A beginners guide to data analytics

This is Part I of our three-part June 2015 print cover story on healthcare analytics. Part I focuses on the first steps of launching an analytics program. Part II focuses on intermediate strategies, and Part III focuses on the advanced stages of an analytics use.

This first part may sting a bit: To those healthcare organizations in the beginning stages of rolling out a data analytics program, chances are you’re going to do it completely and utterly wrong.

At least that’s according to Eugene Kolker, chief data scientist at Seattle Children’s Hospital, who has been working in data analysis for the past 25 years. When talking about doing the initial metrics part of it, “The majority of places, whether they’re small or large, they’re going to do it wrong,” he tells Healthcare IT News. And when you’re dealing with people’s lives, that’s hardly something to take lightly.

Kolker would much prefer that not to be the case, but from his experiences and what he’s seen transpire in the analytics arena across other industries, there’s some unfortunate implications for the healthcare beginners.

“What’s the worst that can happen if Amazon screws up (with analytics)?…It’s not life and death like in healthcare.”

 

But it doesn’t have to be this way. Careful, methodical planning can position an organization for success, he said. But there’s more than a few things you have to pay serious attention to.

First, you need to get executive buy in. Data analytics can help the organization improve performance in myriad arenas. It can save money in the changing value-based reimbursement world. Better yet, it can save lives. And, if you’re trying to meet strategic objectives, it may be a significant part of the equation there too.

As Kolker pointed out in a presentation given at the April 2015 CDO Summit in San Jose, California, data and analytics should be considered a “core service,” similar to that of finance, HR and IT.

Once you get your executive buy in, it’s on to the most important part of it all: the people. If you don’t have people who have empathy, if you don’t have a team who communicate and manage well, you can count on a failed program, said Kolker, who explained that this process took him years to finally get right. People. Process. Technology – in that order of importance.

“Usually data scientists are data scientists not because they like to work with people but because they like to work with data and computers, so it’s a very different mindset,” he said. It’s important, however, “to have those kind of people who can be compassionate,” who can do analysis without bias.

And why is that? “What’s the worst that can happen if Amazon screws up (with analytics)?” Kolker asked. “It’s not life and death like in healthcare,” where “it’s about very complex issues about very complex people. … The pressure for innovation is much much higher.”

[Part II: Clinical & business intelligence: the right stuff]

[Part III: Advanced analytics: All systems go]

When in the beginning stages of anything analytics, the aim is to start slow but not necessarily to start easy, wrote Steven Escaravage and Joachim Roski, principals at Booz Allen, in a 2014 Health Affairs piece on data analytics. Both have worked on 30 big data projects with various federal health agencies and put forth their lessons learned for those ready to take a similar path.

One of those lessons?

Make sure you get the right data that addresses the strategic healthcare problem you’re trying to measure or compare, not just the data that’s easiest to obtain.

“While this can speed up a project, the analytic results are likely to have only limited value,” they explained. “We have found that when organizations develop a ‘weighted data wish list’ and allocate their resources toward acquiring high-impact data sources as well as easy-to-acquire sources, they discover greater returns on their big data investment.”

So this may lead one to ask: What exactly is the right data? What metrics do you want? Don’t expect a clear-cut answer here, as it’s subjective by organization.

First, “you need to know the strategic goals for your business,” added Kolker. “Then you start working on them, how are you going to get data from your systems, how are you going to compare yourself outside?”

In his presentation at the CDO Summit this April, Kolker described one of Seattle Children’s data analytics projects that sought to evaluate the effectiveness of a vendor tool that predicted severe clinical deterioration, or SCD, of a child’s health versus the performance of a home-grown internal tool that had been used by the hospital since 2009.

After looking at cost, performance, development and maintenance, utility, EHR integration and algorithms, Kolker and his team found for buy versus build, using an external vendor tool was not usable for predicting SCD but that it could be tested for something else. And furthermore, the home-grown tool needed to be integrated into the EHR.

Kolker and his team have also helped develop a metric to identify medically complex patients after the hospital’s chief medical officer came to them wanting to boost outcomes for these patients. Medically complex patients typically have high readmissions and consume considerable hospital resources, and SCH wanted to improve outcomes for this group without increasing costs for the hospital.

For folks at the Nebraska Methodist Health System, utilizing a population risk management application that had a variety of metrics built in was a big help, explained Linda Burt, chief financial officer of the health system, in Healthcare IT News’ sister publication webinar this past April.

Katrina Belt

“The common ones you often hear of such as admissions per 1,000, ED visits per 1,000, high-risk high end imaging per 1,000,” she said. Using the application, the health system was able to identity that a specific cancer presented the biggest opportunity for cost alignment.

And health system CFO Katrina Belt’s advice? “We like to put a toe in the water and not do a cannon ball off the high dive,” she advised. Belt, the CFO at Baptist Health in Montgomery, Alabama, said a predictive analytics tool is sifting through various clinical and financial data to identify opportunities for improvement.

In a Healthcare Finance webinar this April, Belt said Baptist Health started by looking at its self-pay population and discovered that although its ER visits were declining, intensive care visits by patients with acute care conditions were up on upward trend.

Belt recommended starting with claims data.

“We found that with our particular analytics company, we could give them so much billing data that was complete and so much that we could glean from just the 835 and 837 file that it was a great place for us to start,” she said. Do something you can get from your billing data, Belt continued, and once you learn “to slide and dice it,” share with your physicians. “Once they see the power in it,” she said, “that’s when we started bringing in the clinical data,” such as tackling CAUTIs.

