When Buying a Company, Use Customer Feedback to Improve Due Diligence

Due DiligenceI have been doing some work on how investment professionals can use customer feedback as part of their valuation process. I include a case study of an investment firm that used customer feedback to help confirm the valuation of the target company (it did), and also where to start in terms of managing the business to secure its future.

Investment professionals take a huge risk when they purchase or make a significant investment in a business. To identify and minimize their investment risk, these professionals conduct due diligence of the business. Due diligence is an investigation or audit of a potential investment. Investors typically examine such matters as the business’ finances, proprietary information, employees, insurance, equipment and property, and litigation claims, to name a few (Entrepreneur.com offers these due diligence questions and a downloadable checklist. Forbes has a their checklist. Inc.com has their own checklist and even offers some advice for conducting due diligence.).

While some due diligence efforts include an examination of customer data, they typically focus on identifying the number and types of customers (e.g., where they are located, size). Even in cases where customer feedback is included in the due diligence process, the business sellers hand-pick a few customers to be interviewed by the buyer, resulting in potentially biased information about the health of the business and inflating the perceived value of the business. If customer feedback is to be of value, a more rigorous approach using customer feedback is needed. In this post, I will outline a more in-depth approach at using systematic customer feedback in the due diligence process.

1. Ask a Representative Sample of Customers

When soliciting customer feedback, take steps to ensure the feedback is representative of all possible feedback from the population of customers.  Ask for a complete customer list from the seller and randomly select the customers you want to give you feedback. If you are particularly concerned about specific customer segments, use stratified random sampling (random selection occurs within each customer segment) to ensure you get enough respondents for the segments in question.

While a census is unnecessary to get a reliable picture of the entire customer base, I recommend that, when possible, you invite all customers to provide feedback. For B2C companies, surveys need to be targeted to the buyer of the products/services. For B2B companies, due to the nature of the buying process, surveys need to be targeted to all parties who are directly and indirectly involved in buying the company’s products/services (e.g., decision makers and decision influencers).

Verify the quality of the sample of customers by comparing the demographic make-up of the sample to that of the entire customer base. The extent to which the sample is representative of the population will determine the quality of the inferences you are able to make about the population. To make any meaningful conclusions about the value of the target company, the customers you ask need to be a representative sample of the population of customers.

2. Ask about Customer Loyalty

The value of a company in directly impacted by customer loyalty. The greater the customer loyalty, the higher the company value. Customers can increase the value of a company by engaging in three different types of customer loyalty behaviors. As is illustrated in the Customer Loyalty Measurement Framework, these three types of customer loyalty include: 1) retention loyalty (valuable customers stay around for a long time), 2) advocacy loyalty (customers tell family/friends about the company to drive new customer growth) and 3) purchasing loyalty (customers increase their share-of-wallet to drive average revenue per user/customer (ARPU) growth).

Because customers can exhibit their loyalty to a company in different ways, you need to ask the right loyalty for your specific needs. Does the target company have a history of high defection rates? If so, ask customers about their intention of staying. Does the target company have stagnant ARPU growth? If so, ask customers about their intention of buying different products. Does the target company historically have low new customer growth? If so, ask customers about their intention of recommending the company to their friends.

As a starting point, consider  including a loyalty question for each of the three general types of loyalty behaviors: retention, advocacy and purchasing (see the RAPID loyalty):

  1. Likelihood to switch providers (retention)
  2. Likelihood to renew service contract (retention)
  3. Likelihood to recommend (advocacy)
  4. Overall satisfaction (advocacy)
  5. Likelihood to purchase different solutions from <Company Name> (purchasing)
  6. Likelihood to expand use of <Company Name’s> products throughout company (purchasing)

3. Ask about Customer Experience (CX)

Customer loyalty is impacted by the customer experience. According to Wikipedia, the customer experience (CX) is the sum of all experiences a customer has with a supplier of goods and services over the duration of that relationship. Customers who are satisfied with their experience with the supplier stay longer, recommend more, and buy more from the supplier compared to customers who are less satisfied with their experience.

