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Analytics Business Practitioner

What Should be the Analytics Organization Structure? | killexams.com real questions and Pass4sure dumps

Analytics organizations tend to be setup in very different ways dependent on the specific companies that set them up, be it in terms of reporting lines, segmented or central teams, or in terms of overall focus (project vs. product/business based). Each of these attributes brings its own set of tradeoff that need to be understood and managed.

Over the years as an analytics professional, I had the chance to work in a variety of industry and businesses. Sometimes analytics was placed under business or products, other time finance and sometimes technology. Amazon for instance tended to place analytics and business intelligence under Finance while the data engineering was placed under the technology department, while at Facebook there was a central analytics team with sub-segments detached to the different product pillars.

The reporting lines dictate to a large extent the priorities that end up being set forth on the analytics team. Finance having a focus on control, product a focus on prioritization and business cases and technology on building data-flows.

One of the other impact of reporting line, is on the hiring selection process for data professionals, especially in areas that cannot staff a full data team.

There are different advantages and disadvantages of Segmented teams and Central teams that need to be considered. The structure has impact on hiring, knowledge sharing, collaboration, career path, focus and objectivity of the organization. Each of these tradeoffs will need to be managed in order to provide for a successful organization.

Centralized Analytics team tend to bring about a more structured approach hiring than segmented teams. They are able to set and enforce hiring standards in terms of the functions they managed. They generally have a standard interview loop and are better able to get interviewers from different analytics specialization. Segmented teams would in general have less emphasis and ability to test the analytical knowledge of the different candidates and may thus value more highly different set of skills.

Another advantage in terms of hiring is the ability to more easily place good candidate in a different domain function. For instance if a good candidate was interviewing for an analytics position that became filled in the meantime, s/he would have to potentially re-interview for a different analytics position at a company with a segmented analytics team while at one with a central team, so long as there is overall headcount at company level s/he could directly be offered the job.

An aspect that distinguishing segmented vs. central team is the approach towards knowledge sharing and collaboration within the analytics teams. In central analytics teams knowledge sharing can happen in a variety of ways, through working on the same project together when the needs come, through project rotation and hand overs, through mentoring, or typical presentations or code reviews. In segmented analytics teams, the knowledge sharing within data will need to be pushed at a higher rate, segmented teams tend to benefit from higher involvement with the specific business they are in, at the expense of wider analytics involvement.

Given that a large part of growth within data roles come from working hands on on projects with other data professionals and being handed stretch projects in certain analytical domains, the segmented data team format hinders growth within the analytics career ladder. More often than not, for people having done their career in segmented data teams, the natural career progression is towards business or product roles rather than progression towards more senior analytics roles.

Segmented analytics team tend to hire more generalists than central analytics teams. Central teams benefit for economics of scale and specialization and can therefore more easily divide responsibilities between different job descriptions such as analysts, data engineers, data-scientists, … While in segmented teams there might only be 1 or 2 data person supporting a given business leaving no scope for specialization. The data person in these teams need to be a jack of all trade.

Segmented data teams by virtue of focusing on a single business area tend to have a deeper focus than central analytics team that may more easily rotate across project and business areas. The deeper focus has advantage in enabling the analytics practitioner to more easily know what kind of behavior is expected and what are the kind of anomalies that you would see in the data, it also allows the analytics practitioner to better avoid the different pitfalls in the data. The broader focus offered by central analytics team however bring also their own set of advantages such as being able to leverage best practices and be able to more easily port methods from other part of the business.

While segmented teams reporting to finance may have a certain independence bringing a degree of objectivity, this is not always the case when the data persons report to the business. Analytics practitioners might be pressured to report numbers in a certain way, or making leaps of thoughts they are not comfortable with. A central analytics team help bring a certain independence in these cases.

Matrix organization help blend the benefits of a segmented analytics team and the benefit of a central team by detaching their members to the specific business cause. Analytics practitioner are able to get a deeper business understanding while retaining all the advantages of a central analytics team in terms of hiring, career path, knowledge sharing and objectivity.

In some organizations beside the direct line management there are also questions as to how to staff analytics practitioners. Whether it is by project or by assigning them directly to a business or product line.

A lot of the value of analytics is obtained by setting a performance measurement process and in putting processes in a state of control. As such it is important to have at least an analytics owner that is available to monitor the performance and take actions such as refreshing a machine learning model or directly deep dive into the datasets to correct a data or identify a business issue in need of fixing.

Nevertheless project focus can have value for certain analytics tasks, whether it is to provide a temporary relief in case of peak workload or in needing to first prove value for investing in certain areas. These types of project based work can also be useful for analytics practitioners to discover different domain areas.