But some argue an organization shouldn’t start with an analytics platform. Rather, as Booz Allen’s Escaravage and Roski wrote, start with the problem; then go to a data scientist for help with it.

One federal health agency they worked with on an analytics project, for instance, failed to allow the data experts “free rein” to identify new patterns and insight, and instead provided generic BI reports to end users. Ultimately, the results were disappointing.

“We strongly encouraged the agency to make sure subject matter experts could have direct access to the data to develop their own queries and analytics,” Escaravage and Roski continued.  Overall, when in the beginning phases of any analytics project, one thing to keep in mind, as Kolker reinforced: “Don’t do it yourself.” If you do, “you’re going to fail,” he said. Instead, “do your homework; talk to people who did it.”

To read the complete article on Healthcar IT News, click here.

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3 Vendors Lead the Wave for Big Data Predictive Analytics

Enterprises have lots of solid choices for big data predictive analytics.

That’s the key takeaway from Forrester’s just released Wave for Big Data Predictive Analytics Solutions for the second quarter of 2015.

That being said, the products Forrester analysts Mike Gualtieri and Rowan Curran evaluated are quite different.

Data scientists are more likely to appreciated some, while business analysts will like others. Some were built for the cloud, others weren’t.

They all can be used to prepare data sets, develop models using both statistical and machine learning algorithms, deploy and manage predictive analytics lifecycles, and tools for data scientists, business analysts and application developers.

General Purpose

It’s important to note that there are plenty of strong predictive analytics solution providers that weren’t included in this Wave, and it’s not because their offerings aren’t any good.

Instead Forrester focused specifically on “general purpose” solutions rather than those geared toward more specific purposes like customer analytics, cross-selling, smarter logistics, e-commerce and so on. BloomReach, Qubit, Certona, Apigee and FusionOps, among others, are examples of vendors in the aforementioned categories.

The authors also noted that open source software community is driving predictive analytics into the mainstream. Developers have an abundant selection of API’s within reach that they can leverage via popular programming languages like Java, Python and Scala to prepare data and predictive models.

Not only that but, according to report, many BI platforms also offer “some predictive analytics capabilities.” Information Builders, MicroStrategy and Tibco, for example, integrate with R easily.

The “open source nature” of BI solutions like Birt, OpenText and Tibco Jaspersoft make R integration simpler.

Fractal Analytics, Opera Solutions, Teradata’s Think Big and Beyond The Art and the like also provide worthwhile solutions and were singled out as alternatives to buying software. The authors also noted that larger consulting companies like Accenture, Deloitte, Infosys and Virtuasa all have predictive analytics and/or big data practices.

In total, Forrester looked at 13 vendors: Alpine Data Labs, Alteryx, Agnoss, Dell, FICO, IBM, KNIME, Microsoft, Oracle, Predixion Software, RapidMiner, SAP and SAS.

Forrester’s selection criteria in the most general sense rates solution providers according to their Current Offering (components include: architecture, security, data, analysis, model management, usability and tooling, business applications) and Strategy (components include acquisition and pricing, ability to execute, implementation support, solution road map, and go-to-market growth rate.) Each main category carries 50 percent weight.

Leading the Wave

IBM, SAS and SAP — three tried and trusted providers — lead this Forrester Wave:.

IBM achieved perfect scores in the seven of the twelve criteria: Data, Usability and Tooling, Model Management, Ability to Execute, Implementation Support, Solution Road Map and Go-to Market Growth Rate. “With customers deriving insights from data sets with scores of thousands of features, IBM’s predictive analytics has the power to take on truly big data and emerge with critical insights,” note the report’s authors. Where does IBM fall short? Mostly in the Acquisition and Pricing category.

SAS is the granddaddy of predictive analytics and, like IBM, it achieved a perfect score many times over. It’s interesting to note that it scored highest among all vendors in Analysis. It was weighed down, however, by its strategy in areas like Go-to-Market Growth Rate and Acquisition and Pricing. This may not be as a big problem by next year, at least if Gartner was right in its most recent MQ on BI and Analytics Platforms Leaders, where it noted that SAS was aware of the drawback and was addressing the issue.

“SAP’s relentless investment in analytics pays off,” Forrester notes in its report. And as we’ve reiterated many times, the vendor’s predictive offerings include some snazzy differentiating features like analytics tools that you don’t have to be a data scientist to use, a visual tool that lets users analyze several databases at once, and for SAP Hana customers SAP’s Predictive Analytics Library (PAL) to analyze big data.

The Strong Performers

Not only does RapidMiner’s predictive analytics platform include more than 1,500 methods across all stages of the predictive analytics life cycle, but with a single click they can also be integrated into the cloud. There’s also a nifty “wisdom of the crowds” feature that Forrester singles out; it helps users sidestep mistakes made, by others, in the past and get to insights quicker. What’s the downside? Implementation support and security.

Alteryx takes the pains out of data prep, which is often the hardest and most miserable part of a data worker’s job. They also offer a tool for that helps data scientists collaborate with business users via a visual tool. Add to that an analytical apps gallery to help users share their data prep and modeling workflows with other users, and you’ve set a company up with what it needs to bring forth actionable insights. While Alteryx shines in areas like Data, Ability to Execute, and Go-to-Market Growth Rate, there’s room for improvement in Architecture and Security.

Oracle rates as a strong performer, even though it doesn’t offer a single purpose solution. Instead its Oracle SQL Developer tool includes a visual interface to allow data analysts to create analytical workflows and models, this according to Forrester. Not only that, but Oracle also takes advantage of open-source R for analysis, and has revised a number of its algorithms to take advantage of Oracle’s database architecture and Hadoop.

