What is Data ingestion, and how to automate it?
2.5 quintillion bytes of data are generated daily.
But the data exists in silos, away from where they can be used.
In order to use data properly, businesses need to invest in data ingestion to collect data from silos into one single unified storage system. Data ingestion is easy to implement and automate too. Let’s learn what data ingestion is, how it works, and how to automate it.
What is data ingestion?
Data ingestion refers to the process of collecting and importing data from various sources into a storage system or a data processing system.
In other words, data ingestion is the process of extracting data from multiple sources as social media platforms, websites, sensors, and more, to make it useful for further analysis. Data ingestion helps in identifying trends and generating insights that can be used to make informed business decisions.
Types of Data Ingestion:
There are mainly three types of data ingestion, which are as follows:
Batch Data Ingestion:
Batch data ingestion is a process where data is ingested at regular intervals. The intervals can be such as hourly, daily, or weekly. This process involves ingesting data in large volumes, and most of the data processing is done offline.
Batch data ingestion is best suited for scenarios where the data is not time-sensitive, and a delay of a few hours or days will not impact the analysis.
For instance, ingesting data from a CRM system or a financial system.
Real-Time Data Ingestion:
Real-time data ingestion is the process where data is ingested as soon as it is generated or received. This type of ingestion is a perfect fit for scenarios where the data is time-sensitive and requires immediate analysis.
An example would be stock market data, social media posts, or website clicks.
Near-Real-Time Data Ingestion:
Near-real-time data ingestion is a process where the data is ingested within a few minutes of its generation.
This is best when data needs to be analyzed and acted upon quickly, but a few minutes of delay in processing is acceptable. For instance, when IoT devices generate user data and share it with servers.
How does data ingestion work?
Here are the steps which show how to do data ingestion:
- Identify data sources: You need to identify the data sources to gather data. These can be CRM databases, folders, APIs, and more.
- Data extraction: Once the sources have been identified, you can start extracting data from sources. Platforms like Nanonets can help you extract data from any kind of source, document, or image.
- Migrate Data: Now, you need to move the extracted data to a centralized location, a data warehouse, or a data lake.
- Transform data: Now, the data needs to be validated and transformed. This may involve cleaning and data enrichment to make it more useful for analysis.
- Sync Data to Data Storage: Finally, the validated and transformed data is loaded into the central location, where it can be analyzed using various tools and techniques.
How to automate data ingestion?
Data ingestion follows mechanical rules and can be automated using data ingestion tools.
Data ingestion tools are software applications that automate the process of collecting, integrating and processing data from multiple sources. These tools typically have features such as:
- Data connectors: easy integrations with various sources to collect data
- OCR: In case you have to extract data from documents, you need to have OCR in build into the system too.
- Data wrangling: Easy way to automate data transformation, cleaning, and data formatting in real-time.
- Data validation: The data ingestion tool allows you to validate data from third-party sources to ensure the accuracy and completeness of the data.
- Data processing and loading to store the data in a centralized repository.
Nanonets for Data Ingestion
Nanonets is an AI-based data entry automation software that connects over 500+ unconnected data sources in real-time. Nanonets has in-built OCR software and workflow automation capabilities to automate any manual data process in minutes.
Nanonets can be used for:
- Data Aggregation
- Document Data Extraction
- Data Cleaning
- Data Mapping
- Data Wrangling
- Data Entry Automation
Here’s how simple it is to automate data ingestion from PDF invoices from Gmail
Select the invoice OCR model.
You can select Gmail and connect your Gmail account from the import file options. Whenever you receive an invoice, it will be processed, and data will be stored in the place of your choice.
Document import options on Nanonets
Now comes rules. What do you want to do with the data? You can set up rule-based no-code workflows to do many tasks like date formatting, lookup in the database, matching the data, removing commas, capitalizing the data, and more.
Data transformation options on Nanonets
Once you've processed the data, you can share the data with your business applications using data export options on Nanonets.
Data export options on Nanonets
It's very straightforward to set up data ingestion on Nanonets. You can start doing it yourself or contact our experts, who can help you set up workflows for your use case.
What are the challenges you face during data ingestion?
Data ingestion involves collecting data from multiple sources. Ensuring the quality of data being ingested as all sources have different formats, syntax, and missing values. Also, some of these sources might be confidential or sensitive, leading to privacy and security risks.
Performing data ingestion for single-digit sources is different than handling more data sources. The infrastructure and bandwidth of resources required to handle large-scale data ingestion can be complex and costly if not automated.
How to mitigate the data ingestion challenges?
You can implement automated workflows to perform data quality checks, profiling, and cleansing to improve data quality. Normalizing data across multiple sources automatically can save time and money.
You can scale data ingestion with automated platforms like Nanonets, which can handle large-scale data ingestion without requiring significant infrastructure investments. Such a platform can also ensure the steady implementation of data security protocols, thus improving data security.
The Future of Data Ingestion:
According to a report by ResearchAndMarkets, the global data ingestion market is expected to grow at a CAGR of 23.3% from 2021 to 2028. This high growth is due to the increasing adoption of automated solutions and the need for real-time data processing for instant insights.
Another report by Gartner predicts that by 2023, over 50% of organizations will use automated data processing platforms to simplify and streamline data integration.
With the increasing volume, variety, and velocity of data being generated in today's digital landscape, data ingestion will continue to play a critical role in enabling enterprises to harness the full potential of their data.
What are the benefits of data ingestion?
- Data ingestion allows for collecting and integrating large amounts of data from disparate sources into a centralized system, providing a more comprehensive view of a company's operations and performance.
- It helps to improve decision-making by enabling faster and more accurate analysis of data, leading to better insights and informed decisions.
- Data ingestion can save time, reduce costs, and improve efficiency by automating data ingestion processes and reducing the manual labor required to collect and integrate data.
- It enables data-driven innovation by facilitating the exploration of new data sources and enabling experimentation and testing of new ideas based on the analysis of the integrated data.
- Data ingestion can also provide a competitive advantage by allowing companies to respond more quickly to changing market conditions and customer needs by providing real-time insights into customer behavior and market trends.
How does data ingestion help enterprises?
- A recent study showed that companies that implement data ingestion processes could save up to 50% of their time spent on data integration and processing, reducing the time required to analyze data and make informed decisions.
- Another study found that data ingestion automation can lead to cost savings of up to 70% compared to manual data ingestion processes, reducing the need for human resources and increasing efficiency.
- By implementing data ingestion processes, companies can reduce the risk of data errors, which can save both time and money in the long run. According to a study, data errors cost US businesses an estimated $3.1 trillion annually.
Nanonets has many interesting use cases that could optimize your business performance, save costs and boost growth. Try Nanonets to see how you can automate data processes on the go.