You Don’t Need a Data Scientist, You Need a Data Culture by @rahulbot via @wordpressdotcom

Most of the larger non-profit organisations we work with are scrambling to figure out how to deploy complex technologies like machine learning and “AI” in service of the social good.

This is a great article and I'm sure many of you will recognise some of the barriers described in the article - I know I do.

2018 will be the year of the chatbot by Patrick Nguyen via @infomgmt

In the coming year, AI will increasingly shape the customer experience.

I think this is an interesting development and although there are plenty of imperfect chatbots I'm hoping over time they will get better and better as their use if more established.

Central banks are turning to big data to help them craft policy via @infomgmt

Collection of micro data increased after the financial crisis, when policy makers realised they lacked the depth of information to make appropriate decisions.

I found this interesting to see comparisons of how the various central banks are all starting to use big data to make decisions and understand the impact of certain actions in the market.

7 Industries that will be taken over by Robots by @cbcandmore via @Datafloq

Robots are taking over. You can see it already happening at McDonald's with its automated ordering kiosks, or your nearest supermarket with its self-checkout machine. Soon, it will be normal to see driverless cars and people walking their robot dogs outside. And this is only the beginning. With advancements in technology, many jobs typically performed by humans are being replaced by artificial intelligence (AI) and robots. Here are seven industries that are significantly impacted by automation.

This is really interesting. I think we need to remember that there are also opportunities for us to have humans in these areas so there is nothing to panic about.

Artificial intelligence cracks 'unbreakable' Enigma code in just 13 minutes by Laura Stevens via @BT

A demonstration at the Imperial War Museum showed how the latest AI technology takes just minutes to decipher messages once thought unbreakable.

Proof on just how far we have come from those days.

Trustworthy Data: The Goal of Data Quality and Governance by Lindsay Stares via @TDWI

Is poor data governance and slow data prep really a problem? It is when it erodes confidence in the quality of your data.

I completely agree with Lindsay - you need to have complete trust in your data in order to make business decisions based on it. She has some good suggestions for areas to comcentrate on.

How blockchain will underpin the new trust economy by @lucasmearian via @computerworld

Over the next two years, businesses will increasingly turn to blockchain to establish trust among parties looking to transfer everything from money and movable goods to property.

I love the diagram, the way he has explained what blockchain actually is, and some of the potential uses which are great - so will companies actually implement blockchain - I hope so.

Predictions 2018: Organisations turn their attention from big data to smart data by Sandy Steier via @infomgmt

The new focus will be on the information that business users and business-focused analysts can utilise in their everyday decision-making.

I like these predictions and it looks like an interesting year we have to look forward to.

Understanding the role of information rights management by Larry Alton via @infomgmt

IRM focuses on restricting access and improving security for documents, spreadsheets, PDFs and other important files intended to preserve or share information.

Interesting thoughts from Larry. I have to say this is sadly an area that needs much more attention in many organisations. I think it is very sad as senior business managers appear to be unaware of the reputation damage and cost of any breach of data.

The Ascent of Blockchain-Based Trading Platforms by Vincent Stokes via @datafloq

Why is Blockchain gaining so much traction and how is blockchain-based trading platforms changing the finance world?

A real implementation of blockchain which has recently been written off as hype and a fad.

Four important best practices for assessing cloud vendors by Nick Sorensen via @infomgmt

It can be challenging to know how best to communicate the requirements of your assessment process and ultimately select the right partner to help your business move forward.

I think this is really useful and worth reading if you are about to do this.

Getting started with artificial intelligence using the Value Pyramid by Suffiyan Syed via @infomgmt

These guidelines span cost reduction, increased efficiency, enhanced insights and customer engagement, and new business automation for executives looking to invest in AI, but unsure where to start.

This is really useful and something worth reading - the pyramid is definitely worth keeping and referring to.

Incorporating weather data into IoT is a game changer by:Franklin Morris via @IBMIoT

Weather has long been a source of headaches for businesses across all industries.

This is a hugely beneficial development and I can imagine countless examples where weather data would have made a difference. As I write this comment in the UK we have snow, ice and low temperatures. If this could be predicted from  a weather forecast it would have a huge number of benefits.

Google Has Released an AI Tool That Makes Sense of Your Genome by Will Knight via @techreview

AI tools could help us turn information gleaned from genetic sequencing into life-saving therapies.

This is a great progression in knowledge and technology and I'm looking forward for it to be used to create some great results.

Leveraging data to elevate the customer experience by Marc Wilczek via @infomgmt

Many organisations are harnessing only a fraction of the potential value of analytics and thus missing out on the chance to turn insights into a competitive advantage.