Ask customers about their experience with the company. While you could ask customers literally hundreds of CX questions about each specific aspect of their experience, research shows that you only need a few CX questions to understand what drives their loyalty. For example, ask customers how satisfied the are with the target company across each of these areas:

  1. Ease of doing business
  2. Product quality
  3. Account Management/Sales
  4. Customer service
  5. Technical support
  6. Communications from the Company
  7. Future Product/Company Direction

4. Ask about Relative Performance

Companies do not perform in a vacuum; competitors are vying for the same customers and limited prospects as the target company you are purchasing. If the target company has plenty of competitors in its space, you need to understand where you rank relative to the competition. After all, top ranked companies receive greater share of wallet compared to their bottom-ranked competitors. All things equal, a company that is ranked the lowest is less valuable than a company that is ranked the highest.

Ask customers about how the company compares to its competitors. Toward that end, the Relative Performance Assessment (RPA), a competitive analytics solution, helps investors understand the relative ranking of the target company and identify ways to increase their ranking, and consequently, increase share of wallet. In its basic form, the RPA method requires two questions:

  1. What best describes Company’s performance compared to the competitors you use?
  2. Please tell us why you rank Company’s performance the way you do. This question allows each customer to indicate the reasons behind his/her response about your ranking. The content of the customers’ comments can be examined to identify underlying themes to help diagnose the reasons for high rankings or low rankings

To understand the value of the company you are purchasing, you need to know how you measure up to the competition. More importantly, after the purchase, the RPA will help you know what you need to do to improve your ranking in the industry.

5. Ask about Company-Specific Issues

Investors may have a need to ask additional questions that are specific to the target company. These questions, driven by specific business needs, can include demographic questions (if not included in their CRM system), open-ended questions, and targeted questions. Typical questions in B2B relationship surveys include:

  • Time as a customer
  • Job function (e.g., Marketing, Sales, IT, Service)
  • Job level (executive, director, manager, individual contributor)
  • Level of influence in purchasing decisions of <Company Name> solutions (Primary decision maker, Decision influencer, No influence)

Include one or two open-ended questions that allow respondents to provide additional feedback in their own words. Depending on how the questions are phrased, customers’ remarks can provide additional insight about the health of the customer relationship. Text analytics help you understand both the primary content of words as well as the sentiment behind them. To understand potential improvement areas, a question I commonly use is:

  • If you were in charge of <Company Name>, what improvements, if any, would you make?

Customer relationship surveys can be used to collect feedback about specific topics that are of interest to executive management. Give careful consideration about asking additional questions. As with any survey question, you must know exactly how the data from the questions will be used to improve customer loyalty. Some popular topics of interest include measuring 1) perceived benefits of solutions and 2) perceived value. Some sample questions are:

  • How much improvement did you experience in productivity due to <Company Name’s> solutions?
  • Satisfaction with price of the solution given the value received

Next, I will present an example of how one investment firm used customer feedback to help in their due diligence process.

Case Study

An investment firm wanted to expand their portfolio of companies by purchasing an existing B2B company. As part of the due diligence process, the investment firm worked with the target company to acquire their customer email list for a Web-based customer survey. The investment firm used the Customer Relationship Diagnostic (CRD) to collect customer feedback. The CRD is a brief survey that asks customers about different types of customer loyalty, satisfaction with general CX touch points, relative performance and a few company-specific questions.

RAPID Loyalty Results
Figure 1. Customer loyalty ratings for target company.

The response rate for the survey was about 70% and consisted primarily of decision makers and decision influencers (~80%) and were Managers, Directors or Executives (~70%).

