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Jeff Felice
President
CertNexus
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We shared an article in March explaining how and why it’s important to get business leaders trained and accredited in the areas of AI, machine learning, data science, and beyond. You can view that article here. As a continuation to that article, we compiled this article explaining the “Now what?” after getting business leaders trained up. It’s then time to trickle down that knowledge to the manager-level roles to ensure that your company is fully implementing the digital transformation journey.
Manager-level roles like project managers, product owners, leaders of data teams, and so forth are the drivers on your digital transformation journey.
These team members act as translators between business leaders and the engineers who work hands on with the technology. For the engineers, drivers can communicate the goals of the business from a strategic perspective. For business leaders, drivers can explain how technology can be used to achieve those goals from a technical perspective. A Driver’s job is to align everyone to the same goals and expectations—the destination and the route you will take to get there.
AI, Machine Learning, Deep Learning, and Data
Drivers need a deeper technical understanding than the passengers (business leaders). They need to know the pros and cons of different approaches to machine learning and deep learning. They should understand the data lifecycle and how the tool will evolve over time.
Most importantly, drivers should be able to articulate why and how a particular digital solution will facilitate the business goals. Drivers will need to communicate why their team is going about pursuing the project in a particular way. The appropriateness of a particular solution depends on several factors:
- What types of data will you be working with?
- What data sources are available to the business?
- How much computational load can your infrastructure handle?
Developing a Data Ethics Policy
The responsibility of being a good driver falls to this managerial role. Behind the wheel of a car, there are written rules of the road that enforce safe driving. There are also unwritten rules of courtesy. Data ethics address the moral element of gathering and employing data. It doesn’t ask, “Can we?” but “Should we?” In this area, some of the biggest companies in the world have gotten it massively wrong and suffered severe reputational damage. In formulating your data ethics policy, you must fully understand the rights that every individual possesses around their data — the rights of ownership, transparency, and privacy, to name a few.
Drivers must also understand the process of building an AI solution to avoid unethical outcomes. For instance, AI that unintentionally targets vulnerable populations or exhibits racial bias. Many businesses think of data ethics as an obstacle, as red tape they have to finagle their way around, but a good data ethics policy offers myriad benefits to the business:
- Customers are more aware than ever of the ways they can be taken advantage of through technology. A commitment to the ethical use of technology can make a company stand out and appeal to customers.
- If the AI is biased or unethical, it will not deliver a clear, accurate picture of the business or industry landscape. As a result, you may chase the wrong opportunity or let the right opportunity slip by under the radar.
- As problems result from unethical tech and data practices, laws like the General Data Protection Regulation, the California Consumer Privacy Act, and the EU Artificial Intelligence Act are coming into place to protect consumers. Ethical data practices mitigate compliance risk.
Technology will continue to become more powerful and more synonymous with business strategy. It is vital to use this great power responsibly and ethically.
What Makes a Good Driver
At the end of the day, the best drivers reach the right destination within a reasonable timeframe. The car arrives intact, and no one is hurt along the way.
When the time comes to take the next trip, they’ll turn to you for a smooth ride.
Learn more about credentials for drivers (manager-level roles):
- Data Science for Business (DSBIZ) offers business leaders, sales and marketing managers, project managers, and other stakeholders a streamlined course to help make decisions and drive organizational data science strategies. DSBIZ candidates will learn data science concepts, methods of use, challenges, and benefits using relevant business examples.
- Data Ethics for Business (DEBIZ) is designed for business leaders and decision makers, including C-level executives, project and product managers, HR leaders, marketing and sales leaders, and technical sales consultants, who have a vested interest in the representation of ethical values in technology solutions.
- Artificial Intelligence for Business (AIBIZ) offers business leaders, project managers, and other stakeholders a streamlined course and associated credential to drive their AI strategy. AIBIZ candidates will learn AI concepts, approaches to machine learning and deep learning, fundamentals of AI implementations, and the impact of AI including business use cases.
- Internet of Things for Business (IoTBIZ) offers business leaders a streamlined course and associated credential to open collaboration and drive informed business decisions for their IoT strategy. IoTBIZ candidates will learn IoT terminology to understand the components of IoT infrastructure, uncover challenges for consideration, and discover the impact that IoT has on their organization.
- Emerging Technologies for Business (ETBIZ) is a combination of three CertNexus credentials (AIBIZ, DSBIZ, and IoTBIZ) which cover the most often used technologies to generate data, extract insights from data, and leverage data to predict future outcomes. Upon successful completion of this credentialing assessment, candidates will earn the capstone ETBIZ credential and receive a badge which can be posted on social media platforms to identify your dedication to emerging technologies.
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