Predictive Analytics In Healthcare: Top 7 Use Cases by Terry Wilson via @datafloq

How exactly does predictive analytics contribute to healthcare? Which risks hospitals are facing when deploying such tools? Keep reading this article to learn which type of events predictive analytics can reliably forecast.

This was interesting and it is always good to read about real-life uses of techniques.

Predictive Maintenance Drives Big Gains in Real World by Alex Woodie via @datanami

If you’re looking for a data project that demonstrates a clear ROI, predictive maintenance is worth a look. Delta Air Lines, for example, says predictive maintenance helped it reduce maintenance-related cancellations from 5,600 in 2010 to just 55 in 2018.

This definitely tells you of a real example of how predictive maintenance works and saves time and money for one company.

Getting better at predicting organised conflict by Tate Ryan-Mosley via @techreview

New techniques, machine learning, and better data gathering have made predictions both more useful and more granular. In this MIT Technology Review article, one predictive model is applied to look at violence in Ethiopia since the election of Abiy Ahmed, the new Nobel Peace Prize winner.

I loved this really insightful article which has some great diagrams that help with understanding.

The Seven Patterns Of AI by Kathleen Walch via @forbes

AI use cases tend to fall into one or more of these seven common categories. Kathleen Walch explains in this article from Forbes.

This is a great list and I think could be used in order to work out what COULD be done and use it to plan a roadmap for the future.

WEBINAR; Predictive Analytics: Practical Applications 4th April 2019

Data Science Central Webinar Series Event

Predictive Analytics: Practical Applications Join us for the latest DSC Webinar on April 4th, 2019 From optimizing processes to increasing revenue and driving business growth, executives are realizing the value in artificial intelligence. However, many organizations struggle to bring the necessary people, processes, and technology stacks together. In this latest Data Science Central webinar, we’ll focus on the business processes needed to implement AI-driven insights within an organization. We will discuss: Investments in new platforms and roles for your strategic roadmap End-to-end business-focused delivery strategy good for the whole team Facilitating change with the right tools and people Attend to hear real ROI use cases of an end-to-end AI rollout in retail and the financial services industry and some of the successes and setbacks along the way. It's time to empower everyone to solve anything. Speakers: Razvan Nistor, Chief Data Scientist -- Keyrus Melissa Burroughs, Product Manager -- Alteryx Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Predictive Analytics: Practical Applications Date:  Thursday, April 4th, 2019 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Why the ranks of citizen data scientists will grow and thrive by Mike Flannagan via @infomgmt

The citizen data scientist is anyone who works with data and predictive analytics, but isn’t a data scientist or an expert in stats and analytics.

I agree with the principal but not his definition of citizen data scientist. I view them as someone who is not employed by the organisation and is not acting for them as a paid consultant of some sort. So for example someone who entered a competition in Kaggle or somewhere similar.

Can crowdsourcing mitigate a dearth of data scientists for banks? by Penny Crosman via @infomgmt

Data scientists from NASA, Google and hundreds of other places are working for financial firms in their spare time. Is this a good idea?

I would add to this that Data Scientists and those studying to become one also enter competitions on the Kaggle platform which a) might win them a prize but b) also fixes a need set by the organisation who has submitted it.

WEBINAR: Optimize The Data Supply Chain - 31st January 2019

Optimize The Data Supply Chain Join us for the latest DSC Webinar on January 31st, 2019 Every organization is aiming to produce more comprehensive understanding of their customers, their business operations and their risks, through data. Most organizations are still learning best practices that allow them to leverage in-house data science resources more effectively. A big piece of the puzzle is enabling better collaboration between data science teams and the lines of business. A team-driven approach is necessary to help. In this latest Data Science Central webinar, led by Mike Ferguson of Intelligent Business Strategies Limited, an independent analyst and consultant who specializes in BI, analytics, data management and big data, you’ll learn: What it means for an organization to be ‘data intelligent’ – and what it takes to get there Optimization of the data supply chain that can help create insight and foresight How a team-driven approach reduces two of the most costly enterprise resources: data and effort Best practices for data science groups to easily find and understand curated and trusted data sets to feed and influence their predictive models A brief overview and demo of Datawatch’s integrated platform that combines best-in-class self-service data preparation, a centralized data marketplace, predictive analytics and data governance Speakers: Mike Ferguson, Managing Director -- Intelligent Business Strategies Michael Rowley, Director Product Marketing -- Datawatch Angoss Ellen Wilson, Product Marketing Manager -- Datawatch Angoss Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Optimize The Data Supply Chain Date:  Thursday, January 31st, 2019 Time:  9 AM - 10 AM PST

Register here

6 predictions for the future of analytics by Beverly Wright via @infomgmt

The dynamic nature and improved capabilities for analytics continues to excite and enable companies and even individuals to do more and in better ways.

