Sep 17, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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SQL Database  Source

[ AnalyticsWeek BYTES]

>> Speed and Trust with Azure Synapse Analytics by analyticsweekpick

>> Mastering Deep Learning with Self-Service Data Science for Business Users by jelaniharper

>> UX Says, UI Says by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

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If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:What is an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset
A: Outliers:
– An observation point that is distant from other observations
– Can occur by chance in any distribution
– Often, they indicate measurement error or a heavy-tailed distribution
– Measurement error: discard them or use robust statistics
– Heavy-tailed distribution: high skewness, can’t use tools assuming a normal distribution
– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean
– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:
– No rigid mathematical method
– Subjective exercise: be careful
– Boxplots
– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:
– Depends on the cause
– Retention: when the underlying model is confidently known
– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cook’s distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesn’t perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:
– Mahalanobi’s distance
– Used to calculate the distance between two random vectors
– Difference with Euclidean distance: accounts for correlations
– Discard them

Source

[ VIDEO OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

 @JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

 Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

Subscribe 

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[ FACT OF THE WEEK]

IDC Estimates that by 2020,business transactions on the internet- business-to-business and business-to-consumer – will reach 450 billion per day.

Sourced from: Analytics.CLUB #WEB Newsletter

The Qualcomm FTC Case Goes To Appeal: The Reasons Qualcomm Should Prevail

I’ve covered a lot of trials over the years, and it has been rare that I’ve seen a US regulatory agency misbehave as much as the FTC (Federal Trade Commission) has in this case. It started as a result of Apple allegedly fabricating evidence and then presenting to a reduced panel of outgoing commissioners during […]

The post The Qualcomm FTC Case Goes To Appeal: The Reasons Qualcomm Should Prevail appeared first on TechSpective.

Source: The Qualcomm FTC Case Goes To Appeal: The Reasons Qualcomm Should Prevail by administrator

White paper: Data integrity in an uncertain world – how to achieve operational resilience

Any successful business must show a level of operational resilience in challenging times.

Whether obstacles arise from internal factors such as expansion and restructuring, or external factors such as increased trading pressures, firms must be able to adapt and thrive operationally.

Is your operations function effective?

This paper explores how data integrity is of paramount importance to successful, resilient firms and examines how automating your data management processes can help reduce costs and eliminate the potential for human error.

Fill in your details to download your free copy:




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Sep 10, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> How Manual vs. Automated Occupancy Counting Makes Or Breaks Your Business by analyticsweekpick

>> From Crisis to Competitive Advantage with Data Catalog Collaboration by analyticsweekpick

>> March 20, 2017 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:How do you control for biases?
A: * Choose a representative sample, preferably by a random method
* Choose an adequate size of sample
* Identify all confounding factors if possible
* Identify sources of bias and include them as additional predictors in statistical analyses
* Use randomization: by randomly recruiting or assigning subjects in a study, all our experimental groups have an equal chance of being influenced by the same bias

Notes:
– Randomization: in randomized control trials, research participants are assigned by chance, rather than by choice to either the experimental group or the control group.
– Random sampling: obtaining data that is representative of the population of interest

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data matures like wine, applications like fish. – James Governor

[ PODCAST OF THE WEEK]

@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

 @JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter

Conversation on racism and robotics

Talking about racism and it’s impact on robotics and roboticists was the first conversation in our new biweekly online discussion series “Society, Robots and Us” on alternate Tuesdays at 6pm PDT. It was a generous, honest and painful discussion that I hope has left a lasting impact on everyone who listened. There is systemic racism in America, and this does have an impact on robotics and roboticists in many many ways.

The US Senator Elizabeth Warren in conversation today with Alicia Garza from Black Futures Lab said, “America was founded on principles of liberty and freedom, but it was built on the backs of enslaved people. This is a truth we must not ignore. Racism and white supremacy have shaped every crucial aspect of our economy, and our political system for generations now.”

The speakers in ‘Society, Robots and Us’ were Chad Jenkins, Monroe Kennedy III, Jasmine Lawrence, Tom Williams, Ken Goldberg and Maynard Holliday explored the impact of racism in their experiences in robotics, along with explicit information about changes that we all can make. And we discussed learnings for allies and supporters and what a difference support could make. Please listen to the full discussion but Chad Jenkin’s notes capture some of the critical insights.

