To Achieve Advanced Analytics, Start with Big Data Integration by John Thielens via @infomgmt

Big data requires new forms of processing and thus, innovative technology to support and create enhanced decision-making and greater insights. It’s no easy task given the scale at which we’re doing business today.

I know myself from my experience organisations have many disparate systems with the same data in fields with different names, different formats, etc. Integrating all this data is an art which needs careful consideration. In a perfect world everything would be designed to be the same in all the various data source but the world is not perfect.  In order to get insights and value out of all your data you need to integrate it first.

SLIDESHOW: 9 Essential AI Technologies by Mike Gualtieri via @infomgmt

Artificial intelligence is the newest ‘big thing’ in data management, but defining just what AI is isn’t so easy. As it turns out, there are at least nine significant technologies that make up this complex topic.

Some that you will know, some you might not have thought they were in this list.  Worth reading.

Wal-Mart Tackles Food Safety With Test of Blockchain Technology by Olga Kharif via @infomgmt

With the blockchain, Wal-Mart will be able to obtain crucial data from a single receipt, including suppliers, details on how and where food was grown and who inspected it.

This is really exciting and also shows how blockchain can be used for something different to financial systems.

The Foundations of Algorithmic Bias by Zachary Chase Lipton via @kdnuggets

We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realise that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.

Please note this is a three page long post.

I really like this article and it really makes you think as you read through it - definitely recommended reading.

An overview of gradient descent optimization algorithms by Sebastian Ruder.

"Gradient descent is one of the most popular algorithms to perform optimisation and by far the most common way to optimise neural networks." Here's a look at different algorithms for optimising gradient descent.

This is really clear and useful - you can use it for learning or revision.

WEBINAR: How to create powerful visualisations to explore, share, and analyse data - 1 December 2016

December 1 • 1:00 pm ET/ 10:00 am PT

Organizations need a way to get extremely fast insight from any size data and share it with co-workers of varying skill sets — wherever they happen to be.

In this webinar, learn how to create data visualizations that let everyone — even those without significant analytics skills — explore and analyze data. Users can quickly see connections and correlations, then immediately share results via the Microsoft Office tools they use every day.

 Attend this webinar to examine:

• How common Microsoft Office tools infused with SAS® Visual Analytics capabilities can help you quickly visualize very large amounts of data and share easy-to-understand analytics insight via the web, mobile devices, and Microsoft applications
• How to empower analysts with data preparation and analytics within a familiar environment
• Ways to explore data and glean insight that surpass traditional visualization and reporting, allowing recipients to slice

Register here

WEBINAR: Get Modernized: 5 Steps to Better Analytics - 6 December 2016

Webinar Event Details

Date: Tuesday, December 6, 2016
Time: Noon ET/ 9:00 am PT
Duration: 60 minutes (including Q&A)

What You'll Learn While analytics is not new to business, the technology available today for supporting analytics is new. It has led to a dramatic increase in the speed and scale with which analytics can be performed and integrated with business processes. Missed opportunities to modernize your analytics technology can result in substantial time and resource costs, and can have a significant impact on the quality of your business decisions. In this webinar, Tom Davenport, renowned analytics author, along with analytics leaders from Deloitte, SAS, and Intel, share strategies and steps for modernizing analytics. You can learn how to: • Evaluate the analytics solutions that are available today • Avoid the risks faced by organizations that are a step behind their competitors due to outdated technology • Get started on the path to analytic modernization by following five critical steps Presenters Thomas Davenport,  Co-Founder, International Institute for Analytics Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative on the Digital Economy. and a Senior Advisor to Deloitte Analytics. He teaches analytics and big data in executive programs at Babson, Harvard Business School, MIT Sloan School, and Boston University. He pioneered the concept of “competing on analytics” with his best-selling 2006 Harvard Business Review article (and his 2007 book by the same name). Jordan Wiggins,  Principal, Deloitte Consulting LLP Jordan Wiggins is a leader in Deloitte’s Analytics and Information Management practice. He has more than 15 years of experience in the technology industry with a focus in analytics. Jordan drives strategic, analytics-based solutions that enable clients to utilize raw data more effectively to support the decision-making process. Scott Van Valkenburgh,  Sr Director Partner Relationships SAS With more than 800 partners in the portfolio, Scott Van Valkenburgh leads the organization that is responsible for SAS’ global partner relationships. His teams are responsible for SAS partner go-to-market strategies and execution. Scott is a thought leader in how companies can improve by modernizing their analytic infrastructure, processes and systems. Prior to joining SAS, Van Valkenburgh was the founder and managing partner of the Sequoia Architecture Group and served as a Principal for PricewaterhouseCoopers within the Management Consulting Services Information and Technology Practice. Pat Richards,  Industry Partner Solutions Manager Intel Pat leads Intel Big Data Service Enablement Organization in the South East, helping companies implement and realize true business value with Big Data solutions. Before joining Intel Pat was VP of Professional Services for Canada and US for NCR. He also served as CTO at SCIenergy providing leadership and management of the company's software-as-a-service (SaaS) product development team. Pat spent three years as Vice President, Technical Operations, at Fox Audience Network and before that, ten years at IBM in a number of progressive technology roles in the areas of grid computing, virtualization and Web 2.0.

