Data Ingestion 101: Solving the First Mile of Data Ingestion

Part 1 in this series defined the nuances of the first mile of data problem. In Part 2, we covered why companies often overlook customer data. In Part 3, we established the importance of striking a balance between people, processes, and technology when finding a solution for customer data ingestion. 

Now that we have a thorough understanding of first mile data ingestion and the challenges that come with it, we’ll cover why you need a first mile data solution and how you can solve that problem for your organization.

Executive Summary

Why you need a first mile data solution

• First Mile data problems are too expensive to ignore.
• Customer satisfaction is paramount. Churn is real. Frustration is real. Empowered teams make for happy customers. 
• Frontline teams, like implementations and operations, must own data ingestion if we are to streamline processes, reduce costs, and increase efficiency.
• Data engineers and developers should focus on business-critical tasks. Troubleshooting messy data files is a costly, inefficient use of their time.
• The Modern Data Stack isn’t scalable if we hope to reduce complexity. We need a better way.

What to look for in a first mile data solution

• If implementation and operations are going to own data ingestion, they’ll need powerful no-code tools.
• Capability, usability, and self-serviceability are paramount when considering first mile data solutions. 
• Osmos takes on the heavy lifting of data ingestion, alleviating the burden from already thin tech teams.
• Get source-agnostic tools that dramatically reduce the time it takes to address schema changes, pipeline failures, and data format issues. 
• Effortlessly scale your data onboarding with Osmos.

Why you need a First Mile Data Solution

First-mile data problems are too expensive to ignore. Unfortunately, the industry is late to the game, having paid little attention to the issue until recently. To solve first mile data challenges, we must acquire the will to thoroughly examine how we solve data ingestion at scale today. If we wait, we risk the problem growing so large that it permanently hampers critical facets of day-to-day operations.

Schema changes and volume anomalies aren’t self-contained. They spread to your downstream warehouse tables and plague business processes. They reach beyond the control and scope of your data team, bleeding into engineering as a product infrastructure problem.

Customer, vendor, and partner data sources represent potential points of failure that cannot be ignored. Here’s how to start thinking about solving data ingestion challenges for your organization.

Define who owns first-mile data 

Getting valuable business information, “the data,” out of external sources and cleanly ingested into operational systems is no simple feat. Deciding who owns that challenge isn’t simple, either.  The honest answer to who owns the problem for most organizations is “it all depends.” 

Data transformation tasks are often split between customer service teams and data teams. The people who receive the data, frontline teams, do their best to facilitate the data ingestion process until they encounter a problem beyond their technical ability. As a last resort, folks in implementation or operations create support tickets, transferring “the problem” to a technical counterpart. 

Data, engineering, and development teams, who are always stretched thin, pause their regular business to take care of this auxiliary task. Rarely can they get to a ticket right away. This waiting game results in pain on all sides. With the workload shifted to data teams, the customer is left waiting, customer onboarding is stalled, and frustration ensues.

Not only do data ingestion support tickets obscure ownership of data ingestion challenges, they are also highly interruptive to the data onboarding process. The inefficiency of passing a data problem back and forth is an operational nightmare. 

Implementation and operations teams are already responsible for data onboarding. They are likely spreadsheet power users. They receive data from customers, vendors, and partners at a regular cadence. They understand the business context of the information they receive. The most logical and efficient way to solve the problem is for front line teams to see the data ingestion process through to fruition. 

The problem is spreadsheets can’t handle tremendous volumes of data. Even as a power user, performing validations on a .CSV file often leads to human error and eventually involves manual cleaning.

The importance of streamlining data ingestion

If you’re going to solve data ingestion challenges once and for all, streamlining is key. The new challenge is reducing the number of people and tools needed to make data ingestion work without further complicating the data ingestion process.

That brings us to the elephant in the room: the Modern Data Stack (MDS). This clunky set of tools is glued together with brittle, one-off scripts that require highly technical specialists with expertise in SQL and Python to keep the operation afloat. Issues with code and persistent bug fixes bog down processes and contribute to the chaos. The complexity of the MDS process misses the point of streamlining first-mile data ingestion. Why force teams to usher first mile data into the data warehouse dealing with cleanup and validation at such a late stage? Why take on the extra tooling and added expense? 

All this to say, if you are already using MDS tools for data ingestion tasks, it is perhaps time to consider a more efficient solution. If you are considering investing in MDS tools, be prepared to manage complexity, scalability challenges, and the great expense of managing a behemoth like the Modern Data Stack.

What to look for in a first mile data solution

If implementation and operations are going to own data ingestion, they’ll either need comprehensive no-code data management tools, or they’ll need to provide customers with a foolproof, self-serve means of uploading their data for transformation.

This is why capability, usability, and self-serviceability are paramount when considering first mile data solutions. Ask yourself, do your frontline teams already possess the skills necessary to use the new tool? Is the new tool user-friendly enough for specialists to hit the ground running? Can teams get up to speed without lengthy training or specialized skills?

Empower your front line teams and lighten the MDS load

Osmos does the heavy lifting early, alleviating the burden of data ingestion from already thin tech teams. Now, you can empower your implementation and operations specialists with no-code tools that transcend data cleaning and data mapping, allowing them to independently transform and validate incoming data as they receive it. Mismatched schema is no problem for Osmos’s AI-powered data transformation engine. The technology automatically detects and maps columns from source to destination. Osmos’s source-agnostic tools dramatically reduce the time it takes to address schema changes, pipeline failures, and data format issues so you can effortlessly scale your data onboarding.

The solution to first mile data ingestion lies in the people, both customers and teams. Osmos streamlines data ingestion by reducing user friction with an embeddable self-serve data upload experience. So your customers, vendors, and partners can easily share data, and your teams can ingest clean, validated data every time.

First-mile data problems are cumbersome and expensive. Save time and money with all-in-one future-proof solutions that streamline data ingestion and accelerate data onboarding.

Hundreds of growing companies trust Osmos. 

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