Data Analytics in the Philippines: Unlocking Unlimited Potential

In recent years, the Philippines has seen a growing interest in data analytics, with organizations and companies investing in this field to improve their decision-making processes and gain a competitive edge. This trend is especially clear in the finance, healthcare, and e-commerce industries, where data analytics has become an essential tool for success.

The Philippine government has also recognized the importance of data analytics and has taken steps to promote its use. The Department of Science and Technology (DOST) and the Technical Education and Skills Development Authority (TESDA) have started to offer free online training courses for data analysts to fix the skills mismatch and shortage that has been seen in this field. This training is intended to cater to unemployed individuals, including high school graduates, and equip them with the necessary skills to enter the job market.

The Analytics Association of the Philippines estimates that by 2028, the country will need 500,000 analytics professionals. This need is not just in the IT field; data analysts are also needed in many other business fields. The entry-level salary for data analysts in the Philippines can go as high as P25,000 per month, making it an attractive career option for many.

To meet the demand for skilled professionals in this field, universities in the Philippines are starting to offer courses and degree programs in data analytics. The University of the Philippines Diliman has a Master of Science in Data Science program, while Ateneo de Manila University has a Bachelor of Science in Data Science program. This move by universities is important for making sure that the Philippines has enough skilled workers to meet the growing demand for data analysts.

It is also important for companies and organizations to talk to people in the industry to find out what skills are needed for jobs. As Sherwin Pelayo, executive director of the Analytics Association of the Philippines, said, “We talked to our industry members, and they said that if these skills were taught in senior high school, we would hire those who possess them.” The executive director of Philippine Business for Education, Justine Raagas, agreed with this point of view and stressed the need to talk to these sectors because they know what is needed in the workplace.

The Department of Trade and Industry has also emphasized the importance of knowing the skill sets needed by companies and training people based on these needs to ensure their employability after graduation. This approach ensures that individuals are equipped with the necessary skills that are in demand in the job market.

In conclusion, the Philippines is slowly but steadily embracing data analytics, and we can expect more changes and improvements in this field in the coming years. The government, universities, and private sector must continue to work together to make sure that the workforce has the skills it needs to meet the growing demand for data analysts. By doing so, the Philippines can position itself as a hub for data analytics in the region, and its workforce can contribute to the country’s economic growth and development.

Don’t miss out on more of Dan’s expert content. Follow his social media channels for exclusive tips, insights, and valuable information on data science and analytics.

Daniel Meyer is the head of Sonic Analytics, an analytics firm that has been in the Big Data industry for over 20 years and has offices in Manila, the San Francisco Bay Area, and Ocala, Florida. He is an accomplished author, public speaker, and business expert specializing in virtual staffing and process automation.

Dan is known for providing big data analytics solutions, including business intelligence and data storytelling, to small businesses seeking to improve their use of data, virtual staffing, and technology. He strongly believes in using analytics for civic responsibility, and offers training, consulting, and education to promote this advocacy.

With his experience in training over 10,000 Filipinos, Dan is passionate about empowering the youth with valuable skills, such as graphic design, video editing, and data analytics. His objective is to equip them with the necessary abilities to harness the dynamic employment opportunities that lay ahead for millions of Filipinos.

Why is A.I. So Scary?

When we think of artificial intelligence, a lot of us think of what we see coming from Hollywood. Movies like the Terminator, War Games and the Matrix color our imaginations to see A.I. as a villain and something to be feared. But it shouldn’t be so scary.

“AI is everywhere in tech right now, said to be powering everything from your TV to your toothbrush. In short, it’s making decisions that affect your life whether you like it or not.” 1

Today we all walk around with A.I. in our pockets (Siri), running our homes (Alexa), giving us directions (Waze), how we watch TV (Netflix) and soon in all of our cars (Waymo). So why the disconnect? What is about the way we use A.I. in our personal lives makes it so easy to use and not nightmare inducing?

At this point in time, we’ve only scratched the surface of examples of A.I. in day-to-day life. Specific industries and hobbies have habitual interaction with A.I. far beyond what’s explored in this book. For example, casual chess players regularly use A.I. powered chess engines to analyze their games and practice tactics, and bloggers often use mailing-list services that use a machine to optimize reader engagement and open-rates. 2

In fact, the applications of artificial intelligence is already so ingrained in what we do every day, it’s just mostly out of sight or at least not staring us in the face. We just have this double standard between what we see as harmless and what we see as a menace.

How will A.I. affect daily life on a grand scale in the near future? Futurist and Wired magazine co-founder Kevin Kelly predicts that, as AI becomes more deeply integrated in our lives, it will become the new infrastructure powering a second industrial revolution. ”The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable,” says Kelly — and technology is much the same, driven by patterns that are surprising but inevitable.

