How data quality improves artificial intelligence – Part 2
Jim Harris shares three more examples of how data quality improves AI in Part 2 of his series.
Tag: data quality
Which data sources will organizations use to make AI as effective as possible?
Phil Simon weighs in on using data to make the most of AI.
How to improve data accuracy
Phil Simon says that the downsides of even a few discrepancies can be enormous.
How data quality improves artificial intelligence
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
7 ways analytical methods improve data quality
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
Data stewardship for artificial intelligence
Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
The importance of data governance: An interview with UNT
Learn why Jason Simon from UNT calls data governance critical.
Data stewards – Why they’re the unsung heroes of the business
Expect to lose time if you don't include a data steward in your project until you're reviewing the data model.
No, data is not the new oil
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
Analytics in policing: it starts with data
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
Data steward: The concierge of analytics
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
The devil’s in the data: processing data for successful AI
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
Without data management, forget AI and machine learning in health care
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
Data governance implications for the chief data officer
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
Trust in the data: Ensuring quality analytics (continued)
How should a data trust process work? David Loshin elaborates.
Amazon's data train just keeps a rollin'
Think that the company has let up in the last two years? Think again.
Data management for analytics: Best practices and examples
Focus on data governance, quality and storage if you want to do data management for analytics right.
Trust in the data: Ensuring quality analytics
David Loshin raises questions about what needs to be done to ensure quality analytics.
Data governance policies: Key to analytics innovation?
Better decisions and analytics innovation – fringe benefits of having comprehensive data governance policies.
Analytics to the rescue
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
Data quality: The not-so-secret ingredient of customer data management
Kim Kaluba explains why good customer data management starts with trusted data quality.
Calling Dr. Data: Unlocking AI’s potential for the NHS
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
Data retention strategy – Part 2
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Data governance – The topic that never dies
Todd Wright says data governance is more relevant than ever – especially in light of the GDPR.
Data retention strategy – Part 1
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
How to improve diagnostic coding through technology
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
Cost factor data quality in insurance companies
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
How to handle increasing demands for high-quality health care data
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
Data quality monitoring - step 3.
In the previous posts of the series, there were described two first steps - data defining, and then their profiling. After precise data collecting and setting business rules, we can move on to their creating.
Monitorowanie jakości danych - krok 3.
W poprzednich postach z serii dotyczącej monitorowania jakości danych opisane zostały dwa pierwsze kroki - definiowanie danych, a następnie ich profilowanie. Po dokładnym zbadaniu danych i dopracowaniu reguł biznesowych, możemy przejść do ich faktycznego tworzenia.