By Maureen Andrei Lepatan


We create data everyday. How? We, especially in this generation spend many hours in accessing our social media accounts, doing online shopping, playing games, watching movies online. Part of our daily routine includes internet and technology. By doing so, all of our hobbies generate data that are captured in various places and in different ways.

Every time we post pictures on Instagram, rant something on Twitter and post our status and photos on Facebook, we create a lot of data. There is a corresponding data point every time we comment or like something online. Imagine how many data we can generate everyday if every person of this planet accesses online.The data become closer and closer to infinity. That is why the term “big data” was created.

 With that being said, data analytics is key to handle pool of data. Analytics is about searching for clues that will enable us to find answers to our problems. We find, we analyze and we present our data.

Primary people for conducting analytics are called analysts. The problem would be that they are overwhelmed by massive amount of data and have trouble to handle them properly.

In order to be effective, analysts should master effective and current business intelligence (BI) tools that could help them to interpret the data properly and guide the companies and businesses regarding their strategies and decision making processes.

I started having interest in dealing with data when I was 3rd year in college. Before, I was a Math person. I am the kind of person who likes challenging activities and work on complex subjects. In the pursuit of my Economics degree, I used a lot of data and created graphical representations in order to survive essay crises and  loads of research papers.

Somehow, economics has the same idea as data analytics which is to tell a story out of the representations. The difference lies upon the frequency of the usage of business intelligence tools in data analytics.

Why did I dive into data analytics? It fits my personality, hobbies and skill sets. I am curious in nature and love to learn new things.  I love editing videos, photos and creating infographics and graphical representations. And data analytics made me combine all of these hobbies in data analytics. It enables me to be creative, analytical and communicative all at once. There is no wrong and right approach. I can be my own self. As long as I get the right data, visualize and verbalize them well, I’m good to go.

Data analytics gave me a sense of purpose. I think in this generation, being an effective analytics talent is what the world needs. I do not mean to disregard other jobs. I just want to be realistic about the present and the future. More and more businesses will build their companies using online platforms requiring more data analytics talents. If businesses do not adapt to the demands of the society, they will most likely fail. As a student and future professional, I need to prepare for these changes. Although I have a background in dealing with data, I need to learn timely business intelligence tools and to train myself to be a better data storyteller.

DMAIPH can help analysts and aspiring individuals who want to learn data analytics. The company conducts trainings to help increase effective and efficient analysts in the Philippines and meet the demands of the society when it comes to data enthusiasts.

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Last September 25 and 26, I attended the training of Sir Dan Meyer regarding Data Management and Data Visualization. In a span of two days, I was able to have an overview of how data analytics works and how to use business intelligence tools to tell a story. Moreover,  I was also assigned to tasks like to welcome the guests and to assist Sir Dan in helping the participants to use Tableau since I also need to fulfill my duties as a business analytics trainee.

At first, I was really intimidated with the participants when they introduced themselves. I never thought that the people whom I say “Good Morning/ Hello” to are CEOs and various kinds of analysts in their respective companies. This really reflects that the demand for analytics talents in the Philippines is greater than the supply. When I talked to some of them, they said that companies have sent them to have trainings with Sir Dan and some of them personally wanted to learn to help their companies.

Training people is really a must to adjust in this day and age. As time goes by, more and more data are generated and unstructured data gradually increase. If data continue to produce increments, the world needs more and more analysts to handle them. In the case of the Philippines, Excel still dominates the analytics industry and is used primarily by professionals to conduct data analysis despite the evolution of  business intelligence (BI) tools. On the second day of the training, the practical application of the concepts taught in Day One were applied. Sir Dan tackled about business intelligence tools, data visualization, business dashboards and data storytelling.

I have 5 major takeaways that I want to share with you:

  1. Data Visualization is just half the job. We need to interpret the data correctly and relay the information such that a grade school student can understand the story behind the data. This is in order to create an impact to various kinds of people and encourage decision-makers to make relevant changes in their businesses. Just be simple and precise!
  2. Learning data science and analytics is all about experimentation. We shall be ready for mistakes along the way. We must continuously attend trainings in order to guide us and persistently practice on our own to obtain mastery.
  3. Companies are enchanting because people like them and trust them. As part of a company, we want to reflect the enchantment our companies have to give to the customers. Without the right strategies to be enchanting, people will not believe us leading to a low profitability and a bad reputation. We can be enchanting as analysts if we can deliver the data persuasively and we can work well with other people.
  4. Being an effective data scientist is a combination of being mobile when it comes to changes in technology and being adaptable in dealing with people.
  5. There are three types of analytics which include descriptive, predictive and prescriptive. How do we use them properly? Descriptive analytics can be effectively utilized if we want to know what happened to have insights in present trends. For example, we want to know about the profits in each month from 2015-2017. Secondly, predictive analytics is used to develop projections and provide information what might happen in the future. Expected sales can be best represented by predictive analytics. Lastly, prescriptive analytics is used to know what to do. We can use this especially if we want to build a model out of multiple sources and include many variables.


