Trusted Data Leads to Trusted Machines When bringing Machine Learning into an organization, the level of quality and trust of the data used to train the machine has a direct correlation to the trust that can be put in the output of those Machine Learning processes..Machine Learning processes and pipelines typically execute with little to no human interaction until late in the process – and often only at the time of output..This reality is quite different from more traditional transactional processes and analytics where humans play a hands-on role during the processes’ execution and therefore have the ability to manually apply high reasoning abilities against the data and almost naturally deal with data inconsistencies, faults and confusions..When machines are working with the data, they function best when the data is already well understood and high quality – both from a semantic and technical perspective..As Machine Learning becomes embedded throughout enterprise solutions, the ability to enable trusted data from inception through removal becomes even more important..When working with Machine Learning in a Data Science construct, a common approach is to dig a metaphorical “moat” around the Data Science laboratory in order to establish a strong Data Governance process around what data goes into the lab and who can access the results outside..While this approach works well in the case of Machine Learning developed by dedicated teams in house, it quickly becomes infeasible to draw the same kind of hearty boundary around all data across all systems..Instead, a pragmatic approach is recommended: as data evolves along its journey, the policies and rules that govern it are strategically enforced to ensure the right investment is made the right time based on business impact..For example, the techniques used to ensure high quality and protected patient data in a hospital setting will likely be more robust than the enforcement strategy around data trust for real estate data for corporate offices. Many organizations are investing in major transactional systems, ERP systems, CRM, HCM, PLM, and analytics environments..In these cases, performing a data migration can help ensure the data that goes into these systems is of the highest trust and quality so that any embedded Machine Learning functionality will yield the greatest benefits possible..Once live on these new systems, organizations need to maintain the data quality and trust levels via ongoing Data Quality and Data Governance initiatives. Machine Learning presents opportunities for real innovation both today and well into the future..In many ways, it offers the promise of technology delivery catching up to the ideas of the innovators and meeting the needs of users.. More details