Register here

WEBINAR: Innovation driven by strategy - 8 December 2016

Webinar Event Details

Date: Thursday, December 8, 2016
Time: 1:00 pm ET/ 10:00 am PT
Duration: 60 minutes (including Q&A)

What You'll Learn The right analytics help to improve business performance. However, not all organisations succeed every time in gaining tangible value from their data. What is often missing from the analytics equation is an actionable strategy for using analytical insights to drive better decision-making. This one-hour webinar will explore the development of an analytics strategy, enablement of behavioural changes and operational transformation to gain the competitive upper hand. • Presenters Bernard Marr,  Bestselling author, keynote speaker, strategic performance consultant, and big data guru, Data Informed Board of Advisers Bernard Marr is a bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and big data guru. In addition, he is a member of the Data Informed Board of Advisers. He helps companies to better manage, measure, report, and analyze performance. His leading-edge work with major companies, organizations, and governments across the globe makes him an acclaimed and award-winning keynote speaker, researcher, consultant, and teacher. Radu Miclaus,  Senior Manager Analytics Pre-Sales, SAS Institute Radu is a creative analytics professional with more than 8 years of experience architecting enterprise analytics infrastructure that focus on transforming raw data into actionable insight for National accounts and commercial accounts at SAS Institute. With deep expertise in business application related to risk analytics, supply chain, IoT, customer intelligence and personalization, Radu focuses on engineering platforms that prepare analytics data, model it and deploy analytics decisions back into operations systems at the scale and speed needed by customers. Currently Radu leads a team of data scientists and pre-sales engineers who support customer engagement cycles related to Big Data initiatives using SAS technologies like Grid, In-Database, Hadoop, In-Memory and Event Stream Processing for high-availability, near-real-time and real-time analytics application.  Register here

GPS, IoT, and the Future Tech that Could Replace it All by @audgepauge93 via @Datafloq

Whether you’re lost on a lonely country road, or simply want to check into a cute café in New York City, GPS always has your back. Now that it’s so fully integrated into our cars, our phones, and our lives, it can be hard to remember a time before location services were available on almost every device. Besides making it tough to get lost as long as your battery lasts, GPS has become more sophisticated and useful than we could have imagined twenty years ago, but where will it go 20 years from now?

I like this idea of IoT being used with GPS to create something like GPS Plus.  I think the future might be interesting.

WEBINAR: The DNA of a Data Science Rock Star - 29 November 2016

Overview

Title: The DNA of a Data Science Rock Star

Date: Tuesday, November 29, 2016

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

The DNA of a Data Science Rock Star

Data Scientists are tasked with transforming their organizations with data. Yet many are struggling to realize their true Rock Star potential, and organizations are missing out on what these Rock Stars could do with the right environment.

Join us for this latest Data Science Central Webinar and learn what skills, tools, and behaviors are emerging as the DNA of the Rock Star Data Scientist. We will explore best practices for Big Data Analytics through Open Source technologies (i.e. Apache Spark, R, R Studio, Python, Jupyter), techniques including machine learning and behaviors around collaboration, sharing and learning.

Speakers:
Carlo Appugliese, Hadoop & Spark Evangelist -- IBM Analytics 
Greg Filla, Associate Offering Manager, Data Science Experience -- IBM Analytics

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

3 challenges for artificial intelligence in medicine Brandon Ballinger via @OReillyMedia

Here's how we can overcome the dearth of labelled data, deployment issues, and regulation fears in order to increase the use of AI in health care.