[youtube https://www.youtube.com/watch?v=_PHMUJdPs_o?feature=oembed&w=500&h=281]

“I have been in computing for nearly 30 years and a roboticist for over 20 years.  Thus, I have been able to experience firsthand many of the systemic problems that face our field. Let me build on some of the recommendations from the blackincomputing.org open letter and call to action. “

In particular, I believe we can bring equal opportunity to STEM quickly by upholding Title VI of the Civil Rights Act of 1964 and Title IX of the Educational Amendments of 1972 for institutions receiving federal funding, and public funding more generally.  We now incentivize systemic disparate impacts in STEM.

Like law enforcement, university faculty are asked to do too much. Given our bandwidth limits, we have to make hard choices about what gets our attention and effort.

This creates a dilemma in every faculty member about whether to bolster their own personal advancement (by gaining social acceptance in the establishments of the field that control access to funding, hiring, and publishing through peer review) or further create and extend opportunity to others (taking a professional sacrifice to provide mentorship and empathy to future generations towards broadening participation in the STEM workforce).

It is clear STEM incentivizes the former given systemic exclusion of underrepresented minorities, with disastrous results thus far.

I believe we are a vastly better society with the upholding of Title VII of the Civil Rights Act of 1964 yesterday by the Supreme Court to prohibit employment discrimination against LGBTQ+ citizens.  Discrimination is wrong.  My hope is that we can apply this same standard and attention for Title VI of this statue to outcomes in STEM. This is not an issue of altruism, it reflects our true values at a nation and affects the quality of our work and its impact on the world.

There are placeholder measures that can be enacted to incentivize equal opportunity.  For example, universities could decline sabbatical and leave requests from faculty seeking to collaborate with companies that have failed to provide equal opportunity, such as OpenAI and Google DeepMind.

To achieve systemic fairness in robotics, however, we must go beyond token gestures to address the causal factors of inequity rooted in the core economic incentives of our universities.  It is universities that are the central ladder to opportunity through the development of future leaders, innovators, and contributors to our society.

We have the tools at hand today to create equal opportunity in STEM.  The question is whether we have the will.

Equal opportunity cannot be true for anyone unless equal opportunity is true for everyone.

Odeste Chadwicke Jenkins, Associate Professor University of Michigan Robotics Institute

Our next episode of “Society, Robots and Us” on June 30 is going to discuss the role and the roll out of killer robots, but we’ll be coming back to talk more about racism, diversity and inclusion in robotics because we’ve only just scratched the surface.

Source by analyticsweekpick

Chinese regulators consider AliPay and WeChat antitrust probe

The Chinese central bank is considering the launch of an antitrust probe into the conduct of digital payments giants Alipay and WeChat Pay.

AliPay sign

Alipay and WeChat have a firm grip on the Chinese payments market

According to Reuters sources, the State Council has gathered evidence on the two firms for more than a month.

It plans to investigate whether the payments giants have used their dominant market position to prevent competition from gaining a significant foothold in China.

Both Alipay, owned by Ant Group and Alibaba, and Tencent Holding’s WeChat Pay are lobbying government officials to prevent the probe.

The investigation would be ill-timed for Ant Group, as it pursues a dual listing in Hong Kong and Shanghai. The firm is seeking a $200 billion valuation.

Reuters sources report that the People’s Bank of China (PBOC) is taking the suggestion of an investigation “very seriously.”

Saturation

AliPay and WeChat enable payment through the scanning of a QR code. The two payments systems dominate a Chinese payment sector valued at more than $16 trillion.

Alipay has 520 million active users, while competitor WeChat Pay claims more than 800 million. The latter also reports more than one billion commercial transactions per day.

Tencent’s mobile payment systems have a market penetration of 84%, with AliPay marginally behind with a 63.6% penetration, according to Ipsos data.

The closest competitor to either is China Unionpay’s Quick Pass solution, with 11.6%.

Chinese authorities have planned to reign in the dominance of the payment giants for some time. In April 2018 the PBOC announced intentions to standardise and regulate QR code payments.

