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Where should I start with my data science project?
The first step that needs to be completed before starting any data science project is clearly defining the goal, or use case, of this project. Data science projects that are executed without having a clearly defined goal(s) almost always fail.
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 needs to be solved, 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 are data products and their different types?
Data products are any products or services where data is used as primary source for generating insights, visualisations or value to a user. Examples of common types of data products include:
- Dashboarding: To help users gain insights, monitor key metrics and take data-driven decisions.
- Recommendation systems: By giving customers relevant recommendation, they can increase sales, customer satisfaction engagement, etc.
- Demand prediction models: By predicting demand more precisely, they can optimize operations to save costs and reduce waste.
What are the benefits of developing data science products?
Overall, developing data and analytics products empowers organizations with valuable insights. This enables them to make data-driven decisions, drive innovation, and gain a competitive advantage in today's data-driven business landscape. This can be achieved through:
- Taking 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.
- Taking 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. In other words, automating decision making. This way, more decision can be taken over a shorter period of time (a.k.a scaling decisions).
- 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.
- Improving 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 should start by clearly answering some fundamental questions:
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What are the steps to successfully create a data project?
Over the course of developing new products, we identify three distinct phases: Exploring, productionalizing, and scaling & optimizating. All phases have four different steps: Refinement, building a walking skeleton, developing a solution(s), and documentation & delivery.
Our Approach:
Explore new data product
Project’s use case is identified and a proof-of-concept (POC) is developed to demonstrate the potential solution(s). This includes data collection and analysis, feature engineering and model development. The goal of this phase is to validate the feasibility of the proposed solution.
Productionalize data product
Building on the POC, this phase focuses on developing a minimal viable product (MVP). This includes further refinement of the model, implementation of the necessary infrastructure and integration with other systems. The MVP is a functional solution that can be tested with real users in production.
Scale & optimize data product
In the final phase, the focus is on scaling the MVP to handle an increased workload and optimize the model for performance. This includes fine-tuning the model, implementing data pipelines, monitoring and testing to ensure the solution is stable. The goal is to ensure the solution can handle the intended usage and deliver the desired business value continuously.
What are the different types of data science use cases?
Different types of data science use cases are summarized in the table below:
What are the common mistakes to avoid in your data science project?
- Creating a model / data product that doesn't solve the right problem(s): If you don’t have a clear scope before you create your model, you might end up with a solution that doesn't solve the problem at hand, or that addresses a completely different use case.
- Creating the solution without enough flexibility: Ideally, the solution should address the needs of many users.
- Not taking ethical considerations in account when creating the model.
- Feeding the model an insufficient amount of high quality data, or biased data.
- Model overfitting
- Lack of automated data quality checks: This might result in data drifts, influencing the output a model creates.
- Lack of scheduled model retraining: 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 (depending on the model):To make informed decisions, you want to be able to know how models make decisions. Therefore, explainability of model output sometimes matters.
How to scale, optimize and future-proof your data product?
- Scheduled regular retraining of the model: The frequency of this varies by use case.
- 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 data quality requirements based on business insights, and come up with custom metrics to assess the quality of your data.
- Checks on model quality and model drift: This 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 model performance.
- Detailed documentation and codebase: This includes comments in your code, and creating visual representation of your data / model pipeline. For example, you can use markdown to walk through your code in more detail.
Get a free consultation from our data & AI experts
Annemieke
- +31207728425
- info@xomnia.com