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Looking for a refresher on R? Checkout the following talk covering in depth about R and answering some of the important question concerning it’s adoption.

A presentation on the history, design, and use of R. The talk will focus on companies that use and support R, use cases, where it is going, competitors, advantages and disadvantages, and resources to learn more about R. Speaker Bio

Joseph Kambourakis has been the Lead Data Science Instructor at EMC for over two years. He has taught in eight countries and been interviewed by Japanese and Saudi Arabian media about his expertise in Data Science. He holds a Bachelors in Electrical and Computer Engineering from Worcester Polytechnic Institute and an MBA from Bentley University with a concentration in Business Analytics.

Sponsor: MediaMath | HackReduce

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Originally Posted at: The History and Use of R

Big data analytics is being adopted at a rapid rate across every industry. It enables businesses to manage and analyze vast amounts of data at ultrafast speeds, and obtain valuable insights that can improve their decision-making processes.

One of the industries that are reaping the benefits of this technology is the construction industry. Construction companies are using big data to perform a wide range of tasks, from data management to pre-construction analysis.

Here is a look at how big data is transforming the construction industry…

How Construction Companies are Leveraging Big Data Analytics

Handling Large Amounts of Data

Many construction companies need to juggle many projects at the same time, and they have to collect, produce, organize and analyze a lot of data because of these projects.

Other than creating work reports and progress reports, they also have to manage technical information on various aspects of their projects. All the unstructured data that is collected and generated can burden their databases.

Big data solutions make it possible for construction companies to process massive amounts of data at unprecedented speeds, enabling them to save substantial time and effort, and focus more on the job site instead of IT issues.

Depending on which big data tools they use, they can improve almost every data-related process, from database management to report creation.

According to an article entitled “How Big Data is Transforming the World of Finance“, big data can help businesses create reports on their operations more frequently, or in real time, so that they can make well-informed decisions on a consistent basis.

Predicting Risk

In order to plan and execute projects effectively, construction companies need to be able to predict risks accurately through intelligent use of data.

By implementing big data analytics, they can gain valuable insights that enable them to improve cost certainty, identify and avoid potential problems, and find opportunities for efficiency improvements.

One example of a construction company that is using big data analytics to predict risk is Democrata.

Democrata conducts surveys to gain a better understanding of the impact of new roads, high rail links and other construction projects, and uses big data analytics to perform searches and queries on data sets to obtain insights that can lead to better and faster decision-making.

Solving Problems

The ability to solve problems quickly can contribute significantly to the successful completion of construction projects.

Liberty Building Forensics Group is a company that investigates and solves construction and design problems, and it has provided consultation on over 500 projects worldwide, including a Walt Disney project.

According to the company, forensic issues usually occur in major construction projects, and they can cause big problems, such as failure to meet deadlines, if they are not properly assessed.

In order to fix forensic issues efficiently, construction companies have to be able to collect the right data in an organized way and make the data accessible to the right people at the right time. This can be achieved through the implementation of big data solutions.

Presently, big data analytics is relatively immature in terms of functionality and robustness.

As it continues to become more advanced, it will be more widely adopted in the construction industry.

John McMalcolm is a freelance writer who writes on a wide range of subjects, from social media marketing to technology.

Originally posted via “How big data is transforming the construction industry”

Originally Posted at: How big data is transforming the construction industry

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A confluence of new capabilities is creating an innovative, more precise approach to performance improvement. New approaches include advanced analytics, refined sales competency and behavioral models, adaptive learning, and multiple forms of technology enablement. In a prior post (The Myth of the Ideal Sales Profile) we explored an emerging new paradigm that is disrupting traditional thinking with respect to best practices: the world according to YOU.

However, with only 17% of sales organizations leveraging sales talent analytics (TDWI Research), it seems that most CSO’s and their HR business partners are gambling — using intuition as the basis for making substantial investments in sales development initiatives. If the gamble doesn’t pay off, then the investment is wasted.

Is your sales talent aligned to your company’s strategy of increasing revenue? According to the Conference Board, 73% of CEO’s say no. This lack of alignment is the main reason why 86% of CSO’s expect to miss their 2015 revenue targets (CSO Insights). The ability to properly align your sales talent to your company’s business goals is the difference between being in the 86% or the 14%.

What Happens When You Assume?

Historically, sales and Human Resource leaders based sales talent alignment decisions — both development of the existing team and acquisition of future talent — on assumptions and somewhat subjective data.