Case Study: Loyalty Results

Customer loyalty results are located in Figure 1. As you can see, customers reported moderate levels of customer loyalty for most of the loyalty questions (e.g., advocacy and retention). For purchasing loyalty, customers reported low likelihood of buying different products and low likelihood of expanding the use of the target company’s solutions.

Case Study: CX Results

CX Results
Figure 2. Customer Experience (CX) ratings for target company.

Results of the CX ratings can be found in Figure 2. Based on the survey results, the customers were moderately satisfied with their experiences across the touch points, except for Communications from the Company and Future Product/Company Direction.

Between 20% and 50% of the customers said they were dissatisfied with each of the seven customer touch points.

Case Study: Relative Performance Assessment Results

Results of the Relative Performance Assessment ratings are located in Figure 3. As you can see, customers said that only 42% of the customers indicated that the company was better than the competition. Almost 60% of the customers indicated that the company was the same worse than most other competitors.

Relative Performance Assessment
Figure 3. Relative Performance Assessment ratings of target company.

After re-scaling the values of the 5-p0int rating scale (1 = worst to 5 = best) to a 0-100 scale, I estimated that the target company falls roughly at the 54th percentile in their industry; that is, the company’s performance is typical when compared to their competition.

Case Study: Determining Dollar Value of Loyalty

To estimate the expected revenue gains/losses of the target company, I worked with the investment firm to translate the customer loyalty ratings into a dollar value. We employed subject matter experts (SMEs) as well as analyzed existing financial reports of the target company to arrive at our best guess of expected annual revenue gains through new customers (~$300k)) and existing customers (through purchasing new/different products – ~$160k) and estimated the annual revenue at risk due to churn (by customers stop using the company ~ $450k).

Case Study: The Decision

Overall, the customer feedback confirmed the valuation of the company. While the target company was perceived to be in the middle of the pack in their industry (ranked at 54th percentile) and the future direction of their products/company appeared dismal (50% are dissatisfied), investors believed they had the management team that could address these shortcomings. The investment company decided to buy the company.

Case Study: Where to Make Improvements

Driver Matrix
Figure 4. Driver Matrix help you identify the best areas to allocate company resources (e.g., money, time) to maximize ROI of your investment dollars.

The investors now became the business owner, and, consequently, needed to manage the business to secure its future. The survey results were analyzed to help decide where to best allocate resources in areas that will improve customer loyalty (and revenue) while minimizing the improvement costs.

Using driver analysis on the existing data, the investment firm found that there were three key drivers of customer loyalty: 1) product quality, 2) communications from the company and 3) future product/company direction. Again, using SMEs, we were able to estimate the ROI for improving each of the three key drivers. It turns out that the greatest ROI for CX improvements would be achieved by improving communications from the company and future product/company direction.

Benefits of Using Customer Feedback in your Due Diligence Process

You can significantly enhance your due diligence process through a systematic approach of collecting and analyzing customer feedback. Using the questions I proposed above, here are some benefits you can achieve when you use customer feedback as part of your due diligence process when purchasing a company:

  • Identify investment opportunities others miss and avoid investing in poor opportunities. Discover the quality of products and services from the people who matter: The customers.
  • Estimate revenue gains/losses.  Using survey data and financial data, you can estimate annual revenue at risk due to customer churn and revenue growth due to new customers and expanding relationships with current customers.
  • Understand your competitive advantage/disadvantage.  Your relative performance will impact how much incremental money your customers will spend with you. Collecting customer feedback can help you identify what you need to do to beat your competition to improve your growth.
  • Understand the ROI of different improvement efforts.

Summary

Investors can gain valuable insight about a target company they are buying by simply asking customers the right questions. Be sure you ask a representative sample of customers so the feedback you get is meaningful and reflects the entire customer base. Ask customers about different types of loyalty behaviors in which they are likely to engage. This feedback can help you estimate revenue gains and risks. Ask customers about their customer experience to identify company strengths as well as potential problems. Ask customers about the company’s relative performance compared to other companies. This insight can help you understand the competitive landscape in the company’s industry and identify ways to improve/maintain your competitive advantage.

