Taking the Data-Centric Approach to AI
What is Data Project Management?
AI projects, brimming with potential, often face a high failure rate despite abundant talent and resources​​.
Fortunately, we have a solid approach we can tackle for AI project, which at their core are data projects. Data project management is at its core managing projects focused on the different steps of a data project lifecycle.
What are the 5 Steps of Data Project Life Cycle?
Understanding and managing data is paramount. Project managers must be adept in data analysis, understanding data sources, and data preparation techniques​​.
Step 1: Problem Identification
AI projects must begin by identifying the problem and validating AI as the optimal solution​​. Diving into AI without this clarity can lead to wasteful investments.
Step 2: Evaluating AI’s Fit
It’s vital to recognize the limitations of AI and consider viable alternatives like rules-based systems or non-intelligent automation​​.
Step 3: Cost/Benefit Analysis
Undertaking an AI project requires a thorough cost/benefit analysis, ensuring that the project delivers promised benefits and ROI​​.
Step 4: Data Acquisition and Quality
The success of AI hinges on the availability and quality of data​​. Before embarking on the project, assess the data volume, sources, and quality​​.
Step 5: Data Preparation
Preparing data is a crucial step, involving cleaning, transforming, and ensuring its usability for the project​​.
However, despite these 5 steps, applying these to AI projects is not easy. More is needed.
How AI projects are really data projects
To excel in AI project management, one must understand not just the technology but also the methodologies that ensure the delivery of the project’s intended value​​.
The Pitfalls and Triumphs of AI Project Management
AI’s journey has been a mix of limited adoption and specialized success in areas like computer vision and natural language processing​​. The key to project success lies in understanding the problem at hand and determining if AI is indeed the right solution​​.
The CPMAI Approach
The Cognitive Project Management for AI (CPMAI) method offers a structured approach to AI project management, involving six phases: Business Understanding, Data Understanding, Data Preparation, Model Development, Model Evaluation, and Model Operationalization​​.
There are six main phases of CPMAI, illustrated below, provide guidance for each stage of AI development and continued project iteration to make sure your project is successful.
Source: Cognilytica
CPMAI Phase I: Business Understanding
The first step of CPMAI methodology for any AI project is gathering an understanding of the business requirements and understanding the business needs.
After all, if you’re not solving a real business problem for your organization then why are you even doing the project at all?
In phase one of your project iteration you should focus on understanding the project objectives and requirements from a business perspective, then converting this knowledge into an AI and ML problem definition and a preliminary plan designed to achieve the objectives.
CPMAI Phase II: Data Understanding
The second phase of CPMAI methodology for any AI project is understanding your data.
The most important part here is understanding what data is required to address the business problem, whether or not that data is available, and what format(s) your data is in.
Data is what fuels your AI projects so you should make sure you have a firm understanding of your data before getting too far along in your project.
CPMAI Phase III: Data Preparation
The third step of the CPMAI approach to AI project management is Data Preparation.
Once you have figured out what problem you are solving and what data you have, next you need to make sure the data you have is usable for your project.
In this step you need to do tasks such as data cleansing, data aggregation, data augmentation, data labeling, data normalization, data transformation and any other activities for data of structured, unstructured, and semi-structured nature.
CPMAI Phase IV: Model Development
Once we have a business understanding that lends to a data understanding and the data prepared with pipelines in place, we can finally get to AI model development.
In CPMAI, the fourth step of your AI project is the creation and development of machine learning models and supporting AI system artifacts.
This includes model technique selection and application, model training, model hyperparameter setting and adjustment, model validation, ensemble model development and testing, algorithm selection, and model optimization.
By the time you are ready to build your very first model you’ve already determined the business needs, the data requirements, and gotten the data in the right format and quality. If you haven’t, then you need to revisit these steps before moving forward.
CPMAI Phase V: Model Evaluation
Once a model is created, it needs to be evaluated to make sure it performs according to the business requirements and other factors set in the previous steps of a machine learning project.
In this fifth phase of CPMAI, you are now ready for model evaluation.
From an AI perspective this includes model metric evaluation, model precision and accuracy, determination of false positive and negative rates, key performance indicator metrics, model performance metrics, model quality measurements, and a determination as to whether or not the model is suitable for meeting the goals or whether earlier phases should be iterated upon to reach those goals.
Phase VI: Model Operationalization
The sixth step of each AI project lifecycle iteration is putting the model you just created into operation.
CPMAI Phase VI makes sure to address model versioning and iteration, model deployment, model monitoring, model staging in development and production environments, and other aspects of getting the model in a position to provide value to meet the stated purpose.
Iterating to Success
Each of these CPMAI phases are iterative with each other and allows for progression backwards or forwards during AI project development depending on the need and challenges met in the real world and provide a solid ML project structure to follow.
