Along with poor data quality, these issues combine to deprive organizations' data platforms--and the machine learning and analytics models they support--of the speed and scale needed to deliver the desired business results. To understand how data management and the technologies it relies on are evolving amid such challenges, MIT Technology Review Insights surveyed 351 CDOs, chief analytics officers, chief information officers (CIOs), chief technology officers (CTOs), and other senior technology leaders. They are succeeding thanks to their attention to the foundations of sound data management and architecture, which enable them to "democratize" data and derive value from machine learning. Pushing these to the edge with advanced data technologies will help end-users to make more informed business decisions -- the hallmarks of a strong data culture. Organizations' top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms: improving data management, enhancing data analytics and ML, and expanding the use of all types of enterprise data, including streaming and unstructured data.

Machine learning has the potential to generate millions of dollars in savings and revenue growth for organizations. But unless ML models are actually put into production, it's just a bunch of useless code. This is the big data science takeaway from New York Life, which recently adopted an MLOps solution from Domino Data Labs to streamline model deployment. Since it was founded in 1845, statistics have played a central role for New York Life. Like all life insurance companies, New York Life dedicates resources to maintaining accurate actuarial tables, which play a big role in determining premiums, payouts, and profits.

Nearly two-thirds of C-level AI leaders can't explain how specific AI decisions or predictions are made, according to a new survey on AI ethics by FICO, which says there is room for improvement. FICO hired Corinium to query 100 AI leaders for its new study, called "The State of Responsible AI: 2021," which the credit report company released today. More than two thirds of survey-takers say the processes they have to ensure AI models comply with regulations are ineffective, while nine out of 10 leaders who took the survey say inefficient monitoring of models presents a barrier to AI adoption. Seeing as how the regulatory environment is still developing, it's concerning that 43% of respondents in FICO's study found that "they have no responsibilities beyond meeting regulatory compliance to ethically manage AI systems whose decisions may indirectly affect people's livelihoods," such as audience segmentation models, facial recognition models, and recommendation systems, the company said. At a time when AI is making life-altering decisions for their customers and stakeholders, the lack of awareness of the ethical and fairness concerns around AI poses a serious risk to companies, says Scott Zoldi, FICO's chief analytics officer.

The demand for artificial intelligence (AI), data, and digital tools is soaring as the Covid-19 pandemic continues to put a strain on many enterprises. This is among the findings in a new study conducted by Corinium and sponsored by Fico. The report, "Building AI-Driven Enterprises in a Disrupted Environment", surveyed more than 100 c-level analytic and data executives and conducted in-depth interviews to understand how organisations are developing and deploying AI capabilities. The study found that the uncertainties caused by the pandemic have forced many organizations to adopt a more committed, disciplined approach to becoming an AI-driven enterprise, with more than half (57%) of the chief data and analytics officers saying that Covid-19 has increased demand for AI, digital products and tools. Enterprises are seeking new AI-driven ways to mitigate risks and navigate through uncharted territories in the current economic environment. The report reveals the central role AI has in shaping the future as global markets work through and begin to recover from Covid-19; as well as how to mitigate future risk and disruption going forward.

Conducted by Corinium and sponsored by FICO, the report – Building AI-Driven Enterprises in a Disrupted Environment – surveyed more than 100 c-level analytic and data executives and conducted in-depth interviews to understand how organizations are developing and deploying AI capabilities. The study found that the uncertainties caused by the pandemic have forced many organizations to adopt a more committed, disciplined approach to becoming an AI-driven enterprise, with more than half (57 percent) of the chief data and analytics officers saying that COVID-19 has increased demand for AI, digital products and tools. Enterprises are seeking new AI-driven ways to mitigate risks and navigate through uncharted territories in the current economic environment. The report reveals the central role AI has in shaping the future as global markets work through and begin to recover from COVID-19; as well as how to mitigate future risk and disruption going forward. Most data-driven enterprises are now aggressively investing in their AI capabilities, in fact 63 percent of respondents have started scaling AI capacity within their organization. However, enterprise chief data and chief analytics officers are facing a wide range of challenges as they increasingly look to grow AI. 93 percent say ethical considerations represent a barrier to AI adoption.

