data quality


Big Data Meltdown: How Unclean, Unlabeled, and Poorly Managed Data Dooms AI

#artificialintelligence

We may be living in the fourth industrial age and on cusp of huge advances in automation powered by AI. But according to the latest data, our great future will be less rosy if enterprises don't start doing something about one thing in particular: the poor state of data. That's the gist of several reports to make the rounds recently, as well as interviews with industry experts. Time after time, the lack of clean, well-managed, and labeled data was cited as a major impediment for enterprises getting value out of AI. Last month, Figure Eight (formerly CrowdFlower) released a study about the state of AI and machine learning.


The new CxO gang: data, AI, and robotics

#artificialintelligence

Apparently, it is a new role born in a lighter form straight after the financial crisis springing from the need to have a central figure to deal with technology, regulation and reporting. Therefore, the CDO is basically the guy who acts as a liaison between the CTO (tech guy) and the CAO/Head of Data Science (data guy) and takes care of data quality and data management. Actually, its final goal is to guarantee that everyone can get access to the right data in virtually no time. It is not a static role, and it evolved from simply being a facilitator to being a data governor, with the tasks of defining data management policies and business priorities, shaping not only the data strategy, but also the frameworks, procedures, and tools. In other words, he is a kind of'Chief of Data Engineers' (if we agree on the distinctions between data scientists, who actually deal with modeling, and data engineers, who deal with data preparation and data flow).


Data Modeling in the Machine Learning Era - DATAVERSITY

#artificialintelligence

Machine learning (ML) is empowering average business users with superior, automated tools to apply their domain knowledge to predictive analytics or customer profiling. The article What is Automated Machine Learning (AutoML)? These are not just empty promises to worldwide business leaders; in 2017, the age of automated, ML-powered analytics and BI dawned, and has since transformed one industry sector at a time. The automation revolution has not paused and is likely to storm global businesses in years to come. The era of AutoML is beginning to enable business users to tune existing data models and apply custom models to their everyday business situations as well.


Global Big Data Conference

#artificialintelligence

For years, the sheer messiness of data slowed efforts to launch artificial intelligence (A.I.) and machine learning projects. Companies weren't willing to wait a year or two while data analysts cleaned up a massive dataset, and executives sometimes had a hard time trusting the outputs of a platform or tool built on messy data. Data pre-processing is a well-established art, and there are many tech pros out there who specialize in tweaking datasets for maximum validity, accuracy, and completeness. It's a tough job, and someone has to do it (usually with the assistance of tools, as well as specialized libraries such as Pandas). But now IBM is trying to apply A.I. to this issue, via new data prep tools within AutoAI, itself a tool within the cloud-based Watson Studio.


Machine learning for data cleaning and unification

#artificialintelligence

The biggest problem data scientist face today is dirty data. When it comes to real world data, inaccurate and incomplete data are the norm rather than the exception. The root of the problem is at the source where data being recorded does not follow standard schemas or breaks integrity constraints. The result is that dirty data gets delivered downstream to systems like data marts where it is very difficult to clean and unify, thus making it unreliable to utilize for analytics. Today data scientists often end up spending 60% of their time cleaning and unifying dirty data before they can apply any analytics or machine learning.


Poor data quality causing majority of artificial intelligence projects to stall

#artificialintelligence

A majority of enterprises engaged in artificial intelligence and machine learning initiatives (78 percent) said these projects have stalled--and data quality is one of the culprits--according to a new study from Dimensional Research. Nearly eight out of 10 organizations using AI and ML report that projects have stalled, and 96 percent of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence, said the report, which was commissioned by training platform provider Alegion. For the research, Dimensional conducted a worldwide survey of 227 enterprise data scientists, other AI technologists, and business stakeholders involved in active AI and ML projects. Data issues are causing enterprises to quickly burn through AI project budgets and face project hurdles, the study said. Other findings of the survey: 70 percent of the respondents report that their first AI/ML investment was within last 24 months; more than half of enterprises said they have undertaken fewer than four AI and ML projects; and only half of enterprises have released AI/ML projects into production.


Artificial intelligence in cybersecurity- Caleb Fenton answers readers' questions

#artificialintelligence

Is AI the Silver Bullet of Cybersecurity? Two years ago, I talked about how we were in the early stages of the artificial intelligence revolution and how to evaluate AI in security products. Since then, AI research continues to blow minds, particularly with Generative Adversarial Networks (GAN), which are being used to clone voices, generate big chunks of coherent text, and even create creepy pictures of faces of people who don't exist. With all these cool developments making headlines, it's no wonder that people want to understand how AI works and how it can be applied to different industries like cyber security. Unfortunately, there's no such thing as a silver bullet in security, and you should run away from anyone who says they're selling one.


Enterprise AI Strategy : What you must Consider? ThinkSys Inc

#artificialintelligence

A chatbot is the most in-your-face use case of AI, but it's easy to underestimate the opportunities that AI can help us realize. By some estimates, by 2023 around 40% of all internal operations teams in Enterprises will be AI-enabled. The flip side is that even though the growth opportunities are huge, it will take time, effort, and a concerted strategy to realize the true potential. Let us look at the key considerations to factor in while embarking on the AI journey. It is imperative to have a definite use case in mind before one thinks of implementing AI in your Enterprise.


96% of organizations run into problems with AI and machine learning projects

#artificialintelligence

Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.


Global Big Data Conference

#artificialintelligence

Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.