Working on an Analytics Internship/OJT program…

400 Hour DMAIPH Data Science & Analytics OJT/Internship Program

The end goal is to develop a DSA strategy presentation for manager. Start out by getting to know the physical data environment, the tools being used and the main players in the business. Move on to assessing the maturity of the analytics culture and it’s use of DSA talent, techniques and technology. Design a business dashboard prototype and deliver a compelling data story to improve management reporting.

Three tracts for interns… HR Analyst, Business Analyst and Data Analyst.

Interns will spend 60% of the internship at the place of business and 40% of the internship in a classroom. This will facilitate the application of theory to real business data in order to help managers get a better idea of the what’s working and what’s nor when it comes to the data in their business.

Based on the APEC DSA Competencies which is close to being adopted by 20+ countries across Asia and the Pacific as a guide for current and future DSA training efforts.

 Week 1 – Fundamentals of DSA

  • APEC DSA Competencies
  • Company Background
  • How This Internship Works

Exercise: LinkedIn Profile

Company Deliverable: Company/Organization DSA Profiles

Week 2 – DSA in the Philippines

  • Putting Data into Context
  • Emerging Trends
  • Cultures of Innovation

Exercise: Glossary of Data

Company Deliverable: Defining Where the Cutting Edge Is

Week 3 – Data Management & Governance

  • Data Management Macro View
  • Data Governance
  • Information Security

Exercise: Data Survey

Company Deliverable: Info Security Risk Assessment

Week 4 – Data Analytics Methods & Algorithms

  • Data Management Micro View
  • The Right Data
  • Machine Learning

Exercise: Who’s Who of Data in the Business

Company Deliverable: Data MVPs

Week 5 – Data Science Engineering Principles

  • Data Map
  • Identify Right App
  • Feedback Loop

Exercise: A Visio Data Map

Company Deliverable: Map of Business Data Lake

Week 6 – Computing and Computational Thinking

  • MS Excel
  • Query Data
  • Programming Languages

Exercise: Top 10 Excel Tips Video

Company Deliverable: Top Ten Data Tips

Week 7 – Statistical Techniques

  • Getting IT
  • Analytics Maturity Model
  • Predictive Analytics Model

Exercise: Flight Risk Model

Company Deliverable: Results of Maturity Assessment

Week 8 – Operational Analytics

  • Management Reporting
  • Public Big Data
  • Business Dashboards

Exercise: Tableau Public Mock Up

Company Deliverable: Business Dashboard Prototype

Week 9: Data Visualization & Presentation

  • Data Visualization
  • Enchantment
  • Data Storytelling
  • Exercise: D.R.A.P.S
  • Company Deliverable: A Business Data Story

Week 10 – Final Project/DSA Strategy Presentation

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My goal is to create and promote a hybrid approach that offers both supplemental education and hands on experience. We need to get past the days of having OJT do data encoding or simple research projects… they need skills that they can apply day one.

They need it, we need it, the country needs it.

Any ideas or suggestions? This is just the first draft.

Hoping to roll this out in the next month or so.

Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. As a key parnter of the Asia Pacific Economic Cooperation’s Project DARE initative, DMAIPH champions the use of using data. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. We can empower students and their instructors with the knowledge they need to prepare for careers in data science and analytics. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can set a guest lecturer date, On-the-Job Training experience or other analytics education solution specifically tailored to your needs.

The Analytics Puzzle for Higher Education in the Philippines

When you look at the picture on the box of puzzle pieces, you generally think it won’t be so hard to fit all the pieces together. But then when you lay out all the pieces and connect them one by one it can often feel like a sense of this is a lot harder then I thought.

In many way, that’s how I feel about efforts to date regarding the teaching of Data Science and Analytics in the Philippines. The end product is clear, just about all the 2,000+ HIEs across the Philippines offering some level of DSA education to a wide range of students.

Everyone agrees that we need more education to meet both the high current demand and the expected huge future demand for DSA talent for both domestic and global consumption. We have seen a lot of awesome initiatives popping up trying to train educators to teach DSA subjects and have seen a number of industry-academe partnerships. CHED has even set aside significant resources to promote the training of faculty and the incentive to offer DSA programs.