Great areas pointed out by Marc in this article.  Worth a read if you are likely to have to do this in your organisation.

Using the scientific method in data analytics for better results by Erick Harlow via @infomgmt

Answers without questions are simply objects that exist. To really get answers out of your 'big data' you need the right questions.

I agree with Erick - you really need to do all the "normal" methodology work to make sure that you do this properly otherwise the result could be incorrect and you waste time, money and worse reputation by coming to the wrong conclusions.

4 ways small businesses can effectively use big data by Charlene Glidden via @infomgmt

There are lots of simple methods an organisation can use to effectively gather, analyse and make sense of information it already has to enhance business insights — without breaking the bank.

Great suggestions by Charlene.  Google Analytics is fairly easy to use and should be well within the capabilities of a Small Business. Google has a lot of help online and just searching should give you plenty of help.

Deep Learning and Google Street View Can Predict Neighbourhood Politics from Parked Cars by @BotJunkie via @IEEESpectrum

It's likely that your car says something about you. The make and model, whether it's foreign or domestic, and how expensive it is can provide information about who owns it.

This is very interesting.  I think this is not true for everyone, but you can certainly make all of these assumptions.

Google’s artificial intelligence built an AI that outperforms any made by humans by Dom Galeon and Kristin Houser via @Futurism

Google's AutoML project, designed to make AI build other AIs, has now developed a computer vision system that vastly outperforms state-of-the-art-models. The project could improve how autonomous vehicles and next-generation AI robots "see."

Wow - the future is definitely on the way and there are so many uses for this technology.

Fraud detection in retail with graph analysis by Jean Villedieu via @Analyticbridge

Fraud detection is all about connecting the dots. We are going to see how to use graph analysis to identify stolen credit cards and fake identities.

This is great and we can only hope that others are doing something similar to this in order to help us all avoid fraud.

Using data to drive a smarter way to faster insights by Maxim Lukichev via @infomgmt

Deep integration between master data management and analytics execution can provide schema synchronisation and offer new decision-making strategies.

A nice aim and something I hope sticks. At the very least lower paid people should be the ones looking at data cleanup as that should be easier to do.

Predictions 2018: The blockchain revolution will have to wait a little longer by Martha Bennett via @infomgmt

In the coming year, we expect to see a number of projects stopped that should never have been started in the first place.

It's a sad reality that if it is new and people or organisations don't fully understand it, that many projects will fail. A shame as I still think this technology has benefits, but maybe the wider business/IT world are not in the right place for it yet.

Backing Up Big Data? Chances Are You’re Doing It Wrong by @PeterSmails via @datanami

The increasing pervasiveness of social networking, multi-cloud applications and Internet of Things (IoT) devices and services continues to drive exponential growth in big data solutions.

I like that this contains two case studies and it makes perfect sense. You need to make sure that backups and recovery and always included in a big data project and that you treat it as a big data project not just a "normal" database.

WEBINAR: America’s data quality crisis - 12 December 2017

Dec. 12, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Your company may have grand ambitions for analytics and AI. But you can’t 

get anywhere with bad data. And research reveals that only 3% of enterprise data fulfils 

even the most basic data quality standards. Worse yet, poor quality costs companies like 

yours an astonishing 20% of revenue

Join this interactive webinar – featuring renowned data quality expert Thomas “the Data Doc” 

Redman – to learn how you can turn your data quality issues around effectively and sustainably.

You’ll learn:

• The cold, hard facts about enterprise data quality – and its business impact
• What it takes to permanently fix your data issues and your data culture
• How your data quality efforts generate quantifiable near- and long-term ROI

Dr. Thomas Redman President Data Quality Solutions (Speaker)	Lenny Liebmann Contributing Editor SourceMedia (Moderator)

Sponsored By:

Register here

Preparing For Machine Learning: 5 Questions Enterprises Must Consider by @IE_James via @IEGroup

Evidence of machine learning's potential to change the world is now everywhere, from lights-out factories through to Netflix's recommendation engine. However, adoption is still not reaching the levels many predicted.

James had it exactly right - there is absolutely no reason to implement machine learning unless you and your organisation are ready for it, and I fear that is no is many cases at the moment.  By all means design your infrastructure and systems to be ready for ML but don't try and implement it until/unless you are really ready.

Implementing Successful Big Data and Data Science Strategy by Ashish Sukhadeve via @DataScienceCtrl

Big Data and Data Science are two of the most exciting areas in the business today. While most of the decision makers understand the true potential of both the fields, companies remain sceptical on how to implement a successful big data strategy for their enterprises.

I liked this and thought it warranted a share.  He is right in saying a pilot project is necessary - you have to do something that gives you a proven quick benefit in order to justify the technology, the methodology and the benefits that can be achieved.

