What are the top data analysis skills for risk management professionals in technological innovation?

?? You should also consider the evolving landscape of data visualization as it becomes increasingly integral to risk management in tech innovation. ?? Advanced Visualization Techniques Beyond basic charts, understanding complex visualization methods such as heat maps and network diagrams is essential. ??? Real-time Data Visualization The ability to create dashboards that update in real-time can provide instantaneous insights into emerging risks. ?? Interactive Visuals Proficiency in creating interactive visual elements that allow users to drill down into data layers is critical for nuanced analysis. ?? Mastering advanced visualization and real-time data is crucial for modern risk management.

An important skill regarding data analysis and risk management is making sure you are not fooled by looking at the wrong data, bad data or looking at it the wrong way. For example, a security might flag as high volatility when it is not simply because of a transposed price: $29 instead of $92 for one day. Data may look uncorrelated until you use a log of the data. Etc. Read “How to Lie with Statistics” to get some ideas of what to look for.

Top data analysis skills for risk management professionals in technological innovation include statistical analysis, data modeling, machine learning, and predictive analytics. Proficiency in data visualization tools, cybersecurity knowledge, and an understanding of emerging technologies are crucial. Strong communication and problem-solving abilities are also essential to interpret data insights and make informed decisions in the ever-evolving landscape of technological innovation.

Once data is analyzed there are various customers - internal statistically sophisticated ones can be given the statistics themselves. But management with great business knowledge but without advanced statistical knowledge would prefer a selection of well presented graphical representations of the data. And if we can make this selection interactive then the management can take ownership of the results and use variants of the data sliced by time and parameters. So we present the data using dashboards to encourage usage and ownership of solvency and var type data, of sales type data, reserving data and many other types.

I think statistical analysis is the foundation for more advanced analytics, especially machine learning. However, statistical analyses are incredibly difficult to convey to decisionmakers, in my experience. From my perspective, it's heavily dependent on whether the decisionmakers either understand the actual analysis or are willing to trust/accept the analysis put forth by their analysts. As in, not every decisionmaker needs to fully understand how an answer was derived in order to work with that answer (though it certainly helps, if they've got a handle on the more complicated aspects).

Research recently has shown that in life assurance the traditional model for parametric mortality is now being out performed by non parametric statistics and by machine learning in projected estimates. This can lead to more accurate estimates for survival and mortality going forward.

I think machine learning has a lot of untapped potential, especially for smaller organizations. I also think it can be incredibly overhyped. There are lots of discussions about machine learning I've come across & engaged with over the years where machine learning seems to be a panacea for every possible problem and oftentimes the most complicated possible model is put forth as the best. I don't think super-complicated models are generally necessary for most applications, assuming good data. Interpretability and ability to capture non-linearity or weirdness/noise in the data is much more valuable to me. I really like decision trees for this reason, but there are plenty of models that have been around for 50 or more years that work, too.

There was a YouTube lecture I saw some years ago (I forget the specific one, unfortunately) where the lecturer was discussing TensorFlow which had just been released in its earliest iteration. The lecturer posed a question to the audience: "Why would Google release this powerful tool for free/open-source?" He suggested two answers. First, Google would now have a larger talent pool of machine learning engineers. The second was that "most organizations' data is garbage". That stuck with me. Most machine learning or statistical analysis is utterly useless with bad data. And, on top of that, more complicated models have to be created to compensate for bad data (I think Sundar Pichai pointed this out not too long ago).

I was surprised to find - in my experience - data wrangling is more important than I thought. All the great statistical approaches and algorithms are amazing but without comprehensive data we may be missing some key insights. A good data wrangling approach, that you can consistently rely on, is a critical first step.

Storytelling is the increasingly difficult part as we get more and more swamped with data (and therefore feel we have to digest it all before we can form an opinion and present it): how to extract the essential and important message from the sea of data, and at reasonable speed, is critical

Understanding data and what it is telling us and the ability to explain it without using technical jargon is probably the best but most underrated skill in risk management.

The most important for me - this is the power to understand the data, to link with the other processes in the industry and go beyond the numbers. The power to choose the next step.

In my opinion, ability to connect data to business is one of the most important skills. Data analysis tool can be a matter of the individual preference, but the key thing is to have the ability to read trends, comparative analysis and Impact on business. Also keeping the focus on the objective of the analysis helps to navigate the pool of data and information.

As innovation facilitator of 100+ projects I like to share how you can MITIGATE PROJECT RISKS in 10 WAYS 1. Market Research: Understand your target audience and their needs. 2. Prototyping: Build and test prototypes to identify flaws. 3. Cross-functional Teams: Bring together experts from various disciplines. 4. Iterative Development: Break the process into phases. 5. User Feedback: Involve users to gather insights and validate. 6. Risk Analysis: Identify potential risks and their impact. 7. Minimum Viable Product (MVP): Launch an MVP. 8. Agile Method: Embrace agile practices to adapt. 9. Quality Assurance: Implement robust testing procedures to catch bugs. 10. Communication: Maintain clear communication within your team.