I like this and I can definitely see that there could be huge advances if we use AI in order to advance medical treatment and care.

A Look at Insurance's Big Data Trajectory by John McCormick via @infomgmt

Mitch Wein, VP of research and consulting at Novarica, sat down with Editorial Director John McCormick to discuss where insurance carriers are with the technology and where he sees them going in the near-future.

This is interesting and I can definitely see that insurance has a lot to learn from big data.

Blockchain And IoT: Not Ready For Primetime, But Now’s The Time To Start by @martha_bennett

Well-architected blockchain-based systems can help deliver those requirements, but they’re not available or even feasible today. In many ways, that’s a good thing, because it opens up great opportunities.

I really like this blog and she is definitely saying the kinds of things I agree with so I advise everyone to read it.

How Artificial Intelligence Will Redefine Management by Vegard Kolbjørnsrud, Richard Amico and Robert J. Thomas via @HarvardBiz

How can managers, from the front lines to the C-suite, thrive in the age of AI? To find out, the Harvard Business Review surveyed 1,770 managers from 14 countries and interviewed 37 executives that are responsible for digital transformation at their organisations. Here's what they discovered.

I found this really interesting. It definitely seems clear to me that any business needs AI and/or machine learning in order to get competitive edge and be able to gain and keep customers. In order to that and understand the need and outputs management need to understand and use these tools.

How Bayesian Inference Works by Brandon Rohrer via @kdnuggets

Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.

I really enjoyed reading this as it reminded me of some aspects that I had either forgotten or didn't appreciate fully.

Top 10 Amazon Books in Data Mining, 2016 Edition by Matthew Mayo via @kdnuggets

Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.

Definitely worth considering these books if you look at the contents list and can see areas you been to learn or brush up on.

SLIDESHOW: 6 Best Practices for Managing BYOD Technology by Richard Allen via @infomgmt

The mobile workforce population is expected to surpass 105 million by 2020, according to IDC. Keeping all those workers and devices from causing security risks is becoming increasingly hard. Here are 6 tips on how to best manage it all.

I definitely agree with him that prevention is better than cure.

France Creates Big Brother Data File Raising Privacy Concerns by Alexandre Boksenbaum-Granier via @infomgmt

Anyone with an identity card or a passport will be registered in the new database. A single file will hold details including name, height, and eye color, along with bio-metric information such as finger prints.

Wow - this could become a hackers paradise.

Big Data Is All Relative—or Relational by Mike Azevedo via @infomgmt

But as we foam at the mouth over the next great revelation that will emerge from the Hadoop cluster, a new wave of cloud-enabled applications are testing the limits of our traditional relational database systems.

RDBMS are so ensconced into our systems that it has to be better to have new innovations able to use them rather than the expense of redeveloping every thing . Yes I admit things would work better and faster with the new databases, but sometimes there is not the time nor the resources to move the data.

WEBINAR: Getting Your BI to Deliver Actionable Insights.- 21 November 2016

Join the Live Webinar

Date: Monday, November 21, 2016, 1:00 p.m. EST
Presenters: Boris Evelson, VP Principal Analyst at Forrester Research & Sisense Head of Product Strategy, Ani Manian

Organizations are growing their data environments up to 50% a year, in both size and scope. Yet only a fraction of this data is actually being translated into actionable insights and better decision making. So how do you achieve actionable insights from your BI?

• Translate growing data into actionable insights and better organizational decision making
• Uncover the latest discoveries in BI, Agile BI, big data and artificial intelligence
• Discover best practices for merging business and technology management, to receive valuable insights from your BI
• Register here

Big Data and Low Expected Returns by Matt Levine via @infomgmt

What is interesting about "big data" is how it has changed what information is useful, and to which investors.

Interesting - I'm tempted to want to go play with all that data.

SLIDESHOW: 7 Reasons Why Data Science Lacks Ethics, and How to Retrofit Morality by Dave da Silva via @infomgmt

Capgemini Senior Data Scientist Dave da Silva dicsusses the reasons why big data and data analytics seem to have abandoned ethics, and what can be done to correct the situation

I particularly like Slide 8 because organisations run the same reports over and over again and if they no longer provide value or insights they should not be run using up resources.