How Facebook's oracular algorithm determines the fates of startups by Burt Helm via @NYTmag

The platform is so good at “microtargeting” that many small e-commerce companies barely even bother advertising anywhere else.

I found this interesting and it suggests that they really have their algorithms and machine learning right and that many others could learn from that.

The 3 most important data metrics for retaining customers by Matthew Tharp via @infomgmt

In an era where every customer is valuable, it’s important to use information to more deeply understand the most profitable and high-growth segments and tailor the business to them.

Some great suggestions on metrics as keeping customers is definitely cheaper than getting new ones.

It’s time to solve deep learning’s productivity problem by Hillery Hunter via @VentureBeat

Deep learning is fuelling breakthroughs in everything from consumer mobile apps to image recognition. Yet running Deep learning-based AI models poses many challenges. One of the most difficult roadblocks is the time it takes to train the models.

Hillery makes some good points and gives a lot to think about.

What the world's central banks are saying about cryptocurrencies by listed authors via @infomgmt

Eight years since the birth of bitcoin, finance execs around the world are increasingly recognising the potential upsides and downsides of digital currencies.

I like this overview of how banks are handling the currencies which might help you to understand how to understand it yourself.

Three tips for mastering digital transformation by Michael Gale and Chris Aarons via @infomgmt

Organisations that saw real results had and built a unique digital DNA that enabled them to succeed where other similar companies failed or saw far fewer results.

I really liked this and agree with the points made in the article.  Organisations really need leadership with the vision and will to do all these things in order to ensure success.

How Machine Learning Boosts Personalisation in Travel by/via @7wData

This article walks through a scenario and points out how machine learning can help to personalise travel and in effect help both the customer and the seller.

I liked this and can imagine the kind of analytics that could be produced off the data they obtained from the OTA clients.  You can also apply the learning and method from this example into other areas just as easily.

WEBINAR: What's big in big data: Predictions for 2018 - 4 December 2017

Web Seminar  What's big in big data: Predictions 
for 2018

Dec. 4, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Information assets, especially big data, are increasingly seen as a 

critical corporate asset. Not surprisingly, more organizations are 

implementing big data projects and they are looking for methods to best 

measure ROI and insights on where big data is heading. To answer that, on December 4, 

Information Management will present a webinar on “What’s Big in Big Data: Predictions for 2018.”

Participating in the event will be Gartner vice president and distinguished 

analyst Doug Laney, who will offer examples of leading organizations at big data 

projects, provide Gartner research on what to expect with big data in 2018, and 

share highlights from his new book, Infonomics: How to Monetize, Manage, and Measure 

Information for Competitive Advantage. Also participating will be Tyler Kleykamp, 

chief data officer for the State of Connecticut, and Mark Stange-Tregear, director of 

analytics at Ebates, who will both share their experiences with big data projects, 

what they have planned with big data in 2018, and lessons they have learned to 

help guarantee success with those efforts.

Douglas B. Laney VP & Distinguished Analyst Chief Data Officer Research Gartner (Speaker)	Tyler Kleykamp Chief Data Officer State of Connecticut (Speaker)	Mark Stange-Tregear  Director of Analytics Ebates (Speaker)	David Weldon Editor-in-Chief Information Management (Moderator)

Sponsored By:

 Register here

Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey by/via @kdnuggets

This is a great analysis and list of Masters courses to learn much more about Analytics or Data Science  so you can get a qualification in it.

Yes there are expensive full time options where you have to be there physically, but there are also fully online options that can be done fairly cheaply. You also need to factor into that how respected the qualification might be.

WEBINAR: Getting the EDGE on data governance - 5 December 2017

Dec. 05, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Data governance ensures that an organization’s data assets can be discovered, understood, governed and then socialized for greater visibility, control and value. It enables an enterprise to get a handle on how it handles its data in the context of achieving larger business goals – from regulatory compliance to growing topline revenue.

Such comprehensive and effective data governance requires a shift from isolated IT program to strategic initiative, with a persona-based approach that ensures everyone – from executives on down – invested in and accountable for data use.

Join Information Management and erwin, Inc. for this webinar on how to create an enterprise data governance experience (EDGE) to mitigate risk, improve operational performance, and accelerate growth.

We’ll look at the five factors of an EDGE that join IT and business functions in managing data risks while maximizing its opportunities.

Jamie Knowles Product Manager, Data Governance Products erwin, Inc. (Guest)	Mariann McDonagh Chief Marketing Officer erwin, Inc. (Guest)	Eric Kavanagh CEO Bloor Group (Moderator)

Sponsor Content From:

Register here

Mastering change management to drive digital transformation by Elliott Hultgren via @infomgmt

When faced with the task of altering the entire technology infrastructure of an organisation, CIOs should adopt a mission-oriented mindset.