FICO, yes, Forrester’s talking about the credit scoring people, has taken its years of experience in actionable predictive analytics, built a solution and taken it to the Cloud where its use is frictionless and available to others. It could be a gem for data scientists who are continuously building and deploying models. FICO’s market offering has lots of room for improvement in areas like Data and Business Applications, though.

Agnoss aims to make it easier for non-data scientists to get busy with predictive analytics tools via support services and intuitive interfaces for developing predictive models. While the solution provider has focused its go-to-market offerings on decision trees until recently, it now also offers Strategy Tree capability, which helps advanced users to create complex cohorts from trees.

Alpine Data Labs offers “the most comprehensive collaboration tools of all the vendors in the Forrester Wave, and still manages to make the interface simple and familiar to users of any mainstream social media site,” wrote Gualtieri and Curran in the report. The fact that not enough people buy Alpine products seems to be the problem. It might be a matter of acquisition and pricing options, it’s here that Alpine scores lowest among all vendors in the Wave.

Dell plans to go big in the big data and predictive analytics game. It bought its way into the market when it acquired Statistica. “Statistica has a comprehensive library of analysis algorithms and modeling tools and a significant installed base,” say the authors. Dell scores second lowest among Wave vendors in architecture, so it has a lot of room for improvement there.

KNIME is the open source contender in Forrester’s Wave. And though “free” isn’t the selling point of open source, it rates; perhaps only second to the passion of its developers. “KNIME’s flexible platform is supported by a community of thousands of developers who drive the continued evolution of the platform by contributing extensions essential to the marketplace: such as prebuilt industry APIs, geospatial mapping, and decision tree ensembles,” write the researchers. KNIME competes with only Microsoft for a low score on business applications and is in last place, by itself, when it comes to architecture. It has a perfect score when it comes to data.

Make Room for Contenders

Both Microsoft and Predixion Software bring something to the market that others do not.

They seem to be buds waiting to blossom. Microsoft, for its part, has its new Azure Machine Learning offering as well as the assets of Revolution Analytics, which it recently acquired. Not only that, but the company’s market reach and deep pockets cannot be overstated. While Microsoft brought home lower scores than many of the vendors evaluated in this Wave, it’s somewhat forgivable because its big data, predictive analytics solutions may be the youngest.

Predixion Software, according to Forrester, offers a unique tool, namely (MSLM), a machine learning semantic model that packages up transformations, analysis, and scoring of data that can be deployed in.NET or Java OSGI containers. “This means that users can embed entire predictive workflows in applications,” says the report.

Plenty of Good Choices

The key takeaways from Forrester’s research indicate that more classes of users can now have access to “modern predictive power” and that predictive analytics now allow organizations to embed intelligence and insight.

The analysts, of course, suggest that you download their report, which, in fact, might be worthwhile doing. This is a rapidly evolving market and vendors are upgrading their products at a rapid clip. We know this because there’s rarely a week where a new product announcement or feature does not cross our desks.

And if it’s true that the organizations who best leverage data will win the future, then working with the right tools might be an important differentiator.

Originally posted via “3 Vendors Lead the Wave for Big Data Predictive Analytics”

 

Source: 3 Vendors Lead the Wave for Big Data Predictive Analytics

See what you never expected with data visualization

Written by Natan Meekers

A strong quote from John Tukey explains the essence of data visualization:

“The greatest value of a picture is when it forces us to notice what we never expected to see.”

Tukey was a famous American mathematician who truly understood data – its structure, patterns and what to look for. Because of that, he was able to come up with some great innovations, like the box plot. His powerful one-liner is a perfect introduction to this topic, because it points out the value of seeing things that we never expected to see.

With the large amounts of data generated every day, it’s impossible to keep up by looking at numbers only. Applying simple visualization techniques helps us to “hear” what the data is telling us. This is because our brain exists in two parts. The left side is logical, the mathematician; the right side is creative, the artist.

Mercedes-Benz, the luxury carmaker, illustrated the value of visualization in its “Whole Brain” campaign in 2012. Ads showed how the two opposing parts of the brain complement each other. They juxtaposed the left side responsible for logic and analysis with the creative and intuitive right side. Through visualization, the campaign communicated that Mercedes-Benz, like the brain, is a combination of opposites. Working together, they create technological innovation, breakthrough engineering, inspiring design and passion.

Mercedes ad depicting left and right brain functions

Visualizing data, i.e. combining left and right sides, lets you optimize decision-making and speed up ad-hoc analysis. That helps you see trends as they’re occurring and take immediate action when needed.

The most impressive thing is that accurate and informative visualizations are just a click away for you, even as a business user. NO technical background or intensive training required at all. With self-service capabilities of modern tools, you can get much more value out of your data just by pointing and clicking.

Data visualization plays a critical role in a world where so much data is pouring in from so many sources every day. It helps us to understand that data more easily. And we can detect hidden patterns, trends or events quicker than ever before. So start using your data TODAY for what it’s really worth.

To read the original article on S.A.S. Voices, click here.

Source: See what you never expected with data visualization by analyticsweekpick

Democratizing Self-Service Cognitive Computing Analytics with Machine Learning

There are few areas of the current data landscape that the self-service movement has not altered and positioned firmly within the grasp of the enterprise and its myriad users, from novices to the most accomplished IT personnel.

One can argue that cognitive computing and its self-service analytics have always been a forerunner of this effort, as their capability of integrating and analyzing disparate sources of big data to deliver rapid results with explanations and recommendations proves.

