Surprise! The data team does more than you think to implement certain legislative actions.
Tag: data quality
Jim Harris shares three more examples of how data quality improves AI in Part 2 of his series.
Phil Simon weighs in on using data to make the most of AI.
Data scientists spend a lot of their time using data. Data quality is essential for applying machine learning models to solve business questions and training AI models. However, analytics and data science do not just make demands on data quality. They can also contribute a lot to improving the quality
Learn why Jason Simon from UNT calls data governance critical.
Expect to lose time if you don't include a data steward in your project until you're reviewing the data model.
By now you’ve seen the headlines and the hype proclaiming data as the new oil. The well-meaning intent of these proclamations is to cast data in the role of primary economic driver for the 21st century, just as oil was for the 20th century. As analogies go, it’s not too
In my first post I looked at the role of analytics in policing and how analytics could and should be used to benefit modern policing. However, a key point that can be forgotten is analytics is only as good as the data it is based on. It’s vital to have
AI is everywhere these days, whether in reality or just as a hyped-up label for some simple rules based decisioning, and this has led to some interesting problems. The first of these is mistrust, as noted by the incoming president of the British Science Association, Professor Jim Al-Khalili: “There's a
Data management gets lost in the enthusiasm around Artificial intelligence (AI) and machine learning (ML). Not surprising, when it's an algorithm that decides what search results to show you, guides the self-driving cars on the roads, and powers the anti-fraud bots that monitor every credit card transaction we make. Charles
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
How should a data trust process work? David Loshin elaborates.
Focus on data governance, quality and storage if you want to do data management for analytics right.
Better decisions and analytics innovation – fringe benefits of having comprehensive data governance policies.
The future of successful compliance will go through extremely skilled data scientists, who will become heroes in your organisation. Human instinct and analytics will help uncover financial crime together. For the last 15 years, banks and other financial institutions have been deploying AML and CFT solutions. In Europe, in line
Kim Kaluba explains why good customer data management starts with trusted data quality.
The NHS is one of the UK’s most beloved institutions, but it’s also in need of some new thinking. This is especially true when it comes to technology. The first major wave of investment in artificial intelligence (AI) has seen entire industries transformed by the speed and capabilities of machine
Today it is possible to use freeform text from patient journals in data management to support healthcare professionals in their daily decision making. This blog post elaborates on how hospitals can automate their diagnostic coding to be more efficient, reduce errors, and improve quality and patient safety. This is the second blog
Data quality is a topic that is often discussed in insurance, but also plays a subordinate role in the project day. I asked Karen Prillwitz about the importance of data quality at large insurers. For many years Karen has advised insurance companies, and as a project manager in a large
Excellent quality of patient treatment and patient safety is highly prioritised on the health care agenda in the Nordic countries. As a result, the demand for measuring quality has increased, and is closely followed by certain special demands for documentation and data quality. If not handled intelligently, this can be