7 Best Practices for Data Science

Data science is increasingly essential to modern business as enterprises become more and more reliant upon data to fuel their decision-making, provide competitive advantage, and engage their customers. Encompassing the process of assembling data stores, conceptualizing frameworks, and building models to drive analysis, data science incorporates statistics, artificial intelligence and machine learning (AI/ML), and other technologies to help businesses answer deeper questions.

As their data strategies grow to keep pace with the volume of information they collect, process, and store, data science best practices are critical to making the most of their efforts. This article explores those best practices and how enterprises can incorporate them into their data operations.

Here are seven data science best practices organizations should follow to maximize their investments in data in 2024.

Invest Heavily in Data Models

Requirements change and business objectives evolve, and data models need to evolve with them. No single data model is eternal. There’s an old saying that “the best time to repair a roof is when the sun is shining” that can be applied to data models—the best time to invest in them is when the existing ones are running smoothly.

Having the luxury of time allows you to better understand what is working and what could work better for your customers, your business model, or to unearth new opportunities. Here are a few ways to proceed:

• Review feature engineering to create new features, remove redundant data, and retrain your models.
• Validate and cross-validate the updated models and perform A/B testing for efficiency and to predict their chances of success.
• Make model updates a regular practice—especially for models deployed in dynamic environments that change frequently.

Learn more about Hierarchical vs. Relational Data Models or Logical vs. Physical Data Models.

Create and Maintain Documentation

Keeping track of data science processes and results to document progress can go a long way toward ensuring transparency and replicability. It can also minimize the chance of tribal knowledge, or unwritten knowledge not widely known within a company, getting in the way of success.

Log project details in as much detail as possible. This will make future investigations into data models easier and fix problems as they arise. Here are some must-follow practices for documentation:

• Use a standardized tool.
• Document data sources firsthand, including where the data was obtained, the data schema, and whether it has been processed.
• Keep track of missing values, processing steps, and feature engineering.
• Make documentation reflective of your project needs—if and when the project evolves, the documentation should evolve with it.

Build Infrastructure to Support Data Operations

IT costs follow data volume—the more your volume expands, the greater the costs of storing, maintaining, and securing data pipelines. An open infrastructure is a one-stop solution to managing data processes and running analytics smoothly across departments.

Infrastructure that’s scalable can adapt to growing resource demands and changing workloads. Cloud platforms like AWS, Azure, or Google Cloud can help you optimize for data storage, processing, and analysis. Alternatively, build your own on-premises solution—just make sure the data science team is involved either way. Here are other ways to ensure that your infrastructure supports your data work:

• Consider outsourcing custom infrastructure needs to third-party data science companies and vendors.
• Deploy data pipeline and extract, transform, and load (ETL) tools to automate data processing from multiple sources.
• Make sure your infrastructure can scale without compromising security.
• Automate connections between your infrastructure and server providers, databases, and essential machines.

Track Metrics To Measure Project Success

Metric-driven data campaigns ensure potential roadblocks are flagged and remediated before they stop a project cold. Metrics also help in optimizing data projects, gauging project health, and improving the return on investment of your efforts.

Key performance indicators and metrics can offer insights into the value of an individual data science project. These objective numbers on a single pane view can help in predictive and retroactive analysis and lead to success.

Deploy Self-Service Tools

Bringing all stakeholders together to reduce data friction is one thing, but communicating the real value of your data science process to them is something else entirely. Using self-service data analytics tools like PowerBI or Tableau, you can convert technical know-how into plain language or visualizations that non-tech stakeholders and decision-makers can understand.

Self-service tools make critical discoveries easy to interpret and act upon. Business intelligence (BI) powered dashboards and visualizations are easier to navigate than manually feeding data pipelines, reducing the turnaround time for data requests and allowing data leaders to focus on more complex tasks.

Teams can also deploy BI tools to track and manage experiments, including hyperparameter settings, results, and model versions—especially in the context of specific user domains—to ensure that resulting prediction and discoveries are more targeted and focused.

Opt for Non-Linear Scalability

Data science is as dynamic as weather patterns—as businesses grow in complexity and size, so do their sample size and data models. Linear scalability has gone cold given how exponentially demand for resources and maintenance has grown. Constant change is part and parcel of data science projects, and non-linear scalability ensures the sustainability of your operations without having to multiply your headcount.

Scaling linearly often means adding resources in a 1:1 ratio, often neglecting the constraints of demand-supply gap. Non-linear scalability optimizes resource utilization, leading to faster insights and focused decision making. Planning for non-linear scalability needs stakeholder engagement, and a workable proof of concept—here are the steps to include:

• Parallel processing to map resources with growing data volume and distribute tasks across multiple processors.
• Distributed computing through tools like Hadoop and Spark to handle large-scale data processing in a distributed and non-linear manner.
• Auto-scalable infrastructure to automatically add or remove resources as and when needed.
• Data partitioning to segregate data into manageable partitions.
• Resource elasticity through cloud services that easily scale up or down.

Save Time and Effort With Automation

Embracing non-linear scalability also means incorporating automation as a standard practice in your project workflow. Netflix uses automation in its data workflow to recommend personalized content to users, and Amazon scaled its inventory management through automated data analysis and forecasting models.

It’s a data science best practice to automate and standardize repetitive, complex, and rote tasks in the data lifecycle, from monitoring to deployment to visualization. Here are other ways you can use automation:

• To pause and restart projects without affecting other running pipelines.
• To force stop and roll back pipelines during downtimes, maintenance errors, or failed deployments.
• Linking pipelines to create a continuous network of data orchestration and implement changes to all data nodes in one go.
• For reporting, dashboards, and powered visualizations to communicate business value to board members and executives.

Bottom Line: Best Practices For Data Science

As data continues to be a driving force for enterprises, businesses need to find ways to meet the demand for data in all stages of its lifecycle. They need to invest in the technologies to support it, the staff expertise to take advantage of it, and the methods to ensure the success of their efforts. Applying the best practices cited here is an iterative process that needs continuous investment, ongoing commitment, and adaptability—data science is just one component of an enterprise data management strategy, but it’s critical to the success of every other component.

Read 10 Best Practices for Effective Data Management to learn more about enterprise data strategies.