AI is a priority for governments and businesses worldwide. Poor data quality is a key aspect of AI that has been overlooked. AI algorithms are based on reliable data in order to produce optimal results. However, if the data is incomplete, incorrect, or not sufficient, it can have devastating consequences. Poor data quality can result in adverse outcomes for AI systems that identify patients' diseases. These systems can produce inaccurate diagnoses and predictions, which can lead to misdiagnosis and delayed treatment.

Effective data management is crucial for organizations of all sizes and in all industries because it helps ensure the accuracy, security, and accessibility of data, which is essential for making good decisions and operating efficiently. Properly organizing and maintaining your data can help ensure that it is accurate and up to date. This is important because inaccurate data can lead to incorrect conclusions and poor decision-making. Well-managed data is easier to access and use, which can help you save time and reduce the risk of errors. In some cases, proper data management is required by law, such as the General Data Protection Regulation (GDPR) in the European Union. Database management system vendors are now deploying artificial intelligence, particularly machine learning, into the database itself.

But is the data ready? How prepared are businesses to take full advantage of the insights that artificial intelligence affords? The tools may be ready, and talented people may have come onboard, but it's likely there's a gap in the data. Yes, there is plenty of data flowing through enterprises, but harnessing it in a productive and unbiased way is another story. At this point, only 24% of organizations consider themselves to be data-driven, and only 21% have what can be considered "data cultures," a new survey of senior data and analytics executives out of Wavestone NewVantage Partners finds.

An MIT-sponsored study in 2019 pointed out that 7 out of 10 companies that invested in Artificial Intelligence (AI) initiatives say that it had minimal or no impact on their business. Let that sink in for a moment. AI and machine learning have taken center stage in the digital economy and as a result, nearly every business wants to ride the wave. However, simply diving in headfirst to invest in AI capabilities doesn't always lead to successful outcomes. One of the most common ways companies hide their failure with AI implementation is to simply blame the technology. The truth is that technology is not the reason why many AI initiatives fail to realize their intended objectives and goals.

A clear majority of employees (87%) peg data quality issues as the reason their organizations failed to successfully implement AI and machine learning. That's according to Alation's latest quarterly State of Data Culture Report, produced in partnership with Wakefield Research, which also found that only 8% of data professionals believe AI is being used across their organizations. For the report, Wakefield conducted a quantitative research study of 300 data and analytics leaders at enterprises with more than 2,500 employees in the U.S., U.K., Germany, Denmark, Sweden, and Norway. The enterprises were polled regarding their progress in establishing a culture of data-driven decision-making and the challenges they continue to face. According to Alation, 87% of professionals say inherent biases in the data being used in their AI systems produce discriminatory results that create compliance risks for their organizations.

Artificial intelligence systems usually learn by example and are likely to learn better with high-quality examples. Low quality or insufficient training data can lead to unreliable systems that make poor decisions, reach the wrong conclusions, introduce or perpetuate bias and cannot handle real-world variation among other issues. Besides, poor data is costly. According to IBM, poor data quality in the US costs the country about 3.1 trillion dollars each year. To build a successful training data strategy, have a well-designed strategy that will collect and structure the data you need to tune, test, and train AI systems.

A new study commissioned by Trifacta is shining the light on the costs of poor data quality, particularly for organizations implementing AI initiatives. The study found that dirty and disorganized data are linked to AI projects that take longer, are more expensive, and do not deliver the anticipated results. As more firms ramp up AI initiatives, the consequences of poor data quality are expected to grow. The relatively sorry state of data quality is not a new phenomenon. Ever since humans started recording events, we've had to deal with errors.

Increasingly, insurance companies are leveraging artificial intelligence (AI) and machine learning to optimize processes, reduce costs, and increase efficiency. For example, Ant Financial's Dingsunbao app is able to make damage assessment and provide detailed analysis including claim amount, damaged parts and repair plan by leveraging AI technologies such as image recognition. Similarly, a public insurer in Germany employs an algorithm to manage the large amount of email correspondence by detecting keywords, sorting correspondence according to topics, urgencies and departments, and suggesting next best actions. In addition to unlocking greater efficiencies and lowering costs, AI and machine-learning technologies can also be applied to help insurance companies acquire new customers, cross-sell and grow revenues. For example, AI and machine learning can provide insights to support more effective customer segmentation, automate and personalize product recommendations, and enable more intelligent and customized self-service product research for customers.

Smarter Humans, Smarter Machines was the core theme of our closed-door #RefinitivSocial100 UK roundtable held in September 2019, with 10 of the UK and Europe's most influential thinkers and thought-leaders on social media in the world of FinTech. Hosted by Refinitiv CEO David Craig and Ben Shepherd, Chief Strategy Officer, and moderated by Amanda West, SVP Innovation Refinitiv Labs, we held a near 2 hour-long discussion on the future of artificial intelligence and the challenge of poor data quality in financial institutions. The main question that everybody was seeking to answer was: Is poor data quality hindering the deployment of machine learning (ML) by financial services companies? "Banks have been saying for a very long time that the data they have is messy and needs to be cleaned. "When we imply that the data might be wrong, we are of course implying that the ultimate decision you make, the automated, autonomous decision made by the AI, having gone through machine learning, could be wrong". FinTech and digital payments advisor, Neira Jones (@neirajones) agreed that using bad data in ML somewhat defeats the object. "Years ago, when we had huge data warehouses… you would have time to clean up your data.

Artificial intelligence (AI) and machine learning (ML) are frequently used terms these days. AI refers to the concept of machines mimicking human cognition. ML is an approach used to create AI. If AI is when a computer can carry out a set of tasks based on instruction, ML is a machine's ability to ingest, parse, and learn from that data itself in order to become more accurate or precise about accomplishing that task. Executives in industries such as automotive, finance, government, healthcare, retail, and tech may already have a basic understanding of ML and AI.