He makes some good points.  I would add that it is important to find a few quick wins for the new strategy to help you get the rest of the organisation on board.

Why Blockchain and Analytics Don't Mix Well by @billfranksga via @iianalytics

Blockchain and analytics might not mix as analysing data within a blockchain environment would be different from how we analyse data within other platforms.

Bill has some great points in this article and it's only when you sit and think hard about this that you realise that this is not the same as any traditional database and that you could get lost in processing and searching the chains to get the anwser to all the questions you are faced with.

Dirty Data Is OK, How You Cleanse It Matters by Chirag Shivalker via @DZone

It has been an unsolved mystery for companies if they should get their data cleansed first to opt for data analytics or if they should opt for data analytics to conclude whether their data is dirty.

There are some really good points in this article.  I cannot emphasise enough the single source of truth point.  We must all have worked for organisations where department A's figures don't match department B's.  You cannot run an organisation if the numbers in your reporting don't match, and even worse you have no idea why they don't match. You need data management, agreed definitions for data, and just the one source of the truth across the entire company.

Why analytics will be the next competitive edge by Gary Cokins via @infomgmt

Organisations are drowning in data but starving for information. The application of data science is becoming commonly accepted, but will senior executives realise it?

I would add to this article that the data has got to be clean and of a consistent quality so that any analysis or decisions you make based on the data are correct. If you can't rely on the data or it is of a dubious quality you are better off not using it.

WEBINAR: Deep Learning: From Basic Principles Through Training Models - 28 November 2017

Overview

Title: Deep Learning: From Basic Principles Through Training Models

Date: Tuesday, November 28, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Deep Learning: From Basic Principles through Training Models for Deployment into Production

What are neural networks, but more important, how are they trained in practice?  How can data scientists design an optimal neural network when a single training run can take 2 weeks? In this Data Science Central webinar we will start from the foundation of what deep learning is then fast forward through what it takes to train a production quality neural network.  You won't be able to train a network when this talk is over, but you'll understand enough basics to start smart conversations about our customers' practice of deep learning.

Speaker: Anthony Stevens, Offering Manager, Watson Deep Learning -- IBM Watson and Cloud Platform

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

 

Register here

WEBINAR: 7 GDPR “Gotchas” and how to avoid them - 28 November 2017

Web Seminar  7 GDPR “Gotchas” – and how to avoid them

Nov. 28, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

As GDPR deadlines loom, most data managers are already working to ensure compliance. But 

the first wave of GDPR compliance efforts reveal some serious potential GDPR “gotchas.” 

And many of these “gotchas” are the result of basic misconceptions about what compliance really requires.

Protect yourself and your organization by attending this interactive expert-led webinar. You’ll learn:

• How European business partners can expose you to unanticipated risk
• What the “data portability” requirements of GDPR really require
• Why you need to select and empower an EU representative ASAP

William Beckler GDPR Compliance Lead ALICE (Speaker)	Lenny Leibmann Contributing Editor SourceMedia (Moderator)

Sponsored By:


Register here

4 Pain Points of Big Data by @DigitalMcKinsey via @Medium

Big data analytics is an amazing tool at the epicenter of the digital revolution. But it’s not foolproof. Here’s how successful companies deal with its potential drawbacks.

I really like this article - it's very clear and says all the things that I find important.  Worth a bookmark so you can refer back to it.

Personalising the retail supply chain with predictive analytics by Michelle Covey via @infomgmt

It has never been more critical for retailers to leverage data to create the convenience and the experiences that consumers crave.

I definitely think personalisation is a key way of differentiating your own organisation from the others in the marketplace.

The titans of AI are getting their work double-checked by students by @davegershgorn via @qz

In an effort keep AI research a science, Joelle Pineau, an associate professor at McGill University and head of Facebook's AI research lab in Montreal, is pushing back against unreproducible AI research. Her challenge, coordinated with five other universities, is to reproduce the work in submitted papers. Students are tasked with reproducing research from some of the world's top AI labs—from universities to companies like Google, DeepMind, Facebook, Microsoft, and Amazon.

I find it strange that not only is it almost impossible to reproduce the research, but that they publish papers with deliberate missing information.  When learning R and how to publish a paper it was made clear to me that I must publish my code so that others can reproduce my results.

WEBINAR: Advanced Tool for Dynamic Visual Analytics - 30 November 3027

Overview

Title: Advanced Tool for Dynamic Visual Analytics

Date: Thursday, November 30, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Advanced Tool for Dynamic Visual Analytics

As you journey through the age of self-service analytics it’s important to understand and explore your data in impactful new ways. There are critical drivers for making better decisions – can you see them? Why did something happen? Have you examined all options and uncovered opportunities hidden deep in your data? 