DMAIPH really provided me a brand new experience. Although I love dealing with data and graphical representations before I become an intern, I felt more impactful when I started my training. I got to help the participants how to navigate Tableau and had to work with wonderful people.  I was able to apply what I learned in the past and at the same time acquire new skills that will be beneficial for me in the future. I look forward to the trainings and more involvement that I can get from the company.

So far, so good.

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Maureen Lepatan is an Economics student in De La Salle University and currently a business analytics intern in DMAIPH. She has a passion in data analytics especially using business intelligence tools such as Tableau and Excel. She has an eagerness to learn data structures such as SQL.



The Rock Stars of Data: Big Data Analytics & Data Management

How to master big data analytics and data management?

The Rock Stars of Data: Big Data Analytics & Data Management

2-day Class: Big Data Analytics and Data Management

June 27-28, 2017

Discovery Suites, ADB Ave., Ortigas Center, Pasig City


Rock Stars of Data Series: Big Data Analytics & Data Management

Data Rock Stars Dan Meyer (DMAIPH) and Dominic Ligot (Cirrolytix) have joined forces to offer a unique training focusing on both the Analysis and the Management of Big Data.

To find out more about our next scheduled public learning session on May 18-19, 2017 in Ortigas or to set-up an in-house training, send an e-mail to

Learning Session Description

Building The Data Value Chain. Data is pervasive – everything we do in the modern world uses and generates data in some shape or form: from web sites we surf, the social media we consume, to the mobile devices we use to connect and communicate. Modern businesses also use and generate data, from financial data, to customer data, to transaction data and sensor data.

But data is only a raw material. Regardless of amount, the real importance of data is only determined by the value people and businesses derive from it. Getting data is the first step. Then the challenge becomes transforming the raw material into a processed good: information. Information enables decisions, and decisions create value.

This session is about the basics of transforming data into information: the data value chain. Attendees will learn how to identify the right data, about how data can be efficiently stored, then transformed into a friendly form for analysis, and finally how data analysis can yield insights.

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This seminar will lightly touch on each aspect of data identification, collection, storage, transformation, and analysis and involve hands-on use of common data management and analysis tools such as Excel, Tableau and SQL, but is designed for those with little to no prior experience with these tools.

Learning Session Objectives

  1. Apply cutting edge technologies to organize, interpret, and summarize Big Data in your business.
  2. Create a process to analyze data and identify patterns not apparent at first glance
  3. Understand the components of The Data Value Chain: Ingestion, Storage, Transformation, Analysis – and how they are all important to deriving value from data.
  4. Learn database manipulation and processing basics using the Structured Query Language (SQL)
  5. Connect a data analysis tool such as MS Excel or Tableau to a database to be able to perform analysis on processed and stored data

In this session, your organization will be able to use:

  • Specific skills to effectively frame the problem you’re addressing to uncover key opportunities and drive growth
  • Critical marketing steps of orientation necessary before engaging tools and technology
  • How to simply and quickly amplify decision making by separating the signal from the noise
  • A framework for asking the right questions, allowing the ability to link analytics to business strategy

In this session, your participants will be able to:

  • Learn the best practices for organizing, summarizing, and interpreting quantitative data
  • Create a repeatable process for analyzing your data
  • Shorten the time between analysis and action to avoid “analysis paralysis”
  • Know how to get from hard data to well-reasoned conclusions

Who Should Attend

  • Business Analysts, Data Analysts and other Analytics Professionals
  • Business professionals who are involved in day-to-day analysis of data.
  • Data analysts who are already performing analysis using spreadsheets but struggle with manual data processing.
  • Managers of analysts or staff who spend a significant amount of their time collecting, analyzing and reporting data.
  • IT and Development Staff that work closely with business leaders and decision-makers.