So things are going well, but when you look at the simple math of how many educators need to be training in the very near future, some like me get a little concerned. Current programs train a few dozen here and maybe a few hundred there, bit by bit. But if you need thousands then current efforts are just going to come up short.

What we need is a unified front. Bringing together all the interested parties, many of whom are already working on this issue, is the only way to get to critical mass. By my estimation we should be looking at training 5,000 educators in the next 3 years. And a one week overview is just the start. To really become adept at teaching DSA, educators need an apprenticeship that lasts months to really learn the tools of the trade like data storytelling, business intelligence and predictive analytics.

And that is just the faculty… when you think about the 100,000s of students who need to taught DSA, you start to see that this puzzle is gonna take a lot more effort to complete then it may have looked like at first.

So thats where I am at now… both evangelizing and empowering. Raising awareness of what the puzzle looks like when solved and why we need to solve. And empowering to build collaborations to connect the pieces faster then each puzzle expert can work on their own.

And that is exactly why I started Augment BPO.

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Augment BPO. The Augment BPO Data Science and Analytics Advocacy Project (Augment BPO) is empowering BPO Companies, Executives, and Workers in the Philippines to prepare for and address the clear and present danger posed by Artificial Intelligence Chatbots (AI Chatbots) to BPO revenue growth and jobs through Data Science and Analytics strategy planning, awareness building and upskill training.

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DMAIPH Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. As a key parnter of the Asia Pacific Economic Cooperation’s Project DARE initative, DMAIPH champions the use of using data. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. We can empower students and their instructors with the knowledge they need to prepare for careers in data science and analytics. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can set a guest lecturer date, On-the-Job Training experience or other analytics education solution specifically tailored to your needs.

APEC Data Science & Analytics Key Competency #4: Domain Knowledge and Application

According to the APEC (Asia Pacific Economic Cooperation) Advisory Group, Domain Knowledge and Application is one of the key competencies of a Data Science & Analytics professional working in the region.

By definition, a DSA professional can apply domain-related knowledge and insights to effectively contextualize data, achieved by practical experience (e.g. apprenticeships) and exposure to emerging innovations.

In my own experience, I knew Wells Fargo data like the back of my hand, but my domain knowledge would have easily allowed me to the same great things with other big banks. When I toyed with the idea of moving into the health services industry, it was obvious my skills would be useful but I had a lot ot learn about the domain knowledge of healthcare data.

Since, domain knowledge represents knowledge and insight that is unique to the organization or industry and that analysts need to consider when conducting any data project. Without this knowledge, analytics solutions may not entirely address the real business problem.

In my experience, domain knowledge about the data being analyzed can sometimes be acquired through exploration of the raw data.  Often, good analysts become subject experts just by playing with the data and asking questions to domain experts about the data.

Given the dearth of analytics talent in many areas, reality will dictate that a lot of data projects will have to be done without sufficient domain knowledge. However, most experts would agree the best results come when the ones using the data, know the data.

So, it behooves companies to invest more in educating and enabling internal resources then looking outside for DSA talent. My solution to this is to introduce apprenticeship programs where subject matter experts train current staff with high DSA affinity who are currently working in other roles.

As an example, there are likely thousands of current call center agents who have the aptitude to be analysts an data scientists, but never had the opportunity to of into DSA. Given they are already employees with proven track records of success, they would be much more likely to have the domain knowledge needed.

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Analytics Training – DMAIPH offers a wide range of analytics centric training solutions for professionals and students via public, in-house, on-site, and academic settings. We tailor each training event to meet the unique needs of the audience.

If you need empowerment and skills enhancement to optimize the use of analytics in your organization, we are here to help. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to set up a free consultation to learn which of our DMAIPH analytics training solutions is best for you.

Most Impactful Ways To Becoming a Great Analyst

As a follow up to a survey I sent to several hundred of my LinkedIn connections a few months back. I sent the survey to connections working with data analytics on a regular basis.

I told them I was talking with a young professional just getting started in his analytics career. During our conversation we discussed what is most important to being a great analyst. I asked for their opinion, of the following ways to learn about analytics, which one has been the most important in your career path?

