Occam's Razor
FEBRUARY 6, 2018
If you bring sharp focus, you increase chances of attention being diverted to the right places. That in turn will drive smarter questions, which will elicit thoughtful answers from available data. The result will be data-influenced actions that result in a long-term strategic advantage. It all starts with sharp focus. Consider these three scenarios….
O'Reilly on Data
DECEMBER 19, 2018
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Since that time, Andrej Karpathy has made some more predictions about the fate of software development: he envisions a Software 2.0 , in which the nature of software development h
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Rocket-Powered Data Science
DECEMBER 16, 2018
In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. An example of a cold start problem is k -Means Clustering, where the number of clusters k in the data set is not known in advance, and the locations of those clusters in feature space ( i.e., the cluster means) are not known either.
BA Learnings
JANUARY 10, 2018
Swiss cheese has holes in various places on different slices of cheese when you cut it up. Let’s imagine these holes reflect weaknesses in the system where mistakes can pass through, afterall no system is perfect. One mistake passing through a hole in one slice of cheese might remain unnoticed and not lead to a business catastrophe, if it's corrected.
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David Menninger's Analyst Perspectives
DECEMBER 3, 2018
IBM's Analytics University (held in both Miami and Stockholm) brought about some large changes. Big announcements this year included a consolidation of IBM's Watson Analytics into Cognos 11.1, helping provide some clarity to their analytics offerings, along with new visualizations and better data preparation. This also includes a new conversational assistant to help generate narrative explanations of displays and interactive queries.
CIO Business Intelligence
DECEMBER 4, 2018
With more and more data at our fingertips, it’s getting harder to focus on the information relevant to our problems and present it in an actionable way. That’s what business intelligence is all about.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
Data Science and Beyond
DECEMBER 23, 2018
I’ve been interested in the area of causal inference in the past few years. In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. Now, I believe I’ve finally found a book with practical techniques that I can use on real problems: Causal Inference by Miguel Hernán and Jamie Robins.
Bruno Aziza
SEPTEMBER 17, 2018
Now more than ever, businesses are deploying machine learning to drive business results. Learn about the state of machine learning in business today.
IBM Big Data Hub
MARCH 16, 2018
March 16, 2018 is the 25th anniversary of the Db2 relational database product on Linux UNIX and Windows. Over the past 25 years, this team has built the Db2 brand for the distributed product, complementing IBM’s Db2 mainframe offering and creating a market force.
Martha Bennett
NOVEMBER 7, 2018
“The visionaries will forge ahead; those hoping for immediate industry and process transformation will give up.” This was the opening sentence of my blog post accompanying Forrester’s DLT/blockchain predictions for 2018. I’m repeating it here, because it’ll continue to hold true for 2019 — with one proviso: There’s a real risk that we’ll experience the beginning […].
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Getting off of Hadoop is a critical objective for organizations, with data executives well aware of the significant benefits of doing so. The problem is, there are few options available that minimize the risk to the business during the migration process and that’s one of the reasons why many organizations are still using Hadoop today. By migrating to the data lakehouse, you can get immediate benefits from day one using Dremio’s phased migration approach.
Occam's Razor
JANUARY 8, 2018
Like me, I'm sure you are working on complex challenges when it comes to data. Multi-petabyte data warehouses. Multi-touch, cross-channel attribution analysis. Media mix modeling. Predictive analytics. Human-centric analysis. Oh, and let's not forget the application of machine learning to every facet of your work. It is genuinely fun to work on these opportunities.
O'Reilly on Data
NOVEMBER 21, 2018
The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science. In this episode of the Data Show , I spoke with Vitaly Gordon , VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people.
Rocket-Powered Data Science
JULY 30, 2018
Below are the individual links to these Data Science, Artificial Intelligence and Machine Learning books, all of which are top sellers on Amazon… “The Book of Why: The New Science of Cause and Effect” “Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems” “Deep Learning (Adaptive Computation and Machine Learning)” “Applied Artificial Intelligence: A Handbook For Business Leaders
BA Learnings
JANUARY 3, 2018
When we have a strong belief about something or a bias towards a particular opinion, we consciously or unconsciously seek out evidence that validates what we already believe. When we come across contrary evidence, our default behaviour is to ignore it, diminish it or in some cases, conclude that it’s wrong prematurely without exploring its merits. This behaviour is due to a cognitive bias known as confirmation bias.
Speaker: William Hord, Senior VP of Risk & Professional Services
Enterprise Risk Management (ERM) is critical for industry growth in today’s fast-paced and ever-changing risk landscape. When building your ERM program foundation, you need to answer questions like: Do we have robust board and management support? Do we understand and articulate our bank’s risk appetite and how that impacts our business units? How are we measuring and rating our risk impact, likelihood, and controls to mitigate our risk?
David Menninger's Analyst Perspectives
NOVEMBER 21, 2018
Once again I attended Tableau's Users Conference, along with 17,000 other attendees, affectionately self-referred to as "data nerds". Pushing the envelope in data capabilities and access, Tableau introduced the "Ask Data" feature, allowing users to prose natural language queries and receive a response, along with new data preparation capabilities and other enhancements to help data analysts.
