Build Vs. Buy: Data Management Solutions for Early-stage Diagnostic Companies
Product & Engineering

Build Vs. Buy: Data Management Solutions for Early-stage Diagnostic Companies

Deepthi Das, Trisha Dhawan
August 3, 2022

To build or to buy- a choice that can make or break an early-stage company.

The long-standing 'build vs. buy' conundrum- what are the pros and cons, what could be an ideal strategy and how could early-stage diagnostic companies make the most from building/ buying to address the biomolecular data management challenges- is more common than you think.  As is the million dollar question (literally!)- what could be an ideal solution to the data management woes of emerging diagnostic companies? Read on to find out!

Major Pain Points:

The most logical approach to solving any problem is to identify the right pain points and take it from there to set a corrective action. Here, we identified a few- can you relate to some of them?

Funding restrictions: In most early-stage diagnostic companies, funding is limited, and financial decisions are critical. Most of the funds are routed to exploring and developing their key idea into a sellable product. This leaves only a small fraction of funds to be spent towards developing a robust data infrastructure.

Resource limitations: The core team of a diagnostic startup would be wet lab scientists or biologists who develop a particular idea for a product. Once the experiments start giving good results, they would need the help of bioinformaticians/ computational biologists to analyze the data and come up with actionable insights. However, hiring more experts may destabilize the available budget.

Scalability: Data management in lab scale experiments can be carried out manually, using basic tools and cloud storage, whereas high throughput screening data requires proper pipelines and some degree of automation to produce actionable insights.

Data silos: Startups tend to accumulate data - with varying data types- at a swift pace as they are at an exploratory stage. Data silos tend to be created naturally as the organization grows and these could hinder data use.

Other barriers: Incompatible systems/data/workflows, concerns about security, and lack of analytic skills and/or the right technology infrastructure are other barriers that stagger the pace of the startup growth.

Build Vs. Buy - The Pros and Cons

A Harvard business survey states that only 15% of healthcare organizations worldwide are fully equipped to make quick data-driven decisions. The need of the hour is an efficient and robust way to organize the data generated in-house to ensure maximum value extraction. The data has to go through a few key scrutiny processes to standardize it and make it FAIR (findable, accessible, interoperable, and reusable) compliant in order to derive insights and encourage collaboration across teams. This brings us to the big question- what is better- building or buying the technology expertise, infrastructure, etc.? What would propel growth faster?

The need of the hour is an efficient and robust way to organize the data generated in-house to ensure maximum value extraction. The data has to go through a few key scrutiny processes to standardize it and make it FAIR (findable, accessible, interoperable, and reusable) compliant in order to derive insights and encourage collaboration across teams. This brings us to the big question- what is better- building or buying the technology expertise, infrastructure, etc.? What would propel growth faster?

The Best Solution for Early-Stage Diagnostic Startups

The enlisted comparison between build and buy clearly shows that buying a data management tool or platform far outweighs the option of building it indigenously for early-stage startups.

The ideal scenario is where different data types are ingested, annotated, and stored in an atlas where integrative analysis can be performed.

The current technological advancements in cloud computation and data-centric AI have helped in the development of data warehouses and pipelines designed specifically for clinical/experimental data which can take care of annotation, curation, and harmonization in an automated manner. These mundane tasks demand resources and can be time-consuming but can also be outsourced. Early-stage diagnostics can greatly benefit from these products by just plugging in their data and focusing instead on research and innovation thereby accelerating the time from concept to commercialization.

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