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.



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