This genie is not going back in the bottle. Over the next 20 years, he says, “our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. “Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. “The most popular AI product 20 years from now that everyone uses has not been invented yet,” Kelly says. “That means that you’re not late.” 3

With this in mind, I want to bring things back to ground level and share with you my story about how I got started with artificial intelligence. The idea behind my next book is really to explain to you my path, from where I started with data and technology to where I am at now. As I share my story I will also explain what I’ve seen across hundreds of businesses, and highlight the importance of starting to think about how to use artificial intelligence for massive success.

Daniel Meyer heads Sonic Analytics, an analytics firm with offices in Manila, the San Francisco Bay Area and Ocala, FL. With over 20 years in Big Data, Dan is one of the most sought-after public speakers in Asia and offers big data coaching and analytics training seminarson both sides of the Pacific. Dan has also recently joined the Powerteam International family as a small business analytics resource speaker.

Sonic Analytics(www.sonicanalytics.com) brings big data analytics solutions like business intelligence, business dashboards and data storytelling to small and medium sized organizations looking to enhance their data-driven decision-making capabilities. We also advocate the use of analytics for civic responsibility through training, consulting and education.

As citizens of this great democracy, we need to look at the data (analytics), plan a course of action (strategy) and share our data-driven viewpoints (presentation). This approach to a data savvy work force starts in school. So, we started an internship program to empower our youth to use Analytics, plan Strategy and Present their insights… ASP!

When not training current and future analysts, you can find Dan championing the use of analytics to empower data-driven citizenship by volunteering his expertise with schools and non-profits dedicated to evidence-based social progress like Saint Leo University’s Women in STEAM 2020 Conference.

Get A.I. Ready – Ortigas

One of the biggest myths of getting started with A.I. is that it is too complex, to expensive and will take too long to set up.

But that is not the case.

We started GET A.I. READY in June 2019, to help businesses trying to compete with the Amazons, Apples and Googles of the world. We have delivered the training at a half-dozen business expos, for over two dozen American companies and have trained 300+ people across the United States.

From that experience, we have developed the following training program, taking the best of what is happening in the U.S. and modifying it for application by companies doing business in the Philippines.

LEARNING SECTIONS:

Section 1 — Good Data Governance requires us to collect, store, access, and analyze our data in an orderly way. Additionally, A.I. is not a standalone software. A.I. is designed to be used as part of a well-organized system; to accomplish a set of tasks using algorithms. This allows us to use A.I. in three different areas of our business processes. Data is the currency of an A.I. driven Organization.

A.I. Process #1 — Augmentation
A.I. Process #2 — Automation
A.I. Process #3 — Machine Learning

Section 2 — Business Intelligence — A simple conversation about the data collected in a business, how it is stored and accessed, what can be done with it will lead you towards A.I. Demonstrate the ability to ride the A.I. wave, rather than be rolled over by it, which all starts with augmenting your data analytics.

Section 3 — Process Automation — Sometimes the most powerful function of A.I. in a business is simply automation. We have to be able to either delegate, outsource or automate every part of our business that is manual, human resource heavy and/or repetitive. Automation saves time and money, but of course can come at a human cost.

Section 4 — Influencing Customer Behaviour — Machine Learning involves building mathematical models that A.I. will use for predictive data analytics and for learning. We then merry ML with Data Storytelling to craft narratives that drive a desired outcome from our customers.

Participants will have a deeper understanding of A.I. terminology, including Big Data Analytics, Deep Learning, Natural Language Processing, Neural Networks, Predictive Modelling and Supervised Learning.

The end results of attending the one-day training will include the following:

An Assessment of your Current Analytics Maturity & a Plan to Level Up
A Map of your Data Lake, including Identified Data Silos
Learning about the Data Families/Skills Need for A.I.
A list of what manual processes you can Automate (Collection, Blending, Modelling, Analysis)
Tips on how enhance your Data Visualization & Data Storytelling skills
Identify the Key Customer Behaviour Influencers you need to maximize business profits

The bottom line, is that if your business is not already moving towards using A.I., you are at an increasingly perilous disadvantage. If you are not investing in this new technology, you can sure bet your competitors are.

You can sign up here: https://www.sonicanalytics.com/get-ai-ready-ph

Daniel Meyer heads Sonic Analytics, an analytics firm with offices in Manila, the San Francisco Bay Area and as of February 2019, Ocala, FL. With over 20 years in Big Data, Dan is one of the most sought-after public speakers in Asia and offers big data coaching and analytics training seminars on both sides of the Pacific. Dan has also recently joined the Powerteam International family as a small business analytics resource speaker.

Grab a Big Data Surfboard or Get Left Behind!

