Data Analytics vs Data Science

Data Analytics vs Data Science: Focus and Goals

Similarities

At a high level, both data analytics and data science fall under the broader umbrella of data analysis. Both fields fundamentally aim to leverage data to add value to an organization. Both fields aim to find actionable insights. Here are three key similarities between the two fields:

Data Dependency: Both data analytics and data science are fundamentally reliant on data. They require accurate, high-quality data to produce meaningful results. Whether the task is descriptive, diagnostic, predictive, or prescriptive, data is the bedrock on which all analysis stands.

Purpose of Insight Generation: The overarching goal for both fields is to generate insights from data. The goal is to understand trends, patterns, and relationships within the data to inform decisions, drive efficiency, or innovate. The insights might be derived from past patterns (as is more typical in data analytics) or predictive models (as is more typical in data science), but the end goal is insight-driven action.

Use of Tools and Technologies: While the specifics of the toolsets might differ, both data analysts and data scientists utilize programming languages (like Python and R), databases, data visualization tools, and statistical techniques to parse, analyze, and interpret data. They both often engage in data preprocessing activities such as data cleaning, normalization, and transformation.

Differences

Data analytics focuses on interpreting historical data to find patterns and trends that can inform business decisions. It focuses primarily on inspecting, cleaning, and transforming data to discover useful information, draw conclusions, and support decision-making.

In other words, the emphasis is on summarizing and describing data to extract useful insights. Common data analytics tasks include data cleaning, data exploration, reporting and ad-hoc analysis. Data analytics helps answer questions like “What happened?” and “Why did it happen?”. Within a business context, example questions might include “Which products have had the highest demand?” and “Is revenue trending up or down?”.

On the other hand, data science incorporates data analytics tasks but focuses more on predictive modeling and algorithm development to uncover insights. Data Science doesn’t stop at analyzing past or current trends but also delves into predictive analytics and machine learning to forecast future events. It asks questions like “What will happen next?” and “How can we make it happen?”

In other words, a data science project relies more heavily on machine learning techniques like clustering, neural networks, and decision trees to make predictions. The focus of data science is to make predictions that can unlock strategic insights and drive innovation and process optimization. Data science helps answer questions like “How much revenue will this customer generate in the next 6 months?” and “Which factors most influence product quality in our manufacturing plants?”.

Key Insights

In short, data analytics focuses on summarizing and visualizing data to uncover trends while data science uses advanced analytic techniques to derive deeper insights and make predictions. But let’s explore in a bit more detail…

Data Analytics vs Data Science: Methods and Tools Comparison

Data Analytics Tools

As previously noted, the data analytics is primarily focused on descriptive analysis. This includes aggregation, summary statistics, and visualization. Typically, data analytics relies more heavily on statistical tools and data visualization software such as Tableau (An industry-leading data visualization tool for dashboards), Excel (for analyzing smaller datasets and creating reports) and Business Intelligence Software (such as PowerBI) to connect data sources and visualize analytics.

Data Science Tools

On the other hand, Data science leverages a broader set of advanced tools and techniques, such as machine learning algorithms, complex simulations, and advanced statistical modeling. The project often involves deep learning, neural networks, and natural language processing. In addition, it is common for lower-level programming tools to be used, such as Python (A popular programming language used for manipulations, analysis and modeling), R (An open-source programming language specialized for statistical analysis) and  Apache Spark (An open-source cluster computing framework for big data). These tools allow data scientists to wrangle large datasets, engineer features, develop and refine machine learning models at scale. Instead of just describing data, data scientists use predictive modeling and algorithms to make data-based recommendations.

Data Analytics vs Data Science: Project Management Comparison

With a basic understanding of these data analysis sub fields, we can now explore the similarities and differences from a project management perspective.

In fact, there are many similarities in the challenges of executing both data science and data analytics projects.

