{"id":708,"date":"2025-03-25T21:56:15","date_gmt":"2025-03-25T16:26:15","guid":{"rendered":"https:\/\/blogs.infosys.com\/infosys-consulting\/?p=708"},"modified":"2025-07-08T15:18:35","modified_gmt":"2025-07-08T09:48:35","slug":"enabling-collaborative-dataops","status":"publish","type":"post","link":"https:\/\/blogs.infosys.com\/infosys-consulting\/technology\/tech-transformation\/enabling-collaborative-dataops.html","title":{"rendered":"Enabling Collaborative DataOps"},"content":{"rendered":"

Collaborative DataOps \u2013 A \u201chot-button\u201d for modern businesses<\/h3>\n

Race to embrace data to enable analytics driven digital transformation is at its peak with the explosion of data evolving at a breakneck speed as the total data created, captured, copied, and consumed globally is likely to increase from 149 zettabytes in 2024 to 394 zettabytes in 20281<\/a>\u00a0<\/sup>. However, the companies that can effectively use these data and able to run analytics are more likely to be successful. Until now, companies have been leveraging the limited available data for operations. However, AI has opened the vast variety of data for consumption and decision making.<\/p>\n

In our experience, we have seen that many of the digital transformation programs fail with data management being the most important contributor. It is becoming increasingly clear that traditional method of data management and governance such as disconnected decision making and static, central data strategy are not likely to bring results in today\u2019s fast moving data world. In fact, Gartner goes to the extent of stating by 2027, 80% of data and analytics (D&A) governance initiatives will fail unless right governance strategies are deployed2<\/sup><\/a>.<\/p>\n

That is exactly where DataOps, a practice popularized by Andy Palmer back in 2015, plays a role by combining technology, automation, architecture and collaborative culture helping bring transparency from source to consumer of a data product. This paper briefly discusses a nuanced mode of DataOps, which we call \u201cCollaborative DataOps\u201d and why and how it can minimize the risks of failure to a great extent.<\/p>\n

Why do businesses need to embrace collaborative DataOps?<\/h3>\n

A global provider of financial market data partnered with Infosys Consulting to analyze proliferation of applications in their data and operations space, and provide a solution to make the portfolio streamlined, lean and agile. The core work of securing underlying data from various sources, processing and storing it, running analytics on top, and producing a consume-friendly data product was being performed at varied degree of efficiency. One of the fundamental reasons of these performance inconsistencies was due to different functions within the organization operating in fragmented ways, often through manual, sequential process and ad-hoc decision making without proper handshake between business and technology, thereby compromising on speed and quality.<\/p>\n

This incident is not unique. \u201c27% of chief data and analytics officers report that their most pressing challenge is lack of involvement and support from business stakeholders3<\/sup><\/a>.\u201d<\/p>\n

These businesses often face challenges in breaking silos and enabling collaboration between different stakeholders. This has resulted in noteworthy pitfalls such as:<\/p>\n

I.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Challenges in maintaining data quality which impacts the success and return on investment of AI initiatives. 99% GenAI adopters have encountered roadblocks with 42% of data leaders citing data quality as the main obstacle4<\/sup><\/a>.<\/p>\n

II.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Businesses incurring significant cost of data security breaches because of ineffective handling of data and inadequate collaboration with Cybersecurity CoEs. In 2024, data breaches globally resulted in an average financial impact of $4.88 million5<\/sup><\/a> which highlights the importance of collaboration with Security teams and identification of potential vulnerabilities and training of employees on effective handling of data.<\/p>\n

III.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Risk of impacting decision making because of the challenge of managing dark data. At least 20% of a company\u2019s data is either redundant, outdated, or trivial6<\/sup><\/a>, which poses the risk of generating correct insight.<\/p>\n

DataOps, philosophy of framework, tools and processes was envisioned to eliminate these challenges. DataOps, a word borrowed from DevOps which is a concept to enable seamless handshake and greater accountability between Development and Operation. Likewise, DataOps is a similar methodology in data space to design, develop, implement, transform, operationalize and govern distributed data in the overall data life cycle.
\nIt principally consists of<\/p>\n