John Wheeler
DECEMBER 18, 2018
Last week, I had the distinct privilege to join my Gartner colleagues from our Risk Management Leadership Council in presenting the Q4 2018 Emerging Risk Report. We hosted more than 500 risk leaders across the globe in our exploration of the most critical risks. The Q4 2018 Emerging Risks Survey, designed by Gartner, captures and analyzes senior executives’ opinions on emerging risks and provides actionable insight on identifying and mitigating these risks.
Boris Evelson
NOVEMBER 14, 2018
Over the last five years, most large enterprises have slowly but surely matured from being data-aware to data-driven. They all collect data from operational and transactional applications; process the data into data lakes, data hubs, data warehouses, and data marts; and build business intelligence (BI) and analytics applications to understand what the data is telling […].
erwin
DECEMBER 13, 2018
This past year witnessed a data governance awakening – or as the Wall Street Journal called it, a “global data governance reckoning.” There was tremendous data drama and resulting trauma – from Facebook to Equifax and from Yahoo to Marriott. The list goes on and on. And then, the European Union’s General Data Protection Regulation (GDPR) took effect , with many organizations scrambling to become compliant.
Advertiser: ZoomInfo
When economic headwinds pick up, sales leaders are the first to sound the alarm — and chart a new course. Longer sales cycles, larger buying committees, increased price pressure, and smaller teams can quickly combine to reduce your margin for error and increase the urgency to find a solution. To thrive in a challenging environment, sales teams need a rock-solid grasp of the fundamentals and the biggest force-multipliers they can get their hands on.
Bruno Aziza
JUNE 17, 2018
Establishing trust in data is critical. Organizations are now employing AI, Machine Learning, Blockchain to ensure data reliability and integrity.
IBM Big Data Hub
NOVEMBER 15, 2018
If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all.
ScienceSoft
APRIL 3, 2018
Our big data consultants have come up with an easy guide to real-time big data analytics. We explain the term and describe a typical architecture, as well as share our thoughts about whether real-time analytics can be a competitive advantage.
Occam's Razor
MARCH 19, 2018
I worry about data’s last-mile gap a lot. As a lover of data-influenced decision making, perhaps you worry as well. A lot of hard work has gone into collecting the requirements and implementation. An additional massive investment was made in the effort to perform ninja like analysis. The end result was a collection trends and insights. The last-mile gap is the distance between your trends and getting an influential company leader to take action.
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Dashboard design can mean the difference between users excitedly embracing your product or ignoring it altogether. Great dashboards lead to richer user experiences and significant return on investment (ROI), while poorly designed dashboards distract users, suppress adoption, and can even tarnish your project or brand. That’s one of the many reasons we wrote The Definitive Guide to Dashboard Design—to help you avoid common pitfalls, including… Cramming too much onto one screen and expecting the u
O'Reilly on Data
NOVEMBER 13, 2018
Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Let’s begin by looking at the state of adoption.
Rocket-Powered Data Science
JULY 30, 2018
Machine Learning is (or should be) a core component of any marketing program now, especially in digital marketing campaigns. The following insightful quote by Dan Olley (EVP of Product Development and CTO at Elsevier) sums up the urgency and criticality of the situation: “If CIOs invested in machine learning three years ago, they would have wasted their money.
Birst BI
DECEMBER 18, 2018
According to Gartner, more than 3,000 CIOs ranked Business Intelligence (BI) and Analytics as the top differentiating technology for their organizations. If BI and Analytics is such a game-changer, then why is the average adoption rate in organizations only 32%? Despite the efforts of Cloud BI vendors making it easier for users to acquire, explore, and analyze data sources without IT dependency, lack of data literacy and analytic skills still hinder widespread adoption for data-driven decision m
Timo Elliott
DECEMBER 11, 2018
These are some quick answers to some common questions I get about Business Intelligence, Big Data, and Analytics: Big Data. The term has been around for quite some time. Why is it still important for innovative businesses? It’s clear that data is one of the most important assets of the future. Organizations want to optimize their end-to-end customer experience, to improve productivity, and to engage the workforce in new ways.
Speaker: Lynnette Khalfani-Cox, The Money Coach®
Niche markets represent a huge opportunity for the financial services industry in America. From college students and women to communities of color and low-to-moderate-income households, niche populations have specialized financial needs – but they often underutilize many valuable financial products and services. How can you better connect with these consumers?
MLWhiz
DECEMBER 16, 2018
With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning. For those who don’t know, Text classification is a common task in natural language processing, which transforms a sequence of text of indefinite length into a category of text. How could you use that?
Boris Evelson
MARCH 2, 2018
In most enterprises, data access is a fait accompli: 72% of global data and analytics decision makers say that they can access the data they need to obtain insights in a timely manner. However, even the most modern BI tools that make data more accessible still require significant subject matter expertise to find the right […].
The Unofficial Google Data Science Blog
JULY 17, 2018
by BILL RICHOUX Critical decisions are being made continuously within large software systems. Often such decisions are the responsibility of a separate machine learning (ML) system. But there are instances when having a separate ML system is not ideal. In this blog post we describe one of these instances — Google search deciding when to check if web pages have changed.
Bruno Aziza
OCTOBER 29, 2018
Since the concept of “machines learning” was introduced in the 1950s, the field has gone from a cryptic domain understood by a few (Turing, Markov, Legendre, Laplace or Bayes) to a technology that every company must deploy.
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