This I Why I Do What I Do…

This is why I do what I do…

Ron: Hi Mr. Dan Meyer, I have attended Data Science Philippines Meetup last February 22. And listened to your talk. I am a graduating student, and a former Data Scientist Intern. I want to be a Data Scientist, but Companies need an experienced person to be their data scientist. I just want to ask, what job should I take first to become a Data Scientist soon? Thank you for reading my message, hope you’ll answer me.

Dan: Data Analyst. IT Staff. Development Staff. Anything where you get to use data everyday. This will give you some hands on experience that you can use to more towards data science. Basically you need to get practical experience coding and managing data. Hope that helps. SQL is probably the most versatile language to learn, but any of the ones you see listed in a data science job posting will help.

Ron: Thank you for the response Mr. Dan Meyer, I’ll keep it in my mind, that really helps. I am hoping to be just like you soon.

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 or connect with me directly to set up a free consultation to learn which of our DMAIPH analytics training solutions is best for you.

So Just How Many Filipino Data Scientists Are There Anyway?

One Question I have been asking myself for awhile is just how many Filipino Data Scientists are there anyway?

I ask this  question because based on my observations of job postings and job titles and industry buzz, there are way more data scientist jobs open here in the Philippines then there are actual data scientists to fill them.

And I’m not even trying to separate the “true” data scientists with the “quasi” data scientists that are doing data science but without all the pre-requisites required.

Lumping everyone together into one number, I wonder what it is. An educated guess would be a few hundred at best. Yet, there are literally 1000 job postings out there right now for data scientist.

This speaks to a fundamental lack of awareness across the country as to what truly a data scientist is and what they do. But that’s for another blogpost.

Right now Im simply going to do some research, survey my network and try and come up with a best guess on exactly how many data scientists there currently are here in the Philippines.

Please feel free to contribute to the conversation… its not going to be easy to get a universally acceptable answer without a lot of discourse first.

Let the hunt for a unicorn head count begin!

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Analytics in the Philippines – The Philippines is at the center of the action when it comes to solutions to the global need for analytics. Blessed with a solid foundation of young, educated and English speaking workforce, companies around the world are look for Filipino analytics talent to fill analytics positions. DMAIPH was set up to facilitate these solutions and bring the talent and the business together. Contact DMAIPH now at or connect with me directly so we can help you take advantage of this unique global opportunity.

What % of Companies Can Both Afford Data Science Teams and Understand How to Use them?

Woke up this morning thinking about the future of analytics and data science in the Philippines.

I created this image to help visualize one of the biggest challenges. From my perspective the majority of companies out there don’t really understand data science and probably cant afford a traditional data science team.


Best guess is ever 50% of companies operating in the Philippines aren’t yet at a place where data science is practical.

The smallest group are ones who both understand data science and know how to use it. These are the ones who are players in the data science consulting, training and outsourcing business.

Best guess its about 10% of the companies operating here and just about all of them are big corporations with lots of resources.

The next segment is the ones who do understand the value of data science, but don’t have the resources to compete for a the talent it takes to build a top data science team.

I’ll peg this at about 15%. But its  growing quickly. Awareness and adoption are actually ahead of the talent, as you see so many “data scientists” in name only out there filling open jobs right now.

That leaves the 25% of companies operating in the Philippines who have money and resources but just don’t know how to get started using data science and building a team. This is the target market of companies like mine.

DMAIPH has the capability to educate your managers and decision-makers on how to use data science to add value to the business. We can also train your team in the basics of data science to allow you to cultivate a data driven culture and promote from within to build a data science team. And we can also take some of the load off your shoulders in the terms doing some of the data science for you.

Analytics Consulting – DMAIPH specializes in a variety of analytics consulting solutions designed to empower analysts, managers and leaders with the tools needed for more data-driven decision-making. We have helped dozens of companies get more analytics in their business. Contact DMAIPH now at or connect with me directly so we can tailor an analytics solution made just for your unique requirements.

Data Science with Talas at the Mind Museum

Really looking forward to meeting some of the top minds in Data Science in the Philippines this Saturday, November 26th at the Mind Museum in Taguig.