Join us for this Data Science Central webinar to see the new version of SAS® Visual Analytics. It introduces new data visualizations – including third party custom ones, a refined user experience to improve productivity, self-service data preparation, and the power of location analytics to visualize data in new contexts. These enhancements will help you automatically highlight and understand key relationships, outliers, clusters and more in your data, revealing vital insights that inspire action.  

Speakers:

Tapan Patel, Principal Product Marketing Manager, Business Intelligence and Analytics -- SAS
Steve Mellgren, Senior Solutions Architect, Business Visualization Practice -- SAS

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

5 mistakes to avoid when implementing data lakes by Matt Maccaux via @infomgmt

Many organisations are making debilitating mistakes that will ultimately hinder their ability to have a scalable, elastic data-monetisation platform.

I have to agree with his comments on too much Hadoop and not enough governance.  The data lake is only going to be useful if it is efficient and contains good and proper data - the old adage about garbage in and garbage out can definitely apply to a data lake without data stewards and some kind of of quality control.

Developing a successful data governance strategy by Federico Castanedo via @OReillyMedia

Multi-model database architectures provide a flexible data governance platform.

Great article by Federico that is worth reading. There is a free e-book you can download too.

The battle over bank customer data may finally be over by Penny Crosman via @infomgmt

Guidance from the CFPB and reduced friction between banks, fintechs and data aggregators are easing bank-fintech partnerships at Wells Fargo, Capital One and others.

I can see that this has a lot of benefits, but I can also see that there are a lot of potential drawbacks as it could become a security risk. I'm also interested in how it verifies that you are the right person.

Majority of firms now adopting AI; more investments planned by David Weldon via @infomgmt

One in three business leaders believe their company will need to further invest in artificial intelligence over the next 36 months to keep pace with competitors.

AI and ML are becoming much more mainstream and I can only see that expanding over time.

What Intelligent Machines Can Do, And What They Can't by @jessicadavis via @InformationWeek

From Alpha Go to cancer detection to data centre efficiency, we talk to analytics practitioner and SAS CTO Oliver Schabenberger about what AI can do and what it can't do.

Good to read something that talks about limitations and not all of the time promising things.

4 Ways Cities Can Change Their Data Game by @tnewcombe via @GOVERNING

From who they hire to how they share, adjusting municipal data use is a must for any city looking to improve its services.

An interesting look at a few possibilities of how data could help cities.

Top Big Data Skills To Help You Stand Out from the Crowd by Sarah Shannon via SmartDataCollective

Big Data is the latest buzzword hitting the technology sector with data analytics fast becoming the newest technique implemented by businesses to monitor their IT networks, and stop impending threats.

Definitely something to read and work out which skills you might be missing or would add to your offering if you worked on it.

How to tap into Splunk's big data analytics engine by @kenhess via @TechRepublic

Read this primer on using Splunk as a big data analysis tool. You'll learn how to analyse data and filter results, as well as create visual results from that data.

This is great and I think a great resource that should be bookmarked so you can refer back to it.

SLIDESHOW: 6 trends shaping the future of data analytics by David Weldon via @infomgmt

Machine and deep learning and natural language processing are among technologies that will help drive organisational success.

Interesting list.

Mastering data architecture to enable digital transformation by Joshua Satten and Nicolas Papadakos via @infomgmt

The information model is the gas powering the engine, enabling organisations to more effectively communicate and reach their specific goals.

Some good points. I would add to it that you need to try and balance the needs of the business, the availability of data and as mentioned in the article you need to focus of granularity in order to get that right.  Business knowledge is key.  Also be aware that documentation can sometimes we scarce so be prepared to have to generate your own for some older data/systems.

How to unit test machine learning code by @keeper6928 via @Medium

Unit tests can save you weeks of debugging and training time.

This article by Chase is really useful and makes a very valid point. I would reiterate that the earlier you find errors the easier it is to fix them and the less costly.

The top 10 technology trends for 2018 by David Weldon via @infomgmt

Artificial intelligence, the IoT, cloud computing and cryptocurrencies are among the technologies that will dominate new IT investments, according to Forrester Research.

I found this interesting.

WEBINAR: Govern first, then ask many questions - 16 November 2017

Govern first, then ask many questions

Nov. 16, 2017 | 1 PM ET/10 AM PT
Hosted by Information Management

Who did what with your corporate data, and when? Knowing that information is more than useful in today’s business world. It’s often a prerequisite, or even mandated by law. That’s why data governance has become a hot button issue. More and more organisations realise that they must design, manage and enforce appropriate rules and regulations regarding the creation, use and retirement of data.