Some interesting predictions. Certainly with items 3 in her list that has been a priority for a while now but I certainly see this area becoming increasingly more important especially as we start to use AI and ML more and more.

4 best practices for tapping the potential of prescriptive analytics by Peter Bull via @infomgmt

This technology considers business objectives, constraints and inputs to recommend the best action forward, showing the impact of each decision on relevant KPIs

I agree with Peter - you don't need a maths degree to use predictive analytics. I really think you need to have clear requirements, exactly what you are trying to achieve and a clear way of judging the results. Just give it a try.

Watching the continued convergence of analytics products and services by Boris Evelson via @infomgmt

Just like product vendors, consultants find it harder and harder to compete just on people and prices, so now they compete on products, too.

I have to agree - it's harder and harder to find a difference between the big consultancies. However they do need to find a way to differentiate so that people notice them and choose them - this could be where a small company could nip in and get the business from them.

Road Map for Choosing Between Statistical Modeling and Machine Learning by/via @f2harrell

All hype aside, just because you can use machine learning doesn't mean you should. So how do you decide between statistical modelling and machine learning? Here's a look at the strengths and weaknesses of each approach.

I love this article which explains very clearly how to make the choice and the impact of either choice. Something to read and maybe even bookmark so you can refer back to it.

Seven fundamentals to set an analytics team up for success by Allison Hartsoe via @infomgmt

How do leaders create factories of analytics and positive ROI? First, they nail the basics, then they build up the case for budget through ROI. Here are the steps to get there.

#4 is very apt - you have to have a single version of the truth that everyone is going to work from.

9 key mistakes organizations make when analyzing data by Larry Alton via @infomgmt

The accessibility and ubiquity of information has led to an increased number of amateur mistakes in analysis. Here are some of the most common, and how to overcome them.

I think this list needs to be bookmarked, printed out and more importantly referred to in order to try and check for all of these in order to improve the standard of your analytics.

8 Common Pitfalls That Can Ruin Your Prediction by Norbert Obsuszt by @kdnuggets

A good prediction can help your work and make it easier. But how can you be sure that your prediction is good? Here are some common pitfalls that you should avoid.

This is good and deserves a bookmark so you can refer back to it.

SLIDESHOW: 20 best practices of top chief digital officers by David Weldon via @infomgmt

Growth in digital transformation efforts in healthcare is driving the need for more CDOs, who need to be versed in strategy, governance and execution.

Slide 11 if really key - you need to get the C-level people behind you and seeing the benefits of the advantages of what you are trying to achieve.

Understanding the difference between machine learning and predictive analytics by Shailendra Kumar via @infomgmt

The two are closely related and both are focused on efficient data processing to enhance accurate predictions, but there are also many differences between them.

Great explanation from Shailendra and I particularly like his examples so that you can put it into context (maybe even if your own organisation).

SLIDESHOW: 20 top platforms for analytics and business intelligence by David Weldon via @infomgmt

Tableau, Olik and Microsoft are among the leading vendors in the data analytics and BI space according to a new Gartner Magic Quadrant report.

I've used a few of these - it's an important thing to understand the differences between these tools - I know there are often company wide decision about the preferred tool, but sometimes it is worth investing in a different tool if it gives you a much better result for what you need to do.

Taking advantage of applied AI in Manufacturing by Beena Ammanath via ‎@xplorexit

The manufacturing digital revolution is happening as the factory floor gets connected and we are able to achieve production at a scale with lower cost and increased quality.

Manufacturing is definitely an area that can benefit greatly from analytics, AI, IoT etc.  In order to survive these skills are becoming a necessity not a nice to have.

Most organisations slow to reap real benefits from analytics strategies by David Weldon via @infomgmt

Despite being the number one investment priority of CIOs today, few say they have reached a level of maturity in their data management efforts.

There are several reason for this - they don't have the staff, the knowledge of their data and they have no idea what could be achieved to start with. This is a prime example of a need for knowledge of what data you have and a background in analytics so you can think about what could be possible.