Data Science VS Data Analytics: Their Difference and Business Impact

How Does Data Science Work for Business?

Data science blends several fields—statistics, mathematics, computer science, domain expertise, and visualization. It’s all about turning raw data into valuable insights that can shape business decisions and contribute to new innovations.

By turning this tangled web of data into clear, actionable insights, you can avoid stock shortages, prevent overproduction, and align your strategy with what’s really happening in the market.

Data science can be your valuable tool to: 

• Understand your customers on a deeper level
• Optimize daily operations and cut unnecessary costs
• Innovate new products and services based on real-world demand
• Mitigate risks and detect fraudulent activities
• Stay competitive by reacting faster to market changes

Let’s say you need to forecast product demand for the coming year. You could simply look at last year’s numbers, or you could dig deeper. For example, analyze monthly sales by product category, price trends, the impact of marketing campaigns, global macroeconomic indicators, and competitor data. Turning all this chaos into actionable insights is where data science comes in. The result? No stock shortages or overflows.

— Maksym Trostyanchuk, Inoxoft’s Head of Delivery

Key stages in Data Science

To gain a clearer understanding of how data science can be applied in a specific company, let’s envision a large retailer and walk through the process step by step.

1. Data collection 

Companies often rely on standard datasets like sales figures and revenue. However, some sources of information can be less obvious but equally important. For example, weather can be important for a retailer, as it can influence sales. In winter, customers are more likely to buy gloves and hats, right? Data sources are all around us; you might even discover valuable insights by analyzing your inventory connected to IoT devices.

2. Data cleaning and preparation

Not all data is accurate or reliable, so it’s essential to “clean” it by removing missing values and anomalies. For example, if a retailer runs a large promotion, it may lead to a spike in sales. However, this data can be skewed, so it needs to be excluded from the analysis. There are also times when certain information is simply missing, and in those cases, you need to fill in the gaps. One effective approach is to use data from previous similar periods. 

3. Exploratory data analysis 

With clean data in hand, you can begin exploring it for patterns and trends. For example, retailers might employ visual tools like line graphs to track seasonal sales fluctuations or calculate statistical relationships, such as the impact of major marketing campaigns on sales. EDA helps you identify trends and frame more targeted questions for further investigation.

4. Feature engineering

Feature engineering involves creating new data points to highlight important trends. For example, retail chains might calculate ‘days since last promotion’ to predict the timing of future sales. Categorical data, like product types, need to be converted into a numerical format that machine learning models can understand.

5. Machine learning

Once the right features are in place, you can develop machine learning models that continuously train to enhance their predictive capabilities. This allows you to forecast various outcomes, such as predicting revenue for the next month or even anticipating what a specific customer might order next. 

6. Model evaluation and selection

You can experiment with different models, but it’s essential to evaluate their effectiveness. For instance, set a goal to predict sales levels for the upcoming month and see which option performs best at achieving that task.

7. Deployment and monitoring 

Once your model is ready, it’s important to monitor its performance regularly. This may sometimes require adjustments, but it’s the only way to stay ahead of market trends and ensure continued success.

Applications in business

Nowadays, it’s hard to find an industry that doesn’t leverage data science. Let’s dive into how it’s applied across different sectors and the various purposes it serves.

Customer analytics

Data science allows you to gain insights into customer behavior and preferences. For example, retailers can recommend products based on a client’s purchase history or browsing habits, which improves the shopping experience and boosts retention.

Supply chain optimization

By forecasting demand, you can ensure the right amount of stock while avoiding excess. A manufacturer, for example, can adjust its production schedule based on predicted trends to reduce costs and improve production efficiency.

Fraud detection

Data science excels in identifying unusual patterns in transactions, enabling banks to detect and prevent fraudulent activities before they escalate. This proactive approach helps safeguard against significant financial losses.

Predictive maintenance

Predictive maintenance uses data to foresee when equipment is likely to failand schedule maintenance accordingly with predictive maintenance. Airlines, for example, analyze data to predict when aircraft engines require servicing, minimizing costly disruptions.

Healthcare analytics

Data science helps hospitals analyze patient data to improve treatments and outcomes. Just like that, they can identify patients at high risk of complications and provide targeted care, ultimately improving patient well-being and reducing readmissions.

Complete Guide to Predictive Analytics and Big Data Analytics

The Role of Data Analysis in Business

Data analysis combines various techniques—data cleaning, transformation, modeling, and interpretation—to turn raw data into actionable insights. It involves diving deep into data sets to uncover patterns, trends, and relationships that can inform smarter business decisions. 