Section One – Big Data—It’s Not Just Size That Matters

  • Understand the 3 T’s of Analytics: Talent, Technique and Technology.
  • Describe the importance of effectively, analyzing big data in Business today.
  • Develop a Data Map to analyze the Big Data in your Business.
  • Recognize when to employ Descriptive, Predictive or Prescriptive Analytics.
  • Establish clear objectives when analyzing Big Data.

Section Two – Assess Your Current Analytics Culture

  • Define What Is an Analytics Centric Culture.
  • Describe the issues and trends in today’s analytics field.
  • Discover how to find the most important KPIs.
  • Learn how to build better management reports.
  • Optimize your use of MS Excel for Big Data Analytics

Section Three – Using Business Intelligence Tools

  • An overview of BI Tools.
  • Tableau Public Demonstration,
  • Discuss the Concept of Data Visualization.
  • Build A Business Dashboard Prototype.
  • Apply a Process to Present Big Data Clearly.

Section Four – Interpreting Your Data and Analysis

  • Articulate the importance of accurately interpreting Data.
  • Determine how to validate your data analysis.
  • Mitigate and analyze Risk, Uncertainty, And Probability.
  • Spot patterns and trends through Statistical Analysis.
  • Use findings from Big Data to Drive Decisions within your Organization.

Section Five: Presenting the Data Value Chain and Databases

  • Discuss the components of The Data Value Chain and the various users and roles involved in transforming data to value: Database and ETL engineers, Data analysts, Business users.
  • Learn about basic data architecture and the role of databases in processing data.
  • Understand the basics of databases, tables and views.
  • Learn about the Structured Query Language (SQL) and SELECT statements.

Section Six: Data Processing with SQL

  • Discuss the additional value that can be derived from using SQL for Data Processing.
  • Go into detail on various ways of processing and preparing data using SQL.
  • Learn about aggregates, conditions, how to join tables, and run queries within queries.

Section Seven: Accessing SQL Tables with Excel

  • Learn about Open Database Connectivity (ODBC) and how Excel uses ODBC to connect to external data sources.
  • Discover how SQL tables and views can be read by Excel into instant Pivot Tables and Pivot Graphs.
  • Understand how changes in database table or view via SQL Inserts, Deletes, and Updates are reflected on Excel.

Section Eight: Performing analysis of SQL-based data using Excel

  • Learn about how SQL data can be dissected using the Data Analysis functions in Excel.
  • Talk about form tools and macros that can automate manual reporting.
  • Discuss tips for reporting and sharing the results of your analysis.

Minimum Hardware and Software Requirements.

  1. Laptop with Intel Core i3 and 4GB RAM.
  2. Windows OS with Excel 2007 or greater.
  3. ODBC and database connections will be provided during class.

Case Studies and Exercises

Dan and Doc will use case studies and group exercises throughout the two-day class. In these activities, the group is divided into teams. Each team will analyze datasets using the principals learned in the various learning sessions. These exercises will also use elements from the case studies as we progress from finding data, to conducting analysis on the data and finally presenting the data.


Learning Investment for 2-day Seminar:

Exclusive Offer!!

Early Bird Rate

P 12,000.00 + VAT

(Pay the full amount on or before April 20, 2017)

Group Rate (Minimum of 5)

P13,000.00 + VAT

Regular Rate:

P 14,600.00 + Vat

(starting April 21, 2017)

All investments includes: 2-day Analytics Seminar with two of the most in-demand Analytics and Data Management Guru in the Philippines, complete with Training Materials, AM/PM Snacks, Lunch and Certificates.


Dominic Ligot, Data Scientist

Doc’s areas of expertise focus on Fintech, Big Data Analytics, and Digital Transformation.

Click here to see Doc’s full speaker/trainer profile >>>

Daniel Meyer, Analytics Champion

Dan specializes in a variety of analytics themed training and speaking option including HR& Recruitment Analytics, Data Analytics, Data-Driven Decision-Making and Analytics for CEO’s.

Click here to see Dan’s full speaker/trainer profile >>>

Reserve your seat here >>>


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.

Quick Data Science Survey

If you are in the Philippines and work with data everyday in your job, I’d like to invite you to take my survey.

Next week I will be speaking about data science in the Philippines, specifically trying to answer the question, “Just How Many Data Scientists Are There In The Philippines Anyway?”

It’s a short 7 question survey that will help me validate some of my research.

Here’s the link:

Thanks for taking a few minutes to help address on of the biggest questions facing the Philippines today.

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.

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.