Doing this property can keep your organisation in bounds, while also fuelling the kind of analytics that can genuinely improve the business. How can your company stay on top? Register for this episode of IM Live to find out! Host Eric Kavanagh will interview several guests.

Adam Famularo CEO erwin (Speaker)	Rick Sherman Managing Partner Athena IT Solutions (Speaker)	Eric Kavanagh  CEO Bloor Group (Moderator)

Sponsor Content From:

Register here

Pig vs Hive vs SQL – Difference between the Big Data Tools by Manisha Nandy Mazumder via @Hadoop360

Great article comparing the three tools.

This is great for understanding the differences and which one might be best for you.

WEBINAR: Building a Compelling Argument with Data - 14 November 2017

Overview

Title: Building a Compelling Argument with Data

Date: Tuesday, November 14, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Building a Compelling Argument with Data

Telling stories with data sounds like a great idea, but often you simply want to make a clear and compelling argument. How do you do that? In this Data Science Central webinar, Robert Kosara will walk you through a particular structure for stories that works well in news graphics and describe how and why it applies to data. He will also talk about the differences between classical story and story with data based on his research and experiences.

Speaker: Robert Kosara, Research Scientist  -- Tableau 

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

 

Register here

Create editable Microsoft Office charts from R by David Smith via @rbloggers

Embedding an R graphic into a Microsoft Office Document can easily be done. But can you make it editable in Microsoft Office? This post introduces two packages that allow you to do this.

This is a great article and could help you even if you didn't want to embed it into MS Office.

Object detection with TensorFlow by Justin Francis via @oreillymedia

How to create your own custom object detection model. (Includes Python code and iPython notebook.)

This is great and you can access the code on Github.

Evolving technology calls for more disciplined approach from auditors by Mohammed J. Khan via @infomgmt

As the maturity of our applications increases, how we code in our environments must adapt to keep up the pace.

Interesting - I guess auditors need to adapt all the time for all innovations no just in technology.

Machine Learning Algorithms: Which One to Choose for Your Problem by Daniil Korbut via @statsbotco

Intuition of using different kinds of algorithms in different tasks.

I like this as it clearly explains which machine learning algorithm to use.

Your Data Are Probably Biased And That's Becoming A Massive Problem by @Digitaltonto via @Inc

Nobody sets out to be biased, but it's harder to avoid than you would think.

I really liked this and he makes some very good points.  I believe if you are aware of bias and the ways it can happen you are part way to get around it as you can look out for it and adapt your ways of working to avoid it.  Peer reviews are also great as the more eyes on a problem the less chance you are all going to have the same bias - especially if you are a from different backgrounds and skillsets.

TensorFlow: Building Feed-Forward Neural Networks Step-by-Step by Ahmed Gad via @kdnuggets

This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.

Please note this is a 3 page article.  I love that this is so clear and I think easy to understand.

Navigating the future of IoT one step at a time by Wael Elrifai via @infomgmt

At stake are new ways of working, new skills and resources, new types of contractual arrangements and significant cultural change up and down the supply chain.

Great 8 points that you should read and make note of because it will save you pain.

How to master machine learning logistics by Ted Dunning and Ellen Friedman via @infomgmt

90% of the effort in a successful implementation is about proper model management, say authors Ted Dunning and Ellen Friedman.

I agree with them completely. There are some things that may appear boring and not as sexy as machine learning, but if you do them then your machine learning will be implemented more successfully and you will get the results that you intended.

19 top paying Internet-of-Things jobs by Bob Violino via @infomgmt

The Internet of Things remains one of the hottest trends in technology. Here's how the demand is translating to salaries for experienced professionals.

Good if you want to know what skills and roles you need to aim towards in your own career.

How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist by Kathryn Hume via @HarvardBiz

Having an intuition for how machine learning algorithms work - even in the most general sense - is becoming an important business skill.

Interesting and definitely worth a read.

Graph Databases Help Companies Unlock Connections Within Their Data by Tom Smith via @DZone

Once you become familiar with graph databases, you’ll expand your view of how to ingest and analyse unstructured data at speeds you cannot imagine.

This is in the form of an interview with Jim Webber, Chief Scientist at Neo4j. Really interesting.

The Python and R Graph Gallery by/via @R_Graph_Gallery

This could be handy for your next Python or R data viz project: hundreds of charts along with the reproducible R and Python code.

Something to bookmark and keep for the next time you need to find a a great chart for your data.