  1. Formal Education – A degree or certificate in an analytics related field.
  2. Self-Learning – Using trial and error and online resources.
  3. Subject Matter Experts – Being trained/mentored by an expert.
  4. Seminars/Workshops – Attending events to acquire new knowledge.
  5. Technical Training – Attend training on specific technical areas.
  6. Other – Please provide a brief description

 

Here are some pearls of wisdom from some of the replies I got… keep in mind these are all from well established analytics professionals:

“the most important in my career path is self-learning as most of the things we’re doing like journal reading and implementing algorithms needs less dependency with the people you are working with but requires collaboration with them and this requires you to solve problems by yourself and implement things by yourself but communicate them properly to the people you’re working with.”

If you don’t have the drive to do this, you will be an analyst in name only.

“Self-Learning – Using trial and error and online resources. World changes too fast for any of those others to matter.”

In the 15 years I have been doing analytics without any formal education in data-related fields I can say that this is 100% true. To be a great analyst you have to always been learning.

“your ability to tell the story behind all the numbers will make you an indispensable asset and an outlier in the increasingly growing population of analysts. Management almost always don’t have the time to read all the numbers in tables, spreadsheets, and reports, but they will surely appreciate instantly seeing the big picture presented in a “one-pager” report prepared by analysts. That almost always makes an analyst “great” in the eyes of the report consumers.”

Yes! If what you spend your blood, sweat and tears on does influence decisions, then you are working on the wrong place.

#2 and #3 have been important. Having a mentor to coach you side by side gives you accountability to progress in your work and learning curve. Naturally, this will push you to self-learn: trying out what you’ve learned and testing.”

Having a mentor is so important to help you get access to the actual learning experiences you need to evolve and excel as an analyst.

“spending time with the consumers of the analytics is especially important. Learning how they look at the data, and/or want to look at the data. What is important to them. What really matters. Too much non-essential information and you’ll loose them. Targeting their needs with relevance and precision will win them.”

Another big point that is often overlooked, if you are spending time producing repots and they don’t take customer insights into account, you have a big blindside.

“The most important thing to being a great analyst is to have a great sense of awareness. For me, self-learning has been the most important.”

I cannot imagine anyone being a great analyst if you didn’t have at least a bit of an ego mixed in with a Superman complex. Great analysts live to fix things.

It’s interesting to note that only 2 of the 50+ replies I got form surveying my LinkedIn connections said formal education is the most important.

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Analytics Survey – DMAIPH conducts quarterly analytics surveys to collect data on current trends in analytics. We specialize in surveys that assess analytics culture and measuring how aligned an organization is to using data and analytics  in its decision-making. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to find out more about how DMAIPH can conduct surveys to help you assess the analytics culture in your business.

 

 

Analytics and Data-Driven Decision Making – Webinar on Feb 15

With Daniel Meyer, President and Founder, Decision-Making, Analytics & Intelligence Philippines… that’s me! 🙂

I have the honor of conducting  a webinar next month in partnership with American University.

Here is the promotional copy for the webinar… hope some of my followers like you are able to join us. 

Evaluators can learn from the ways that the corporate sector uses business analytics to understand, interpret, and display Big Data. Key aspects from the corporate sector that are useful for monitoring and evaluation include identifying what data is important, and finding ways to visualize it for consumption. In this webinar, Daniel Meyer discusses analytics solutions relevant to measurement and evaluation.

Daniel Meyer is passionate about solving problems by bringing together the best talent, cutting edge technology, and successful methodologies. He is an expert on data-driven decision-making, multi-industry analytics and business intelligence. He is the author of Putting Your Data to Work and the Fundamentals of Business Analytics. Learn more about Dan Meyer.