There are tradeoffs that need to be considered when setting up an Analytics Organization, these usually are simplified into what’s the degree of data focus versus business focus. There are multiple aspect to it however and ways to mitigate certain tradeoffs. Dedicated central analytics organization can, for instance, be setup in a matrix like structure to help them get more into the specific business mindset and priorities.

MYnd Analytics Provides Business Update | killexams.com real questions and Pass4sure dumps

MISSION VIEJO, Calif., Feb. 14, 2019 (GLOBE NEWSWIRE) -- MYnd Analytics, Inc. (NASDAQ: MYND), a predictive analytics company aimed at improving the delivery of mental health through the combination of telemedicine and data analytics, today provided the following business update and letter to shareholders from the Company’s CEO, Patrick Herguth.  The Company also reported it has filed its quarterly report on Form 10-Q for the period ending December 31, 2018, which is available at: www.sec.gov.

Recent highlights:

Patrick Herguth, CEO of MYnd, stated, “We are off to a very auspicious start in 2019. Having recently joined the company in December of 2018, I have spent the last month conducting an extensive bottom-up review of the business with management, the board, customers, potential customers, and strategic partners. I have been tasked with evaluating the business and developing a plan that grows revenue and reduces losses, while delivering on our mission to assist practitioners in delivering better access to behavioral health services and practices.

A year ago, MYnd acquired Arcadian Telepsychiatry Services, LLC ('Arcadian'), which provides a suite of complementary telemedicine services that can be combined with PEER™ (Psychiatric EEG Evaluation Registry), including telepsychiatry, teletherapy, digital patient screening, curbside consultation, on-demand services, and scheduled encounters for all age groups. In the upcoming year we intend to both expand products and services within our current Arcadian client base, while continuing to win new business. For example, one of our partners is a leading web-based behavioral healthcare company that serves millions of members and offers extensive behavioral health resources.  We have successfully completed a pilot program at 12 of their primary care clinics to provide tele-behavioral health, including psychiatric services, and are in the process of expanding this on a broad scale to dozens of clinics across several states.  This program is an excellent example of our ability to develop solutions that are tailored for a partner’s specific tele-behavioral health needs.

We are also seeing rapid expansion of our EAP services.  Our inbound call volume for these services has increased by 189% from fiscal Q1 2018 to fiscal Q1 2019.  In addition, we are exploring the opportunity to create a Virtual Intensive Outpatient (VIOP) program for patients in substance abuse programs.  This is a program that we expect will provide scale to our business as these sessions can be delivered in a virtual group therapy setting.

To support the growth in the Arcadian business we are focusing on our internal operations.  We are launching several projects that we believe will enable us to scale rapidly and improve our operating margins.  One of our larger projects is the deployment of a new tele-behavioral health focused electronic health record (EHR) system that will allow clinics to directly schedule appointments with Arcadian physicians and provide a secure video connection all within a single application.  Our practitioner recruiting efforts are also ramping up and we have credentialed 199 clinicians to be a part the Arcadian network between October 2017 and December 2018.  Recruiting will remain a key focus area for our team to ensure we have the capacity to meet our client demands.

We are in an exciting area, as evidence by companies such as Teladoc, MD Live and American Well and believe our expertise in behavioral medicine gives us advantageous positioning within the telemedicine arena.

On the augmented intelligence side of the business, we are gaining traction with PEER™, a predictive analytics decision support tool that provides information to help physicians reduce trial and error treatment for behavioral health conditions. PEER provides the physician a personalized care report with treatment analysis based on a patient’s unique brain markers, reducing treatment time and treatment costs. We hope to continue to penetrate new markets with pilot programs so that PEER can be used as a tool to assist physicians as they combat the rising incidence of suicides and expand on trials to reaffirm the benefits of this clinical system.

Our New Jersey Blue Cross Blue Shield paid pilot program in partnership with COTA, Inc. and Hackensack Meridian Health continues to grow enrollment.  The Walter Reed PEER trial is being expanded to include a large, private provider as an investigator partner, which should allow us to increase the patient data in the study.  The clinical trial with the Canadian Armed Forces and University of Ottawa is also being expanded to include patients from a new facility allowing us to increase our study participants and accelerate the study schedule. These are both randomized controlled studies that will augment our already published clinical data on the effectiveness of PEER versus current standard of care treatment.

