them succeeding and providing what is anticipated of them.

Focus piece: “How to build a data strategy to scale AI ”?

Executive Summary

While understanding the why and what of Data and AI strategy is important, it is also important to understand how to build it up, or your organization will become like those that know a lot but never get around to doing it. While every company's process will be different, this piece by Accenture asks deep questions whose answers will provide a path on how to build an AI and Data strategy.

Key Takeaways

• Begin with a Mindset (Create a Data-Driven Culture): Alignment of all stakeholders, including employees, managers, shareholders, and senior leadership, is required for developing a sustainable and functional AI and data strategy. The responsibility for alignment rests with those at the top. According to an excerpt from the article, "senior leaders must show employees what is possible with data and invest in the tools and resources that will empower their employees to achieve those possibilities." Without alignment, some people's relaxed attitude has a negative impact on the attitude of others.
• Aim for Quality Over Quantity: Let your data and AI strategy prioritize quality over quantity. As these new fields or initiatives are being introduced within a company, employees need to know how much they can trust the data and systems being built. Furthermore, the strength of Data and AI systems is highly dependent on the quality of data used. While data volume is important, it will not benefit the organization unless it is of high quality.
• Always keep ethics in mind: The Facebook Cambridge Analytica Data Scandal is a stark reminder of why you must integrate ethical values into your company's drive for innovation and transformation. By developing and implementing your AI and data initiatives ethically, you assist to ensure that your users' data is secure and free of bias, while also encouraging justice, transparency, and a feeling of accountability inside your business. Today's post concludes with an excerpt from the article, which states that "without ethical and responsible use, data strategies and AI solutions may work technically but may not deliver the expected outcome."

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