Regulatory initiatives including BCBS 239, Solvency II and MiFID II have brought data governance to prominence throughout the financial services industry. The basic principles defined in the regulations are best practice in the data space and an area where Kinaesis bring competitive advantage. By focusing on the organisation, management and understanding of data assets, business can create huge opportunity and rapidly improve performance.
Many organisations focus on governance, quality and lineage as ends in themselves. They create a raft of documentation that becomes hard to maintain once the organisation has lost focus on the outcomes.
To achieve lasting quality and clarity of insight from the data then the governance needs to run through the key stakeholders of the data platform and form part of the change lifecycle.
Kinaesis has developed best practice governance, quality and lineage techniques and methodologies through the practical implementation of numerous data projects in the financial services.
Our approach is to combine the governance and lineage into the project process and architecture of the solution and embed quality into the culture of an organisation to create lasting change.
The client in question had a poor track record in delivering insight and reporting capabilities to the business. Many of their projects failed due to poor understanding of their data and the requirements needed to turn it into knowledge. They like many organisations were in a cycle where they were looking to technology to solve the problems, however this was not the cause of their failing projects.
Kinaesis provided a 10 day review assignment for the clients most recent failed project. The assignment reviewed the technical architecture, the project process and the governance. The findings were not what the team were expecting to hear. Our focus on all aspects of the project enabled us to highlight that regardless of what technology had been employed then the project could not have succeeded due to the data not being able to support the target outcomes.
The key victory on the project was that by highlighting a data focused methodology with embedded data quality and lineage then the IT teams would be able to implement successful projects. The architectures they could implement would be much simpler and the projects much easier to implement.
They could bring forward discussions on the right answer to complex data problems prior to the major expense for the project being committed. The business were brought much closer to the implementation detail and to create an environment where they owned the data problems as much as IT all which helped to improve on the success of future projects.