The Competitive Landscape for Machine Intelligence by Shivon Zilis and James Cham via @HarvardBiz

Two years ago, Shivon Zilis of Bloomberg Beta first published her widely circulated report, The Current State of Machine Intelligence. In this year's update, Shivon Zilis and James Cham offer insights into how the "Stack" of building blocks is maturing and what all businesses need to do NOW in order to survive and outlast their competitors. This is a must read for everyone interested in the business of this rapidly evolving space.

This is great and definitely s must read.

SLIDESHOW: 19 Top Companies for Enterprise Content Management by David Weldon via @infomgmt

Which are the top companies for enterprise content management? Gartner Group thinks it knows, and has released its Magic Quadrant for the top 19 Enterprise Content Management vendors.

Interesting, particularly the smaller ones towards the end.

The End of Analytics? by Thomas H. Davenport via @Data_Informed

Thomas H Davenport looks at the trend of companies (Salesforce is an example) providing self service analytics capabilities.

I found this really interesting.

WEBINAR: Big data, cognitive bias, and data quality’s new frontiers - 15 November 2016

Big data, cognitive bias, and data quality’s new frontiers

Complimentary Web Seminar
November 15, 2016
11 AM ET/8 AM PT
Hosted by Information Management

For many years we’ve treated data quality as a set of measurements related to a specific set of data (Is it complete? Is it valid?) or at the intersection of two sets of data (Are they consistent?). But with the advent of Big Data, we suddenly face a deluge of data from known and unknown sources, with highly varied formats, and potentially very disparate meanings and uses. Into this mix we add the human factor, the individual sets of assumptions and biases that are both built into the systems producing the originating data and incorporated into the integration and interpretation of the resultant data.

Working from the assumption that all this Big Data is supposed to yield more insight and better business decisions, how do we ensure that we can trust not only the original data, but the subsequent data we’re acting upon? To address this challenge, we need to consider new frontiers and dimensions for data quality to ensure that the Big Data we are using is not only relevant and fit for purpose, but data that we can trust and act confidently upon.

Featured Presenters:

Moderator: Jim Ericson Editor Emeritus Information Management	Speaker: Harald Smith Director, Product Management Trillium Software

Sponsor Content From:

Register here

Master Data Management Sees Large Gains Among Special Interest Groups by David Weldon via @infomgmt

There has been a considerable uptick in job openings on the web, in social media that reference master data management and data governance, and in membership for MDM and data governance groups

I've seen that with recruitment agencies - if you have MDM on your LinkedIn or CV they are emailing and calling more.

Open Source Data Sharing Software Takes Aim At Cancer by Fred Bazzoli via @infomgmt

The new resource can assist investigators in sorting through genomic cancer data to determine better methods of cancer prevention, diagnosis and treatment.

This looks really exciting and I believe could also be used for other illnesses than cancer.

Investing in Data: How Tomorrow’s Companies Profit from Data Centricity by Michael Pumper via @infomgmt

Tomorrow’s world is all about data, and today’s companies should consider becoming data-centric to remain relevant.

This is not the time for existing companies to sit on their laurels and carry on as normal. Today is about using the internet of things to monitor objects and processes, artificial intelligence and machine learning to get an insight out of data in order to gain a competitive edge - without these your business will fail.

Organizations Turn Focus to Data Reliability, Security and Governance by David Weldon via @infomgmt

With the rapidly growing investments in data analytics, many organisations complain they lack a combined view of all the information being created.

I'm so glad these areas have more focus - you wouldn't let a pilot fly the plane you are in if he hasn't passed all his exams after training so why are you risking you business by making decisions on data that is not managed properly?  However I would point out that apart from the points raised in this article you need to validate your data to make sure it is good quality.

SLIDESHOW: Gartner Identifies Five Domains for the New Digital Platform by David Weldon via @infomgmt

If organisations are to succeed in the future, they must embrace a new type of “civilisation infrastructure,” insists Gartner Group’s Peter Sondergaard. Speaking at the Gartner ITxpo in Orlando last week, Sondergaard explained what this means for chief information officers today.

I can completely agree with the observations in this article from Information Management.

WEBINAR: The citizen data scientist: Can you democratize analytics for better business outcomes? - 10 November 2016

The citizen data scientist: Can you democratize analytics for better business outcomes?