The speaker line up looks awesome and the networking opportunities will be plenty.

I understand the event is sold out, but I believe there is a waiting list. Contact my good friends at Talas to find out more.

This has been the best month yet for analytics and data science in the the Philippines. The sky truly is the limit!

Hope to see many of you there!  #DSCONPH and #DSCON2016


News & Events- DMAIPH is a highly engaged leader, sponsor and participant in analytics events across the U.S. and the Philippines. As an Analytics Champion I write, blog, speak and lecture about analytics in a wide variety of forums. I authored several publications on analytics including my latest book, Putting Your Data to Work. Contact DMAIPH now at or connect with me directly to learn more about where I will be talking about analytics next.

Quick Analytics Career Question

Greetings to You My Valued LinkedIn Connection,

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. With that in mind, I’d ask you to share your thoughts.

In your opinion, of the following ways to learn about analytics, which one has been the most important in your career path?

  • Formal Education – A degree or certificate in an analytics related field.
  • Self-Learning – Using trial and error and online resources.
  • Subject Matter Experts – Being trained/mentored by an expert.
  • Seminars/Workshops – Attending events to acquire new knowledge.
  • Technical Training – Attend training on specific technical areas.

Thanks for sharing. As always I will roll up all the replies I get and blog about it.

Dan Meyer, Analytics Champion,


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 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.

Which Analytics Training Is For You?

From my perspective analytics training options fall into the following categories:

  1. Introduction to and/or Overview of Analytics
  2. Technical Training on Specific Analytics Topics
  3. Data Science/Advanced Analytics Training

Knowing which type of training you are selecting is super important as people just starting to get comfortable with analytics will probably be lost in a data science training.

On the flip side, a seasoned data geek will get bored in a introduction or overview class.

SO how can you tell which is which?

Here are a few suggestion on how to separate the intro classes from the technical classes from the data science classes .

First, most people who train on predictive analytics, using lots of math, statistics and building data models are probably talking data science. To get your bang for your buck in these classes you need to have a lot of exposure to the science side of analytics and be completely comfortable finding, analyzing and reporting data.

Second, if you are already working with data and you have specific tools you are working with for specific data functions, then you might be best served by going to a technical centric training. Like if you are using a tool like Tableau or Qlik or IBM Cognos to do marketing analytics or sending workforce management reports.

That leaves people who are either new to the idea of analytics or are just not sure where to start. Then you need an overview of analytics to figure out what you need in your business. Once you have a handle on your data, then you can really focus on certain technical aspects and get into data science.

The training I am conducting on November 22 is of the third kind. I will be giving an overview of various analytics techniques and technologies and introducing you to a variety of concepts to make the idea of using data to make decisions a reality.

On February, 21, 2017 I will be hosting a training on Data Analytics. E-mail us at to register or get more info.

As a bonus, all attendees will also get a copy of my new book, Putting Your Data to Work. It’s a guidebook specifically designed for Filipino professionals looking to up their analytics games.


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 or connect with me directly to set up a free consultation on which of our DMAIPH analytics training solutions is best for you.

10 Points Where There is a Need for a Data Science Consultancy

My good friend Albert Gavino recently posted about why there is such a strong need right now for data science consultancies.

Bert is a data scientist in the truest sense of the word. So when he listed 10 reasons, which I think are spot on, I asked him if I could share. The 10 reasons are:

  1. Some (if not most) companies want to get into it, but are not sure if they need it.
  2. They need direction on how to do it.
  3. They need information on how much to invest in data science infrastructure
  4. They need people with skill sets to be able to implement data science
  5. Some are biased towards proprietary software while some like the open source guys like Apache.
  6. CEOs think it’s all about big data
  7. Data Science is continually evolving so don’t ask me about AI and deep learning….it’s still transforming things
  8. How much does it cost to consult for a data science? pretty high because we all know demand and supply in this industry
  9. Recruiters confuse programming languages and tools such as R, Python, SPSS, SAS, matlab, spark, scala, hadoop, hive, mahout (there are just too many out there they would get lost)
  10. There is a gap in our Academic Curriculum where they just teach electives such as Business Analytics which does lack a lot of information to the needs of the industry.


