Without a clear and quick process your dev, sales, and customer success teams can become overwhelmed by the amount of work required to delight new customers and ingest clean validated data.
Data onboarding is different for every customer — some have hundreds of rows in various formats once a month, while others only need to import large CSV files daily. This variance makes it difficult for teams to develop a streamlined data onboarding process. Instead, companies rely on manual processes and a backlog of requests to import customer data.
The thing is, data onboarding doesn't happen in a silo. The faster a dev team can import data, the faster customers begin to see value in a product. Time to value is important for retention, and anonboarding study by Wyzow found that 55% of customers say they’ve returned a product because they didn’t fully understand how to use it.
So if one team is overwhelmed, that lag negatively impacts all other teams. To help prevent data onboarding from becoming a burden for your teams, we've identified three warning signs that reveal when it's time for a better process and system.
New customer onboarding usually begins with the front-line sales and customer success (CS) teams. They're responsible for gathering data to import into a product so can customers use it.
You get nervous as you ask customers a ton of questions about their data - 'Where is it? What format is it? How much are we talking?' - because you know that you need the 'right' answers to give them a good onboarding experience.
Their data is often emailed as CSV files, which may have vastly different naming conventions and formatting from one company to the next. Not only do these files take time to collect, but cleaning data isn't a core skill of CS and sales teams.
Yet they wind up emailing back-and-forth with customers to correct errors and fill in gaps. Why? Because your dev and data teams are focused on the product and business objectives, the file cleaning and importing tickets and requests get deprioritized making the onboarding process take weeks or even months.
If your team struggles with a backlog of data onboarding related tasks that slow down the customer onboarding process, then it’s time to reevaluate your process.
All of those one-off data cleaning requests have to go somewhere, and they usually fall to technical teams. Dev and product management teams get bogged down with tickets to import customer data, create custom solutions for high-level clients, or help clean up messy data before it's ingested into the company's operational system.
So your frontline teams say to customers, ‘'Of course we can handle your data," but they know that it's going to take you a lot of all-nighters, a lot of stress, significant cost, and some potential missed deadlines to (hopefully) be able to work with the data they have.
Rahi, a company that helps organizations scale faster by improving supply chain efficiencies, ran into this exact issue. They needed to respond to customer purchase orders (POs) faster, but it ran into a number of challenges when receiving a variety of complex customer POs. The data in the POs wasn’t organized, came in different formats, and had to be manually verified against multiple ERP systems before finally loading the order into the management systems.
This manual data wrangling and importing process took Rahi's sales team over 60 hours a week to complete, resulting in longer fulfillment times. To accelerate the process, Rahi leveraged Osmos Pipelines. Instead of the sales team manually cleaning the incoming PO data, the company uses Osmos’ AI-powered data transformations to validate, clean up, and restructure the messy PO data to fit the ERP schema and format.
"We saved over 60% on delivery costs with our largest clients by simply removing tedious copy-paste and manual data wrangling activities," said Rahi CTO Matt Robinson.
By automating data wrangling, your teams can focus on delivering great customer experiences with the time saved.
Custom data onboarding solutions work well — until they don’t. Writing custom Python scripts, building data uploaders, and maintaining data pipelines are all possible solutions, but a lack of repeatable, human-in-the-loop processes limits company growth.
ForMosaic, a resource management software company that enables real-time collaboration, importing clean customer data is critical. They needed one flexible data onboarding solution that could handle different types of scenarios, so they began building a data importer tool. But the team quickly realized that it would take 6-12 months of dedicated engineering time to develop the necessary validations and customizations needed for their customers.
They ultimately decided that building a data importer tool was not part of its core business. Instead, the team decided to configure and embed a smart data uploader right into their application using Osmos Uploader.
"Osmos Uploader gives us all the features we need to provide our end-users a delightful data importing experience, and I get the time back to focus on our core product," said Nima Tayebi, CTO of Mosaic.