Related: Mastercard extends cross-border services in China with Bank of Shanghai

Source

Sep 03, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy check  Source

[ AnalyticsWeek BYTES]

>> The Four Horsemen of SASE (Secure Access Service Edge) by administrator

>> R now supported in Azure SQL Database by analyticsweekpick

>> Social Media Analytics – What to Measure for Success? by thomassujain

Wanna write? Click Here

[ FEATURED COURSE]

Artificial Intelligence

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This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances…. more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:How would you define and measure the predictive power of a metric?
A: * Predictive power of a metric: the accuracy of a metric’s success at predicting the empirical
* They are all domain specific
* Example: in field like manufacturing, failure rates of tools are easily observable. A metric can be trained and the success can be easily measured as the deviation over time from the observed
* In information security: if the metric says that an attack is coming and one should do X. Did the recommendation stop the attack or the attack never happened?

Source

[ VIDEO OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp posts—for support rather than for illumination. – Andrew Lang

[ PODCAST OF THE WEEK]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

Numpy Tutorial for Beginners

What is NumPy? Numpy is a tool for mathematical computing and data preparation in Python. It can be utilized to perform a number of mathematical operations on arrays such as trigonometric, statistical and algebraic routines. This library provides many useful features including handling n-dimensional arrays, broadcasting, performing operations, data generation, etc., thus, it’s the fundamental […]

The post Numpy Tutorial for Beginners appeared first on GreatLearning.

Source by administrator

Aug 27, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data analyst  Source

[ AnalyticsWeek BYTES]

>> Getting Started with Predictive Analytics in Construction by analyticsweekpick

>> Predictive Analytics in Action: 5 Industry Examples by analyticsweek

>> How to integrate big data In security systems? by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Explain what a local optimum is and why it is important in a specific context,
such as K-means clustering. What are specific ways of determining if you have a local optimum problem? What can be done to avoid local optima?

A: * A solution that is optimal in within a neighboring set of candidate solutions
* In contrast with global optimum: the optimal solution among all others

* K-means clustering context:
It’s proven that the objective cost function will always decrease until a local optimum is reached.
Results will depend on the initial random cluster assignment

* Determining if you have a local optimum problem:
Tendency of premature convergence
Different initialization induces different optima

* Avoid local optima in a K-means context: repeat K-means and take the solution that has the lowest cost

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[ FACT OF THE WEEK]

Facebook users send on average 31.25 million messages and view 2.77 million videos every minute.

Sourced from: Analytics.CLUB #WEB Newsletter

Getting Started with Predictive Analytics in Construction

How current and historical data is bringing future insights to construction projects, and changing the course of the industry forever.

More and more, the industry is acknowledging that data plays an important role in construction. Still, projects produce massive quantities of data, and only a small portion of it is being used to inform decisions. One way of using data, however, is becoming more advanced and increasingly accessible for both small and large contractors: predictive construction analytics.

Using predictive analytics can help reduce risk and improve your decision making process. Read on to discover what predictive construction analytics are, why they’re important to the industry, and how you can start using these tools for better project outcomes.

Predictive Analytics 101

At the heart of predictive analytics is the ability to use current and historical data to forecast future outcomes. In other words, these tools make predictions about the future using techniques including statistical modeling and machine learning.

These techniques give the future insights generated by predictive analytics a significant degree of precision, especially with the use of machine learning. By generating algorithms based on current and historical data, machine learning is designed to solve business problems and streamline decision making, allowing you to choose the best path forward for your project.

Why Are Predictive Analytics Becoming More Important in Construction?

Each day, construction teams are managing a number of moving parts on site, from subcontractors to change orders, and beyond. The more complex construction projects become, especially in the era of social distancing and increased remote work, the more you need the kinds of tools that can take all available information into account and guide your next big decision. Enter, predictive construction analytics.

“What technology like data analytics, and even more specifically machine learning and artificial intelligence, is doing for us [construction] is unlocking our ability to harness the project data – organize it, interpret it to uncover patterns faster,” said Allison Scott, Director, Construction Thought Leadership & Customer Marketing at Autodesk, on a recent webinar. 

These tools can reduce issues, lower costs, and mitigate risk for construction projects by making the work more predictable. As an example, consider the preconstruction process. One of the biggest challenges for design teams during preconstruction is creating a realistic budget that can be applied to current and future project stages. On the construction side, teams frequently find it hard to manage the budget they receive from a project’s architecture or contractor teams. Predictive construction analytics allow preconstruction teams to create budgets that account for all possible factors that could emerge during a project, including regional labor and material costs, among other items.