When purchasing a company, a systematic approach to surveying the customers (and analyzing the data correctly) can significantly augment the information in your due diligence process and provide a lot of insight about the value of the company. Asking the customers of the target company could mean the difference between acquiring a valuable company or a lemon.

Learn more about the Customer Relationship Diagnostic (CRD) for your due diligence

To learn more about the Customer Relationship Diagnostic, please complete the form below.

[si-contact-form form=’1′]

Source by bobehayes

The Practice of Customer Experience Management: Paper for a Tweet

To get paper: 1) click image, 2) Give feedback via tweet, 3) download paper

I have been writing (books, articles, blog) on the topic of customer feedback and related fields (CRM, CEM, VOC)  for many years and am accumulating a lot of content. In the process of organizing this content, I wrote a short paper about the practice of customer experience management (CEM) that provides a solid foundation for a larger book on the topic.

The Practice of Customer Experience Management: The Paper

This 5-page overview explains the practice of CEM using a 6-component model and presents best practices in each component. If you are learning about CEM for the first time or understand the CEM basics, you will find this paper valuable. While the paper provides a good overview of CEM programs, it also includes over 20 links to richer online content on specific topics to help you:

  • use customer feedback to build a customer culture
  • understand the power of business linkage analysis
  • measure the customer experience more efficiently
  • measure three types of customer loyalty to grow business through new and existing customers
  • select the right customer metrics for an effective CEM program

Understand how businesses put all the CEM pieces together to create a coherent set of activities to support a customer-centric culture.

Get the Paper and Provide Feedback: The Tweet

My blog readers are a diverse crowd of professionals, each with different perspectives and insights on the CEM topic.  I am considering using this paper as a foundation for a larger book, but, before I start, I would like to see what you think.  Do you think a book on CEM is a good idea? What topics should a CEM book cover? Should I target the writing for senior executives / others?

I set up a way for you to get the 5-page paper and give me short feedback on topics you want covered in a book on CEM. All you have to do is tweet about the paper and it is yours. Simply click the cover of the paper above and send a tweet about the book (add your topic ideas!) via Pay with a Tweet  and you will have immediate access to download the paper. Download requires a tweet or a Facebook like. If you do not have a Facebook or Twitter account, don’t worry. I will post the contents of the paper on my blog in a couple of weeks.

Thanks in advance for your feedback!

Source by bobehayes

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

 

Issue #15    Web Version
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>> Colleges are using big data to identify when students are likely to flame out by analyticsweekpick

>> Rise of Data Capital by Paul Sonderegger by thebiganalytics

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Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE JOB Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
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Boeing creates data analytics group

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Boeing Defense, Space & Security has stood up a new data analytics organization to help customers leverage IT across their organizations and has named Dewey Houck as chief data analytics officer.
The appointment kicked off several personnel moves as the company created the new organization.
Chris Raymond will take Houck’s spot as deputy of Boeing Network and Space Systems, reporting the unit’s president Craig Cooning. Raymond is currently vice president of business development and strategy for Boeing Defense, Space & Security.
As chief data analytics officer, Houck will report to Darryl Davis, president of Phantom Works’ advanced technology group. Houck was vice president and general manager of electronics and information solutions within Boeing Network & Space Systems.
“We’re evolving and repositioning core capabilities,” said Chris Chadwick, president and CEO of Boeing Defense, Space & Security. “This is all part of how we’re shaping the organization to support the innovation and growth we need in Boeing’s second century.”
Houck’s group will bring new information capabilities to current and future platforms, which the company sees as a growth opportunity and a competitive advantage, the company said in a release.
The changes take effect June 1.
The company also is bringing back Tom Bell to replace Raymond in the retitled position of vice president of sales and marketing. The name change is significant in that it emphasizes the importance of accelerating sales and global growth, the company said. He’ll report to Chadwick.
Bell is a 20-year veteran of Boeing, but left the company in 2012 to join Rolls-Royce. He begins his new position on June 5.
Boeing also named Steve Nordlund as vice president of strategy for Defense, Space & Security. He’ll report to Chadwick. He starts June 1. He previously led the unmanned systems organization for Boeing Military Aircraft.

