APRIL 2019CIOAPPLICATIONSEUROPE.COM9data warehouses for that purpose [6]. In our age of big data, data lakes tend to replace them [7]. Data scientists should be able to work with any chosen data store and the choice of the right architecture should be left in the hands of data architects and data engineers. The key point here is to have a centralised repository of all the important data.Stage 3 - Data DemocratisationGovernance considerations will arise from the collection and centralisation of data [8]. Managing the availability, usability, integrity, and security of the data is critical and has to be in the hands of a senior manager. Could it be that a chief data officer has to be hired before a data scientist [9]?Implementing what has been described so far should allow any person in the business to have access to past data. Reports showing how business decisions influenced results should now be generated by data analysts and business intelligence developers. At this stage of democratisation of the data, more and more advanced questions should be brought to the data analysts and the need for more advanced analytics should be growing [10]. This is probably the best stage to start thinking about hiring data scientists.Stage 4 - Data MaturityThe arrival of data scientists in an environment where data is available and where people starts understanding the kind of questions that analytics can answer is ideal. They should be involved in many projects and help assessing the potential of new ideas. The organisation should rapidly starts feeling the benefits of data-driven decisions and data scientists should be excited by the variety of projects brought to them. This situation should lead to an evolution where data products based on machine learning start to be developed. Machine learning engineers might be required at this stage to help with the release of more data products - some of them being branded AI [11]!Following this process [Fig. 1] should ensure that organisations hire the right data professionals at the right time and avoid building teams of data scientists not fully engaged as they do not have access to the data they need and lack the management support allowing them to contribute building a data-driven organisation. Are you ready for the journey? Hervé Schnegg
< Page 8 | Page 10 >