Common practices include:

• Polling the field to determine the focus for sales training
• Hiring sales talent based largely on the subjective opinion of interviewers
• Defining your “ideal seller profile” based on the guidance of industry pundits
• Making a hiring decision based on the fact that the candidate made Achiever’s Club 3 of the last 5 years at their previous company
• Deploying a sales training program based on what a colleague did at their last company

Aligning sales talent based on any of the above is likely to land your company in the 86% because these approaches fail far more times than they succeed. They fail to consider the many cause-and-effect elements that impact success in your company, in your markets, for your products, and for your customers. As proof of their low success rate, a groundbreaking study by ES Research found that 90% of sales training [development initiatives] had no lasting impact after 120 days. And the news isn’t any better when it comes to sales talent acquisition; Accenture reports that the average ramp-up time for new reps is 7-12 months.

Defining YOUR Ideal Seller Profile(s)

So how does your organization begin to apply the “new way” (see illustration below) as an approach to optimize sales performance? It begins with zeroing in on the capabilities of your salespeople that align most closely to the specific goals of your business. In essence, it means understanding what the YOUR ideal seller profiles are.

Applying the new way begins with specific business goals of your company. What if market share growth was the preeminent strategic goal for your organization? Would it not be extremely valuable to understand which sales competencies were most likely to impact that aspect of your corporate strategy? The obvious answer is yes; and the obvious question is how align and optimize sales to drive increased market share?

How does a CSO identify where to target development in order to have the biggest impact on business results?

By using facts as the basis for these substantial investments. Obtaining facts requires several essential ingredients. The first is a rigorous, comprehensive model for sales competencies; that is, a well-defined model of “what good looks like” for a broad range of sales competencies. This model can be adapted for a specific selling organization, and provides the baseline sales-specific assessments (personality, knowledge, cognitive ability, behavior, etc.).

Then, by applying advanced analytics, including Structural Equations Modeling (SEM) – we can begin to identify cause-effect relationships between specific competencies and the metrics and goals of YOUR organization. With SEM, CSO’s can statistically identify the knowledge and behavior that set top-performers apart from the rest of their team. With this valuable insight, the organization can now align both talent development and acquisition to the company’s most important business goals.

Sales Talent Analytics Provide Proof

Times have changed. The days of aligning sales talent based on gut feel, assumptions or generally accepted best-practices are over. By leveraging sales talent analytics, today’s sales leader can apply a proven 3-step approach to stop gambling and get the facts to statistically pinpoint where to focus development of the sales team, quantifiably measure the business impact / ROI of that development, and improve the quality of new hires. But buyer beware; not all analytical approaches are equal. The vast majority leverage correlation-based analytics which can lead to erroneous conclusions.

By the way we’re not eschewing well designed research that provides insights into broader application of best practices. Aberdeen Group found that best-in-class sales teams that leverage data and analytics increased team quota attainment 12.3% YOY (vs. 1% for an average company) and increased average deal size 8% YOY (vs. 0.8%)

It’s time to define the ideal seller profile for YOUR company. In our next post in this series, we answer the question – how do we capitalize on that understanding to drive the highest impact on our business goals?

Source: Aligning Sales Talent to Drive YOUR Business Goals by analyticsweekpick

HHS’s Price Affirms Commitment to Health Data Innovation: Secretary Price emphasized the need to decrease the burden on physicians.

Mayo Clinic uses analytics to optimize laboratory testing: The company Viewics makes software for the facility, which uses it to look for patterns and increase efficiency.

Nearly 10,000 Global Problem Solvers Yield Winning Formulas to Improve Detection of Lung Cancer in Third Annual Data Science Bowl: The winners of the competition, which challenged contestants to accurately diagnose lung scans, were announced.

Gene sequencing at Yale finding personalized root of disease; new center opens in West Haven: The Center for Genomic Analysis at Yale opened and is intended to help diagnose patients.

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Big data — the cutting edge of modern marketing or an overhyped buzzword? Columnist Kohki Yamaguchi dives in to some of the limitations of user-centered data.

As everyone knows, “big data” is all the rage in digital marketing nowadays. Marketing organizations across the globe are trying to find ways to collect and analyze user-level or touchpoint-level data in order to uncover insights about how marketing activity affects consumer purchase decisions and drives loyalty.

In fact, the buzz around big data in marketing has risen to the point where one could easily get the illusion that utilizing user-level data is synonymous with modern marketing.

This is far from the truth. Case in point, Gartner’s hype cycle as of last August placed “big data” for digital marketing near the apex of inflated expectations, about to descend into the trough of disillusionment.

It is important for marketers and marketing analysts to understand that user-level data is not the end-all be-all of marketing: as with any type of data, it is suitable for some applications and analyses but unsuitable for others.

Following is a list describing some of the limitations of user-level data and the implications for marketing analytics.

1. User Data Is Fundamentally Biased

The user-level data that marketers have access to is only of individuals who have visited your owned digital properties or viewed your online ads, which is typically not representative of the total target consumer base.

