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Build vs. Buy - Data Quality and Ingestion Guide

How to access Build vs. Buy as a product manager? How can you avoid wasting precious engineering resources on data import and data quality headaches?

Product managers are consistently looking for low-investment, high-payoff functionality to add to their products. Specifically, what can be done to improve the product without wasting your limited engineering resources. Below you will find a quick guide to help you understand the complexities of building an advanced data importer with AI to detect data quality issues. 

Some factors to consider before making your decision:

  1. Sacrificed Product Roadmap
  2. Developer Time
  3. Number of Developers Involved
  4. Complexities
  5. Opportunity Costs
  6. Maintenance
  7. Go-Live Time
  8. User Friendliness

#1 - Sacrificed Product Roadmap

SaaS companies have to balance customer feature requests, corporate strategic vision, and tech debt when planning engineering sprints and roadmap planning. Sacrificing your product roadmap for anything other than your core competencies is reducing your company’s resources efficiency. 

The question you must ask yourself: Is building a data importer the highest priority objective for the business? If not, partnering with Qluster is your answer. 

# 2 - Developer Time

Developer resources are probably the most scarce within your organization. It wouldn’t be prudent to have them spend time on projects that aren’t integral to your product’s long-term strategy. Building a data ingestion tool within your ETL pipeline is extremely time-consuming. The most simple data importers take 1-3 weeks to build. Depending on the complexity of your incoming data and required transformations, your team will need to build many layers of data quality validations to ensure data is anomaly-free. 

#3 - Number of Developers Involved

With limited developer resources and the list of roadmap items increasing, organizations must be frugal with the tasks assigned to developers. Most organizations are operating with less than ideal numbers of technical talent. In today’s competitive labor market, your engineering team should be focused on revenue-generating projects. Based on conversations with customers who considered building a complex data ingestion tool, it will require 3 engineers (~$500k annually) to build and maintain such a product. 

#4 - Complexities

While you might think that building a data import tool is a simple task, it is not! It is important to consider the various complexities before embarking on the journey to build your own tool in-house. Complex features such as AI anomaly detection and user traceability are two integral parts of a properly functioning ingestion tool. Handling external data from customers, partners, and vendors is messy and time-consuming for your team. To accept these varying data files, the data importer must have validations to detect errors before they pollute the destination. 

Moreover, building integrations to your data sources and destinations require weeks/months of development and QA testing depending on the systems you are trying to connect. Each source and destination needs to be meticulously tested to ensure data can seamlessly flow into your environment. 

#5 - Opportunity Costs

Time is money. Wasting resources on low-impact and non-revenue-generating projects is not an efficient strategy from a product perspective. Partnering with an experienced solution provider will provide your team with the mechanical advantage to move faster with fewer resources. 

#6 - Ongoing Maintenance

After you’ve built your internal tool, you’ll need to constantly keep updating it for bug issues, security vulnerabilities, and integration updates. These are serious challenges to consider when embarking on the journey to build an internal data ingestion tool. You can’t just set it and forget it. Keeping ETL data pipelines running smoothly requires frequent maintenance. Your team will need to bump tickets or projects to the next sprint cycle just to maintain the data importer when an issue arises. 

#7 - Go-Live Time

A data import tool with complex data validations, integrations, and automation requires 9-12 months to build. That’s one year of your engineering team’s time dedicated to a non-revenue-generating product for your company. Most companies need such a tool today, not many months from now. That’s where Qluster’s data ingestion and quality detection platform can help. 

#8 - User Friendliness

User experience is one of the most important aspects of building a successful product. Having a product that is easy for your non-technical internal team and end users to easily upload data hassle-free is imperative. 

Qluster has partnered with some of the best UX researchers to make our data ingestion product user friendly and easy to use. If you have any questions, please click on the “Contact Qluster” button below.

Questions?

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