Make ops-driven data ingestion a reality with Osmos
Data Transformation Hot Tip: Change how you think about data ingestion
In today’s data-abundant landscape, businesses face pressure to streamline operational processes, maximize cost-savings, and scale, all while striving to ingest increasing amounts of data. The sheer volume and complexity can overwhelm traditional data ingestion processes, leading to bottlenecks, inefficiencies, and outright failures. The journey from raw extracted data to clean, usable data is not without its challenges, particularly in the first mile of the data supply chain.
Recent innovations in Artificial Intelligence (AI) and Natural Language Processing (NLP) have opened up exciting new opportunities to tackle data ingestion challenges with novel strategies, that is, if business leaders can tackle the sell innovation up the corporate ladder. By now, the message has reached the C-suite. By harnessing the power of AI and NLP, organizations can automate and streamline previously complex workflows, transforming volumes of data from a diversity of sources with unprecedented speed and accuracy.
In this article, we’ll touch on a traditional approach to data ingestion, building ETL pipelines, and discuss how new tools are revolutionizing data ingestion, challenging the status quo, and changing how we think about solving first-mile data challenges.
Understanding the data supply chain before making a decision
The data supply chain is the end-to-end process of transforming and managing data from its raw form into valuable actions and insights. It includes collecting, ingesting, processing, storing, and analyzing data to generate meaningful outcomes. The first mile of data ingestion, a critical stage in the data supply chain, is the initial step of collecting and bringing in raw data from various sources– including customers, vendors, and partners– into your data infrastructure. This stage sets the foundation for the entire data lifecycle and significantly impacts the quality and reliability of the data downstream.
Before choosing a solution, carefully evaluate your data situation. Assess the complexity and volume of incoming data, your latency needs, and scalability requirements.
Key considerations in choosing a first-mile data solution
- Data Complexity: The data landscape is inherently complex. Businesses need to handle structured, semi-structured, and unstructured data efficiently to make good use of it. Since there’s no universal standard for data and data exchange, effectively managing various sources, formats, and structures is a must.
- Data Volume: Traditional data ingestion processes are easily overwhelmed by masses of data. Extracting, transforming, and loading large datasets is often time-consuming and resource-intensive. It can impact the speed at which organizations leverage data for decision-making or to operationalize it for active use.
- Data Latency: Companies can’t afford data transformation delays. Lag hinders a business’s ability to respond swiftly to market changes and emerging opportunities. Timing from data ingestion to operationalization is critical.
- Scalability: As a business grows, so does the volume and complexity of its data. Scalability becomes a significant concern, particularly when dealing with legacy processes that may struggle to handle large-scale data ingestion efficiently.
When exploring and vetting solutions, companies face another critical decision – choosing between traditional Extract, Transform, Load (ETL) and modern ETL processes in data ingestion. Understanding their differences and benefits is essential to optimizing the data supply chain.
The modern tools we rely on are largely cloud-based, AI-driven, out-of-the-box solutions that can be implemented in an instant. By contrast, traditional or legacy ETL solutions are built on inconvenient processes. With infrastructures built on legacy code, investing in legacy ETL is like paying for a house of cards built from chewing gum and tape. It’s bound to fail under pressure.
The scripts are precarious. The interfaces are rarely intuitive. Managing these long, complicated workflows requires certification, another costly barrier. Workflows are run locally on installed software; licenses are usually sold per seat. Not only will you have to buy a dedicated server, you’ll also need to hire certified resources and purchase their licenses individually.
Legacy ETL’s workflow-centric approach to data management is cumbersome, to say the least. Imagine you hire a crack team of devs and engineers who specialize in strategic planning and tooling architecture. They build you a well-orchestrated workflow and train new team members on the set-up. What they’ve built is essentially a long string of rules and logic, repeated pivots, and nested if-then statements patched together to create a single workflow. You’ll need other workflows if you receive data from multiple vendors or partners. Those will need to be custom-built. All of this will need to be continuously updated and maintained to ensure long-term viability.
This process represents a source-based mentality. It ignores schema considerations, and perhaps most importantly, it doesn’t seek the simplest path forward. This is why shifting your thinking from dev-driven workflows to an ops-driven data ingestion approach is key.
While it is possible to construct transferable workflows, these tools weren’t built with collaboration in mind, so knowledge transfer isn’t easy. You notice that their established knowledge base is quickly eroded as the department churns. Your investment will never be as solid as it was the day it was built.
The most unfortunate side effect of this unwieldy process is that legacy ETL tools virtually eliminate the opportunity for frontline teams to take ownership of the data ingestion process, leaving the people with the closest relationship to the data, the business context of the information, entirely out of the loop.
“The fundamental mistake organizations, teams, people make is they start with the source. The correct pattern in my opinion is to start with the question to be answered.” - Kirat Pandya
- Question: What is the question to be answered
- Destination: What is the data? What is the simplest schema (fields, types, etc.) of the data I need to get my answer?
- Source: Where can I find all of the bits that data will fit into (2)
- Transform: How do I now transform all of source data into (2)
- Maintain: How will I make sure the pipelines keep running?
Why you need modern ETL
This is an exciting moment in data ingestion. For the first time, frontline teams are empowered to execute data ingestion and data transformation tasks without the assistance of technical teams. That means your spreadsheet power users can apply their knowledge of business use cases and context to the data they receive in real-time. Osmos is bridging the skills gap and democratizing access to data ingestion.
Where legacy ETL is built for engineers, Osmos’s modern ETL tools are built for implementation and operations teams. By leveraging AI-powered data transformations to streamline and accelerate data ingestion processes, Osmos enables companies to improve data quality at scale. Giving frontline teams the ability to automate complex data transformations reduces the need for manual intervention and minimizes the risk of errors. This significantly speeds up data ingestion, allowing organizations to quickly process and act on data. With a user-friendly interface that requires no specialized skills and is accessible to all employees, Osmos makes ops-driven data ingestion a reality.
If you’re still relying on data teams to take the reins, it’s time to change how you think about data ingestion. Empower your frontline teams and pave the way for widespread data democratization.
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