Historically, machine learning and its penchant for predictive analytics has functioned as the most accessible of cognitive computing technologies that include natural language processing, neural networks, semantic modeling and vocabularies, and other aspects of artificial intelligence. According to indico co-founder and CEO Slater Victoroff, however, the crux of machine learning’s utility might actually revolve around deep learning and, specifically, transfer learning.

By accessing these technologies at scale via the cloud, enterprises can now deploy cognitive computing analytics on sets of big data without data scientists and the inordinate volumes of data required to develop the models and algorithms that function at the core of machine learning.

From Machine Learning to Deep Learning
The cost, scale, and agility advantages of the cloud have resulted in numerous Machine Learning-as-a-Service vendors, some of which substantially enhance enterprise utility with Deep Learning-as-a-Service. Machine learning is widely conceived of as a subset of predictive analytics in which existing models of algorithms are informed by the results of previous ones, so that future models are formed quicker to tailor analytics according to use case or data type. According to Slater, deep learning algorithms and models “result in better accuracies for a wide variety of analytical tasks.” Largely considered a subset of machine learning, deep learning is understood as a more mature form of the former. That difference is conceptualized in multiple ways, including “instead of trying to handcraft specific rules to solve a given problem (relying on expert knowledge), you let the computer solve it (deep learning approach),” Slater mentioned.

Transfer Learning and Scalable Advantages
The parallel is completed with an analogy of machine learning likened to an infant and deep learning likened to a child. Whereas an infant must be taught everything, “a child has automatically learnt some approximate notions of what things are, and if you can build on these, you can get to higher level concepts much more efficiently,” Slater commented. “This is the deep learning approach.” That distinction in efficiency is critical in terms of scale and data science requirements, as there is a “100 to 100,000 ratio” according to Slater on the amounts of data required to form the aforementioned “concepts” (modeling and algorithm principles to solve business problems) with a deep learning approach versus a machine learning one. That difference is accounted for by transfer learning, a subset of deep learning that “lets you leverage generalized concepts of knowledge when solving new problems, so you don’t have to start from scratch,” Slater revealed. “This means that your training data sets can be one, two or even three orders of magnitude smaller in size and this makes a big difference in practical terms.”

Image and Textual Analytics on “Messy” Unstructured Data
Those practical terms expressly denote the difference between staffing multiple data scientists to formulate algorithms on exorbitant sets of big data, versus leveraging a library of preset models of service providers tailored to vertical industries and use cases. These models are also readily modified by competent developers. Providers such as indico offer these solutions for companies tasked with analyzing the most challenging “messy data sets”, as characterized by Slater. In fact, the vast forms of unstructured text and image analytics required of unstructured data is ideal for deep learning and transfer learning. “Messy data, by nature, is harder to cope with using handcrafted rules,” Slater observed. “In the case of images things like image quality, lighting conditions, etc. introduce noise. Sarcasm, double negatives, and slang are examples of noise in the text domain. Deep learning allows us to effectively work with real world noisy data and still extract meaningful signal.”

The foregoing library of models utilizing this technology can derive insight from an assortment of textual and image data including characteristics of personality, emotions, various languages, content filtering, and many more. These cognitive computing analytic capabilities are primed for social media monitoring and sentiment analysis in particular for verticals such as finance, marketing, public relations, and others.

Sentiment Analysis and Natural Language Processing
The difference with a deep learning approach is both in the rapidity and the granular nature of the analytics performed. Conventional natural language processing tools are adept at identifying specific words and spellings, and at determining their meaning in relation to additional vocabularies and taxonomies. NLP informed by deep learning can expand this utility to include entire phrases and a plethora of subtleties such as humor, sarcasm, irony and meaning that is implicit to native speakers of a particular language. Such accuracy is pivotal to gauging sentiment analysis.

Additionally, the necessity of image analysis as part of sentiment analysis and other forms of big data analytics is only increasing. Slater characterized this propensity of deep learning in terms of popular social media platforms such as Twitter, in which images are frequently incorporated. Image analysis can detect when someone is holding up a “guitar, and writes by it ‘oh, wow’,” Slater said. Without that image analysis, organizations lose the context of the text and the meaning of the entire post. Moreover, image analysis technologies can also discern meaning in various facial expressions, gestures, and other aspects of text that yield insight.

Cognitive Computing Analytics for All
The provisioning of cognitive computing analytics via MLaaS and DLaaS illustrates once again exactly how pervasive the self-service movement is. It also demonstrates the democratization of analytics and the fact that with contemporary technology, data scientists and massive sets of big data (augmented by expensive physical infrastructure) are not required to reap the benefits of some of the fundamental principles of cognitive computing and other applications of semantic technologies. Those technologies and their applications, in turn, are responsible for increasing the very power of analytics and of data-driven processes themselves.

In fact, according to Cambridge Semantics VP of Marketing John Rueter, many of the self-service facets of analytics that are powered by semantic technologies “are built for the way that we think and the way that we analyze information. Now, we’re no longer held hostage by the technology and by solving problems based upon a technological approach. We’re actually addressing problems with an approach that is more aligned with the way we think, process, and do analysis.”

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What Is Happening With Women Entrepreneurs? [Infographics]

Business-Plan-Woman

On this International Women’s Day, it might be a wise idea to learn how women is shaping the entrepreneurial landscape. Not only is the impact impressive, growing but it is also building sustained growth. In some aspects, the impact is equal or better than the male counterparts.

Women entrepreneurs has been on the rise for sometime, more specifically, we’ve grown twice as fast as men between 1997 and 2007, at the pace of 44% growth in women-owned businesses. if it is not a cool stats, not sure what else is?

