Bench International

Buy vs Build for Digitization of Data

Buy vs Build for Digitization of Data

Insight by: Mamatha Shekar

It has bedeviled even great leaders whether to buy or build a solution/product for a challenge or upcoming initiative. One can quote examples from the past when both the models have succeeded or failed depending on the complexity of the solution to be built, how closely it relates to the core business, agility of the company and most importantly the leaders identified to shoulder the responsibility.

In the 1990s, DNA microarrays were invented by integrating technologies from two different fields (photolithography from semiconductor and DNA synthesis from biotechnology). Microarrays revolutionized the field of genomics as this powerful tool could interrogate the whole genome in a single experiment which was a big leap given that researchers could only probe a few genes at a time before this. DNA microarrays were commercialized as GeneChip® by Affymetrix (now part of Thermo) and cost a fortune when it was newly introduced make it less accessible to scientific community. This drove the model of manufacturing one’s own microarrays in academia and industry.  Around the same time, Dr. Patrick Brown of Stanford (also founder of Impossible Foods), published step by step instructions to build DNA printers from scratch and to use them to print DNA microarrays. Many laboratories ventured into printing DNA microarrays and some even building printers from scratch.  Unfortunately, it was short lived as the fabrication of DNA microarrays at times was a challenge even for Affymetrix with expert know-how, ultra clean and fully controlled fabrication facilities.  Eventually, as with any technology, the price of GeneChip®  reduced drastically and retrospectively, “buy” seemed to be the winning option.

What is the winning strategy for the digitization revolution happening today?  Buy or build?  At the onset, digitization might look like an uncomplicated initiative.  Depending on the final objectives of the digitization process, it can be fairly complex as it is infrastructure intensive and requires deep collaboration between two different sets of experts.  Shortage of big data engineers makes it difficult for small companies to attract talent.

Currently, the trend is building it in-house due to lack of products comprehensively covering the requirements of a given industry/company. To fulfil their requirements, companies would have to buy multiple products leading to data silos or preventing centralization of data which is one of the primary goals of digitization. To be successful in an in-house digitization project, the absolute first step is to hire exemplary experienced visionary leaders who understand critical roles to establish a team structure and employ collaborative experts to fill those roles. This core team should be capable of designing the core architecture and identify fundamental components. They can strategize the modules to build internally and find partners as needed as it may not be possible to hire hundreds of engineers. Identifying partners with right expertise is also a crucial step – they have to be the experts in the field and also match culturally. Few early adopters failed when they partnered to develop the entire solution with well-known managing consulting firms, but not necessarily in the technology space. Partners must be provided well defined requirements, specifications and clear-cut qualifying metrics for the goods/services to be built.