Even within the pool of trackable cookies, the accuracy of the customer journey is dubious: many consumers now operate across devices, and it is impossible to tell for any given touchpoint sequence how fragmented the path actually is. Furthermore, those that operate across multiple devices is likely to be from a different demographic compared to those who only use a single device, and so on.

User-level data is far from being accurate or complete, which means that there is inherent danger in assuming that insights from user-level data applies to your consumer base at large.

2. User-Level Execution Only Exists In Select Channels

Certain marketing channels are well suited for applying user-level data: website personalization, email automation, dynamic creatives, and RTB spring to mind.

In many channels however, it is difficult or impossible to apply user data directly to execution except via segment-level aggregation and whatever other targeting information is provided by the platform or publisher. Social channels, paid search, and even most programmatic display is based on segment-level or attribute-level targeting at best. For offline channels and premium display, user-level data cannot be applied to execution at all.

3. User-Level Results Cannot Be Presented Directly

More accurately, it can be presented via a few visualizations such as a flow diagram, but these tend to be incomprehensible to all but domain experts. This means that user-level data needs to be aggregated up to a daily segment-level or property-level at the very least in order for the results to be consumable at large.

4. User-Level Algorithms Have Difficulty Answering “Why”

Largely speaking, there are only two ways to analyze user-level data: one is to aggregate it into a “smaller” data set in some way and then apply statistical or heuristic analysis; the other is to analyze the data set directly using algorithmic methods.

Both can result in predictions and recommendations (e.g. move spend from campaign A to B), but algorithmic analyses tend to have difficulty answering “why” questions (e.g. why should we move spend) in a manner comprehensible to the average marketer. Certain types of algorithms such as neural networks are black boxes even to the data scientists who designed it. Which leads to the next limitation:

5. User Data Is Not Suited For Producing Learnings

This will probably strike you as counter-intuitive. Big data = big insights = big learnings, right?

Wrong! For example, let’s say you apply big data to personalize your website, increasing overall conversion rates by 20%. While certainly a fantastic result, the only learning you get from the exercise is that you should indeed personalize your website. While this result certainly raises the bar on marketing, but it does nothing to raise the bar formarketers.

Actionable learnings that require user-level data – for instance, applying a look-alike model to discover previously untapped customer segments – are relatively few and far in between, and require tons of effort to uncover. Boring, ol’ small data remains far more efficient at producing practical real-world learnings that you can apply to execution today.

6. User-Level Data Is Subject To More Noise

If you have analyzed regular daily time series data, you know that a single outlier can completely throw off analysis results. The situation is similar with user-level data, but worse.

In analyzing touchpoint data, you will run into situations where, for example, a particular cookie received – for whatever reason – a hundred display impressions in a row from the same website within an hour (happens much more often than you might think). Should this be treated as a hundred impressions or just one, and how will it affect your analysis results?

Even more so than “smaller” data, user-level data tends to be filled with so much noise and potentially misleading artifacts, that it can take forever just to clean up the data set in order to get reasonably accurate results.

7. User Data Is Not Easily Accessible Or Transferable

Because of security concerns, user data cannot be made accessible to just anyone, and requires care in transferring from machine to machine, server to server.

Because of scale concerns, not everyone has the technical know-how to query big data in an efficient manner, which causes database admins to limit the number of people who has access in the first place.

Because of the high amount of effort required, whatever insights that are mined from big data tend to remain a one-off exercise, making it difficult for team members to conduct follow-up analyses and validation.

All of these factors limit agility of analysis and ability to collaborate.

So What Role Does Big Data Play?

So, given all of these limitations, is user-level data worth spending time on? Absolutely — its potential to transform marketing is nothing short of incredible, both for insight generation as well as execution.

But when it comes to marketing analytics, I am a big proponent of picking the lowest-hanging fruit first: prioritizing analyses with the fastest time to insight and largest potential value. Analyses of user-level data falls squarely in the high-effort and slow-delivery camp, with variable and difficult-to-predict value.

Big data may have the potential to yield more insights than smaller data, but it will take much more time, consideration, and technical ability in order to extract them. Meanwhile, there should be plenty of room to gain learnings and improve campaign results using less granular data. I have yet to see such a thing as a perfectly managed account, or a perfectly executed campaign.

So yes, definitely start investing in big data capabilities. Meanwhile, let’s focus as much if not more in maximizing value from smaller data.

Note: In this article I treated “big data” and “user-level data” synonymously for simplicity’s sake, but the definition of big data can extend to less granular but more complex and varied data sets.

Originally posted via “7 Limitations Of Big Data In Marketing Analytics”

Originally Posted at: 7 Limitations Of Big Data In Marketing Analytics by analyticsweekpick