Data Science vs Analytics Project Management Similarities

Here are key similarities:

• Reliance on Data Quality: Both types of projects depend heavily on the quality and integrity of the data. The adage “garbage in, garbage out” applies to both fields. Project managers need to ensure that data is clean, relevant, and accurate before any analysis or modeling begins.
• Cross-functional Collaboration: Projects in both domains often require collaboration between various departments or teams. For example, IT might be involved in data provisioning, while business teams provide domain expertise. Project managers must facilitate effective communication across these teams to ensure success.
• Iterative Nature: While the specifics of the iteration might differ, both data analytics (ex. agile business intelligence) and data science projects can be iterative (ex. agile data science). This is helpful, as analysts and scientists often need to revisit their models, analyses, or data sources based on initial findings, stakeholder feedback, or new data.
• Stakeholder Communication: For both types of projects, it’s crucial to have clear communication with stakeholders. Project managers must ensure that findings, recommendations, or models are presented in a way that’s understandable and actionable for the target audience.
• Ethical Considerations: Both data analytics and data science projects often grapple with ethical concerns, especially around data privacy, potential biases, and the implications of their findings or models. Project managers need to be attuned to these issues and ensure that projects adhere to ethical standards and regulations.
• Requirement of Technical Expertise: Even though the depths of technicalities might differ, both fields require a team with technical expertise. This might include familiarity with specific tools, programming languages, statistical methods, or machine learning algorithms. Project managers should be aware of these requirements when assembling their teams or setting project timelines.
• Documentation and Reproducibility: Both data analytics and data science emphasize the importance of documenting processes, methodologies, and findings. Whether it’s for regulatory compliance, stakeholder communication, or future project reference, ensuring that work is documented and reproducible is crucial.

Data Science vs Analytics Project Management Differences

However, below are some key differences:

• Resource Requirements: Data Analytics often requires less computational resources as compared to data science projects.
• Stakeholder Expectations and Communication: Data Analytics projects are often more straightforward to explain to stakeholders (since the results are often based on descriptive statistics), which means the implications of findings are often immediate and actionable. However, data science outcomes can be more challenging to explain, especially to non-technical stakeholders. There might be a need for more in-depth discussions about model assumptions, accuracy, biases, and potential implications. In other words, the forward-looking (predictions or recommendations) might require more detailed explanations to gain and understanding trust in the model’s outputs.
• Project Execution Risk:  In a data analytics project, the team knows if it can accomplish project. However, a data science project often has more uncertainty (ex. can the data generate an accurate predictive model is often not known until mid-way through a project).
• Risk Management: Data Analytic project risks often focus on data quality, timeliness, and reporting accuracy. Data Science projects, on the other hand, have more of a focus on potential model biases, overfitting, and misinterpretation of model outputs.  

Implications for a Team’s Process

For both data analytics and data science, the process tends to be iterative (data science projects might be more experimental as models get tested and refined).  In other words, they should be Agile.

Reviewing Agility

Note that Agile is not about adhering to a set process. Rather, it is a philosophy that empowers teams to self-manage, deliver often, to learn, and to adapt. Teams should first identify the principles they aspire toward. The Agile Manifesto has 4 initial statements and 12 principles that serve as a great foundation that you can use or build off of.

Hence, both data analytics and data science projects require a dynamic agile project management framework such as Scrum, kanban, Data Driven Scrum (or building a custom framework that works for your team).  However, note that Scrum is challenging to implement for many data science projects, due to the uncertain nature of how long a tasks might take within a data science project.

Using an agile framework, work is typically broken down into iterations allowing for rapid prototyping, testing and integration of feedback.

Leverage an MVP

Furthermore, the team should strive to deliver a scaled-down but valuable solution as early as possible via a Minimum Viable Product. In addition, via an agile framework, it is imperative to have stakeholder alignment and clear goals to help surface key insights that create business impact.

In short, data analytics and data science draw from overlapping skillsets and knowledge bases when it comes to extracting value from data. But data analytics focuses on retrospective descriptive techniques while data science emphasizes complex modeling for predictive capabilities. When managed effectively, they can work hand-in-hand to help drive smarter decisions and optimize processes. Organizations need both capabilities to stay competitive in increasingly data-driven markets.

It is also important to understand that there’s a spectrum of skills and techniques across data analytics and data science. Depending on the organization and the specific roles, a data analyst might sometimes perform tasks that overlap with a data scientist and vice versa.