WEBINAR: Predictive Forecasting with Time Series Analysis - 7 November 2017

Overview

Title: Predictive Forecasting with Time Series Analysis

Date: Tuesday, November 07, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Predictive Forecasting with Time Series Analysis

The ability to accurately predict what is likely to happen at a point in the future, and build plans and strategies based on that knowledge, is essential to an organization’s success. But what happens when a forecast is inaccurate? What is the impact on a business, its customers or its partners? For businesses, the ability to catch even a tiny glimpse of what the future may hold can lead to happy customers, improved efficiency and productivity, and highly successful business decisions.

In this Data Science Central webinar learn how time series analysis better enables departments across your organization with actionable, more accurate insights related to the timing of equipment failure, customer offers, and the impact of effects like seasonality.

Speakers:

Murali Prakash,  IBM Product Manager  -- IBM SPSS
Mikhail Lakirovich, IBM Offering Manager  -- IBM SPSS
Douglas Stauber, IBM Offering Manager -- IBM SPSS

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

 

Register here

The Seven Deadly Sins of AI Predictions by Rodney Brooks via @techreview

"Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future."

Great list and I agree with them 100%

The top MDM and data governance consultancies by David Weldon via @infomgmt

The Master Data Management Institute offers its picks for the top global and regional systems integrators, and advice on how organisations can best work with them.

Great lists of consultancies and integrators for MDM.

Introducing R-Brain: A New Data Science Platform by @idigdata via @kdnuggets

R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.

Great article and it sounds like a great platform. I'm hoping to have a go with it next week if I can find the time to play.

Beyond Hadoop by James Ovendon via @iegroup

A company once synonymous with big data is on its way out, but what comes next?

Interesting.  So people are starting to use alternate to Hadoop or using it for other reasons.

AI & Machine Learning : The Most Used Fundamental Terminology of It via @08DevEsh

Suddenly, artificial intelligence (AI) is everywhere. For decades, the dream of creating machines that can think and learn like humans seemed like it would be perpetually out of reach, but now artificial intelligence is embedded in the phones we carry everywhere, the websites we use every day and, in some cases, even in the appliances we use around our homes.

Great list and explanation of terms.

3 ways machine learning is revolutionising IoT by Majeed Ahmad via @networkworld

One of the greatest boons that machine learning and its algorithms have delivered to the IoT is how easily it integrates into the IoT’s platforms.

Interesting article that makes some really good points.

Even This Data Guru Is Creeped Out By What Anonymous Location Data Reveals About Us by DJ Pangburn via @FastCompany

Using code and the web, a data scientist follows two unnamed people and learns just how much our anonymous location data can say about who we are.

Scary. There is some advice on settings at the end that you should read.

How to win Kaggle competition based on NLP task, if you are not an NLP expert by Artem Farazei via @indatalabs

Here is how he got one of the best results in a Kaggle challenge remarkable for a number of interesting findings and controversies among the participants.

This is great for a number of reasons. I like the way it tries to teach you another way of ooking at the problem and yet when you read this it seems so obvious.

WEBINAR: Data Literacy – Closing the Data Skills Gap - 26 October 2017

Overview

Title: Data Literacy – Closing the Data Skills Gap

Date: Thursday, October 26, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Data Literacy – Bridging the Gap

With the volume and velocity of data available in the world today, most industries and companies have a desire to use that data better.  Unfortunately, as data has grown at incredible speeds, there has followed a real and growing data literacy skills gap. This skills gap can lead to major issues within organizations, which is why understanding what data literacy is and how to alleviate this gap is so important. 

Join us for this latest Data Science Central webinar to learn more about the growing data literacy skills gap, what exactly data literacy is, and how your organization can be better prepared in the current data revolution.

Speaker: Jordan Morrow, Program Manager of Data Literacy -- Qlik 

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

  

Register here

Keras Cheat Sheet: Deep Learning in Python by Karlijn Willems via @DataCamp

Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models. With this library, you’ll be making neural network models in no time!

This is great - well worth signing up with DataCamp and getting some tools and courses that are incredibly useful.

WEBINAR: How data archiving supports GDPR readiness - 26 October 2017

Web Seminar  How data archiving supports GDPR readiness

Oct. 26, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

The EU’s General Data Protection Regulation (GDPR) will take effect in May 2018. Whether your organization is located inside or outside Europe, it will be impacted if it holds data on even a single European resident. One element of GDPR preparedness should include your data archiving strategy, or how you handle personal data on either production or legacy systems that is no longer changing. Just because static information might not be actively used, it still is subject to GDPR.

This webinar will address how GDPR impacts organizations in North America and will specifically explore the large volume of static data that is residing on both production systems and outdated, redundant, or otherwise obsolete applications that are common in many organizations. Participants will hear from GDPR experts from OpenText and data archiving specialists with Flatirons Jouve.