Originally posted at: http://washingtontechnology.com/articles/2015/05/27/boeing-data-analytics-group.aspx

Source: Boeing creates data analytics group by analyticsweekpick

4 Reasons Data Driven Small Businesses Are Embracing Big Data

Small businesses are thinking bigger about their data – and it’s about time.

The term big data sounds intimidating – reserved only for the Fortune 500 leaders – but that could not be further from the reality of data analytics in the competitive small business market today.

Previously the exclusive domain of statisticians, large corporations and information technology departments, the emerging availability of data and analytics – call it a new democratization – gives small businesses and consumers greater access to cost-effective, sophisticated, data-powered tools and analytical systems.

For small businesses, big data will deliver meaningful insights on markets, competition and bottom-line business results for small businesses.

For small businesses and consumers, the big data revolution promises a wide range of benefits.

New Tech, New Rules

Today, big data is changing the rules of commerce and business operations, creating opportunities and challenges for small businesses. The convergence of three leading computing trends – cloud technologies, mobile technologies and social media – are creating cost-effective, data-rich platforms on which to build new businesses and drive economic growth for small and large businesses alike. This helps boost local economies as well as global e-commerce and trade.

Optimizing Insights

Digital data will continue to turbocharge the movement to understand analytics, in both small and large businesses. Proprietary data combined with data from the cloud will continue to create new insights and a deeper understanding of what consumers need, what they like and what will keep them happy.

The development of new data sources and unique analytics will drive entrepreneurial growth around the globe over the coming decade.

Better Management

Today, small businesses can leverage business management solutions, including Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) software platforms, to automate operational management tasks and keep better watch over their very own big data – including analytical views of sales and marketing campaigns.

Small businesses can stay on top of accounting, cash flow, budgets, balances and more with financial management software alternatives, as well as tools and applications for inventory management, project management, fleet management, human resources and more.

Real-Time Decisions

By optimizing real-time data analytics, small businesses today are capturing a better view of their administrative, sales and marketing practices – including real-time overviews of what’s working well and what needs scrutiny. Small businesses mining their own big data today routinely deploy a variety of solutions – most originating in the cloud – to improve operational and administrative efficiency and productivity, while reducing manual tasks and redundancies.

Today, small businesses are no longer intimidated by big data. They are embracing it to create and manage bigger opportunities for growth and profitability.

Today’s competitive small businesses realize that optimizing analytics and business intelligence allows them to recognize the full benefits of their very own big data – powering better marketing, sales and operational efficiency, productivity and functional gains. With data-driven tasks and decisions in the mix, a new culture of small business is emerging, powering greater opportunities for the small business community, its vendors and customers.

Angela Nadeau  is CEO of CompuData, an award-winning business technologies leader. Angela maintains a deep knowledge of the trends driving businesses today to be more productive and profitable by leveraging technology. With more than 25 years of expertise, she has advised thousands of businesses on effective ways to leverage technology to increase productivity, profitability and efficiency – guiding businesses of all sizes to new levels of market success and corporate growth.

Originally posted via “4 Reasons Data Driven Small Businesses Are Embracing Big Data

 

Source: 4 Reasons Data Driven Small Businesses Are Embracing Big Data

Apr 13, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

 

Issue #15    Web Version
Contact Us: info@analyticsweek.com

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I hope this note finds you well. Please excuse the brief interruption in our newsletter. Over past few weeks, we have been doing some A/B testing and mounting our Newsletter on our AI led coach TAO.ai. This newsletter and future versions would be using capability of TAO. As with any AI, it needs some training, so kindly excuse/report the rough edges.