Webinar details:

February 15, 2017

1pm Eastern

Webpage with webinar registration links: http://programs.online.american.edu/msme/webinars

 IMG_6912Daniel Meyer

Daniel Meyer is President & Founder of DMAIPH (Decision-Making, Analytics & Intelligence Philippines), an analytics, consulting, training and outsourcing company with offices in Manila and the San Francisco Bay Area. Mr. Meyer is one of the top analytics experts in the Philippines. With a team of over 40 analysts, DMAIPH provides a variety of analytics solutions to companies in the U.S. and Asia. Mr. Meyer is also one of the most sought after public speakers in the country and has personally trained thousands of Filipinos in various analytics functions. Before setting up his own company, Mr. Meyer worked as a Senior Analytics Consultant for Wells Fargo Bank for 15 years. Mr. Meyer provided executive management analytics for the bank’s Remittance Service including developing business dashboards, overseeing competitive intelligence gathering, managing data analytics outsourcing projects and facilitating audit and risk management. Mr. Meyer recently published Putting Your Data to Work, an analytics guidebook designed to provide organizations with a solid foundation in using analytics to empower more data-driven decisions. Mr. Meyer earned a B.A. in History with a minor in International Studies from Sonoma State University and a M.A. in Education with a focus on Student Affairs in Higher Education from the Indiana University of Pennsylvania.

 

It’s No Longer Just Enough To Know

In a recent conversation about using more analytics in the measurement and evaluation of public policies and programs, one of my colleagues said that in today’s world, “It’s no longer just enough to know.”

The point being if you aren’t using data and analysis to enhance your efforts and empower decision-makers with actionable insights, then you are not serving the public to the best of your ability.

A lot of government programs, non-profits and philanthropic organizations are what he called, “Information Rich, but Data Poor.”

Check out my upcoming webinar on Feb 15, 2017! https://dmaiph.com/2017/01/14/analytics-and-data-driven-decision-making-webinar-on-feb-15/

Just because you gather massive amounts of information in the form of data points, does not mean the data is adding value. In fact one of the biggest challenges the corporate world has been dealing with the past few years is how to optimize Big Data.

We live in a world where so much data is produced and captured, then analyzed and published in reports and article, yet the data and analysis alone is often not having the impact our policies and projects were intended to have.

In effect, we might know things, but we aren’t able to influence decisions because our data is not compelling enough.

To this end, I have advocated importing some analytics themed best practices from the corporate world to educate more on what to do with the data and how to put the data to use. To in short, be Information Rich, Data Rich to move towards more Data-Driven Decision-Making.

Starting backwards, I will first focus my training on the How. How do we make more data-driven decisions?

The I will focus on the Why. Why do we need to make more data-driven decisions?

From there we will go into several business analytics concepts like Data Visualizations, Public Data Mining, Data Lakes, Demographic Profiling using Big Data, and Data Blending.

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A final topic of interest that I will bring to the discussion is the Plus Minus Implications for Unstructured and Qualitative Data. Things that at first can be hard to assign a number too, but are just important as any piece of traditional data used in decision-making.

At the conclusion of my work, public policy and project reporting will be much more data rich, influence will improve and decision-making enhanced.

Now we won’t just know, we will be able to champion what we know in ways that will make a difference.

Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. We can empower students and their instructors with the knowledge they need to prepare for careers in analytics. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can set a guest lecturer date, On-the-Job Training experience or other analytics education solution specifically tailored to your needs.

My Blog Post on the Analytics of Measurement and Evaluation

I recently had the honor of writing a blog post for American University’s Measurement & Evaluation program.

The post is about using a business analytics approach to “effectively evaluate projects, programs and processes.” This is in a response to the fact that it is becoming increasingly essential to use big data and analytics to ensure organizational success.

Here is the link: http://programs.online.american.edu/msme/resource/measurement-and-evaluation-analytics

It is my hope that I can inspire readers to look for ways to bring new data into their projects, programs and processes, blend it with current data, provide more dynamic analysis and share more impactful results.

I will also be doing a webinar early next year entitled Its Not Longer Just Enough To Know. Where I will highlight some techniques and technologies that I use to empower more data-driven decision-making.

Facilitating a mastery of the fundamentals of analytics is what I do best.

All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. Though my company DMAIPH, I can equip students and their instructors with the knowledge they need to prepare for careers in analytics. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can set a guest lecturer date, internship, on-the-job training experience or other analytics education solution specifically tailored to your needs.

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The Analytics of Project Measurement

Peter Drucker perfectly summed up why big corporations rely so heavily on analytics when he said “What gets measured, gets managed.”

A successful analyst is able to remove the noise when analyzing data and isolate what matters to his or her organization.

With most companies collecting large amounts of data, you need to be both talented and disciplined to pinpoint key insights that can yield value.

