DECEMBER - JANUARY8 IN MYV EWBY VINCENT KWAN, HEAD OF DATA, CLSATHE FOUR QUADRANTS OF DATA STRATEGYBig Data and Data Science took the lead when various disruptive innovations shook the business world. The business feared lagging, resulting in a rush to invest in Big Data technology. Imagine a dashboard that provides real-time aggregate KPI views across all business lines with the ability to drill into every detail you can ask, e.g., what is the percentage fulfillment of the yearly sales KPI? Why is it over or under? Which customers contribute the most revenue from which transactions? How are business lines impacted if the business environment changes, and how should they respond? To receive answers to all these inquiries, a vast amount of data must be collected, processed, and structured so it can be easily retrieved, analyzed, projected, and referenced. Though in reality, the failure rates of Big Data or Data Science projects are between 60-85%, according to Gartner and other research sources. Failure to achieve the above is usually due to problems with the overarching grand plan. A grand plan is proposed to fulfill traditional business expectations but becomes a sugar-coated poison because it gives a false impression of determinism without taking into account the agility of the implementation and adoption of new technology.Our approach is to deal with the gap between the business expectation and the agility of the technology involved. This is divided into a Data Strategy with four quadrants. Each quadrant will evolve and grow with the plan and link up with each other to construct the whole data ecosystem with the incorporation of governance and intelligence. Here we drill into each quadrant. The first quadrant is Data Governance. This is an overhyped buzzword that has evolved to cover everything in the data field. We take a more realistic approach and confine it to the Policy and Standard with systems designed to monitor governance, implementation, and data quality management. The establishment of a Data Governance Committee is an important step in assuring the awareness of data within business lines and IT. The committee will drive Data Governance measured against nine characteristics of data:Vincent Kwan
< Page 7 | Page 9 >