Understanding customer behavior, improving operations, or navigating market dynamics—data analysis helps you see the bigger picture and make strategic choices.

Data analysis is key to making smart decisions and staying ahead of the competition. By looking at data closely, you can spot new opportunities, reduce risks, and improve how your business operates. It helps understand what’s working, what’s not, and where you can grow.

— Nazar Kvartalnyi, Inoxoft’s COO

Key stages in Data Analysis

Data analysis follows a clear process to uncover useful insights from your information. Here are the main steps:

1. Data collection

Just like in the data science process, data collection involves gathering information from multiple sources to reach —whether it’s internal databases filled with sales or customer information, feedback from customer surveys, or market research reports from third-party providers. The goal is to compile all relevant information to get a complete view of the business or problem you’re analyzing. 

2. Data cleaning and preparation

Errors like missing values, outliers, and formatting issues can easily distort results. Cleaning, as discussed earlier, involves addressing these issues by correcting, removing or adjusting outliers, and ensuring uniformity in how data is represented. Imagine you’re analyzing sales data—if some entries have dates in different formats, or prices are listed in varying currencies, this would need to be fixed to ensure meaningful analysis.

3. Data exploration and visualization

After cleaning, it’s time to explore. Here, tools like charts, graphs, and dashboards help you uncover patterns, trends, or outliers that might otherwise go unnoticed. Visualizing data makes it easier to identify insights, such as how customer purchases vary by season or which products are consistently top performers—like a heatmap to quickly spot which store locations have the highest sales.

4. Statistical analysis

This step involves applying statistical techniques to better understand the data. You might run tests to see if certain factors (like price changes) significantly impact sales, or if customer age is linked to particular buying habits. Using methods like regression analysis, you can quantify relationships, test hypotheses, and draw meaningful conclusions.

5. Data interpretation and reporting

Finally, the insights discovered need to be communicated clearly to stakeholders. This is where you translate complex data into actionable insights through reports, presentations, or dashboards. These findings should be easy to understand and guide business decisions, like a monthly dashboard to track sales growth, customer retention, or product return rates, and adjust strategies in real time.

Applications in business

Data analysis is vital in areas where historical data holds significant importance. By delving into past performance, you can uncover insights and find opportunities for improvement, paving the way for better results in the future.

Sales analysis

You can monitor your sales performance to identify best-selling products and understand customer buying habits. For example, a retailer might use this data to tailor promotions and manage inventory better, ensuring they meet customer demand.

Marketing analytics

If you’re in marketing, data analysis can help evaluate how well your campaigns are working.By looking at where customers come from and how money is spent on ads, you can make smarter decisions. For example, if a company finds that one social media platform leads to more sales, they might shift more of their budget to advertise there.

Financial analysis 

Another important use of data analysis is to review your financial statements and track key performance indicators. This helps you understand your financial health and predict future performance, allowing for smart budgeting and investment choices.

Operational analytics

Companies use data analysis to find inefficiencies in their processes. With identified bottlenecks and streamlined operations, you can boost productivity and cut costs. For instance, a manufacturer might examine production workflows to discover ways to work more efficiently.

Customer analytics

Customer analytics is all about understanding how your clients behave. The insights from data analysis allow you to categorize your customers and personalize marketing strategies. This tailored approach enhances satisfaction and builds loyalty, allowing you to better meet individual needs.

Market research 

You can  use data analysis to explore trends and competitor strategies. This helps you spot new opportunities and make smart choices about your products and market positioning, keeping you competitive in a changing market.

“If your business relies on proprietary data and your competitive edge is defined by that data, it raises the question: how can a company or society extract the full value implicit in technology? The answer is often that they can’t. This scenario is evident in fields like genomics, where technology is pushing the natural scaling of activities beyond traditional institutional boundaries, especially regarding how we typically think about business strategy.”

— Philip Evans, a Senior Advisor at Boston Consulting Group

What Is the Difference Between Data Science and Data Analytics?

To clarify the difference between data science and data analytics, let’s use an analogy—how these two fields function in a retail company that collects data on sales, customer interactions, and market trends.

A data science team might use this information to build a model that predicts future sales. Meanwhile, data analytics specialists analyze which products performed well and which didn’t. They uncover trends and patterns. The results of their work are visual reports that reveal what factors influenced sales. This is similar to intervention between the past and future. 