I am also pleased to report, we were recently awarded a Federal Supply Schedule (FSS) contract as a government supplier. Under the contract, we may offer Healthcare Information Technology services to federal government departments and agencies related to our Psychiatric EEG Evaluation Registry, including the Department of Defense and the United States Department of Veterans Affairs facilities nationwide.  This contract adds MYnd’s data analysis services to the range of treatment support tools for clinicians in the federal health care system.  We see this as an important market, as the Veterans Health Administration alone encompasses more than 1,400 sites of care, serving almost nine million veterans each year.

Lastly, we are very excited about the merger agreement we announced in a press release and Form 8-K on January 7th, 2018 that will result in current MYnd securityholders owning approximately 5.9% of the surviving commercial-stage biopharmaceutical company, Emmaus Life Sciences, Inc. ('Emmaus'), which is engaged in the discovery, development, marketing and sale of innovative treatments and therapies, including those in the rare and orphan disease categories. Its lead prescription product, Endari, demonstrated positive clinical results in a completed Phase 3 clinical trial for sickle cell disease and received FDA approval in July 2017.  Endari is indicated to reduce the acute complications of sickle cell disease in adult and pediatric patients five years of age and older. The results of the trial were published in The New England Journal of Medicine on July 19th, 2018.  Endari is the first approved medication (FDA approved) for sickle cell disease in almost 20 years.

Emmaus launched Endari in the United States in early 2018 and has experienced strong market uptake.  Endari is reimbursable by the Centers for Medicare and Medicaid Services, and every state provides coverage for Endari for outpatient prescriptions to all eligible Medicaid enrollees within their state Medicaid programs. Additionally, Emmaus has distribution agreements in place with the nation’s leading distributors, making Endari available to selected pharmacies nationwide. Emmaus has received Orphan Drug designation from the FDA which provides protection from competition in the United States. While there are only around 100,000 cases of sickle cell disease in the United States1, there are over 20 million cases worldwide2, which represents a $3 billion global market3. Emmaus management also believes the platform technology has the potential to address other clinical indications such as diverticulosis. 

As a result of the proposed transaction, MYnd securityholders will own approximately 5.9% of the fully diluted shares of the 'New Emmaus' post-closing. Presently, Emmaus is not publicly listed or traded and the value of the New Emmaus shares after the closing of the merger will ultimately be determined by the markets. We will be issuing to the shareholders and other equity holders of Emmaus, shares equal to 94.1% of the shares of the 'New Emmaus' on a fully diluted basis. The final exchange ratio will be determined on a fully diluted basis. In addition, we currently intend that MYnd shareholders as of a record date (which we expect to be a day prior to the closing of the Merger) will receive new shares in connection with a spin-off of 100% of the equity in the entity that will own and operate the predictive analytics and telemedicine business, which we expect to begin trading as a new standalone company.  This is subject to change as the approval process progresses.

In other words, as long as you continue to own your MYnd shares on the record date described above prior to the completion of the Merger, we expect that you will continue to own a portion of the 5.9% of Emmaus being allocated to MYnd securityholders and your allocable portion of the existing predictive analytics and telemedicine company.

Our board believes that significant value will be created for MYnd shareholders through these proposed transactions.

This should be an exciting year ahead and we look forward to updating you on our progress,” concluded Mr. Herguth.

About MYnd Analytics

MYnd Analytics, Inc. (www.myndanalytics.com) is a predictive analytics company that has developed a decision support tool to help physicians reduce trial and error treatment in mental health and provide more personalized care to patients. The Company’s Psychiatric EEG Evaluation Registry, or PEER Online, is a registry and reporting platform that allows medical professionals to exchange treatment outcome data for patients referenced to objective neurophysiology data obtained through a standard electroencephalogram (EEG). Based on the Company’s original physician-developed database, there are now more than 40,000 outcomes for over 11,000 unique patients in the PEER registry. The goal of PEER Online is to provide objective, personalized data to assist physicians in the selection of appropriate medications. To read more about the benefits of this patented technology for patients, physicians and payers, please visit: www.myndanalytics.com.

MYnd also operates its wholly owned subsidiarity Arcadian Telepsychiatry Services LLC which manages a suite of services including telepsychiatry, teletherapy, digital patient screening, curbside consultation, on-demand services, and scheduled encounters for all age groups. Arcadian utilizes patient engagement and re-engagement strategies so that care is effectively completed, helping to comfortably move inpatient care to outpatient, assisting patients in readjusting to their life routine, as well as reducing wait times for mental health treatment. Arcadian’s customer base includes major health plans, health systems, and community-based organizations.