Complimentary Web Seminar
November 10, 2016
2 PM ET/11 AM PT
Hosted by Information Management

Analytics is about better understanding customers, markets, and other phenomena that impact business performance. So while PhDs in mathematics and computer science play an important role in analytics success, employees with degrees in psychology, economics, and other fields can also help your company find nuggets of actionable insight in the mass of Big Data.

Join our special webinar on “The Citizen Data Scientist” to learn how the most digitally savvy organizations are leveraging their non-technical talent. Key takeaways will include:

• How to identify top potential non-IT contributors to your analytics initiatives
• What it takes to transform a liberal arts major into an analytics superstar
• Why “democratization” of analytics is a hot trend among Digital First market leaders

You’ll also be able to directly ask our expert panel questions about your own most pressing analytics and BI challenges. So sign up today!

Featured Presenters:

Moderator: Lenny Liebmann Founding Partner Morgan Armstrong

Sponsored By:

Register here

WEBINAR: The Secret to "Enterprise-Grade" Digital Transformation - 10 November 2016

Webinar Event Details

Date: Thursday, November 10, 2016
Time: Noon ET/ 9:00 am PT
Duration: 60 minutes (including Q&A)

What You'll Learn Digital transformation is all the rage today, impacting everyone from the C-suite to the Ops team. Customer pressure and a turbulent business environment are driving IT departments to adopt a broad array of cutting-edge computing technologies. In this mad race to competitive advantage, however, pitfalls abound — immature technologies, archaic business processes, and a lack of visibility into the real-time behavior of businesses are impeding the path to success. What’s missing? An “enterprise-grade” IT structure that places digital priorities into the heterogeneous, middleware-connected, legacy-heavy enterprise context. Companies who effectively master mission-critical tools such as enterprise-grade transaction tracking and real-time application analytics will keep pace with — and remain in control of — the tide of change sweeping across their industries. Those who do not will stumble. With decades of experience exploring the fast-changing landscape of IT technology, leading industry analyst and Forbes contributor Jason Bloomberg will discuss what it means to be “enterprise-grade” and how businesses must leverage enterprise-grade technologies to pursue tangible business outcomes such as: • More sophisticated and reliable real-time digital business services • Greater alignment between IT analytics and business goals • Improved compliance and fraud detection Following Jason, Nastel Technologies’ Charley Rich will share several real-world customer examples where enterprise-grade transaction tracking and analytics capabilities resulted in better, game-changing business outcomes.

Presenters Jason Bloomberg, is a leading industry analyst and globally recognized expert on agile digital transformation. He writes and speaks on how today’s disruptive enterprise technology trends support the digital professional's business transformation goals. He writes for Forbes, his biweekly newsletter called the Cortex, and contributes to several blogs. He also helps technology vendors and service providers communicate their digital transformation stories. His latest book is called The Agile Architecture Revolution (Wiley, 2013). Mr. Bloomberg has published over 900 articles, spoken at over 350 conferences, webinars, and other events, and has been quoted in the press over 1,500 times. Charley Rich, VP Product Management at Nastel Technologies and jKool has extensive experience in Big Data Analytics, SaaS, UI and APM. He was an important contributor to four highly successful start-ups including: InterWorld, Tivoli, SMARTS and Collation/IBM and holds a patent for Application Performance Monitoring. Prior to Nastel, he was IBM’s World-wide Product Manager for the Application Dependency Discovery Manager solution and received the Tivoli General Manager's Award.

Register here

Three blockchain articles by @VanRijmenam from @Datafloq

In this series of posts, he is providing insights in a technology that will change our world. Blockchain has been said to be as important invention as the Internet and Johann Palychata, a research analyst from BNP Baripas, called Blockchain an invention like the steam or combustion engine.

In part 1 of this series he gave an introduction to Blockchain, in part 2 he provided insights in different types of Blockchain and consensus algorithms and in part 3 he will discuss some of the major challenges we will need to overcome to make Blockchain truly change our world for the better.

This is definitely a must read as this is clearly going to be the future,

Operational data governance: Who owns data quality problems? by David Loshin via @SASsoftware

Data integration teams often find themselves in the middle of discussions where the quality of their data outputs are called into question. Without proper governance procedures in place, though, it's hard to address these accusations in a reasonable way. Here's why.

I completely agree with the second point he makes at the end of the article - far too many times I've worked on a data warehouse project where that team is concerned about the data quality far more than the source system team - to my mind that is wrong.