– Team TAO/AnalyticsCLUB

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>> The What and Where of Big Data: A Data Definition Framework by bobehayes

>> The Cost Of Too Much Data by v1shal

>> Unraveling the Mystery of Big Data by v1shal

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 How a Data Scientist’s Job ‘Play in Front’ than other BI and Analytic Roles – CIOReview Under  Data Scientist

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 AI, Machine Learning to Reach $47 Billion by 2020 – Infosecurity Magazine Under  Machine Learning

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Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE JOB Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

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*This Newsletter is hand-curated and autogenerated using #TEAMTAO & TAO, excuse some initial blemishes. As with any AI, it may get worse before it will get relevant, excuse us with your patience & feedback.
Let us know how we could improve the experience using: feedbackform

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What to Look for in a Healthcare Big Data Analytics Vendor

Healthcare big data analytics is a booming business, which is both a good and a bad thing for providers seeking to bulk up their infrastructure to supplement their EHRs with sophisticated tools for clinical analytics, population health management, and predictive insights.

The number of up-and-coming big data vendors is growing every day as providers recognize the need to treat data as a resource instead of a burden, and picking a winner out of the pack isn’t always easy for healthcare organizations constrained by finances and concerned about developing long-term, effective partnerships.

If you understand your healthcare big data analytics technology options, are preparing to put your team into action, and are ready to move forward with a strategy to harness big data as a way to drive quality improvements and organizational efficiencies, it’s time to dive into the murky world of vendor selection.

HealthITAnalytics.com explores what to look for in a healthcare big data analytics vendor in order to ensure that a provider gets the right technology for its needs in the short term while keeping options open for shifting and changing strategic goals.

Matching what you have to what you want

As specialists trying to participate in the EHR Incentive Programs have learned to their cost, one size doesn’t fit all when it comes to health IT initiatives.  A large, well-known corporation may be able to boast about their brand recognition and have a client list a mile long, but not all healthcare organizations – or big data sets – are created equal.

Healthcare organizations must have a clear idea of what their data sets look like before they can match their needs and goals to a service provider.  Those that have invested heavily in structuring their EHR input may wish to begin their big data programs with general clinical analytics, as many hospitals do.  Others focused more on research, complex cases, or bolstering their clinical decision support might want to turn to companies that offer cognitive computing or natural language processing that can comb through bulky narrative text.

Providers must also examine their existing infrastructure and decide whether they can build upon technologies already in place, or if they would prefer to rip everything out and start again.  Can the vendor accommodate your legacy systems?  Do you need to invest in basic infrastructure like a data warehouse or master patient index in order to benefit from your potential vendor’s wares?  What are the costs involved in bringing your infrastructure up to baseline, and how long will it take to see a satisfactory return on these investments?

The majority of healthcare organizations do not feel fully prepared to tackle these questions at the moment, but that is quickly changing as experience replaces trepidation.  Healthcare big data analytics is a messy business at the best of times, but don’t let an overeager vendor trivialize how much work must be done in order to get the most out of a contract.

A commitment to interoperability and data standards

Vendors must treat interoperability as more than a buzzword these days as federal agencies, consumers, payers, and patients all crack down on data siloes that make big data analytics such a headache.  After Congress raised questions about vendors who actively block the type of information sharing that is vital for care coordination and population health management and the ONC responded with a widely-read report on the matter, vendors have started to change their tune on interoperability.

The rise of interoperability coalitions like Carequality and the CommonWell Health Alliance may make it a little easier for healthcare providers to identify vendors who are committed to health information exchange, but even the combined might of both organizations does not include a majority of the big data analytics companies on the market.

It is up to healthcare providers to ask about the foundations of a vendor’s technologies and how they will interact with other products, providers, and partners.  A few important questions to ask include:

• Is your product built on open standards or proprietary architecture?  Does it accept APIs, and is anyone actively developing them?

