DECEMBER 2018CIOAPPLICATIONSEUROPE.COM8An End-to-End Data Science Blueprint ­ Managing Technology, Process and People in Practicehe explosion of big data, coupled with advances in cloud computing technologies, has enabled companies to leverage on data for business insights. It is no surprise that across industries, companies have begun taking on a data-driven approach by charting out long-term roadmaps and setting up data science teams within their organisations. Yet bringing technology, process, and people together is easier discussed than put into practice. How much of an investment do you need to make on infrastructure? How do you ensure consistency, continuity, and scalability in a data science set up? How do you acquire the right composition and talent in a big data team? As more companies transit towards a data-driven approach, we begin to better understand how we can tackle the operational challenges that come with managing technology, people and process in practice.Technology ­ Setting Your Architecture and Capabilities RightMapping out a three to five year data-driven roadmap ensures that companies have a good foundation to embark on the next step of getting the right data capabilities that suit their stage of data-driven development. It also helps companies keep costs at bay and purchase data assets that the business needs. In this regard, the old adage holds true--you can't run before you learn how to walk. For example, companies just starting out may focus on deploying a data science workbench while taking on a modular approach in developing data architecture. Using open sourced frameworks such as Hadoop and Spark are affordable and low cost for fulfilling big data processing and analysis. It also enables companies to perform quick experiments to understand the requirements for the next stage of growth in their data capabilities.Companies that are more advanced in their data-driven strategy will find that they need to scale up in terms of data capacity and complexity. They are at a stage of evolving from just simple analysis to predictive modelling and growing a larger data repository. These companies may customise software and invest in their own R&D and design data applications to improve their company's workflow and productivity. TJOHNSON POH, HEAD DATA SCIENCE / PRACTICE LEAD, DBS BANKIN MYVIEWJohnson Poh
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