In the corporate world, business analytics is widely use to track, analyze and report Key Performance Indicators (KPIs).

KPIs are rolled up to senior leadership to drive business strategy, identify and mitigate risk and to optimize operational productivity.

This approach is very similar to the way projects in the Measurement and Evaluation are tracked, analyzed and reported.

I would define measurement simply as the act of measuring to ascertain the impact, size, level of success, etc. of a specific data set.

There are many components to measuring projects making sure the project is on schedule, stays in scope, is not over budget, the quality of work is up to par, the end goal of the project remains relevant, and finally if the project is ultimately deemed a success.

A foundation in analytics will contribute to a more optimal and efficient process of measurement. Like businesses do with KPIs, you should start will identifying that are the key measurements your project will be judged on.

Once you know those data points, then figure out how to collect them, analyze them, and report them.

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At each step you can look for new data, validate existing data and blend data from other sources to add value to your measurement efforts.

Once you get to the reporting phase you can look for cutting edge techniques in data visualization and interactive reporting like dashboards to help educate and empower your audience.

That is how it is done in the corporate world where business analysts boil down massive amounts of big, often unstructured data into a few bullet points that allow decision-makers to take action.

When it comes to the Measurement of Project Evaluation, understanding various analytics solutions can make all the difference.

Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. We can empower students and their instructors with the knowledge they need to prepare for careers in analytics. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can set a guest lecturer date, On-the-Job Training experience or other analytics education solution specifically tailored to your needs.

The Analytics of Measurement and Evaluation

By taking inspiration from the way corporations use business analytics to optimize their Big Data, our Program Measurement and Evaluation processes can be greatly enhanced.

To understand the connection, let’s start with the mission of the Measurement & Evaluation program.

“The ability to effectively evaluate projects, programs and processes is becoming increasingly essential to organizational success today. American University’s online Master of Science (MS) in Measurement & Evaluation provides you with the knowledge to lead these evaluation efforts and the technical skills needed for analytically demanding roles in upper management.” 1

A good analytics solution constructs a universal framework for collecting, analyzing and utilizing data to determine project effectiveness and efficiency.

Likewise, an efficient measurement and evaluation of projects, programs and policies using analytics should ensure success. An analytics centered approach will likely work with corporate, non-profit and governmental organizations across various sectors and industries.

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We can look specifically to two key business analytics concepts I have used in my twenty plus years of analysis work; Key Performance Indicators (KPIs) and Data Visualization. The key to my success was my ability to answer important business questions using analytics.

Analytics is generally defined as the discovery of patterns in data that provides insight and identifies opportunities. As Carly Fiorina, former CEO of HP said about analytics, “The goal is to turn data into information, and information into insight.” 2

Organizations that invest in analytics generally make much better business decisions then one’s that don’t. In fact, IBM found that organizations who use analytics are up to 12x more efficient and 33% more profitable. 3

In the corporate world, business analytics is widely use to track, analyze and report Key Performance Indicators (KPIs).

KPIs are rolled up to senior leadership to drive business strategy, identify and mitigate risk and to optimize operational productivity.

This approach is very similar to the way projects in the Measurement and Evaluation are tracked, analyzed and reported.

So we need to ask ourselves, what are the KPIs for the project, program or process we are measuring? What points of data need to be captured, analyzed and reported to determine success?

A successful analyst is able to remove the noise when analyzing data and isolate what matters most to his or her organization. That is what is at the heart of measurement, knowing what data is important and what is not.

Once we have the right data, we can measure what the data tells us to determine success, causality, impact… whatever the outcome may be.

A quote often attributed to management guru Peter Drucker perfectly sums up why big corporations rely so heavily on analytics when he said “What gets measured, gets managed.”

Similarly, policy decisions can be made based on what is measured. Project funding can be impacted by what is measured. Process optimization can be directed by what is measured.

Once we are able to measure what is truly important to policy-makers, managers and decision-makers, we need to make sure we present the data in a compelling way.

This is where data visualization comes in.

I often make the analogy that if a picture is worth a thousand words, then a good pie chart is worth a thousand rows of data.

We all know that most people learn more by seeing something then by reading or hearing it. Data visualization takes that a step further.