There are a Dozen interesting factoids about how women is shaping business landscape:

  1. In 2005, there were 7 CEO’s in Fortune 500. As of May 2011, there were 12 CEO’s in Fortune 500 companies, not many but growing.
  2. Approximately 32% of women business owners believe that being a woman in a male-dominated industry is beneficial.
  3. The number of women-owned companies with 100 or more employees has increased at nearlytwice the growth rate of all other companies.
  4. The vast majority (83%) of women business owners are personally involved in selecting and purchasing technology for their businesses.
  5. The workforces of women-owned firms show more gender equality. Women business owners overallemploy a roughly balanced workforce (52% women, 48% men), while men business owners employ 38% women and 62% men, on average.
  6. 3% of all women-owned firms have revenues of $1 million or more compared with 6% of men-owned firms.
  7. Women business owners are nearly twice as likely as men business owners to intend to pass the business on to a daughter or daughters (37% vs. 19%).
  8. Between 1997 and 2002, women-owned firms increased their employment by 70,000, whereas firms owned by men lost 1 million employees.
  9. One in five firms with revenue of $1 million or more is woman-owned.
  10. Women owners of firms with $1 million or more in revenue are more likely to belong to formal business organizations, associations or networks than other women business owners (81% vs. 61%).
  11. Women-owned firms in the U.S. are more likely than all firms to offer flex-time, tuition reimbursement and, at a smaller size, profit sharing to their workers.
  12. 86% of women entrepreneurs say they use the same products and services at home that they do in their business, for familiarity and convenience.

Road is well traveled and boy we have covered a distance. Let us embrace and keep breaking the glass ceiling. At the end, Happy International Women’s Day you all!

Infographic: Women in Business
Courtesy of: CreditDonkey

Source: What Is Happening With Women Entrepreneurs? [Infographics]

7 Characteristics to Look Before Hiring a Data Scientist

datascientist_servertech

Data is being collected in droves, but most of the time, people don’t know what to do with it. That’s why data scientists are hot commodities in the startup world right now. In fact, between 2003 and 2013, employment in data industries grew about 21 percent — nearly 16 percent more than overall employment growth. It’s a fairly new concept, but these people are so valuable because they understand the significance of data for your business and how you can use it.

Using analytics, firms can discover patterns and stories in data, build the infrastructure needed to properly collect and store it, inform business decisions and guide strategy. Access to sufficient and robust data is vital to sustained startup growth.

Companies need to incorporate data science into their business models as early as possible while they’re taking risks and making crucial decisions about the future. But how do you know whether your company is ready to go the extra mile and hire a data scientist?

First, you need to make sure you can afford to hire one. On average, a single data scientist costs a company $100,000 annually. A team of data engineers, machine learning experts and modelers can cost millions.

Smaller companies may need to create software solutions and invest time in building revenue to ensure they can actually utilize a data scientist’s skills. Tools such as Tableau, Qlik and Google Charts can help you plot and visualize the results of your data collection, connect this information to dashboards and quickly glean actionable insights.

Once your business is ready to make a larger investment to gain a competitive edge, there are several key traits to seek out in potential candidates. The best data scientists are:

1. Skilled.
All the data in the world won’t illuminate much if the scientist analyzing it doesn’t possess practical IT skills, experience with the tools mentioned above and a thorough understanding of basic security practices. A solid background in mathematics and statistics is also an indispensable trait; this demonstrates an intellectual rigor and the ability to confidently synthesize and massage many types of data sets.

2. Aware.
Armed with a thorough understanding of the pressures inherent to certain industries, skilled data scientists can effectively enlighten the decision-making process. To this end, interview recruits about how they view the competitive climate at the moment.

3. Proven.
A good way to guarantee you hire the best data scientist for your needs is to ask each contender to develop a sample presentation based on a specific set of data you provide. Then, pursue the candidates who convey real vision, robust understanding and deep insight.

Related: 4 Things a Data Scientist Can Do for Entrepreneurs

4. Entrepreneurial.
Data scientists energize enterprise through discovery. Natural curiosity and enthusiasm for solving big problems coupled with an ability to transform data into a product may place one candidate above the rest.

5. Agile.
Just as successful startup teams depend on across-the-board versatility, data scientists must be agile enough to quickly modify their methods to suit changes within a particular industry.

6. Intuitive.
You want this person to beat you to the punch when it comes to anticipating questions that data could answer. Look for someone who has a keen sense for future data applications.

7. Strong communication.
Insight that can’t be expressed is worthless. Good data scientists are able to uncover data patterns and are willing to explain those patterns in clear and helpful ways through thoughtful and open communication. They should know how to present visualizations of data and tell a story through numbers.

The perfect complement to a scaling, booming startup is a data scientist with a killer skill set. By sharing the burden and excitement of making crucial business decisions, this single hire can take your startup from data zero to data hero in no time.

Note: This article originally appeared in Entrepreneur. Click for link here.

Source by analyticsweekpick

8 Data Security Tips For Small Businesses

In 2015, more than 169 million personal records were exposed, ranging from financial records, trade secrets, and important files from education, government, and healthcare sector. Though big organizations are the usual victims of data breach, there is an ongoing trend which shows that small businesses are rapidly becoming a much-favored victim by hackers nowadays. 2017 should be high on charts for businesses to fix their security loopholes.

Here’s a great cheat sheet on 8 data security tips that will come handy in case one needs to revisit their data security strategy.