Predictive analytics are poised to be a big part of the construction industry’s future. According to McKinsey & Company, solutions using predictive analytics, machine learning, and artificial intelligence will likely bring about major changes to how engineering and construction firms bid on and execute projects. Specifically, predictive analytics can help construction professionals answer questions around whether they should bid on a project, and if so, how much. These tools can also help determine if subcontractors’ bids are reasonable, and if a project is about to run into challenges. Predictive construction analytics can break down the costs and profitability of prior jobs, examine the accuracy of subcontractor bids received, and determine when and how past projects ran into trouble. All of this information can then generate the answers you’re looking for, before a new job has even begun.

Tips for Getting Started with Predictive Analytics in Construction

1. Hone in on our focus area

The best way to start implementing predictive analytics solutions for your next construction project is by first honing in on your area of focus. Going too broad in your adoption of predictive analytics can set you back, resulting in wasted time and disorganization. You should first determine one or two key focus areas where you want to bring in more predictability to your project. For example, do you want to better anticipate and mitigate safety and quality issues? Or perhaps you’d like more visibility into project risk, like budget overruns or labor challenges? Identify where you need more predictability and select a solution from there.

2. Find the right tools to measure

When it comes time to select the best predictive analytics tools, finding the right solutions based on your focus area can help you achieve your overall project goals. The right software for the construction industry can help with risk management around cost, schedule, quality, and safety. This solution can also help you evaluate subcontractor performance and mitigate day-to-day risks for future projects. Predictive analytics can also help safety managers understand the leading indicators to potential behavioral and environmental hazards, and take proactive measures before incidents arise. Moreover, a predictive analytics solution tailored to the construction industry can help executives identify risks across projects and take measures to improve project performance and set any job up for success.

3. Standardize and centralize

Finally, getting the most out of predictive analytics requires you to centralize and standardize your data. The higher quality your data input is, the higher quality, and thus better able to predict, your data output is. This is why it’s essential to establish a centralized data platform with standardized ways to input and structure information for accelerated accuracy in the predictive analytics solutions you use. Implementing a common data environment is one way to achieve this by allowing your team to optimize and utilize information when it’s needed most. Moreover, good data empowers future technologies, including machine learning and AI, to accelerate project delivery.

Predictive Analytics in Action

The use of predictive analytics tools in the construction industry has contributed to a number of successful project outcomes. Over the last few years, BAM Ireland, an operating company Royal BAM Group nv (BAM), has utilized BIM 360 Construction IQ, a predictive analytics software for the construction industry, to manage risk and streamline its workflows.

The software flagged a number of inconsistencies in BAM Ireland’s documents, including issues that were labeled as open despite being addressed and closed by project teams. Additionally, the system identified a number of critical issues that remained open, allowing the BAM Ireland team to address them before they became major challenges.

“A huge problem here for us is overdue issues,” Michael Murphy, digital construction operations manager at BAM Ireland, explained.

“If we fix these problems early, they’re cheaper to fix. If we start with a $25 issue that could be fixed in design, if that gets to construction, that increases to $250 to fix. If it’s spotted during snagging that will be $2,500. If it gets into operation it could cost $250,000. Knowing where the issues are early on is essential.

“If this system [Construction IQ] is taking a lot of heavy lifting away it’s giving us a laser sharp focus in terms of what the genuine health and safety issues are. We don’t have to explain how it works to the team; it just happens! Not only is it pointing at major issues, but it’s giving us more time.”

BAM Ireland has seen a 20% improvement in on-site quality and safety, and a 25% increase in staff time spent on high-risk issues since adopting Construction IQ as its predictive analytics solution. What’s more, as Construction IQ continues analyzing every BAM Ireland project, it is refining its prediction capabilities and improving the accuracy of its insights.

Predict Success Today

Predictive analytics can help you get organized and put your current and past project information to work toward success in the future. Finding the right predictive analytics solution for your next construction project starts with discovering how these tools can work for you. Learn more about available solutions and put predictive analytics to work for all of your future construction projects.

The post Getting Started with Predictive Analytics in Construction appeared first on Autodesk Construction Cloud Blog.

Originally Posted at: Getting Started with Predictive Analytics in Construction by analyticsweekpick