Snowflake Connect: AI on January 27
Unlock the full potential of data and AI with Snowflake’s latest innovations.
Businesses and organizations generate copious amounts of data every day, but in its raw state, it holds more promise than actual value. When making a recipe or building a product on an assembly line, the end result is a sum of its parts, everything coming together to create something useful (or delicious). Likewise, when a company has a mess of raw data, they need to make sense of it all before it can be of any use to anyone. This is done through a series of steps called data processing.
Data processing is how raw and often chaotic data is structured into a useful format. Through a series of operations, businesses reveal the hidden value locked away inside columns of numbers, pages of survey responses, and spreadsheets stuffed with information. It’s at the core of business strategy, and makes everything from business analytics to machine learning (ML) possible.
In this guide, we’ll explore what data processing is and why it’s important, the stages of data processing, types of data processing, data processing methods and data processing tools and technologies. We’ll finish up with some of the most commonly asked questions about this business-critical operation.
Data processing is a systematic series of operations that takes raw, unorganized data and transforms it into usable information from which organizations can draw meaningful insights and make informed decisions. It’s a foundational element of business strategy and is crucial for making data analysis possible.
Historically, data processing has been a very laborious, time-consuming manual process. Human computers — people who were given the job title of “computer” — relied on physical tools like ledgers, forms and calculators, as well as paper-based systems, to collect, store and analyze data. Infamously, it took the United States seven years to publish the results of the 1880 census because of how slow the manual tallying processes were, which led Herman Hollerith, an employee of the U.S. Census Bureau, to invent the tabulating machine. It dramatically reduced the time needed to process census data from years to months and laid the groundwork for the modern data processing industry.
Today, data processing is an electronic process managed by computers and automation, usually handled by data analysts, data processors, data engineers and data scientists. AI and ML play a significant role in handling especially large data sets. Data processing is often described as happening in a cycle, and a number of steps are taken to get data from its raw state to being analyzed, interpreted and then stored.
Without data processing, the vast amounts of data that organizations are generating every second would be nothing more than digital noise. Data processing bridges the divide between unprocessed information that is rarely ever useful in its raw state and key insights that can inform business decisions and give organizations a competitive edge.
Improved decision-making: Businesses can’t rely on assumptions and guesses if they want to compete and grow. The clear insights gained through data processing can improve decision-making in a number of ways, including:
Identifying market trends: Sales data can provide info on which of your products are selling well, what demographics are buying them, what time of the year they’re selling well, etc.
Improving operational efficiency: Analyzing supply chain, logistics and production data can help companies identify waste and bottlenecks and optimize their processes.
Making data-backed predictions: Predictive analytics uses historical data to forecast outcomes, helping businesses to anticipate customer needs, manage inventory and mitigate risks.
Enhanced accuracy and reliability: Unprocessed data very often contains errors, duplicates and inconsistencies. And in many industries, like governance, risk and compliance (GRC), fraud detection and finance, a single error or discrepancy can snowball into even greater complications. The data cleansing step of data processing (which we’ll explain shortly) identifies and corrects these issues, making the data more accurate and trustworthy when it comes time for analysis.
Greater competitive advantage: Effectively processing and leveraging data is a key differentiator for companies that want a leg up on their competitors. Some of the advantages it provides include:
Personalizing customer experiences: Processing customer data gives businesses the ability to offer customers personalized recommendations, targeted marketing and services they may be interested in, which builds brand loyalty and customer retention.
Responding to market changes: Real-time data processing allows businesses to react quickly to market changes, whether it’s a new product drop by a competitor or a shift in customer demand.
Enhanced data security and compliance: Data processing isn’t just about making data useful — it’s also about making it safe. Specific protective measures are built into data cleansing and organization, including data masking, anonymization, encryption and tokenization. Data processing systems also enforce rules about who can access, modify or delete data. Additionally, many data regulations, like GDPR and HIPAA, have strict requirements about which data is allowed to be collected, how it can be used, etc. Data processing systems meticulously document every step of the data lifecycle, from collection to deletion; this creates an audit trail that proves that an organization is complying with regulations.
Earlier in this guide, we compared data processing to a factory assembly line. Much like the stages of building a car, from hammering out the chassis to final paint and polish, data processing follows a structured, multistep workflow. Each step is essential for converting jumbled, raw data into the clean, reliable data that organizations rely on to make educated decisions and build solid strategies.
It’s time to start gathering that data, and it can come from myriad sources — transaction logs and company databases, social media engagement stats and customer surveys. It’s often housed in data lakes and warehouses. It’s crucial that the data that’s extracted during this first step is relevant, accurate and coming from reliable sources. Otherwise, it runs the risk of skewing the final results, completely compromising the project from the start.
Often called pre-processing, this is the most critical and time-consuming stage, in which data is cleaned and organized to ensure quality and consistency. These steps include:
This is where prepped data gets fed into the processing system, and is the first stage in which raw data begins to take on the form of usable data. Examples of processing systems could be either software or an algorithm designed for specific data types or analysis goals, like Apache Spark for large data sets. Manual entry (for small data sets), data import from external sources or automatic data capture are all ways in which data can be input into these systems at this stage.
As the name suggests, this is the core of the data processing cycle. A few different techniques are used to transform the data into meaningful information, depending on the desired outcome or insights needed from the data. They include:
After processing, the data is presented in a format that’s digestible and easy to understand. The output is the final product, which could be a graph, dashboard or some other visual representation. The interpretation phase is the analysis of the output to draw conclusions, identify trends and make informed decisions — and is where the value of the processed data is finally realized.