8 Pointers are:
Designate Computer Access Levels
Enable Two-Factor Authentication
Secure Wireless Network Connection
Use SSL for exchanging Sensitive Data
Use Trusted Resources for storage
Store Encrypted Data Backups
Make your staff aware

8 Data Security Tips For Small Businesses
8 Data Security Tips For Small Businesses

Originally Posted at: 8 Data Security Tips For Small Businesses

Three Big Data Trends Analysts Can Use in 2016 and Beyond

One of the byproducts of technology’s continued expansion is a high volume of data generated by the web, mobile devices, cloud computing and the Internet of Things (IoT). Converting this “big data” into usable information has created its own side industry, one that businesses can use to drive strategy and better understand customer behavior.

The big data industry requires analysts to stay up to date with the machinery, tools and concepts associated with big data, and how each can be used to grow the field. Let’s explore three trends currently shaping the future of the big data industry:

Big Data Analytics Degrees

Mostly due to lack of know-how, businesses aren’t tapping into the full potential of big data. In fact, most companies only analyze about 12 percent of the emails, text messages, social media, documents or other data-collecting channels available to them (Forrester). Many universities now offer programs for big data analytics degrees to directly acknowledge this skills gap. The programs are designed to administer analytical talent, train and teach the skillsets – such as programming language proficiency, quantitative analysis tool expertise and statistical knowledge – needed to interpret big data. Analysts predict the demand for industry education will only grow, making it essential for universities to adopt analytics-based degree programs.

Predicting Consumer Behaviors

Big data allows businesses to access and extract key insights about their consumer’s behavior. Predictive analytics challenges businesses to take data interpretation a step further by not only looking for patterns and trends, but using them to predict future purchasing habits or actions. In essence, predictive analytics, which is a branch of big data and data mining, allows businesses to make more data-based predictions, optimize processes for better business outcomes and anticipate potential risk.

Another benefit of predictive analytics is the impact it will have on industries such as health informatics. Health informatics uses electronic health record (EHR) systems to solve problems in healthcare such as effectively tracking a patient’s medical history. By documenting records in electronic format, doctors can easily track and assess a patient’s medical history from any certified access port. This allows doctors to make assumptions about a patient’s health using predictive analytics based on documented results.

Cognitive Machine Improvements

A key trend evolving in 2016 is cognitive improvement in machinery. As humans, we crave relationship and identify with brands, ideas and concepts that are relatable and easy to use. We expect technology will adapt to this need by “humanizing” the way machines retain memories and interpret and process information.

Cognitive improvement aims to solve computing errors, yet still predict and improve outcomes as humans would. It also looks to solve human mistakes, such as medical errors or miscalculated analytics reports. A great example of cognitive improvement is IBM’s Watson supercomputer. It’s classified as the leading cognitive machine to answer complex questions using natural language.

The rise of big data mirrors the rise of tech. In 2016, we will start to see trends in big data education, as wells as a shift in data prediction patterns and error solutions. The future is bright for business and analytic intelligence, and it all starts with big data.

Dr. Athanasios Gentimis

Dr. Athanasios (Thanos) Gentimis is an Assistant Professor of Math and Analytics at Florida Polytechnic University. Dr. Gentimis received a Ph.D. in Theoretical Mathematics from the University of Florida, and is knowledgeable in several computer programming/technical languages that include C++, FORTRAN, Python and MATLAB.

Source: Three Big Data Trends Analysts Can Use in 2016 and Beyond

Four Things You Need to Know about Your Customer Metrics

Customer Metrics
What Customer Metrics Do You Use?

A successful customer experience management (CEM) program requires the collection, synthesis, analysis and dissemination of different types of business metrics, including operational, financial, constituency and customer metrics (see Figure 1).  The quality of customer metrics necessarily impacts your understanding of how to best manage customer relationships to improve the customer experience, increase customer loyalty and grow your business. Using the wrong customer metrics could lead to sub-optimal decisions while using the right customer metrics can lead to good decisions that give you a competitive edge.  How do you know if you are using the right customer metrics in your CEM program? This post will help formalize a set of standards you can use to evaluate your customer metrics.

Customer Experience Management is EFM & CRM
Figure 1. Customer experience management is about collection, synthesis, analysis and dissemination of business metrics.

Customer Metrics

Customer metrics are numerical scores or indices that summarize customer feedback results. They can be based on either customer ratings (e.g., average satisfaction rating with product quality) or open-ended customer comments (via sentiment analysis). Additionally, customer ratings can be based on a single item or an aggregated set of items (averaging over a set of items to get a single score/metric).

Meaning of Customer Metrics

Customer metrics represent more than just numerical scores. Customer metrics have a deeper meaning, representing some underlying characteristic/mental processes about your customers: their opinions and attitudes about and intentions toward your company or brand. Figure 2 depicts this relationship between the feedback tool (questions) and the this overall score that we label as something.  Gallup claims to measure customer engagement (CE11) using 11 survey questions. Other practitioners have developed their unique metrics that assess underlying customer attitudes/intentions. The SERVQUAL method assesses several dimensions of service quality; the RAPID Loyalty approach measures three types of customer loyalty: retention, advocacy and purchasing. The Net Promoter Score® measures likelihood to recommend.

Figure 2. Advocacy Loyalty Index (customer metric) measures extent to which customers will advocate/ feel positively toward your company (underlying construct) using three items/questions.

Customer Metrics are Necessary for Effective CEM Programs but not Frequently Used

Loyalty leading companies compared to their loyalty lagging counterparts, adopt specific customer feedback practices that require the use of customer metrics: sharing customer results throughout the company, including customer feedback in company/executive dashboards, compensating employees based on customer feedback, linking customer feedback to operational metrics, and identify improvement opportunities that maximize ROI.

