As AI continues to integrate into an increasingly diverse set of applications, the role of an AI project manager becomes increasingly complex and crucial. This post aims to provide a glimpse into the role of AI project management and tries to underscore the effort required to ensure the success of […]
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Data Analytics vs Data Science – Is there a difference between these two fields? With the explosion of the need to analyze data, it can be confusing to understand what the distinction is between the different related fields of data science and data analytics. While data science and data analytics […]
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As AI practitioners We would like a blog post exploring some underlying concepts and practical tips of Agile AI So that we can more effectively drive results with our AI initiatives Agile is a flexible approach for managing initiatives in ever-changing environments. Acknowledging the complexities of AI projects, many organizations […]
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Not stopping at merely utilizing apps like ChatGPT, many companies are building, or exploring the possibility of building, their own Generative AI bots for internal use as well as for use by their clients. However, since Generative AI is so new, people wonder if managing Generative AI projects is different […]
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To drive effective data science outcomes, data scientists and stakeholders need to jointly understand the deliverable requests and their value. And yet, communication breaks down whenever data science team members and stakeholders interpret the project scope differently. To help bridge this gap, teams can write many of the project deliverables […]
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Do data science teams use (or think they use) an agile data science team process framework? To help find out, my student (Sucheta Lahiri) recently presented an academic paper that reported on data science agility across 16 organizations. The key takeaway was that even if your team thinks you are […]
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A data science team’s process is a key driver to their projects’ success. However, as will be discussed below, there is not an existing AI / data science maturity model that is focused on how to evaluate (and improve) a team’s process. Hence, after reviewing several Analytics / Data Science […]
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By their nature, data science products are risky. Building a Data Science MVP can reduce that risk by focusing the early development life cycle on discovery and learning. The onset of a data science project has a lot of unknowns – on the data, algorithm, systems, and business side. You […]
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The machine learning process defines the flow of work that a data science team executes to create and deliver a machine learning model. In addition, the ML process also defines how the team works and collaborates together, to create the most useful predictive model. A High Level Machine Learning Process […]
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Agile Business Intelligence Project Management Best Practices Data by itself is often useless. However, by transforming the data into human understandable insights, organizations can harness the power of data to make meaningful decisions that drive outcomes. But making this happen is easier said than done. And traditional BI approaches often […]
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Data science projects are unique. It’s time to start managing them as such.
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