Additional Information about the Proposed Merger between MYnd and Emmaus and Where to Find It

In connection with the previously disclosed proposed business combination involving MYnd Analytics, Inc. and Emmaus Life Sciences, Inc., MYnd and Emmaus have filed documents with the U.S. Securities and Exchange Commission (the “SEC”), including the filing by MYnd of a Registration Statement on Form S-4 containing a Joint Proxy Statement/Prospectus, filed on February 13, 2019, and each of MYnd and Emmaus plan to file with the SEC other documents regarding the proposed transactions. INVESTORS AND SECURITY HOLDERS OF MYND AND EMMAUS ARE URGED TO CAREFULLY READ THE JOINT PROXY STATEMENT/PROSPECTUS AND OTHER DOCUMENTS FILED WITH THE SEC BY MYND AND EMMAUS BECAUSE THEY CONTAIN IMPORTANT INFORMATION ABOUT THE PROPOSED TRANSACTIONS. Investors and security holders may obtain free copies of these documents and other documents filed with the SEC at the SEC’s web site at www.sec.gov and by contacting MYnd Investor Relations or Emmaus Investor Relations.

MYnd, Emmaus and their respective directors and executive officers may be deemed participants in the solicitation of proxies with respect to the proposed transaction. Information regarding the interests of these directors and executive officers in the proposed transaction has been included in the Joint Proxy Statement/Prospectus described above. Additional information regarding the directors and executive officers of MYnd is also included in MYnd’s proxy statement for its 2018 Annual Meeting of Shareholders, which was filed with the SEC on March 1, 2018, as updated in MYnd’s Annual Report on Form 10-K for the fiscal year ended September 30, 2018, and additional information regarding the directors and executive officers of Emmaus is also included in Emmaus’ proxy statement for its 2018 Annual Meeting of Stockholders, which was filed with the SEC on August 23, 2018.

No Offer or Solicitation

This document does not constitute an offer to sell or the solicitation of an offer to buy any securities or a solicitation of any vote or approval nor shall there be any sale of securities in any jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction. No offering of securities shall be made except by means of a prospectus meeting the requirements of Section 10 of the Securities Act of 1933, as amended.

Forward-looking Statements

Except for the historical information contained herein, the matters discussed are forward-looking statements made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, as amended. These forward-looking statements involve risks and uncertainties, such as statements regarding the commercialization of Endari and other market developments, new products and growth strategies, development in the several business initiatives for MYnd, the ability of the parties to complete the merger and related transactions, the ability of MYnd’s products to successfully produce objective data, to improve efficiency in the treatment of depression and other mental health and psychiatric illnesses, to recognize patterns, to predict outcomes and personalize medicine, to improve patient outcomes and to reduce healthcare costs, as well as those risks and uncertainties set forth in MYnd's and Emmaus’ respective filings with the Securities and Exchange Commission. These risks and uncertainties could cause actual results to differ materially from any forward-looking statements made herein.

Contact:

Crescendo Communications, LLCTel: +1 (212) 671-1020Email: mynd@crescendo-ir.com

______________________________1https://www.cdc.gov/ncbddd/sicklecell/data.html2https://www.hhs.gov/blog/2018/09/13/collaborative-patient-focused-approach-to-cure-sickle-cell.html3 https://www.marketwatch.com/press-release/global-sickle-cell-disease-treatment-market-2019-2023-advent-of-regenerative-therapy-to-drive-growth-technavio-2019-01-23

How to Train Someone to Translate Business Problems into Analytics Questions | killexams.com real questions and Pass4sure dumps

Analytics translators perform some of the most essential functions for integrating analytics capabilities in a company. They define business problems that analytics can help solve, guide technical teams in the creation of analytics-driven solutions to these problems, and embed solutions into business operations. It’s specialized work, calling for strong business acumen, some technical knowledge, and project management and delivery chops. While translators can acquire some of the requisite knowledge for the job through coursework, they make the most impact once they have developed practical skills through on-the-job experience. Translator training is one of the most important analytics investments a company can make, because companies seldom capture the full value of analytics without capable translators. The key to training a translation workforce is a multi-tiered progression, in which employees study concepts in a classroom before mastering new skills through apprenticeships. Translators connect the theory and the practice of analytics; their training courses must do the same.

Analytics translators perform some of the most essential functions for integrating analytics capabilities in a company. They define business problems that analytics can help solve, guide technical teams in the creation of analytics-driven solutions to these problems, and embed solutions into business operations. It’s specialized work, calling for strong business acumen, some technical knowledge, and project management and delivery chops.

