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Where should I start with my data science project?
Before starting any data science project, the first step is to clearly define your goal or use case. Companies that jump to choosing or building data science products before clearly defining their goals will most likely experience failure.
Xomnia’s Analytics Translators have created a free use case canvas that can help you define all the important aspects of your data science use case. This includes defining the goal of the project, the problem(s) that you are trying to solve, and the right stakeholders.
We recommend starting your data science project by filling our use case canavs or similar tools that can provide your project with the necessary focus from the start.
What kind of products can be delivered with data science?
With enough data available, it is possible to build several types of data products. Data products are any products or services where data is used as a primary source for generating insights, visualizations, or value for a user. Some examples of data products include:
Interactive Dashboards
These help users gain insights; monitor key metrics, data trends, and patterns; and make data-driven decisions. Examples: Sales dashboards and financial performance dashboards.
Predictive Analytics Applications
These data-driven tools help business users make better predictions or recommendations based on historical data. Examples: Customer churn prediction apps and stock price prediction apps.
Recommendation Systems
These suggest relevant items or content to users based on their particular preferences or online behavior. By giving customers relevant recommendations, these can increase sales, customer satisfaction, and engagement. Examples: streaming platforms, and data-driven e-commerce websites.
Anomaly Detection Systems
These help to identify unusual behavior (i.e. outliers in data), which may indicate problems. Examples: Fraud detection systems, network intrusion detection systems, and defect prediction systems.
Customer Segmentation Tools
These help businesses categorize their customers based on their behavior and characteristics. Example of application: Marketing campaigns tailored to specific customer groups.
Natural Language Processing (NLP) Applications
These help analyze and understand text data, enabling language-related insights. Examples: Chatbots, text summarization tools, and tools that enable sentiment analysis (how do clients feel about their purchases based on their reviews?)
These help users gain insights; monitor key metrics, data trends, and patterns; and make data-driven decisions. Examples: Sales dashboards and financial performance dashboards.
Decision Support Systems (DSS) are also worth mentioning here. DSSs are designed to assist users in making informed decisions by providing relevant information and analytical tools. DSSs can in fact be integrated into all of the examples mentioned above.
By incorporating DSSs into data science products, users can gain insights, monitor key metrics, and receive actionable recommendations and predictions to enrich their decision-making process. In the customer segmentation use case, for example, a DSS user interface could allow the marketing team to experiment with different combinations of variables such as purchase frequency, location, average transaction value, etc., and visualize their impact on customer segments.
What are the benefits of developing data science products?
Data without proper management, context, and stewardship will hold little value as you cannot access it, analyze it, and trust that it's complete and correct. Data science products enable businesses to extract actionable insights, make data-driven decisions, and gain a competitive advantage. By effectively leveraging data through data products, organizations can drive innovation, enhance operational efficiency, and achieve sustainable growth.
By developing and using data products, organizations can unlock their full potential thanks to:
More accurate decisions: Data and analytics products provide valuable insights that can support informed decision-making. By analyzing large volumes of data, businesses can uncover patterns, trends, and correlations that help them make more accurate decisions.
More timely decisions: Data and analytics products enable organizations to anticipate future changes or challenges by identifying or predicting things even before they happen. They can provide forecasts by leveraging historical data and applying predictive analytics.
Automating decision-making: In some cases, it is possible to leave a decision entirely to an algorithm, and by automating decision-making, more decisions can be taken in a shorter amount of time (i.e. decisions become more scalable)
Increasing efficiency: Data and analytics products can identify bottlenecks, inefficiencies, and areas for improvement in operational processes. This enables businesses to anticipate potential risks before incurring losses due to them.
Improve customer satisfaction and retention: Data and analytics products can provide useful insights into customer behavior and preferences. Companies can utilize this to tailor their services, products, offers, etc., in ways that maximize conversions or sales.
Boosting innovation and product development: Data and analytics products can drive innovation by uncovering market trends, identifying new opportunities, and supporting product development in ways that are not possible without them.
Tracking KPIs accurately and quickly
How to define the right data science use case?
AI and data should be tailored to solve challenges in your company, and not the other way around. Therefore, the journey to create and execute your data-driven strategy and deliver useful data products should start by clearly answering fundamental questions:
- WHAT are the data opportunities for our company & WHY should our company chase these data opportunities? → Define your value proposition.
- HOW might we achieve the selected use cases? → Conduct a capability assessment.
- WHEN to develop which data products and organizational enablers? → Set a data and AI roadmap.