Despite the usefulness of customer metrics, few businesses gather them. In a study examining the use of customer experience (CX) metrics, Bruce Temkin found that only about half (52%) of businesses collect and communicate customer experience (CX) metrics. Even fewer of them review CX metrics with cross-functional teams (39%), tie compensation to CX metrics (28%) or make trade-offs between financial and CX metrics (19%).

Evaluating Your Customer Metrics

As companies continue to grow their CEM programs and adopt best practices, they will rely more and more on the use of customer metrics. Whether you are developing your own in-house customer metric or using a proprietary customer metric, you need to be able to critically evaluate them to ensure they are meeting the needs of your CEM program. Here are four questions to ask about your customer metrics.

1. What is the definition of the customer metric?

Customer metrics need to be supported by a clear description of what it is measuring. Basically, the customer metric is defined the way that words are defined in the dictionary. They are non-ambiguous and straightforward. The definition, referred to as the constitutive definition, not only tells you what the customer metric is measuring, it also tells you what the customer metric is not measuring.

The complexity of the definition will match the complexity of the customer metric itself. Depending on the customer metric, definitions can reflect a narrow concept or a more complex concept. For single-item metrics, definitions are fairly narrow. For example, a customer metric based on the satisfaction rating of a single overall product quality question would have the following definition: “Satisfaction with product quality”. For customer metrics that are made up of several items, a well-articulated definition is especially important. These customer metrics measure something more nuanced than single-item customer metrics. Try to capture the essence of the commonality shared across the different items. For example, if the ratings of five items about the call center experience (e.g., technical knowledge of rep, professionalism of rep, resolution) are combined into an overall metric, then the definition of the overall metric would be: “Overall satisfaction with call center experience.”

2. How is the customer metric calculated?

Figure 3. Two Measurement Criteria: Reliability is about precision; Validity is about meaning

Closely related to question 1, you need to convey precisely how the customer metric is calculated. Understanding how the customer metric is calculated requires understanding two things: 1) the specific items/questions in the customer metric; 2) how items/questions were combined to get to the final score. Knowing the specific items and how they are combined help define what the customer metric is measuring (operational definition). Any survey instructions and information about the rating scale (numerical and verbal anchors) need to be included.

3. What are the measurement properties of the customer metric?

Measurement properties refer to a scientifically-derived indices that describe the quality of a customer metric. Applying the field of psychometrics and scientific measurement standards (Standards for Educational and Psychological Testing), you can evaluate the quality of customer metrics. Analyzing existing customer feedback data, you are able to evaluate customer metrics along two criteria: 1) Reliability and 2) Validity. Reliability refers to measurement precision/consistency. Validity is concerned with what is being measured. Providing evidence of reliability and validity of your customer metrics is essential towards establishing a solid set of customer metrics for your CEM program. The relationship between these two measurement criteria is depicted in Figure 3. Your goal is to develop/select customer metrics that are both reliable and valid (top right quadrant).

Four Types of Reliability
Figure 4. Four Types of Reliability

While there are different kinds of reliability (see Figure 4), one in particular is especially important when the customer metric is made up of multiple items (e.g., most commonly, items are averaged to get one overall metric). Internal consistency reliability is a great summary index that tells you if the items should combined together. Higher internal consistency (above .80 is good; 1.0 is the maximum possible) tells you that the items measure one underlying construct; aggregating them makes sense. Low internal consistency tells you that the items are likely measuring different things and should not be aggregated together.

There are three different lines of validity evidence that help show that the customer metric actually measures what you think it is measuring. To establish that a customer metric assesses something real, you can look at the content of the items to determine how well they represent your variable of interest (establishing evidence of content validity), you can calculate how well the customer metric correlates with some external criteria (establishing evidence of criterion validity) and you can understand, through statistical relationships among different metrics, how your customer metric fits into a theoretical framework that distinguishes your customer metric from other customer metrics (e.g., How is the customer engagement metric different than the customer advocacy metric? - construct validity).

Figure 5. Evidence of criterion-related validity: Identifying which operational metrics are related to customer satisfaction with the service request (SR)

These three different lines of validity evidence demonstrate that the customer metric measures what it is intended to measure. Criterion-related validity evidence often involves linking customer metrics to other data sources (operational metrics, financial metrics, constituency metrics).

Exploring the reliability and validity of your current customer metrics has a couple of extra benefits. First, these types of analyses can improve the measurement properties of your current customer metrics by identifying unnecessary questions. Second, reliability and validity analysis can improve the overall customer survey by identifying CX questions that do not help explain customer loyalty differences. Removal of specific CX questions can significantly reduce survey length without loss of information.

4. How useful is the customer metric?

While customer metrics can be used for many types of analyses (e.g., driver, segmentation), their usefulness is demonstrated by the number and types of insights they provide. Your validation efforts to understand the quality of the customer metrics create a practical framework for making real organizational changes. Specifically, by understanding the causes and consequences of the customer metric, you can identify/create customer-centric operational metrics (See Figure 5) to help manage call center performance, understand how changes in the customer metric correspond to changes in revenue (See Figure 6) and identify customer-focused training needs and standards for employees (See Figure 7).

Figure 6. A useful customer metric (satisfaction with TAM) reveals real differences in business metrics (revenue)

Examples

Below are two articles on the development and validation of four customer metrics. One article focuses on three related customer metrics. The other article focuses on an employee metric. Even though this present blog post talked primarily about customer metrics, the same criteria can be applied to employee metrics.