We’ve also put together a detailed comparison table to break down these two approaches.

Exploring the Applications of Deep Learning

Data Science and Data Analytics Applications Across Different Sectors

Data science and analytics have revolutionized numerous industries. Gone are the days when companies relied solely on intuition and subjective opinions to guide their decisions; that approach is no longer sustainable for those aiming to thrive in today’s market. In fact, it is these two fields that have paved the way for personalization and a deeper, more comprehensive understanding of the market landscape. 

Let’s explore some specific data analytics and data science examples:

Data Analytics vs Data Science: What Does Your Business Actually Need?

The choice between data science and data analytics can feel overwhelming. But don’t worry; it truly hinges on your unique business needs and goals.

To help you navigate this decision, we present you some practical tips for determining which approach aligns best with your objectives.

1. Define your goals

• If you want to predict future trends, uncover hidden patterns, or build innovative solutions, then data science is the way to go.
• If you need to understand past performance, identify areas for improvement, or make informed decisions based on historical data, then data analytics is the better choice.

2. Assess your data

• If you have large and complex datasets with a variety of structured and unstructured data, data science is better equipped to handle the challenge.
• If you have smaller, more structured datasets, data analytics can provide valuable insights without the need for advanced techniques.

3. Consider your resources

• Data science often requires a team with specialized skills in statistics, machine learning, and programming. It may also involve significant investment in infrastructure and tools.
• Data analytics can be performed by a smaller team with expertise in data visualization and basic statistical analysis, and typically requires less investment in resources.

4. Think about the impact

• Data science can lead to groundbreaking innovations, new product development, and significant competitive advantages.
• Data analytics can drive operational efficiency, improve decision-making, and optimize existing processes.

5. Don’t forget about collaboration

• In many cases, data science and data analytics can complement each other. Data analytics can provide the initial insights that guide further exploration and modeling in data science.

“When choosing between data science and data analytics, think about what you want to achieve. If your goal is to forecast future trends, drive innovation, or analyze complex datasets, then data science is the right path. On the other hand, if you’re looking to understand past performance, enhance current processes, or make informed decisions based on historical data, then data analytics is the way to go.”

— Inoxoft’s Senior Data Analyst 

In reality, the choice between these two approaches isn’t always clear-cut. Sometimes, the best solution is to combine both. But no matter the path, having a team of experts to guide you through the process is key. That’s where you can rely on our experience. Reach out to us to discuss your business challenges, and let’s figure out how we can help. 

Data Quality Management: What You Need to Know

Let Inoxoft Help Your Company Leverage Data Science or Data Analytics Capabilities

Our agency offers a variety of data science and data analytics services to help businesses of all sizes extract valuable insights from their data. 

Our Data Analytics Services

Data analytics services we offer are designed to tackle common business challenges by transforming raw data into actionable insights. We help you collect, clean, and organize information that you can transform into intuitive dashboards and comprehensive reports to effectively visualize your data. 

With our expertise, you’ll be equipped to identify trends, patterns, and correlations that can inform your decision-making process, streamline operations, and ultimately enhance your overall performance.

Improving Career Planning with Data Analytics: A Case Study

A USA-based entrepreneur came to us with a vision: to transform how people approach their careers by ensuring a perfect match between skills and job roles. Understanding the complexity of the modern job market, we eagerly dived into the project, working closely with the client to bring their vision to life.

Challenge

Many job seekers find career planning overwhelming, often lacking the insights needed to make informed choices about the job market. After conducting initial research, our team uncovered several key pain points:

• Many job seekers struggle to identify suitable career paths or understand the skills required for a smooth transition.
• A lack of personalized guidance and motivation leaves individuals feeling uncertain about the steps they should take.
• Job market trends can be overwhelming, making it difficult to stay informed about in-demand skills and opportunities.
• Career coaches often have limited time to offer personalized advice to every user, leading to a gap in individual attention.

Solution

We developed a career mapping platform powered by machine learning and data analytics that provides custom recommendations, suggests relevant skills, and connects users with industry experts to help them transition smoothly into new careers.

To ensure the platform meets the diverse needs of users, we implemented:

• Tracking user behavior: The platform leverages machine learning to analyze user profiles, offering tailored career paths based on skills, interests, search patterns, and market trends.
• Real-time job market insights: The platform provides up-to-date data on job postings, industry trends, and salary information, enabling users to make informed decisions about their career goals.