So if the point is not already clear, there is a growing difference between the haves and the have nots when it comes to analytics and data science.

If you want to be with the have and leave all the have nots behind, you have to invest in a good analytics solution, a data science team and some technology to help you handle your big data.


If you want to learn more, please look for my friend Albert Gavino or connect with me. Also, Talas Data Consultancy will be hosting a Data Science Conference on November 26, 2016. I will be there meeting and greeting analysts, data scientists and people interested in how to use data to drive better decision-making.

Analytics Culture – The key to using analytics in a business is like a secret sauce. It is a unique combination of analytics talent, technology and technique that are brought together to enrich and empower an organization. A successful analytics culture is not easy to create, but DMAIPH can show you how. Contact DMAIPH now at or connect with me directly so we can build a strategic plan to turn your company into analytics driven success story.

Q9: Can you please describe the concepts of storing data in a data ware house?

Twenty years ago data was mostly stored in databases. These databases housed all the data a business would need to do analytics. Transaction data, sales data, customer data, demographic data was all neatly collected, stored and analyzed in databases.

A surprising number of companies still store most of their data in databases. It works well for business that just need to look at historical data to conduct basic descriptive analytics.

About ten years ago the amount of data captured in a business and the growing diversity in date sources and data storage brought about the mainstream use of data warehouses in the business world.

Data warehouse are often a collection of databases interconnected so that data can be brought together into one place for reporting and analysis.

Whether you are working with a data base or a data warehouse, you should have a basic understanding of how data is stored. It should be in table format, with header columns and data rows.

A good way to quickly assess the analytics culture of a business is to look at how data is shared among management. Does it look table like? Or is it obvious that most of the time spent by the author was put into decorating? If you can’t easy sort something, then you are not dealing with a good data culture.

The best way to have a good data culture is to have well documented data structures. Any dB admin worth a grain of salt has the data hierarchy mapped out and has a knowledge base to help users know what data is in each field.

Like with finding data, being good at storing data starts with knowing the environment. Any good analyst should have a basic understanding of how to use SQL to pull a query for a data table. Even if you cant do hard core coding, know how data is generally stored in a structure is key.


Another important concept about data warehouses if you have to know how to join or blend data from different sources. When you have multiple data tables in a warehouse you often need to join the data on a common field. Data blending goes on step further as you are often trying to take data that doesn’t have a natural point on common that is easy to join on. Advanced data warehouses and data management tools can blend things easily, but its still important to understand the core concepts of how to join and blend data.

As I mentioned in earlier posts, there is now a new concept taking root that one up data warehouses. Data lakes are being used to address the fact that we have more unstructured data then we have structured data. Data bases and data warehouses were designed only to handle structured data the easily fits into a data able.

Now we have to collect data from images, videos, blogs, comments and other places that are not easily converted to a value. Data blending across both traditional structured data warehouses and new types of data is not easily done in most data warehouses so tools are being developed to bridge this gap.

The lake is no longer a place just to fish, but also to do all the other things a lake can be used for.

So, when it comes to understanding data warehouses, learn who built and/or maintains it and buy them a cup of coffee. Get your hands on the data dictionary, knowledge base, FAQ, metadata.. whatever you can to map out the data environment. If you do that then you can find use the big data stored in a data warehouse to find the right data at the right time.

Q7: What exactly is data science and why the rapid rise of data scientists?

A year ago I might have found it challenging to really answer this question. The first time I had heard of the term data science and a data scientist wasn’t that long ago. And I have been doing some pretty advanced analytics for close to 20 years now.  I know the term has been around in academic and research circles awhile longer, but 2014 is the first time I ever saw a job posting for data scientist in big business.

So what is data science? Besides simply being the study of data, it generally refers to using complex models, machine learning, predictive and prescriptive analytics and powerful technology to analyze business data in much greater volume, velocity and variety then possible a few years ago.