Now, Mosaic has the control to handle multiple data importing scenarios, and their customers have the freedom to upload their data to fit the required schema. With Osmos’ custom validations and AI-powered data transformations, Mosaic’s customers are empowered to send clean data every time.
Our team at Osmos ran into this same issue, which is why we've built AI-powered data transformation tools that speed up the data onboarding process.
Osmos Uploader and Osmos Pipelines help makecustomer data onboarding a stress-free and streamlined process. Our no-code technology makes it easy for your teams to quickly validate, clean, and import customer data. That means fewer support tickets, less time spent cleaning and validating data, and more time on improving the product and customer experience.
So if you're ready to scale and speed up data onboarding then try the world’s most comprehensive customer data onboarding solution today.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
A majority of business leaders believe data insights are key to the success of their business in a digital environment. However, many companies struggle to build a data-driven culture, with a key reason being the lack of a sound data democratization strategy.
Just like data mesh or the metrics layer, active metadata is the latest hot topic in the data world. As with every other new concept that gains popularity in the data stack, there’s been a sudden explosion of vendors rebranding to “active metadata”, ads following you everywhere and… confusion.
In our experience at Secoda working with many data teams, we've seen most data teams do not have the tools they need to succeed. For growing organizations, the data function is usually an afterthought. The first data hire is brought on before raising a Series A and is expected to manage the workload that comes afterward with little to no support.
As the amount of data rapidly increases, so does the importance of data wrangling and data cleansing. Both processes play a key role in ensuring raw data can be used for operations, analytics, insights, and inform business decisions.
Do you know the current status — quality, reliability, and uptime — of your data and data systems? Not last month or last week, but where they stand at this moment. As businesses grow, being able to confidently answer this question becomes more important. That’s because data needs to be clean, accurate, and up-to-date to be considered reliable for analysis and decision-making. This confidence comes through what’s known as data observability.
One reality that many companies face when adopting cloud technology: designing data infrastructure for business in a cloud computing environment is just different. Legacy stacks can indeed suffice for many companies. But as business requirements grow and use cases increase, both in number and complexity, the models and assumptions that worked well enough in the data center become problematic.
In the past years, organizations have been investing heavily to convert themselves into data-driven organizations with the objective to personalize customer experiences, optimize business processes, drive strategic business decisions, etc. As a result, modern data environments are constantly evolving and becoming more and more complex. In general, more data means more business insights that can lead to better decision-making. However, more data also means more complex data infrastructure, which can cause decreased data quality, a higher chance of data breaking, and consequently erosion of data trust within organizations and risk of not being compliant with regulations. The data observability category — which has quickly been developing during the past couple of years — aims to solve these challenges by enabling organizations to trust their data at all times. Although the category is relatively young, there are already a wide variety of players with different offerings and applying various technologies to solve data quality problems.
Data governance is more than just having a strategy – it is about establishing a culture where quality data is achieved, maintained, valued, and used to drive the business. Modern-day businesses are supported by data and information in many ways and forms. In recent years, data has become the foundation for competition, productivity, growth, and innovation. We are seeing successful organizations shift their focus from producing data to consuming it, and data governance strategies becoming increasingly important to support their crucial business initiatives. Executives and shareholders are starting to realize that data is a strategic asset and data governance is a must if they want to get value from data.
If you have spent any time in the data space in the last 10 years, you'll know that data job titles have gotten hilariously complicated and confusing. There are Data Analysts, Data Scientists, Analytics Engineers, Data Engineers, Business Analysts, Business Intelligence Analysts, Product Analysts, Product Data Scientists, Data Product Managers (?), ML Engineers, Data Enthusiasts, People Who Just Really Love Counting, and dozens of other titles floating around.
I started my career as a first-generation analyst focusing on writing SQL scripts, learning R, and publishing dashboards. As things progressed, I graduated into Data Science and Data Engineering where my focus shifted to managing the life-cycle of ML models and data pipelines. 2022 is my 16th year in the data industry and I am still learning new ways to be productive and impactful. Today, I am now the head of a data science & data engineering function in one of the unicorns and I would like to share my findings and where I am heading next.