Data Engineering: Overcoming The Inertia Of Thinking

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Imagine, 4,000 years ago, folks probably scoffed at the wheel: "What's this round thing for? I'm just fine dragging stuff around." But as distances grew, the wheel became essential. And we're at a similar crossroads with artificial intelligence, data science, and data engineering.

It's time to show that making data-driven decisions is better than just winging it. It's offering the exact product a customer needs, not just what we think they need. There are still plenty of people who are dragging their metaphorical boxes on the ground. But here's where the thrill is for us at DATAFOREST: we're providing a whole new digital business way.

Tracing data science origins and rise

All the problems that modern data science solves appeared a long time ago. And the solutions appeared then. Only the scale has changed many times. Previously, cargo quantities could be counted by hand, recorded on paper, and inventory calculated using a pencil. However, the global changes associated with the third industrial revolution increased the data on the problem so much that no pencil would be enough. And in the second half of the last century, the following happened.

Tracing data science origins and rise
  • With the advent of computers in the mid-20th century, the ability to store and process large amounts of data began to take shape. This period saw the development of databases and data processing technologies, laying the groundwork for data analysis.
  • As digital technology proliferated in the 1980s and 1990s, so did the generation and collection of digital data. Businesses and organizations started to realize the potential value hidden in this wealth of data.
  • The explosion of the internet in the late 1990s and early 2000s led to an unprecedented increase in data generation and availability. The "big data" concept emerged, characterized by data sets too large for traditional software.
  • Data science began to crystallize as a distinct discipline in the early 21st century, drawing from statistics, computer science, information science, and machine learning.
  • Developing more powerful computing technologies, including cloud computing and advanced ML algorithms, further propelled data science. These technologies allowed for handling and analyzing data at scales and speeds that were previously impossible.
  • The formal recognition of data science as a distinct profession and field of study accelerated in the 2010s. Universities began offering specialized courses and degrees in data science, and businesses increasingly sought experts in this area.

The evolution of data science reflects the growing importance of data as a resource in the modern world, along with technological advancements that have made such analysis possible. It has also led to the emergence and development of providers in this field. Data engineering and web product development company DATAFOREST was registered in 2018 and now is a TOP-rated custom data-driven solutions provider, helping clients use data for revenue generation and cost optimization.

What instead of pencil and paper

The fields of data science and data engineering encompass a range of services crucial for businesses looking to leverage data for strategic decision-making.

Here are some of the most popular services in these areas:

Data science services

  • Predictive analytics: Using statistical models and machine learning algorithms to predict future trends and behaviors based on historical data.
  • Machine learning model development: Creating algorithms that enable computers to learn from and make data-based decisions.
  • Data visualization: Presenting complex data in an accessible and easily understandable visual format to aid in decision-making.
  • Big data analytics: Analyzing extensive data sets to uncover hidden patterns, correlations, and other insights.
  • Natural language processing (NLP): Developing systems that can understand and interpret human language, widely used in chatbots, sentiment analysis, and more.
  • Customer analytics: Analyzing customer data to gain insights into behavior, preferences, and trends to improve customer engagement and targeting.

Data engineering services

Data engineering services
  • Data warehousing: Building and maintaining a central repository of integrated data from various sources.
  • Data integration: Combining data from different sources and providing a unified view.
  • Data pipeline construction: Developing the infrastructure for data flow from the source to the destination, often for real-time processing.
  • Database management: Designing, implementing, and managing databases to store and organize data efficiently.
  • ETL (Extract, Transform, Load) processes: Extract data from various sources, transform it into a suitable format, and load it into a target database.
  • Cloud data services: Utilizing cloud platforms like AWS, Google Cloud, or Azure for scalable data storage, processing, and analytics services.

Three more services

As for the DATAFOREST company, in its practice, it also offers data scraping, web development, and DataOps services. The company applies advanced data science techniques along with web development expertise to create end-to-end products that improve data management and optimize infrastructure.

Data scraping services

It involves extracting data from various sources, particularly websites. This process is essential for gathering large amounts of information from the internet. In data science, scraped data provides a rich source of raw material for analysis, allowing data scientists to work on real-world data and derive meaningful insights. Data engineering focuses on creating robust and efficient systems to automate the scraping process, handle large volumes of data, and ensure data quality and integrity.

Web development

Web development

Web development refers to creating web applications often used to display and interact with data. These applications can range from interactive data visualizations to complex analytical tools. For data scientists, web applications are a platform to showcase insights and analytical results in an accessible manner. For data engineers, the challenge lies in building the backend infrastructure that powers these web applications, ensuring they can handle data-intensive operations and provide a seamless user experience.

