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An Executive’s Guide to Delivering Business Value Through Data-Driven Innovation and AI

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By Lars Joakim Nilsson, Managing Director of Advanced Analytics and Big Data at Inmeta

The key to making better business decisions is surprisingly simple: take a proactive approach to using data. Every company gathers data in one form or another, but the way a company uses its data has a lasting impact on the ability to compete, innovate, and attract talent.

For many companies and their employees, data is gathered and handled reactively. They collect and use data intermittently on an as-needed basis, but it’s seldom collected for historical analysis to support the creation of AI solutions.

Data-driven companies are different.

Data-driven companies believe that being proactive with their data is the ultimate differentiator. They strive to create a culture that, from the top down, holds dear the transparency, accessibility, and usefulness of data. A data-driven company seeks to learn from data to make predictions about future outcomes, and then bases decision-making on facts derived from the data they collect every day.

At Crayon, an AWS Partner Network (APN) Advanced Consulting Partner with the AWS Machine Learning Competency, we help customers understand what becoming data-driven means and how to challenge common misconceptions and fears that arise around artificial intelligence (AI) and data science.

In this post, I will share insights from my time at Inmeta, a subsidiary of Crayon, and the work I’ve done with executives. We will discuss the philosophy behind why data-driven companies drive better business outcomes using AI, and I’ll share tips for your company to take a new approach on data collection that will help facilitate better insights, outcomes, and customer experiences using AI.

Becoming Thoughtful About Your AI Strategy

Fostering a data-driven culture within your organization isn’t only about technology. It’s also about enabling stakeholders to make better decisions and realizing new opportunities by embracing an AI-driven mentality for solving business problems.


Building a data-driven organization is an opportunity for you to transform your business, discover new insights, and empower your employees. However, it’s crucial to approach this transformation in the right way. Let’s discuss some of the first steps you should take and the essential questions to ask yourself as you thoughtfully develop your company’s relationship with data.

Value the data you collect and maximize its usefulness for building models and using AI.

The data your company collects is often organizational or outcome data from the processes you run. At Crayon, we believe you need as much of your historical data (in both structured and non-structured formats) as possible to get the most from AI.

To get optimal results from your data and begin building and training ML models, you need to combine your data with behavioral data from multiple sources such as Internet of Things (IoT)-connected devices, documents, social networks, weather, geo data, and so on.

Educate your organization and help individual employees become data-driven within their roles.

To create an AI-driven culture, you must educate your entire organization about what it means to use data to make better decisions and improve business outcomes.

Develop a consistent and transparent process for capturing, realizing, and putting into production AI-driven recommendations from across your organization. We encourage customers to consider hiring a dedicated AI strategist and evangelist to serve as the primary stakeholder to whom internal teams can turn to for continuing education.

Building a digitalization strategy without AI is like making macaroni without the cheese. We don’t recommend it.


There is a tight bond between digitalization—using digital technology to change your business model—and AI. Though they form different aspects of your company’s overarching digital transformation, they should be a part of the same strategic roadmap. Unfortunately, we often find that many companies have digitalization plans sans the AI strategy.

As your company digitalizes, you will begin to collect more data points from which you can discover new insights, develop better processes, and create new business opportunities. Building an AI-driven strategy alongside your digitalization strategy enables your company to maximize its ability to innovate and the value it can drive by going digital.

AI doesn’t begin and end with the data scientist. The role of the data scientist is to come together with business owners and developers to create new solutions.

Many of our clients’ data strategies begin and end with the data scientist. However, data science in a silo won’t get your organization very far. Understanding the role of the data scientist within a data-driven organization means letting go of preconceived notions of who a data scientist is and the role they play in a broader data-driven strategy.

The role of the data scientist isn’t to define, drive, and implement your company’s data-driven strategies and educate your organization about the importance of data. An effective data scientist instead collaborates with line of business owners across the organization to identify novel ways to use AI to drive more accurate predictions and better business outcomes.

Don’t expect your data scientists to be your organization’s leading advocates for becoming data-driven; that responsibility lies with both the executive team and with a dedicated internal AI strategist and evangelist.

Lead by example and become an AI advocate.

A data-driven culture begins and ends at the executive level. AI has the power to impact all aspects of your business, from the user experience to operational flows to business models.

We find that executives who can infuse an AI-driven mindset into their existing operations and from the top down can help their teams become more innovative at a much faster pace. As an AI-driven executive, you should be intimately involved in deciding which business outcomes you want your company to achieve using AI.

Don’t fear data; embrace its power and possibilities one step at a time.

Data and AI is whatever you make of it. You have a unique opportunity to use the data you collect every day to drive new value for customers and efficiencies for your internal teams.

When you think about data and AI, think business outcomes. Your company’s data strategy should be driven by—and continuously influence—the business outcomes you seek to encourage in both the short- and long-term.

Video: Crayon Helps TINE Dairy Co-Op Leverage Machine Learning

Next Steps

What can you do with data and predictions developed from your data using AI and ML to help you meet your business goals and delight your clients?

Please connect with us at Crayon for expert guidance as you get started building a data-drive company.

The content and opinions in this blog are those of the third party author and AWS is not responsible for the content or accuracy of this post.


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Crayon is an AWS ML Competency Partner. Their team consists of highly-qualified data scientists and developers with expert experience within the field of AI and deep learning.

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