And of course the ones charged with doing the data science are data scientists. They understand math, statistics, and theories that can be applied to business data using new technologies and methodologies.

The biggest challenge to being a true data scientist is that you have to be adapt at both technology and working with people. Being a business data expert, knowing how to code and doing higher math are only half the job. You have to also share your data, communicate it in ways that drive action, share and engage with non-data centric people. It’s hard to find people who are good at both.


Image from Forbes Magazine. 

In addition, whole some data scientists are educated to be data scientists, very, very few actually have any kind of degree in data science. That kind of degree really didn’t exist until very recently. Instead most data scientists have advanced degrees is related subjects and have migrated into the business world do to market demand.

That demand has been growing at a staggering rate the past few years as every day we generate more and more data across the planet. President Obama first employed a data scientist for his campaign in 2012. The White House now has a chief data scientist position.

If you were to compare results from job board searches form 2012, you’d see maybe 100 data scientist job postings. Now its easily in the 1000’s.  So that’s why the job market for data scientist is one of the hottest around.  Lack of training programs, having both tech and people skills, and the booming demand due to unending new data to being analyzed.

Some people ask me if I’m a data scientist I am careful with my answer. True data science is not something I am academically prepared for nor I have never published anything in a scholarly journal. But my real world experience working with data has made me an expert on many aspects of data science.

I guess I feel more like an analyst, but a freakin awesome analyst who can do a lot of things using data that are super important to a business.


Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. As a key parnter of the Data Science Philippines Meetup Group, 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 analytics. Contact DMAIPH now at 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.

Q3: What are some of the current trends in analytics?

Every few months I devote a day to discover what are the current trends in analytics. I do this both to refresh the slides in my presentation and to refresh my mind to see what I may have missed.

The amount of literature out there on analytics continues to blossom at an amazing rate, making it a true challenge to stay well versed on what’s hot and what’s not. I read a new analytics themed book about once a month and I have well over 200 blogs, web sites and social media groups cataloged. So I like to think I’m pretty well versed on what is current.

Every time I go to list the top 5 analytics trends, I find that some things change and some stay the same. Ever since I have been doing this, data visualization is near the top. Business dashboards continue to be a big need. Business intelligence tools evolve and new ones’ pop up, but Tableau continues to be a market leader. 90% of us still use Excel for 90% of our analytics work.


Still a lot has changed. When I started this just 5 years ago no one was really talking about Big Data or Data Science. People just stared discussing using predictive analytics and now its all about prescriptive, even though most of us are still just doing descriptive analytics. For the newbie, descriptive = historical, predictive = forecast models, and prescriptive = really complicated models with a lot of variables to not just predict the future but to show a lot of alternatives as well.

Now if you talk to experts they make think nothing I have mentioned so far is new. But to the novice analyst or to the manager who really doesn’t care what’s it called, she just want’s results… its all new to them.

So I try each time to really find something really new not just to me but truly new to analytics. Six months ago that was the idea of using a data lake instead of a data warehouse. For those still unsure what a data warehouse is, it’s a collection of databases stored and/or connected centrally. Data lakes are used to describe the reality that more and more data is now unstructured data.

The discussion on what is unstructured data and how best to mine it and integrate it with structured data has really been at the forefront for a while now. Going from 80% structured to 90% unstructured in in just a few short years as mankind generates unprecedented amounts of data not easily captured in a database every day.

As of today, if I had to pick 5 topics to talk about it would be (1) Hiring Data Science and Analytics Talent, (2) Big Data Analytics, (3) Data Warehousing and Data Lakes, (4) Data Blending and (5) Mining Public Unstructured Data

Check back with me in a few weeks and this list will change.

The Fundamental of Business Analytics – Business Analytics is the application of talent, technology and technique on business data for the purpose of extracting insights and discovering opportunities. DMAIPH specializes in empowering organizations, schools, and businesses with a mastery of the fundamentals of business analytics. Contact DMAIPH now at or connect with me directly to find out how you can strengthen your business analytics fundamentals.