As more organizations deploy AI projects and products, many enterprises are looking beyond surface-level AI. They're looking to take their AI projects to the next step. One way to do that is by pursuing an AI-first data strategy, according to Forrester Research. This means creating machine learning and data models that are designed with an AI mindset instead of trying to use already created data to fit their AI. Phases of an AI-first strategy include delivering and deploying data for scale, testing and training models to create trust, and discovering and sourcing data that represents the business model of the enterprise.

STERLING, Va., Dec. 5, 2019 /PRNewswire-PRWeb/ -- EnterWorks, a leading provider of Master Data Management (MDM) and Product Information Management (PIM) solutions, has announced a live webcast event featuring Michele Goetz, Principal Analyst, Business Insights, Information Architecture and Artificial Intelligence, at Forrester. The webinar, "How AI, Machine Learning and Data Strategy Can Enable Compelling New Products & Experiences," will take place on Tuesday, December 10, 2019 from 11:00 am to 12:00 pm EST. It is sponsored by EnterWorks; Amplifi, an information management consultancy that helps the world's leading brands, retailers and manufacturers to harness and unleash the power of their data; and Sisense, a business intelligence software and analytics platform. The webinar will inform participants how artificial intelligence, machine learning and data strategy can enable compelling new products and experiences, and how deploying AI and ML depends on effective master data and its proper governance. According to Forrester's Goetz, many companies have initiated AI and ML projects only to find that they have not established the foundation for success that comes with implementing a comprehensive data management strategy and the platforms needed to make replicable and scalable success possible.

"And so you run out of patience along the way, because you spend your first year just collecting and cleansing the data," said Mr. Krishna, who was interviewed at The Wall Street Journal's Future of Everything Festival last week. "And you say: 'Hey, wait a moment, where's the AI? Mr. Krishna didn't name clients or say how many had halted projects. One well known example of an AI project unraveling happened in 2017 at the University of Texas' MD Anderson Cancer Center, which aimed to use IBM's AI platform, Watson, to improve cancer care. An audit by the University of Texas showed the cancer center was using old data, among other issues. A report this month by Forrester Research Inc. found that data quality is among the biggest AI project challenges. Forrester analyst Michele Goetz said companies pursuing such projects generally lack an expert understanding of what data is needed for machine-learning models and struggle with preparing data in a way that's beneficial to those systems. She said producing high-quality data involves more than just reformatting or correcting errors: Data needs to be labeled to be able to provide an explanation when questions are raised about the decisions machines make. While AI failures aren't much talked about, Ms. Goetz said companies should be prepared for them and use them as teachable moments. "Rather than looking at it as a failure, be mindful about, 'What did you learn from this?'" she said. Mr. Krishna said he couldn't specify what percentage of IBM-related AI projects were halted over the past five years. But he said: "In the world of IT in general, about 50% of projects run either late, over budget or get halted.

You can't do anything with data – let alone use it for machine learning – if you don't know where it is. In the age of big data, this is not a trivial matter. It is also the main driver that's propelling the rise of machine learning data catalogs, which the analysts at Forrester recently ranked and sorted. Just a word of warning: the name at the top of the list might surprise you. According to Michelle Goetz's June 21 Forrester Wave report, the percentage of analytic decision makers managing more than 1 petabyte of data (either structured, semi-structured, or unstructured) has essentially tripled from 2016 to 2017.

According to a recent Teradata study, 80% of IT and business decision-makers have already implemented some form of artificial intelligence (AI) in their business. The study also found that companies have a desire to increase AI spending. Forty-two percent of respondents to the Teradata study said they thought there was more room for AI implementation across the business, and 30% said their organizations weren't investing enough in AI. Forrester recently released their 2018 Predictions and also found that firms have an interest investing in AI. Fifty-one percent of their 2017 respondents said their firms were investing in AI, up from 40% in 2016, and 70% of respondents said their firms will have implemented AI within the next 12 months.

The field of artificial intelligence (AI) is booming. It's expected to create 2.3 million jobs by 2020, and around three-fourths of tech leaders plan on hiring more AI talent, according to a July report. An October 2017 survey of 260 organizations found 80% of companies are investing in AI, with one third believing they need to invest more over the next three years. But is the standard C-suite able to handle the enlarged focus on the emerging technology? Do businesses need a chief AI officer to fully and properly handle AI in the enterprise?

AI became a ubiquitous buzzword in 2017, but the promised rise of the machines has thus far failed to materialise. Despite the buzz, real-world use cases have remained somewhat limited, but this looks set to change in 2018. Companies are already ploughing cash into AI, but much of this investment is yet to reap rewards. According to research firm Forrester, 55 percent of firms have not yet achieved any tangible business outcomes from AI, and 43 percent of them say it's too soon to say whether their investment has been a success. "There's been a lot of pilots and a lot of proof of concepts, but the reality is not only what AI is and what AI can do, but also what is required to build these new intelligent systems is starting to be realised," says Michele Goetz, one of the authors of a Forrester report titled The Honeymoon For AI Is Over.

According to a recent Teradata study, 80% of IT and business decision-makers have already implemented some form of artificial intelligence (AI) in their business. The study also found that companies have a desire to increase AI spending. Forty-two percent of respondents to the Teradata study said they thought there was more room for AI implementation across the business, and 30% said their organizations weren't investing enough in AI. Forrester recently released their 2018 Predictions and also found that firms have an interest investing in AI. Fifty-one percent of their 2017 respondents said their firms were investing in AI, up from 40% in 2016, and 70% of respondents said their firms will have implemented AI within the next 12 months.

The general perception of machine learning is that it involves sophisticated algorithms and predictive models that far surpass the understanding of the business domain expert that relies upon this technique to glean analytic insight. But it doesn't have to. According to Paxata Vice President of Corporate and Product Marketing Michele Goetz--a former analyst for Forrester--an artful combination of machine learning, data visualizations, natural language processing, and a heaping of semantic technologies can render this technology functional to business analyst end users for enhanced analytic insight. "There are analytics and visualizations that pull out of the machine learning that provide a bit of insight without having to go fully down the analytics path to get that type of insight," Goetz commented. "Just having something that you're continuously refining and tuning is interesting when you're working in a much more exploratory fashion."

We report a study performed with a social robot that autonomously plays a competitive game. By relying on an emotional agent architecture (using an appraisal mechanism) the robot was built with the capabilities of emotional appraisal and thus was able to express and share its emotions verbally throughout the game. Contrary to what was expected, emotional sharing in this context seemed to damage the social interaction with the users.