Deploying translators is especially important during a company’s early efforts to use analytics, when much of its analytics know-how resides in a small cohort of data leaders and practitioners. We’ve seen companies hatch ambitious plans to apply analytics in dozens of situations—only to pull back because they employ too few people who can deliver solutions. That gap should shrink in the long term, as analytics pervades business and analytics training becomes a standard part of employee development. But in the face of competitive pressure, companies cannot wait to work with analytics on a large scale. Translators can help businesses climb the analytics learning curve quickly and roll out more use cases than they might otherwise.

While translators can acquire some of the requisite knowledge for the job through coursework, they make the most impact once they have developed practical skills through on-the-job experience. Yet it is all too common for executives to assume that employees can act as effective translators, capable of delivering analytics solutions, once they complete a class on the rudiments of modeling. In fact, employees who only receive classroom training are more like teenagers who sit through a driver’s-education course, then walk outside and try to drive away—with no behind-the-wheel training, supervised practice, or road sense.

Translators can only master their trade by observing seasoned colleagues at work and then working on actual problems with expert guidance. This progressive, real-world learning approach prepares translators to manage diverse teams of specialists, create replicable workflows, and apply business judgment while assessing trade-offs. None of these steps can be skipped if a company hopes to apply analytics widely and generate significant value.

Recruiting translators and positioning them for impact

Before launching a translator-training effort, executives should map out a company’s analytics strategy and priorities. Then they can determine how many translators are needed in each part of the business—and target recruiting and training programs accordingly.

Translators typically sit within business units, in proximity to day-to-day operations in stores, plants, mines, call centers, and other sites where employees make products or deal with customers. These vantage points let them spot uses for analytics and ensure that analytics solutions are embedded into the business for impact.

Ideally, translators will have spent time working in business operations before starting translator training. Existing business staff often make better translators than new hires because they have an important quality that is hard to teach: knowledge of a business domain where analytics will be applied. To put this another way, business operations are the typical translator’s “mother tongue.”

In addition to business acumen, other qualities companies should look for in internal translator candidates include comfort working with numbers, project management skill, and entrepreneurial spirit. Training curricula can then concentrate on the technical knowledge and practical methods that translators need.

Building basic analytics awareness

The first stage of a translator-training program should equip employees with fundamental analytics knowledge: a basic understanding of how analytical techniques can help solve typical business problems, as well as general familiarity with the process of developing analytics use cases.

This level of knowledge is readily attained from a week or so of classroom training covering:

Translators also need the technical depth to hold their own when discussing problem-solving approaches with data scientists. Many take online tutorials to learn common programming languages, such as R or Python, and learn more complex algorithms. To lead delivery of use cases, though, translators must hone their skills through hands-on practice—much as language students reinforce their classroom learning when they are immersed among native speakers.

Developing the ability to deliver analytics use cases

An analytics use case follows an end-to-end process that is applicable to a wide range of business problems. The translator first helps define a business problem and “translates” it to data scientists in technical terms. She then confirms that the selected analytical technique solves the problem cleanly and efficiently, and she might collaborate with designers if the use case calls for a tool for front-line colleagues.

The process concludes with implementation of the analytics solution, which the translator facilitates by helping users incorporate it into their routines. This often includes explaining to end users what takes place inside the “black box” of a model, so they can be comfortable leveraging the insights it delivers.

Most translators learn the delivery process through classroom or online study and then master them during apprenticeships. They start by observing expert translators on the job and gradually assume more responsibility, culminating with responsibility for teaching others. The typical progression consists of the following stages:

There’s no fixed number of use cases that translators must complete at each stage to progress their abilities. The right number is the number that prepares translators to advance to the next stage, and it can vary with the range and sophistication of the analytical techniques and business problems that a translator deals with, among other factors.

Our experience suggests that translators spend six to twelve months in training. Others may be ready sooner. One translator at McKinsey started training with a degree in engineering and several years of consulting experience, which had taught him to structure and solve business problems. After studying data science in a week-long executive-education course, he worked alongside an experienced translator and then began leading use cases. Now he’s not only a productive translator, but he also serves as a teaching assistant in analytics classes.

Since companies that are just beginning to implement use cases usually don’t have experienced translators, some rely on external translators to deliver their first wave of use cases and oversee their initial apprentices. Once three or four employees have learned to deliver use cases, they can train new apprentices.

Translator training is one of the most important analytics investments a company can make, because companies seldom capture the full value of analytics without capable translators. The key to training a translation workforce is a multi-tiered progression, in which employees study concepts in a classroom before mastering new skills through apprenticeships. Translators connect the theory and the practice of analytics; their training courses must do the same.