Data visualization is not only important to presenting our insights but also for exploring the data for insights. Most people find it easier to process information when it is in the form of a picture then a collection of data.

Chip & Dan Heath, Authors of Made to Stick, found that, “Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful.”

The ability to take all of the data gathered in the measurement phase and use it in the evaluation phase will make a significant difference in the success of the project, program or process you are working on.

According to the Office of Planning, Research and Evaluation, “Program evaluation is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs, particularly about their effectiveness and efficiency”. 5

Data Visualization can be used to paint a picture of a program, project or policy that influences outcomes based on the KPIs. And by appealing to the basic human fascination with stories, a persuasive graph, chart or infographic can make all the difference in the world.

By adopting the business analytics concepts of KPIs and Data Visualization, and applying them to the world of programs, policies and projects, you can find the same level of success I found in the corporate world.

  1. American University, “Certificate in Measurement & Evaluation” http://programs.online.american.edu/online-graduate-certificates/project-monitorin Accessed October 20, 2016
  2. Carly Fiorina Speech from December 6, 2004 http://www.hp.com/hpinfo/execteam/speeches/fiorina/04openworld.html . Accessed October 20, 2016
  3. Simon Thomas, Senior Analytics Consultant for IBM https://youtu.be/Zi8jTbXnamY . Viewed October 20, 2016
  4. Chip & Dan Heath, Authors of Made to Stick, http://heathbrothers.com. Accessed October 20, 2016
  5. OPRE, http://www.acf.hhs.gov/opre/resource/the-program-managers-guide-to-evaluation-second-edition. Accessed October 20, 2016

Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. We can empower students and their instructors with the knowledge they need to prepare for careers in analytics. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can set a guest lecturer date, On-the-Job Training experience or other analytics education solution specifically tailored to your needs.

The Analytics of Project Evaluation

When looking at how to use more analytics in program evaluation, let’s start by getting a standard definition.

Per Wikipedia, Program evaluation is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs,[1] particularly about their effectiveness and efficiency”.

This is very much like business analytics in how business leaders look at the analysis of business data to answer questions, identify opportunities and mitigate risks.

Program effectiveness can be measured many ways. Like how a cost-benefit analysis or market penetration report could be used by a company to assess the success of a new product or service.

Program efficiency can be measured using elements of Six Sigma or Lean. Looking for waste or defects in the end results of a project can lead to discoveries of poor implementation or biased data collection.

Another primary goal of project evaluation in both the public and private sectors, is providing stakeholders with information on “whether the programs they are funding, implementing, voting for, receiving or objecting to are producing the intended effect.”

To achieve this goal, you need a system to gather, analyze and report data. Like in any analytics project, the key is finding the right data and using it to answer questions, educate your audience and provide meaningful insight.

Answering questions like, “how much the program costs per participant, how the program could be improved, whether the program is worthwhile, whether there are better alternatives, if there are unintended outcomes, and whether the program goals are appropriate and useful.[2] will indicate the level of success the program achieved.

There are many analytics techniques like data blending to bring in supporting data form outside the program. Predictive models can show where the project would go if it continues to get funding. Data visualization can also be used to help illustrate findings that can be useful in program evaluation.

Just off the top of my head, I can see a lot of opportunity for the use of a business analytics approach to Project Evaluation. There is a lot of common ground in methodology and reporting, but I think bringing in some cutting edge business analytics to the mix would allow even more insightful and actionable project evaluation.

Let’s find out.

1, 2  https://en.wikipedia.org/wiki/Program_evaluation

Evaluators can learn from the ways that the corporate sector uses business analytics to understand, interpret, and display Big Data. Key aspects from the corporate sector that are useful for monitoring and evaluation include identifying what data is important, and finding ways to visualize it for consumption. In my upcoming webinar with American University on analytics solutions, I will be talking about how analytics is relevant to measurement and evaluation.

Webinar details:

February 15, 2017

1pm Eastern

Webpage with webinar registration links: http://programs.online.american.edu/msme/webinars

Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. We can empower students and their instructors with the knowledge they need to prepare for careers in analytics. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can set a guest lecturer date, On-the-Job Training experience or other analytics education solution specifically tailored to your needs.