As many as 57% of chief data and analytics officers have said that Covid-19 has increased demand for AI, digital products and tools in enterprise. The findings were published in the report Building AI-driven enterprises in a disrupted environment, by global analytics software firm FICO and market intelligence firm Corinium. Together they surveyed more than 100 C-level analytic and data executives. The study found that uncertainties caused by the pandemic have forced many organisations to adopt a more committed, disciplined approach to becoming an AI-driven enterprise. In addition to rates of take-up, the survey also questioned respondents on ethical and responsible AI, and competitive advantages AI could generate post-Covid.

As the use of artificial intelligence applications – and machine learning – grows within businesses, government, educational institutions, and other organizations, so does the likelihood of bias. Researchers have studied and found significant racial bias in facial recognition technology, for example, and in particular in the underlying algorithms. That alone is a massive problem. When you more broadly consider the role AI and ML will play in societal and business contexts, the problem of AI bias becomes seemingly limitless – one that IT leaders and others need to pay close attention to as they ramp up AI and ML implementations. AI bias often begins with people, which runs counter to the popular narrative that we'll all soon be controlled by AI robot overlords.

We caught up with Dr. Mark Chrystal, Chief Analytics Officer at rue21 to understand more about how he perceives the role of analytics in retail today, his upcoming talk at NRF's Big Show and the future of retail. MANTHAN: In your role as the Chief Analytics Officer, what would you say is the biggest challenge facing rue21 in 2019? MARK: The biggest challenge I face is the ability to explain what is happening in the industry and more importantly, with our current, lapsed and potential customers. My job is to help the business navigate the environment and provide insights that help chart a course to success. This is particularly challenging in the current retail environment and for a company that is in the midst of a turnaround.

U.S. Bank's Chief Analytics Officer Bill Hoffman thinks the term "artificial intelligence" is a bit of a misnomer – he prefers to think about AI as a customized experience. "The'A' in AI should be'augmented,' not artificial," he said in an interview last year with CMO Australia. "There's nothing artificial about building a high quality, personalized relationship with a customer." Hoffman's less artificial approach to AI is transforming how U.S. Bank interacts with its customers and builds quality relationships with people. Hoffman was instrumental in U.S. Bank's adoption of Einstein, an AI platform offered by Salesforce, late last year.

Blockchain will use AI to search through relationship data, says Dr. Scott Zoldi, chief analytics officer at FICO, in his AI predictions for 2018 Defensive AI will be used to protect systems from malicious AI and machine learning The growing use of blockchain technology in financial services will include a healthy dose of artificial intelligence, as new, automated analytic techniques look for patterns in the "relationship data" about people, contracts and transactions. That merger of two hot tech trends is one of the predictions made by Dr. Scott Zoldi, chief analytics officer at Silicon Valley analytic software firm FICO, and a recognized expert in the field of artificial intelligence. Check out our crypto blog on blogger.com "Beyond its association with cryptocurrencies, blockchain technology will soon record'time chains of events,' as applied to contracts, interactions and occurrences," Dr. Zoldi wrote on the FICO Blog. "Think about renting a car. In the future, you will be able walk up to a car to lease it, but you'll do so with a micro-loan for which you are approved to lease the car for, say, an afternoon. This micro-loan will have insurance contracts attached to the blockchain, and a codified history of the car's history of drivers, events, and maintenance. As you drive through the city and interact with toll roads and parking spaces, all of this information will be automatically recorded and monitored on the blockchain. When you leave the car and lock it, the lease is complete and auditable on the chain. These kinds of data event chains will create new opportunities for graph analytics and novel new AI algorithms to consume relationship data at scale."