• How easy will it be for my organization to participate in large-scale analytics or health information exchange with a state or local entity, my accountable care organization, public health departments, and research organizations?

• How will your product interface with my existing health IT systems?  What sort of user experience can my clinicians and other staff expect?

• Have you considered the growing importance of medical device integration and the Internet of Things?  How will your technology adapt to the need to integrate additional data sources as patient-generated health data becomes more critical to providing quality care?

Transparent business practices and pricing structures

Taking the pledge for interoperability is just one part of having sound business practices that will encourage long-term partnerships.  While the ONC’s data blocking report may have reportedly spooked some vendors into dropping data exchange fees, the question of who has the rights to demand cash for patient data in motion and at rest has sparked some serious debates.

In 2013, the ONC released a guide for providers looking to negotiate EHR replacement contracts, urging them to pay attention to terms that would limit the transfer of patient information to a new system or cut off access to data during a dispute.  The advice about contract negotiation applies equally to an EHR system or a big data technology, each of which can be licensed for use on an organization’s own technology or provided as a service in the cloud.

The ONC warns providers to pay close attention to liability language that may exonerate the vendor from any responsibility should patient harm arise from unexpected downtime, a privacy violation, or an error or omission in the data.  “Developer contract language often includes indemnification language that shifts liability to you without regard to the cause of the problem or whose ‘acts or omissions’ may have given rise to the claim,” the guide says.

“You may want to negotiate with the EHR technology developer a mutual approach to indemnification that makes each party responsible for its own acts and omissions, so that each party is responsible for harm it caused or was in the best position to prevent,” the ONC suggests.

The guide also suggests courses of action for dispute resolution, intellectual property issues, warrantees, and confidentiality agreements.  Most vendors are willing to negotiate these terms to some degree, but be wary of those who insist on an all-or-nothing approach. Before signing on the dotted line, providers should be sure they are clear about their expectations and responsibilities, as well as ensuring they understand the pricing structures for data storage and transfer without falling victim to hidden fees or sudden hikes in a payment plan.

A balance of track record and innovation

Healthcare big data analytics is all about discovering novel and ingenious ways to use information, but providers investing millions of dollars in new infrastructure want to be sure that they aren’t throwing money down the drain.  Despite the general enthusiasm around embracing new ideas for analytics, executive leaders are still a relatively conservative bunch.

This year’s HIMSS Leadership Survey indicated a very high level of board room support for expanding innovative health IT and data analytics capabilities, yet more than a third of organizational leaders would prefer if that innovation had been tested at another organization first.  Just 24 percent of respondents said that their executive leaders were “open to trying ‘bleeding edge’ technology,” which puts big data analytics purchasers in a quandary.  After all, someone has to be the first one to try something new – and to possible reap the rewards of being adventurous.

But investing in start-up technology companies with big dreams and little real-world experience can be a risky proposition for providers who are looking to stretch every dollar they invest.  Venture capital investment in population health management and analytics companies is through the roof, but not every outfit that receives funding gets bought by a major player or scores a huge IPO.

Healthcare organizations should look for vendors who have secured adequate funding for their products, have working, bug-free examples of their software or hardware to display, offer robust customer support services, have firm timelines and plans for implementation, and don’t make promises they seem unlikely to be able to keep.

The ability to expand and grow with you as strategic plans change

Healthcare organizations are constantly being bombarded with new initiatives, shifting goals for federal mandates, and major changes to health IT programs, reimbursement structures, and quality improvement goals.  As the industry begins to embrace value-based payments and care structures driven by the need to provide high quality services and produce better outcomes, organizational needs and goals must be flexible.

Vendors have to be flexible, too, and be able to provide the right insights at the right time for the task at hand.  While technology turnovers are inevitable as new capabilities and standards move through the market, healthcare providers are looking for products that can carry them through at least a few years of turmoil without requiring a complete overhaul.

