Artificial Intelligence and the Data Quality Conundrum … smart factories) leverage cyber-physical systems powered by AI, machine learning (ML) …

Alteryx Machine Learning is a cloud-native, automated modeling solution that helps business users make data-driven decisions.

Enterprises are increasingly pushed for faster insights from their ever-increasing data volumes. A TDWI senior analyst looks at how some vendors are responding. Accelerating speed to insight from data is critical to nearly all types of organizations, especially as managers seek to develop strategies for responding to unexpected and rapidly changing circumstances such as the global coronavirus outbreak. TDWI's recently published Best Practices Report, Faster Insights from Faster Data, takes an in-depth look at practice and technology issues that matter most in reducing delays in data life cycles and putting well-prepared and relevant data in the hands of users sooner. Not long after the publication of the report, I had the opportunity to visit with some technology providers exhibiting at the TDWI Las Vegas Conference and Strategy Summit in February. It was interesting to see how issues brought up in the report are being addressed by vendors.

Fivetran, the automated data integration provider, announced enterprise software veteran Bob Muglia, former CEO of Snowflake, will join its board of directors. Turbine Labs, an AI-powered software and managed-services platform, announced a data integration and collaboration with Tableau, to support the massive amount of COVID-19-related data. Mark Marinelli, Head of Product and Chief Data Evangelist at Tamr, talks about Machine Learning and the breakthrough in Enterprise Data Management, in this article for Forbes. Brian J. Dooley, author, analyst, and journalist with more than 30 years' experience, talks about data center management-as-a-service and data management-as-a-service, in this article for TDWI. Arawan Gajajiva, Principal Solution Architect in Matillion, presents a practical framework for centralising BI and analytics inside the enterprise, in an article for ITProPortal.

TDWI provides individuals and teams with a comprehensive portfolio of business and technical education and research to acquire the knowledge and skills they need, when and where they need them. The in-depth, best-practices-based information TDWI offers can be quickly applied to develop world-class talent across your organization's business and IT functions to enhance analytical, data-driven decision making and performance. TDWI advances the art and science of realizing business value from data by providing an objective forum where industry experts, solution providers, and practitioners can explore and enhance data competencies, practices, and technologies. TDWI offers conferences, summits, and seminars with both in-person and virtual attendance. Every month, you can find us teaching full-day courses on the hottest topics in data.

When it comes to taking a trained machine learning model and putting it into operational use at the company, TDWI found that it took 14 percent of respondents over 9 months just for that step along. But for more than 20 percent, that same step took only a few days to a few weeks. When it comes to successful machine learning, there are several things you can do to ensure that you'll be part of the fast 20 percent, not the slower 14 percent. One of them, of course, involves using your database for machine learning. You've probably heard about some applications of machine learning in the news, like computers creating art and music through machine learning.

Earlier this week at #TDWI 2019 conference at Las Vegas a Silicon Valley startup - Inguo exhibited their category defining product in Causal Analysis. A data scientist with deep domain knowledge or a business owner of data both can use this technology to get actionable insights from their data. It has the potential to change the whole landscape of causal analysis and give the businesses a vital tool to optimize various business outcomes. I love Inguo's tag line: Find the WHYS of your data!

AI technologies have clear value paths in marketing, including sales and enhancing customer experience. When most companies think about artificial intelligence (AI)--and its many sub-categories, such as machine learning, natural language processing (NLP), or cognitive computing--they think about applications such as IBM's Watson technology being used to diagnose patients. What they often miss, according to Fern Halper, VP and senior research director of advanced analytics at TDWI, is that "marketing is often one of the first departments in an organization to use advanced analytics." In a recent webinar, Halper discussed the current potential and future pathways for marketing analytics with David Stodder, senior research director of business intelligence at TDWI, and Wilson Raj, the global director of customer intelligence with SAS. They all agreed that among all the different business departments, applying AI to marketing could provide the most immediate, recognizable return on investment .

AI technologies have clear value paths in marketing, including sales and enhancing customer experience. When most companies think about artificial intelligence (AI)--and its many sub-categories, such as machine learning, natural language processing (NLP), or cognitive computing--they think about applications such as IBM's Watson technology being used to diagnose patients. What they often miss, according to Fern Halper, VP and senior research director of advanced analytics at TDWI, is that "marketing is often one of the first departments in an organization to use advanced analytics." In a recent webinar, Halper discussed the current potential and future pathways for marketing analytics with David Stodder, senior research director of business intelligence at TDWI, and Wilson Raj, the global director of customer intelligence with SAS. They all agreed that among all the different business departments, applying AI to marketing could provide the most immediate, recognizable return on investment .