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More companies today are investing in AI-based cybersecurity technology to speed up incident detection and response, to better identify and communicate risk to the business, and to gain a better understanding of cybersecurity situational awareness. That’s according to ESG research that found 12% of enterprise organizations have deployed AI-based security analytics extensively while 27% had done so on a limited basis. In a recent conversation with SearchCIO, SAP CSO Justin Somaini explained how organizations can implement machine learning algorithms and AI in security to improve their cybersecurity posture. For example, for Concur we have traveler’s safety within the Concur system to help identify at-risk employees while they’re traveling around the globe and making sure they are safe.

Looking back over the years and considering the IT organizations that have succeeded and failed in their IT service management (ITSM) journeys, I have seen three factors that differentiate a successful service management strategy from one that is bound to fail: • Realization that service management is not a project but a lifestyle choice here are many other contributing factors to the failure or success of any ITSM project, but when I consider these other factors — for example, a lack of knowledge of the process framework, process and technology gaps, lack of intentional improvement after deployment, lack of momentum … and the list can go on — they all really are just related symptoms of the three critical factors listed above. IT leadership vs. IT management ITIL, as well as the other frameworks, often get their start as a grassroots effort — ITIL and Lean in operations, IT asset management in operations or procurement, and Agile and DevOps in development. These common IT/business goals and objectives must drive the goals and objectives of each of the service management frameworks, which, in turn, must work together to ensure — through critical success factors and key performance indicators — that they are supporting those goals and objectives or driving meaningful improvements toward that end.

Now dubbed BigQuery ML, the new version lets you use simple Structured Query Language (SQL) statements to build and deploy ML models for predictive analytics. The other two most well-known names are Amazon’s Relational Database Service and Microsoft’s Azure SQL, and you can find more in our recent cloud database service roundup. Now in beta, BigQuery ML enables analysts (and data scientists) to run predictive analytics such as forecasting sales and creating customer segments right on top of the data where it is stored. Topping the substantial list of upgrades after ML are a clustering capability, BigQuery Geographic Information Systems (BigQuery GIS), a new Google Sheets data connector, and a new Google Sheets data connector.

In the context of New York City pre-trial bail hearings, a team of prominent computer scientists and economists determined that algorithms have the potential to achieve significantly more-equitable decisions than the judges who currently make bail decisions, with “jailing rate reductions [of] up to 41.9% with no increase in crime rates. Unfortunately, decades of psychological research in judgment and decision making has demonstrated time and time again that humans are remarkably bad judges of quality in a wide range of contexts. In all the examples mentioned above, the humans who used to make decisions were so remarkably bad that replacing them with algorithms both increased accuracy and reduced institutional biases. Critiques of algorithmic decision making have spawned a rich new wave of research in machine learning that takes more seriously the social and political consequences of algorithms.