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
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
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.
Think that the company has let up in the last two years? Think again.
Focus on data governance, quality and storage if you want to do data management for analytics right.
David Loshin raises questions about what needs to be done to ensure quality analytics.
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, comprehensive 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
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Todd Wright says data governance is more relevant than ever – especially in light of the GDPR.
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
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
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.
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.
The previous post in this series described the first step of the data monitoring process — defining. Therefore, once we have defined the objective of our initiative and, consequently, also data sources and specific requirements towards them, we can move on to studying the data.
W poprzednim poście z tej serii opisany był pierwszy krok procesu monitorowania danych – tworzenie definicji. Dlatego kiedy już mamy zdefiniowany cel naszej inicjatywy, a co za tym idzie także źródła danych oraz określone wymagania względem nich, możemy przejść do zapoznania się z danymi.
To get full value from analytics programs, Todd Wright says be sure you can first access, integrate, cleanse and govern your data.
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
You have to be able to trust the data that you are working with, whether it’s data processing or analysis that you are involved with. And there is a strong correlation between that trust and data quality. Is it possible to determine data quality without monitoring mechanisms?
Die Geburtenrate in Deutschland befindet sich derzeit auf dem höchsten Niveau seit 33 Jahren. Eine erfreuliche Entwicklung, und zugleich stellt es Eltern vor die schwere Entscheidung, welchen Namen der Nachwuchs tragen soll. Zahlreiche Webseiten und Bücher bieten Hitlisten und Namensbeschreibungen an, um die Auswahl zu erleichtern. Oder sollte man das
Get on with your day faster by taking a self-service approach to data preparation.
Niezbędnym elementem wszystkich inicjatyw związanych z przetwarzaniem i analizowaniem danych jest zaufanie do danych, które w znacznej mierze uzależnione jest od ich jakości. Czy można określić jakość danych bez mechanizmów jej monitorowania?