What are the steps to successfully create a data project?
1) Project refinement: In this phase, you define the data value proposition for your company. You collect the necessary information to start the project through various stakeholder engagement sessions. The goal of these sessions is to refine the project's use case and identify potential risks early on.
At this stage, various forms of documentation are used to gather and organize information, such as a problem statement worksheet, use case canvas, responsible AI checklist, and a stakeholder plan.
2) Data Collection and Exploration: With a well-established goal, it is now time to identify and gather the necessary data for your project. This includes exploring and understanding the characteristics of the data, including its structure, quality, and possible issues.
3) Data Cleaning and Feature Engineering: This includes cleansing and preprocessing the collected data to handle any missing values, inconsistencies, and outliers. You can also start transforming variables in your dataset to create new variables based on domain knowledge. For example, if your dataset contains users’ birthdates, you can calculate the age of the users instead of working with birthdates directly, since age is more relevant than birthdate in applications such as in marketing campaigns targeted at specific age groups.
Read more: Practical tips on writing clean code: Improve your coding & enhance your software
4) Model selection, training and evaluation: Now, with a cleansed dataset, you can experiment with several machine learning or statistical models based on the project’s goals. It is important to split the dataset into training and testing sets so that you can train the models with the training data and evaluate them with the test data. That way, you can measure the quality of the different models you experiment with. For evaluating, you can consider metrics such as accuracy, precision, recall, and F1 score.
5) Initial Model Deployment: In this phase, the initial implementation of the project is completed by building and deploying a minimal working version of the project. The deployment environment is also chosen at this phase (e.g. cloud or on-premises).
The project's framework is established, and a preliminary examination of the data is conducted to draw early conclusions. This serves as a foundation for further development, mitigates potential risks, and allows a smoother transition in the future.
By learning from past experiences, the team starts with a technical head start, using a cookie-cutter template with established best practices. Examples of this include an automated test suite, continuous integration, pre-commit, linters, formatting, abstract base classes, and MLflow. This approach ensures a robust, maintainable, and high-quality solution.
6) Iterative Model Refinement: After creating an initial version of the model, you iteratively construct a refined project solution through sprints, with a focus on answering the research question. Stakeholder engagement is maintained throughout this phase to ensure that the solution meets user requirements and aligns with their needs. Your team follows an agreed way of working, including regular check-ins with the business owner, stakeholder involvement, and Scrum meetings.
The solution is developed in multiple steps, with the team gaining a deeper understanding of the business processes as the project progresses. During sprint demos, feedback is gathered from key users to inform further development. The project is evaluated using pre-defined success criteria, with the ultimate goal of delivering a solution that answers the research question and serves as the foundation for building an MVP.
At this stage, it is also valid to enrich the model with more data, create more features when applicable, and experiment with different machine learning models and hyperparameters to arrive at the best solution possible.
7) Deploying to Production, Monitoring, and Maintaining: This important phase of the project includes conducting thorough testing in the production environment to identify and address potential issues, as well as setting up continuous monitoring to track performance and possible anomalies.
8) Delivery and Documentation: This step includes comprehensive documentation of the solution, transfer of ownership, and evaluation of future opportunities. A comprehensive end report is delivered, detailing technical specifications as well as business insights. This phase emphasizes a seamless handover for continued development as an MVP and sharing of key learnings.
How can I decide which data science product is best for my use case?
To help you figure out which kind of solution is more suitable to the problem you might have at hand, you have to take into consideration the type of data you possess:
Labeled data: Data in which each example is accompanied by a clear indication or “label” that describes the outcome it belongs to. Example: Imagine you have a collection of pictures, and for each of them, there is a tag indicating whether it contains a dog, a cat, or a bird. In a business setting, imagine a list of transactions and a tag that indicates whether they are potentially fraudulent or not. If you have labeled data, your solution will most likely fall under the supervised learning category.
Unlabeled data: Data that lacks explicit tags, which requires the algorithm to identify patterns or structures by itself. Imagine the same collection of pictures or list of transactions mentioned above but without the tags.
If you have unlabeled data, your solution will most likely fall under the unsupervised learning category.
In some cases, you need an agent to learn to make decisions by interacting with the environment - in this case, labels are not explicitly provided, and the algorithm has to learn through trial and error - this is called reinforcement learning.
Read more: Machine learning with limited labels: How to get the most out of your domain expertise?