Core features

• Custom TensorFlow Training and Model Deployment: We implemented an ML model that continuously improves by training on new data for accurate career recommendations.
• Web Scraping and Data Collection: The platform gathers the latest job market information from various sources, providing users with valuable insights into current job opportunities and industry trends.
• Emerging Career Path Predictions: The system analyzes collected data to predict emerging career paths, helping users stay ahead of market shifts and make proactive career decisions.
• Personalized Calendars for Experts: Career coaches and industry experts can offer personalized one-on-one consultations, with customized scheduling features.
• Email and Calendar Notifications: We built in notifications that keep users informed about upcoming appointments and important career-related updates.
• Integration with LinkedIn, The Muse, Stripe, and Zoom: Seamless integration with LinkedIn for job searching, The Muse for career advice, Stripe for secure payments, and Zoom for virtual consultations made the platform an all-in-one career planning tool.

The results

Our team successfully implemented the platform, providing a solution that:

• Gathers real-time data from various sources to offer tailored career recommendations.
• Matches users to job roles that align with their skills and interests, optimizing career satisfaction.
• Offers a full suite of tools designed to help users make informed career decisions.

Our Data Science Services

We create predictive models that give you valuable insights, and help you make better decisions. With organized and classified data, it’s much easier to spot trends and patterns that you might miss otherwise. 

Our expertise empowers you to automate routine tasks, freeing up valuable time and resources. We also guarantee a smooth deployment of your machine learning models into production, enabling you to harness their capabilities to enhance efficiency and boost performance. 

With our support, you’ll transform raw data into strategic advantages for your business.

Revolutionizing Ad Targeting with Predictive Text Classification

In another successful collaboration, we partnered with one of the industry’s pioneering data activation, curation, and audience intelligence platforms to enhance their offerings. The client provides brands with actionable data, analytics, and insights, enabling them to optimize the targeting, engagement, and ROI of their online, mobile, and social video ads. 

Challenge

A leading AdTech company aimed to enhance their ability to analyze complex websites and accurately categorize their content for improved ad targeting effectiveness. Traditional methods struggled to keep pace with the diverse and ever-evolving nature of online content, resulting in suboptimal ad placements and reduced engagement.

Solution

We developed a sophisticated machine learning model capable of analyzing millions of web pages daily and accurately categorizing their content based on valuable contextual information. This multilingual classifier empowered the client to:

• Uncover valuable insights: Analyze millions of web pages daily to extract meaningful textual data.
• Deliver targeted content: Accurately categorize web content for it to reach the right audience.
• Scale with efficiency: Process vast amounts of data with exceptional speed and accuracy.

Core features

• Natural Language Processing: The model uses advanced techniques like tokenization, stop word removal, lemmatization, and text-to-sequence conversion to better understand and classify web content.
• Industry-Standard Categorization: We applied the IAB taxonomy, which is a well-known standard, to ensure consistent and accurate categorization of content, making it easier for clients to target ads effectively.
• Custom TensorFlow Training: The model was trained using TensorFlow, a top machine learning tool, to ensure it performs accurately and can adapt to the client’s specific needs.

Results

Our team successfully developed a top-tier machine learning model for the client, enabling them to:

• Analyze Diverse Websites: Effectively process complex websites with varying content types.
• Achieve High Processing Speed: Analyze up to 2 million websites daily, providing inferences in under 10 milliseconds.
• Ensure Accurate Categorization: Deliver precise categorizations based on words and context, enhancing content understanding.
• Boost Advertising Effectiveness: Successfully promote the agency’s services by targeting the right ads to the right audiences.

The model we presented has greatly enhanced the client’s ability to target ads, leading to better audience engagement and a higher return on investment.

If you are looking for a data science or data analytics partner, we can help you achieve your goals. Contact our team to discuss how we can help you harness the power of technology to achieve your business goals.

Final Thoughts

Without getting into complicated explanations and technical jargon, the difference between data science and data analytics is pretty simple. With data science, it’s like looking into the future—you can predict what customers will want next year or develop an innovative product. Data analytics, on the other hand, focuses on the past, using historical data to make informed business decisions. 

Whether you’re crafting a groundbreaking fintech solution or building a robust management system, we are thrilled to be your partner in this exciting journey. Our expert team uses the power of both data science and data analytics, along with cutting-edge AI and machine learning techniques, to tailor solutions that perfectly suit your unique business needs. 

Reach out to us today! We’re here to help you discover the perfect strategy that drives success.