The last step involves securely storing the processed data in databases or data warehouses for future use and retrieval. This step is crucial for a few reasons:
Various methods are used to transform raw data into meaningful, usable information. While there are quite a few, and each is best suited for different scenarios and requirements, batch processing, real-time processing and online processing are three of the most common.
Batch processing is a method in which a large volume of data is collected over a period of time and then processed all at once, in a batch. This approach is ideal for tasks that aren’t time sensitive and can be scheduled during off-peak hours to save computing resources. Ideal use cases could be payroll systems, monthly billing, end-of-day reports and generating bank statements. For example, a credit card company might collect all transactions throughout the day and process them in a single batch overnight to update customer accounts.
Real-time processing handles data as it’s generated, providing immediate results. This method is critical for situations where the turnaround from data input to output needs to be instant, especially for systems where a delay could have serious consequences. Fraud detection in financial transactions, GPS systems and air traffic control systems are all examples of where this type of data processing is used.
Online processing is a type of real-time processing that’s interactive. It processes user-initiated transactions as they occur, providing an immediate response. This is what you experience every day when you interact with websites and apps. In a nutshell, a user initiates a request or inputs data, and the system immediately processes it and provides feedback. These systems are always online and ready to handle user requests at any moment. Ecommerce, online banking, airline reservations and online gaming all utilize online processing. Have you ever bought concert or movie theater tickets online? This is how your payment is processed and the system is immediately updated to ensure no one else can buy a ticket for the same seats.
There are different methods for processing data, and not all methods are compatible with all processing types.
This is the oldest and most traditional data processing method, which involves collecting, organizing and analyzing data entirely by hand, without the aid of machines. It’s slow, labor-intensive, prone to error and not ideal for large volumes of data. But it is a good choice for small-scale operations or businesses or where human judgement is essential, like conducting a hand recount of ballots during an election.
If you’re using simple machines and devices to process data, like calculators, typewriters or punch-card machines, you’re using the mechanical data processing method. The Hollerith tabulating machine we mentioned earlier in this guide is an example of this method. Mechanical data processing is ideal for simple data processing jobs and yields fewer errors than manual data processing, but still isn’t a good choice for huge data sets.
Electronic data processing (EDP) is the most modern and widely used method, relying on electronic solutions like computers, servers and automation to process data. It’s a highly efficient, accurate and scalable approach that can handle massive amounts of data in real time. EDP automates the entire data processing cycle, from input to output, and is used in virtually every industry today for everything from simple payroll systems to big data applications.
Modern data processing relies on a combination of powerful tools and emerging technologies to extract valuable insights from raw, unprocessed data. These solutions enable everything from basic data storage to complex, automated analysis.
These are foundational tools for data storage and management, but have different purposes in the processing pipeline.
Databases are for storing and organizing information from a single data source for one particular function of your business. Think of it as a meticulously organized filing cabinet for a single purpose. They’re designed for quick, frequent tasks and small queries. Popular databases include SQL-based systems like MySQL, PostgreSQL and Microsoft SQL Server.
Conversely, data warehouses are large, centralized repositories for storing vast amounts of historical data from multiple sources. They’re designed for analysis and are essentially the library where data analysts go to find information to answer questions about complex business trends. They’re built for running complex queries on large data sets to generate reports and business intelligence. Data warehouses often use big data technologies like Snowflake, Hadoop, Apache Spark and data lakes.
AI and ML are powerful technologies that automate and enhance every stage of data processing. They go beyond handling simple calculations to uncover patterns and make predictions. AI can automate data cleansing and preparation and automatically detect and correct errors, fill in missing values and standardize data formats. When ML models are trained on historical data, they can make predictions, find anomalies and segment data.
Cloud providers like Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure make it possible for businesses to scale their data processing resources up or down as needed without having to buy or maintain expensive, on-premise hardware. It also makes it possible to process big data at scale, which would be impossible for most companies to do otherwise.
Data analytics platforms are software solutions that are often cloud-based and provide a complete environment for data processing. Snowflake and Tableau offer a unified platform for storing data, running analytical queries, building visualizations and simplifying complex workflows. With Snowflake’s AI Data Cloud, for example, data is optimized for high-performance data operations once it’s loaded into the platform, and it runs on top of major public clouds.
Data processing is the indispensable engine that powers the transformation of raw, unorganized data into the business-critical insights that organizations need to make informed decisions. We’ve come a long way from the purely manual days of logging and analyzing data, and now have powerful, automated solutions fueled by AI and ML to handle the sheer volume of data that businesses produce today, which continues to grow exponentially. Efficient and intelligent data processing is more important than ever to make sense of the sea of data that organizations produce every day to ensure their future growth and success.
Distribute computing frameworks: Apache Hadoop, Apache Spark
Cloud-based data warehouses: Google BigQuery, Amazon Redshift, Microsoft Azure HDInsight
NoSQL databases: MongoDB, Apache Cassandra
Stream processing systems: Apache Flink, Apache Storm
Business intelligence (BI) and visualization tools: Tableau, Microsoft Power BI
Integrated data platforms: Snowflake
Big data comes from a wide variety of sources, which can be broadly categorized into three types: structured, unstructured and semi-structured data.
Structured: Highly organized, follows a predetermined format. Typically stored in tables, making it the easiest type of data to search, manage, and analyze using traditional tools. Examples: financial transactions, point-of-sale (POS) data, healthcare records.
Unstructured: Lacks a predefined format. The most common type of big data, yet it poses the greatest challenges for analysis. Includes text, images, audio and video. Examples: social media data, PDFs and emails, sensor data from smart thermostats or wearable devices.
Semi-structured: A hybrid of the other two types. Lacks a rigid structure like structured data, but has some organizational properties that make it easier to categorize and analyze than unstructured data. Examples: XML and JSON files, log files, webpages.
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