Generative AI/LLM-Based Solutions: When dealing with problems that require the generation of new content, insights, or complex pattern recognition, generative AI, particularly Large Language Models (LLMs), come into play. This approach is ideal for scenarios where you need to:
Create or simulate data (data augmentation): Generative AI can generate text, images, or other forms of data that mimic real-world examples. This is particularly useful in situations where data is scarce or sensitive.
Enhance creativity and innovation: These models can assist in brainstorming, creating novel designs, or suggesting solutions that might not be immediately obvious to human analysts.
Understand and generate human-like language: LLMs excel in tasks that involve natural language understanding and generation, making them perfect for chatbots (customer service), content creation, summarization, and more.
What are the common mistakes to avoid in your data science project?
Lack of clear objectives: If you don’t have a clear scope before you create your model, you might end up with a solution for a completely different use case. Besides scope creep, this also leads to confusion, lack of measurable success criteria, and low-quality models.
Insufficient stakeholder involvement: Not involving relevant stakeholders throughout your project can result in misalignments with business needs.
Poor data quality: Neglecting data quality issues can lead to inaccurate analyses, poor models, and consequently unreliable insights. Poor data can be caused by a handful of factors, for example:
- Not having enough high-quality data
- Highly biased data
- Samples that do not accurately represent all possible input data values
- A training data set that is just too small
Poor model quality: Poor models either overfit (models that learn the training data too well, capturing noise and making it less effective on new data) or underfit (models that are too simple and fail to capture underlying patterns performing poorly on both training and new data). Overfitting and underfitting models can make lots of mistakes in a real-world scenario. This can be caused by:
- Poor data quality (see item above)
- Inadequate training time (too little time leads to underfitting, and too much time leads to overfitting)
- Input features that are too little (underfitting) or just too many (overfitting)
- Lack of automated data quality checks, which might result in data drifts, influencing the output a model creates.
System inflexibility: Ideally, the solution should address the needs of many users, but unfortunately, that is not always the case. Additionally, when you don’t retrain your model, it will become outdated. Therefore, it is wise to schedule retraining to make sure your model stays relevant.
Lack of transparency: To make informed decisions, you want to be able to know how models make decisions. Therefore, the explainability of the model output matters.
Inadequate documentation: It is usually expected for the code to be commented on. Visual representations of model pipelines are also generally appreciated. Without proper documentation, new team members or stakeholders may have trouble understanding the data model and its underlying structures. Additionally, it becomes difficult for developers to identify areas that need modification as the data model evolves.
Ignoring data privacy and ethics: Visit our comprehensive guide about responsible and ethical AI.
Trying to apply data science to a problem that doesn't require complex technology, and could be solved with a simpler solution.
How to scale, optimize and future-proof your data product?
Your ultimate goal is to ensure that the solution can handle the intended usage and deliver the desired business value continuously. Here are examples of important things to keep in mind if you want your solution to be scalable, optimized, and future-proof:
- Implement data pipelines: Since we want to ensure the solution can handle the intended usage, you want to make sure the data is processed automatically. You can implement data pipelines to facilitate that. This will ensure, for example, that your solution receives data on scheduled times and that your data product receives data in specified formats, which can mitigate possible errors while reading the input data.
- Empower your solution with techniques and tools that can enable scalability. For that, make sure you choose technologies that can grow as your data and user base expand, and ensure that the system can handle more users and data without slowing down. It is also important to divide data and workload efficiently across servers for better performance.
- Monitor and test your system performance: Since you don’t want to worry about model or data drift, the best you can do is implement a monitoring system that keeps track of the stability of your model and data. That way, you don’t have to do checks yourself and you can make sure that you will receive notifications when something happens. You can also guarantee that your data science solution is monitored in real-time and that issues are identified and dealt with quickly.
- Perform data quality checks: Monitoring can be a part of your data processing pipelines, or you can use data quality software. This step also depends on the use case; you need to specify requirements based on business insights and come up with custom metrics to assess the quality of your data.
- Check on model quality and model drift: Over time, with unforeseen trends in data, the model can become outdated. Therefore, the data science product needs to constantly adapt to the input data. However, this depends on the use case - you need to specify requirements based on business insights and come up with custom metrics to assess the quality of your model performance. Nonetheless, it is always important to create flexible data models that can adapt to changing business needs.
- Documentation. Ensure to document the system thoroughly for easy understanding and future development.
- Keep an open feedback loop with users. This enables continuous improvement and involves users in testing the solution to ensure its usability.
Want to know how we can help you achieve data driven impact?
Lars
- info@xomnia.com