You will learn:

• How the GDPR impacts U.S.-based companies
• Foundations of an enterprise information management strategy essential for GDPR
• How data archiving supports GDPR readiness
• Specific capabilities of archiving platforms that address privacy by design, consent, data portability, the right to erasure, and other GDPR requirements

This webinar is essential for anyone responsible for compliance, data strategy or management, enterprise information management, information governance or architecture, or application strategy or management.

Bryant Bell Director Product Marketing OpenText (Speaker)	Gino Vicari Principal Value Consultant OpenText (Speaker)	Bill Young, Ph.D. Consultant Flatirons Jouve (Speaker)	Jim Ericson  Consultant, Editor Emeritus Information Management (Moderator)

Sponsor Content From:

Register here

23 more insurers sign on with blockchain consortium by Nathan Golia via @infomgmt

Aon, Chubb, Willis Re and 20 more carriers have joined B3i, a working group looking to develop blockchain-based insurance solutions.

This is great news and means that blockchain is definitely here to stay in the insurance world.

Five big data misbeliefs that could cost organisations millions by Marc Wilczek via @infomgmt

Information is the engine spurring digital growth. Yet, false truths are causing risks and preventing companies from capitalising on their data-driven strategies.

I most definitely agree with numbers 1 and 3!!

WEBINAR: The Fast Path to Success with AI - 26 October 2017

Across industries, AI is transforming business. The organisations that harness the power of their data will overwhelm competitors who do not. Unfortunately, while business leaders know AI is essential to long-term success, they lack the familiarity with these kinds of solutions to successfully execute. What’s more, the data scientists needed to build AI solutions are in short supply and high demand.

The good news is that advanced AI technologies like automated machine learning bring AI into reach for everyone. With these tools and technologies, most companies will make progress without hiring data scientists and without expensive training. These advanced tools make it possible for almost anyone to build predictive models without writing a single line of code or having deep knowledge of the algorithms.

In this webinar, Greg Michaelson, PhD, and Head of DataRobot Labs, will review the practical first steps an organisation takes toward becoming an AI-driven enterprise and remaining competitive in the coming years

You will discover how to:

• Train users across your organisation - including business leaders - to spot AI opportunities
• Systematically identify the opportunities best suited for accelerating your company’s AI efforts and offering the highest ROI
• Quickly experience AI success with small, successful projects comprised of the people and data you have today
• Get AI implementation right the first time

Event details

Thursday, October 26

1:00 pm ET/10:00 am PT

45 minutes with Q&A

Speaker:

Greg Michaelson, PhD

Head of DataRobot Labs

Register here

How can Blockchain Revolutionise Mobile App Security? by @junedghanchi via @Datafloq

Blockchain can increase mobile app security in various domain, ranging from data security to data transactions.

I agree that blockchain needs to be used in other areas than the financial services side of things.  However we do need to look at alternatives too in order to have a more balanced environment.

3 Ways Blockchain Will Transform the Internet of Things by @VanRijmenam via @Datafloq

There is no denying the power of the Internet of Things (IoT). IoT devices are already in 60 percent of U.S. homes using a broadband connection, and an estimated 200 million vehicles will be connected to the internet by 2020, standing to transform entire industries for a good reason.

Great article by Mark and I completely agree that strategies need to be combined and cross referenced as they often need to be handling the same area at the same time.

VIDEO: Build smart applications with your new super power: Cloud AI by @Ppoutonnet via @oreillymedia

In this video Philippe Poutonnet discusses how you can harness the power of machine learning, whether you have a machine learning team of your own or you just want to use machine learning as a service.

I found this really interesting and a great way to have ML for all.

WEBINAR: Moving from BI to Automated Machine Learning - 18 October 2017

Wednesday, October 18, 2017 1:00 pm Eastern / 10:00 am Pacific 35 minutes with Q&A

Machine Learning has become a competitive differentiator in a big data world. Vast amounts of data are already overwhelming existing BI tools and analytics processes. When faced with hundreds of variables, a human's ability to efficiently identify new insights or detect changing patterns manually has also been exceeded. 

To address these challenges, BI and analytics professionals are adopting user-friendly, automated machine learning solutions.

In this webinar hosted by DataRobot, recognised analytics industry expert Jen Underwood will discuss how BI and analytics professionals can get started with automated machine learning. She will cover:

• An introduction to machine learning and popular machine learning use cases

• CRISP-DM Methodology and common algorithms (regression, clustering, classifiers, and more)

• How to get started with Automated Machine Learning 

Guest Speaker: Jen Underwood Founder, Impact Analytix

Register here