Data Uploader

Accelerate Data Ingestion with AI-Powered Data Importers

Written by 
Naresh Venkat
April 26, 2024

Accelerating Customer Activation with User-Friendly Data Importers 

Business teams play a critical role in understanding customer needs and driving growth. They gather valuable insights and often shape customer experiences. Data ingestion is an unnecessary barrier to customer activation; it’s a means to an end.

Key Takeaways

  • The growing complexity of systems and data complicates customer onboarding and slows activation.
  • Organizations must recognize the customer data ingestion challenges business teams experience every day.
  • Osmos empowers business teams to take ownership of data ingestion processes, optimizing your data ingestion framework and significantly reducing the reliance on dev and engineering resources.

Why business teams need a user-friendly data importer

The challenge of customer data ingestion lives with business teams. Specialists in sales, customer onboarding, implementation, or data services are commonly saddled with the tedious task of receiving customer, vendor, and partner data. Handling this data is neither easy nor straightforward.

The Growing Complexity of Data Systems and Ingestion Tasks

As data systems evolve, data ingestion tasks become increasingly complex. The variety of data sources, formats, and schemas makes it challenging for non-technical teams to navigate the data ingestion landscape. Traditional data importers often require high technical knowledge, making them inaccessible to business teams.

Even Excel power users who can dive deep into complex customer data have difficulty with data mapping and cleanup when the source data is too far gone. Efficiently handling complex data ingestion tasks requires setup and technical expertise. Enter developers and engineers. These folks were brought on to create innovative products that accelerate growth. They now manage an endless supply of data ingestion support tickets. This dependency is the source of the bottleneck. It delays data ingestion and slows customer activation, resulting in frustration and customer pain.

Data Mapping and Cleaning is Time-Consuming

The complexity of mapping and cleaning messy data is a significant factor in customer activation drop-off. The data ingestion process usually requires some understanding of data structures and schemas. Not all team members come to the organization with those skills in hand. Business teams spend much of their time on data ingestion tasks, mainly because data never arrives in the needed schema. Every new data ingestion task may require a new approach to solving the messy data problem.

The Importance of Data Importers in Customer Onboarding

Onboarding customer data can involve integrating data from various sources into a company's systems and processes. This data can include customer details, purchase history, preferences, pricing, and transactions, among other information.

It may sound simple to import customer data from any unique source, but historically, this has been far from the truth. Validating data quality and transforming it into the desired format can be tireless when your data is plagued with data entry errors, empty cells, and a non-unified format. 

Implementing data import tools, like Osmos Uploader, is the first step toward accelerating customer onboarding and activation. These tools simplify the data ingestion process and enable business teams to accelerate their data ingestion efforts without extensive technical expertise.

Osmos has streamlined the onboarding process with an easy-to-use interface and AI-powered auto-mapping that puts customer data in the desired schema quickly, with the team member overseeing the entire process. This dramatically reduces the manual effort required and empowers teams to produce accurate and consistent customer data for ingestion faster than ever before.

Optimizing your Data Ingestion Framework For Faster Activation

Let’s start by considering the different methods, files, syntaxes, and systems used to share data—CSV, JSON, TSV, XML, APIs, data warehouses, emails, CRMs, and ERPs. With the diversity of data formats available, an organization’s data ingestion framework becomes central to ensuring fast and effective customer activation. First, we’ll define must-know terminology and then dive into data ingestion optimization strategies.

What is a data ingestion framework?

A data ingestion framework provides a structured approach to collecting, processing, and storing data from various sources. It includes the tools, processes, and best practices required to ingest data into an organization's systems effectively.

What is a data ingestion pipeline?

A data ingestion pipeline, on the other hand, is a series of steps that data goes through from the source to its destination. It involves extracting data from various sources, transforming it into a usable format, and loading it into the target system.

We’ve already established that your data importer is central to accelerating customer activation. The next step is automating data ingestion pipelines for the customers that regularly send data from known sources.

Custom data ingestion scripts are the legacy method of choice for most organizations in this data ingestion situation. New, more robust data ingestion frameworks rely on Osmos tools like Pipelines to ingest customer data that comes in regularly or from the same or similar sources. 

Not only can anyone on the business team build and maintain fully automated system-to-system pipelines, but it’s so easy anyone in the organization can do it. With prebuilt integrations, automated data transformations, and no-code lookups, joins, and aggregations powered by Datasets, this tool is the final step in the acceleration process. Now, a business team member can effectively manage any challenging data ingestion task for any client using the suite of Osmos data ingestion tools.

Business Teams Are Accelerating Data Ingestion with Osmos

We’ve learned how Osmos tools help optimize your data ingestion framework. Now, let’s explore the features that are most important to business teams and their customers.

  1. Ensuring Incoming Data Quality: Osmos tools empower business teams to independently handle custom validations monitoring the AI-driven mapping and cleanup process, ensuring that only clean and accurate data is ingested.
  2. AI-Powered Data Mapping: One of the most time-consuming aspects of data ingestion is mapping customer data to the template schema. Osmos's AI-powered AutoMap feature learns how to automatically map customer data to the desired schema, reducing manual effort and saving time for business teams.
  3. One-Click Data Transformation: Transforming data to fit the required format can be daunting for non-technical users. Osmos tools support one-click data cleanup, making data transformation simple for business users, regardless of their technical abilities, eliminating the need for complex coding or scripting.
  4. 360-Degree Visibility: Osmos data import tools provide a comprehensive view of the data ingestion process, offering 360-degree visibility. Business teams can see a list of errors at a glance, enabling them to identify and resolve issues quickly. The importer allows teams to send error reports to customers or provide them with access to an embeddable history table, facilitating faster uploads and closing the loop on fixing errors.

Empowering Business Teams while Liberating Engineers

We recognize that empowering business teams with accessible tools to manage incoming data efficiently is crucial for organizations to stay competitive. Osmos's adaptive AI paves the way for a more efficient and user-friendly data management landscape.

With Osmos's user-friendly data importers, organizations can unlock the full potential of their business users. They can take ownership of the data ingestion processes and reduce their reliance on IT, dev, and engineering.

Should You Build or Buy a Data Importer?

But before you jump headfirst into building your own solution make sure you consider these eleven often overlooked and underestimated variables.

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Naresh Venkat

Co-founder and COO