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  • Writer's pictureDia Adams

10 Data Science Best Practices

The adoption of data science applications by businesses has witnessed a steady rise in recent years. Despite the buzz surrounding data science, the actual outcomes often fall short of expectations. Many organizations implementing data science methods encounter challenges, and improper project execution is a significant contributing factor. Other common hurdles include a lack of comprehension of business issues, inconsistencies in project design, and struggles in translating data insights into actionable outcomes.

Given the intricate nature of data science, organizations must adhere to certain best practices to enhance the effectiveness of their data science projects.This article will explore some of these best practices that organizations can integrate to elevate the success rate of their data science endeavors. But before delving into that, let's delve into the concept of data science.

Unveiling the Essence of Data Science

Data science has unwittingly become an IT buzzword However, beneath the hype lies a multifaceted field that incorporates elements of mathematical reasoning and computer programming to comprehend data. Contrary to popular belief, data science isn't a new IT term. Its roots, particularly in the late 20th century, tie back to statistics, indicating organized data documentation.

Fundamentally, data science amalgamates disciplines like big data, data mining, and machine learning. Today, it involves the collection and analysis of vast amounts of unstructured organizational data. Data scientists are individuals tasked with deciphering voluminous and noisy data, leverage mathematical acumen, coding skills, and a diverse skill set encompassing databases, computing, and communication. The insights derived from this process enable companies to enhance customer service, product quality, inter-organizational communication, and more. As data science gains prominence, it is poised to become a coveted asset for numerous organizations, steadily gaining traction over the years.

10 Impactful Best Practices for Data Science

Now that the groundwork on the definition and purpose of data science has been laid, let's delve into some best practices that companies can adopt to leverage the benefits of data science effectively.

Establish a dedicated program for data science within the organization

A common hindrance to fully realizing the potential of data science projects is the absence of a specialized infrastructure. Often, companies have small data science teams working concurrently on different tasks without a documented modus operandi or essential metrics for assessing success.

To unlock the latent capabilities of data science teams, businesses should advocate for the creation of a comprehensive data science program. This program should outline the purpose of data science initiatives, equip the organization with the necessary infrastructure (trained experts, essential equipment, etc.), provide a delivery roadmap, and establish performance metrics.

Cultivate a capable team instead of pursuing unicorns

In the context of data science, a unicorn refers to an individual, specifically a data scientist, possessing or capable of acquiring all the desired data science skills—a rarity in high demand. However, it is more prudent for companies to prioritize building cross-functional data science teams instead of seeking a one-size-fits-all unicorn.

A cross-functional team typically includes:

  • Data engineer(s) for collecting, converting, and pooling raw data.

  • Machine Learning Expert(s) creating ML data models to identify patterns.

  • DevOps engineers deploying and maintaining ML data models.

  • Business Analyst(s) understanding company requirements and target markets.

  • A team leader to guide the team effectively.

Cross-functional teams offer advantages such as workload sharing, diverse problem-solving perspectives, and improved decision-making.

Clearly define a problem before embarking on the solution journey

Thoroughly describing data science problems, including minute details, is crucial. This comprehensive approach enables data scientists to examine each component against concrete parameters such as priority, clarity, usable data, and ROI. It also aids in identifying primary and secondary stakeholders required for the problem. Despite its fundamental nature, many companies overlook this step, providing vague problem descriptions that complicate the efforts of data scientists. Therefore, before attempting to solve a problem, companies must dissect it thoroughly to uncover all its components and requirements.

Ensure a Proof of Concept (POC) is run on a definitive use case

POCs are integral to any data science project as they determine the feasibility of a data model or solution. Selecting an appropriate use case is crucial for the success of a POC. Data scientists should choose a use case that offers quantifiable results and addresses critical business issues.

Determine and list all Key Performance Indicators (KPIs)

The success of a company's data science efforts is determined by the Key Performance Indicators (KPIs) aligned with its business goals. While most companies have overarching business goals, many lack specific, delineated KPIs to monitor progress. Companies need to establish measurable KPIs, such as ROI, percentage increase in revenue per consumer, CSAT score, etc., to evaluate the viability of data science projects. For instance, deploying an optimization algorithm to boost revenue requires tracking indicators like monthly sales numbers and website visitors.

Emphasize proper management of stakeholders

Stakeholders, individuals who use the data provided by data scientists, play a crucial role in data science projects. Managing stakeholders effectively involves considering internal (e.g., business analysts) and external (e.g., clients) individuals planning to use the data.

Implementing strategies like transparent communication channels, conveying project outcomes, seeking feedback, and fostering collaboration ensures that data scientists analyze not just data but also the human elements associated with it.

Base data science documentation on stakeholders

Documentation is vital for any data science project, but tailoring it to the requirements and specializations of involved stakeholders is equally critical. Adopting a customized approach instead of a one-size-fits-all method ensures effective utilization of project data.

Match a data science job with appropriate tools

Pairing the right data science project with suitable tools requires expertise and a deep understanding of data science. This best practice involves selecting tools like data visualization software, determining cloud storage capacity, choosing the right programming language, assessing scalability, and approaching the problem strategically.

Incorporate the agile methodology

Applying the agile methodology to data science projects, dividing them into sprints, has proven effective. Sprints, usually lasting a few weeks, involve data scientists working on specific project aspects after interacting with stakeholders. Each sprint concludes with a review to assess progress.

Keep track of data ethics

While data models execute objectively, data scientists, being human, must build models that adhere to data collection, analysis, and interpretation ethics. Failing to uphold data ethics can tarnish a firm's credibility and reputation, as exemplified by incidents like the Cambridge Analytica scandal.

As a rapidly expanding field, data science, if implemented correctly, can significantly contribute to a business's growth. Organizations should equip themselves with proper data science infrastructure, hire the right talent, foster extensive collaboration, and adhere to the aforementioned best practices to maximize the benefits of their data science efforts."


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