DataOps

DataOps, short for Data Operations, is an agile, process-oriented methodology aimed at improving the speed and accuracy of analytics. It requires practices, processes, and technologies for building and enhancing data analytics pipelines. DataOps focuses on streamlining the data lifecycle from collection and preparation to analysis and reporting. DataOps practices ensure that data scientists have timely access to high-quality data and that data engineers have clear feedback on the data infrastructure's performance and needs.

Data services fuel business advantages

Let's explain how the mentioned services are applied in real business scenarios across different industries.

Industries / Services

Data Science

Data Engineering

Data Scraping

Web Development

DataOps

Retail

Personalized product recommendations, predicting buying trends

Efficient management of customer data, enhancing data accessibility

Competitive analysis, market trends, pricing strategies

Improved customer shopping experience, personalized interfaces

Real-time inventory updates, streamlined sales data integration

Healthcare

Predictive health analytics, patient risk assessment

Secure patient data management, regulatory compliance

Medical research, public health trends

Patient portals for easy access to health records, appointment scheduling

Quick integration of patient data, improved access for healthcare professionals

Finance

Market trend analysis, investment strategy optimization

Secure transaction processing data storage for compliance

Market data collection for financial analysis

User-friendly online banking platforms, real-time financial data access

Real-time market data flow, efficient processing of financial transactions

Manufacturing

Predictive maintenance, production optimization

Management of inventory and supply chain data

Supplier data collection for raw material sourcing

Production monitoring dashboards, real-time performance metrics

Integration of sensor data from the production line, immediate analysis for decision-making

Marketing

Customer behavior analysis, campaign performance tracking

Database management for multi-channel customer interactions

Consumer sentiment analysis, competitor strategy assessment

Engaging campaign websites targeted landing pages

Timely integration of customer interaction data, campaign adjustment based on real-time insights

This matrix illustrates how each service uniquely benefits different industries, highlighting the versatility and importance of these services in addressing industry-specific challenges and driving business growth.

Leading industries implement data services

  1. Retail — Amazon: Uses data science for personalized product recommendations, data engineering to manage massive customer and transaction databases, and data scraping for competitive pricing. DataOps ensures seamless integration of real-time data for inventory and pricing.
  2. Healthcare — Mayo Clinic: Implements data science for patient data analysis to improve treatment outcomes. They use data engineering to manage patient records securely and data scraping for the latest medical research. Their patient portal, developed through web development, offers easy access to personal health information.
  3. Finance — JPMorgan Chase: Leverages data science for risk assessment and fraud detection and uses data engineering for secure transaction processing. They also employ data scraping for real-time market insights and have advanced web development for online banking platforms.
  4. Manufacturing — Siemens: Uses data science for predictive maintenance and process optimization. Their data engineering skills are crucial in managing supply chain and production data, and web development is used to create interactive dashboards to monitor manufacturing processes.
  5. Marketing — HubSpot: Employs data science for analyzing customer interaction and campaign performance. Their data engineering enables managing a vast amount of marketing data while scraping is used for gathering trends and consumer behavior. Their website and client portals offer personalized user experiences.

The DATAFOREST company has a demonstrated history of working in the Travel, E-commerce/Retail, Fintech, Marketing, Pharma, Informational Technology, and Services industries.

The increasing volume of unstructured data drives the big data and data engineering services market.

The role of data service providers

In the grand tapestry of modern business, a provider of data services is much akin to those ingenious minds who first envisioned the wheel. As it was a pivotal innovation, these data-centric services transform how businesses operate, compete, and succeed in a digitalized world.

The role of data service providers

A service provider (like DATAFOREST is) in this realm is not merely a vendor. They bring tools and expertise to turn raw data — an abundant but often underutilized resource — into actionable insights and strategic assets.

The company offers such services:

  • Data Science
  • System Integrations
  • Web scraping 
  • Web applications 
  • DevOps

Like the wheel, which moved humanity beyond the limitations of distance and speed, these services enable businesses to transcend traditional boundaries of market understanding, customer engagement, and operational efficiency.

The managing partner and co-founder Oleksandr Sheremeta has helped dozens of companies scale and improve operational efficiency by creating digital workflows and automating all business processes by applying AI & Data Science technologies and expertise in ETL pipelines, API integration, and advanced software engineering.

Vladyslav Zinchenko (Partner, CTO) helps with DataOps , AI adoption, Predictive Analytics and Business intelligence. Olexiy Multykh (Partner) is highly skilled in IT consulting, outsourcing, data reporting, technical support, business analytics as well as in web, SaaS, database, application, and custom software development.

About the author 

Peter Keszegh

Most people write this part in the third person but I won't. You're at the right place if you want to start or grow your online business. When I'm not busy scaling up my own or other people' businesses, you'll find me trying out new things and discovering new places. Connect with me on Facebook, just let me know how I can help.

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