In each article, I present the necessary information needed to critically evaluate each customer metric: 1) Clear definition of the customer metrics, 2) description of how metrics are calculated, 3) measurement properties (reliability/validity), 4) show that metrics are related to important outcomes (e.g., revenue, employee satisfaction). The articles are:

  • Hayes, B.E.  (2011). Lessons in loyalty. Quality Progress, March, 24-31. Paper discusses the development and validation of the RAPID Loyalty approach. Three reliable customer loyalty metrics are predictive of different types of business growth. Read entire article.
  • Hayes, B. E. (1994). How to measure empowerment. Quality Progress, 27(2), 41-46. Paper discusses need to define and measure empowerment. Researcher develops reliable measure of employee perceptions of empowerment, the Employee Empowerment Questionnaire (EEQ). The EEQ was related to important employee attitudes (job satisfaction). Read entire article.
Figure 7. Evidence of Criterion-Related Validity: Satisfaction with TAM Performance (customer metric) is related to TAM training.

Summary

A customer metric is good when: 1) it is supported with a clear definition of what it measures and what is does not measure; 2) there is a clear method of how the metric is calculated, including all items and how they are combined; 3) there is good reliability and validity evidence regarding how well the customer metric measures what it is supposed to measure; 4) they are useful in helping drive real internal changes (e.g., improved marketing, sales, service) that lead to measurable business growth (e.g., increased revenue, decreased churn).

Using customer metrics that meet these criteria will ensure your CEM program is effective in improving how your manage the customer relationship. Clear definitions of the metrics and accompanying descriptions of how they are calculated help improve communications regarding customer feedback. Different employees, across job levels or roles, can now speak a common language about feedback results. Establishing the reliability and validity of the metrics gives senior executives the confidence they need to use customer feedback as part of their decision-making process.

The bottom line: a good customer metric provides information that is reliable, valid and useful.

Source

Why Focus Groups Don’t Work And Cost Millions

030120.focusgroup
We all know what “focus group” is and what it is used for. What we don’t admit quickly is that it has little use and that we all deal with it acting old school. With changing consumer ecosystem, we should think of some other more quantitative technique that is more relevant to the current stage. With ever evolving technology and sophisticated tools, there is no reason to feel otherwise. Focus group was never an efficient way to measure product-market fit. But, considering it was the only thing that was easily available that could provide a decent start; industry went with it. We are now at a point where we could change and upgrade ourselves to harness better ways to measure potential product need and adoption.

Few of the downsides of using focus group

Unnatural settings for participants
Consider a situation where a bunch of strangers come together and discuss about some product that they have not seen before. When in real life would such an incident occur? Why would someone speak honestly without any trust between moderator and the participant? This is not a natural setting where anyone experiences a real product. So why should we use this template to make decisions?

Not in accord of how a real decision process works
Calling people and having them sit in a group and vouch for product is not how we should decide on the attractiveness/adoption of a product. There are several other things that work in tandem to influence our decision making process spend on a product and those are almost impossible to replicate in focus group sessions. For example – In real life, most of the people depend on word of mouth and suggestions from friends and family to try and adopt a new product. Such a flaw induces greater margin of error in data gathered from such groups.

Motivation for the participants is different
This is another area which makes focus group less reliable area to focus on. Consider why someone will ever detach from their day-to-day lives to come to a focus group. The reasons could be many, namely – Money, early adopter, ability to meet / network with people etc. Such variation in experience and motivation for participants induces more noise than signals.

Not a right framework for asking for snap judgment on products
Another interesting point against focus group template is its framework to gather people out of the blue, have them experience product for the first time and ask for their opinion. Everyone brings their own speed to the table when it comes to understanding the product. So, how can it be not flawed when everyone is asked at same short interval to share their opinion? This also induces error in findings.

Little is useless and more is expensive
We all know that the background for the participants is highly variable, and it is almost impossible to carve a niche out of the participants. If few participants are invited, it is extremely hard to pin-point the needs of participants, and if we invite too many, it will be an expensive model and with all the error and flaws in it. This makes focus group model useless and costly.

It is not about the product but the experience
A product never alone work on its own, it often works in conjunction with experience that is delivered by other dependent areas. And cumulative interactions deliver the product experience. In focus group, it is extremely difficult to deliver an exact experience as it has not been built into the mix yet. Experience comes after numerous product iterations with customers. So, in initial stages, it is extremely difficult to suggest anything by just quick hands on with product and no experience build around it.

Innovation suppressant
Consider a case where iTunes is pitched to focus group. “iTunes is a place where you could buy individual songs and not the whole album, yes online and no, No CDs”. Have you ever wondered how that will fly? Focus group is great in suggesting something right in the ally of what is already present today. If there is a groundbreaking product whose market has not yet been explored, it could induce some uneasiness and could easily meet with huge rejection. So, focus groups are pretty much innovation killers.

People might not be honest unintentionally
Consider a case where you are asked about your true feelings for a product in a room full with people who think highly about it. Wouldn’t it skew your observation as well? We all have a strong tendency to bend towards political correctness causing us to skew actual findings. There are other such biases caused by group think, dominating personality in the room etc. that have been identified to invalidate the findings of the focus group sessions. This introduces error in judgment and makes collected data erroneous.

Above stated reasons are few of many that make a focus group obsolete, erroneous and unreliable. So, we should avoid using them and we should substitute it with other more effective ways.

So, what’s next? What should companies do? Let’s leave it to another day, and another blog. Catch you all soon.

Source: Why Focus Groups Don’t Work And Cost Millions by d3eksha