Healthcare providers can help themselves make the right choices by having a solid strategic vision for their organization over the next three to five years as meaningful use winds down and accountable care heats up.  Providers may wish to ask themselves:

  • How will I tackle population health management and the increasingly expensive proposition of caring for patients with complex chronic disease needs?  Will our patient demographics change significantly over the next few years?  How can we be proactive about addressing their needs?
  • How will the shift to value-based reimbursement drive the need for improved operational efficiencies within my organization, and how do I think big data will help?
  • What data exchange and interoperability capabilities do I need to ensure care coordination across the continuum?  How can my business partners and I work together to bring data-driven healthcare insights to our community?
  • What patient safety and care quality goals are we hoping to meet?  How can gaining deeper insights into our clinical care produce better patient outcomes?
  • What revenue cycle management issues do we need to address?  Can we turn patient behavior data into better collections, or will an investment in preventative care keep high-cost services to a minimum?
  • How can we improve our data integrity and data governance to maximize our investment in healthcare big data analytics?  Do we need to retrain our EHR users, hire more health information management professionals, or build a dedicated team of data scientists?

Healthcare big data analytics is such a rapidly expanding field that capabilities that seem commonplace today didn’t exist five years ago, and will probably be outdated five years from now.  But understanding your organizational objectives will help you make the best possible decisions with the information available at the moment, and hopefully set up your big data program for long-term future success.

Choosing the right vendor is a critical component of seeing the benefits of big data, and providers should not underestimate the degree to which open communication during this type of ongoing partnership will be required.

After thoroughly considering how a technology purchase will impact their goals, providers should look for stable, responsible, capable, and innovative vendors that offer high quality products with transparent, reasonable pricing structures if they wish to be pioneers in the field of big data.

Originally posted via “What to Look for in a Healthcare Big Data Analytics Vendor”

Source

Google Offers ‘Preemptible’ Virtual Machines

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If you don’t mind running a virtual machine (VM) that will live for just 24 hours, and can be shut down with only 30 seconds advance notice, then Google may have a great deal for you.

It’s offering Google Compute Engine Preemptible Virtual Machines, a short-lived VM type that’s in beta right now, according to a post on the Google Cloud Platform Blog. “Preemptible VMs are the same as regular instances except for one key difference – they may be shut down at any time,” wrote Paul Nash, Senior Product Manager.

Fixed Pricing — For a Price
By not giving a guarantee that the VM will be available, Nash said, Google can fix pricing for it, allowing a company to have more predictable costs. He said prices will start at $0.01 per core hour.
That low price comes attached with strings the size of skyscrapers, however. For one, the VM’s runtime is limited to 24 hours. And, if Google should need the space or resources being occupied by that VM, the VM will be terminated without prejudice.

That’s why Google says the best types of applications for these VMs are fault tolerant and can afford to be interrupted. It gives an example of batch processing jobs: “If some of those instances terminate during processing, the job slows but does not completely stop.”

Terminated Without Prejudice
Google adds that the probability that a running VM will be shut down is “generally low,” but it will probably happen from time to time. And those VMs will be terminated after running for 24 hours, no matter what.
There are two other important caveats:

In some instances, it may not even be possible to create a Preemptible VM; it depends on whether or not Google has Compute Engine resources available.
Preemptible VMs can’t live migrate, so if it’s shut down, it won’t be spinning back up on another host.
Nash blogged that Preemptible VMs can be created through the Google Developer Console, or by adding “–preemptible” to the gcloud command line. A Google pricing page shows that some of its cheapest per-hour VM charges can range anywhere between $0.03 per hour, up to $0.11 per hour or more. Thus, the Preemptible VMs could result in a substantial savings — for the right workload.

Originally posted at: https://virtualizationreview.com/articles/2015/05/18/google-offers-preemptible-virtual-machines.aspx

Source: Google Offers ‘Preemptible’ Virtual Machines by analyticsweekpick