“Land was wealth 300 years ago. So, the person who owned the land owned the wealth. Later wealth was in factories and production, and America rose to dominance. The industrialist owned the wealth. Today, wealth is in information. And the person who has the most timely information owns the wealth. The problem is that information flies around the world at the speed of light. The new wealth cannot be contained by boundaries and borders as land and factories were. The changes will be faster and more dramatic. There will be a dramatic increase in number of new multimillionaires. There also will be those left behind.”

Two things strike me in this quote by Robert Kiyosaki, author of Rich Dad Poor Dad.

The first is that it incapsulates the world we live in now. A world where information is power. We see this in practice today as the biggest companies now are the ones who use data the same way companies 100 years ago used oil. Nothing new here.

The second thing is that those who control the information will be successful and more importantly those who do, not, will be left behind. This my biggest fear right now. Watching so many businesses being left behind because they don’t have control of information.

Experts have talked about Industry 4.0, the Information Age, the power of Big Data, etc. ad nauseum. But people are not talking much about those being left behind. At least not in the context of being data-driven.

The past two years we have talked a lot about the Trump voter, feeling left behind financially. But we aren’t talking much about the data starved companies that employed a lot of them.

We all know that automation, innovation and globalization combined to spell the end of a lot of companies. But we don’t really talk about how the same companies ultimately ended up failing because they didn’t understand how to use their data better.

Big Data represents a massive wave of disruption that will continue to smash companies like a tsunami. Only those who can ride the wave successfully will be prepared. Analytics is our surfboard to make sure our organization doesn’t get left behind and that we are able to surf the Big Data wave.

Those that master analytics are the new multimillionaire that Kiyosaki mentions. They use data science, build predictive models and have figured out machine learning and artificial intelligence.

The ones that are left behind are the ones still using MS Excel to do rudimentary reporting. Even ones who have started using some basic business intelligence tools and are gaining valuable insight from their data will struggle against the bigger, more data-driven competitors that make multimillionaires of the best data surfers.

One way to know if your organization is in trouble is ask this one simple question. Does the way data is used inside your business look like the way data is used in your personal life? Can we do at work what we can do with Facebook, Netflix, Amazon, Google, etc. at home?

In most cases there is probably a pretty big gap. We process data at work in batches, sometimes only at month end. The analysis takes days and the reports we use to make decisions are only looking at the past. We know who our customers have been, but we are guess who they will be in the future. We hire people not on who is likely to bring the most value to our business, but the one with the best resume and the best interview skills. Does any of this sound familiar?

If it does than that sound you hear in the background, getting louder and starting to impact what you do is the wave of disruption.

If you don’t want to be left behind, crushed by big data, and you want your organization to keep making millionaires than you had better start learning to surf and to do that, you will need a high-quality board.

Dan Meyer heads Sonic Analytics, an analytics advocacy with offices in Manila, the San Francisco Bay Area and as of February 2019, Ocala, FL. With over 20 years in Big Data, Dan is one of the most sought-after public speakers in Asia and has recently begun offering public training seminars in the United States. Dan has also recently joined the Powerteam International family as a small business analytics resource speaker.

Bullish on the Bee

I know I’ve had an interesting career when I get excited about a news article announcing the merger of the Philippines biggest name in fast food with a California based coffee chain.

Why? Because I’ve trained business analysts from both companies and have some insights into how both of them work with their data.

For those of you who aren’t familiar with Jollibee, it’s basically the Filipino McDonalds but with a lot of Filpino-centric fast food options and a much cuter mascot. They dominate their home market and they have expanded around the globle. Where ever your find significant pockets of Filipinos, you can find a Jollibee.

As for Coffee Bean (CBTL) its a California based coffee and tea chain that lives in the Starbuck’s shadow. The have a fairy random footprint and really aren’t the first coffee of choice anywhere I know of.

But for Jollibee it seems like a smart move to acquire Coffee Bean. A lot of CB locations are in malls and business hubs are off to the side and not front in center. They couldn’t compete with the Starbucks for the same floor space and that reality makes them mostly an after thought.

Plus there is nothing special about their menu. Nothing bad, but nothing great either. Plus they have an ever growing amount of competition in a market (coffee) that may be peaking as younger customers like alternatives like milk tea and pearl tea.

On the other hand, Jollibee is an absolute monster of a competitor. They dominate the lower end of the market. Adding CB gives them a presence in more higher end market places. In a lot of malls and business districts, Jollibee is either not present or stuck in the basement with the other fast food brands. But CB can be front in center right. If Jollibee infuses some cash into improving the location of the CB stores, it should be pretty successful.

Plus Jollibee’s marketing is one of the best in the business. They consistently product content that is heartwarming, enchanting and really works. And the bee, that is one of the most beloved mascots I have ever seen. Way better then a quasi creepy clown or a very outdated southern colonel. If they can get people feel the same way about a cup of CB coffee they do about a yumburger, that its a golden marraige.

One more thing, I always mention when I talk about Jollibee. I can 100% guarantee, that in any room full of Filipinos, there is one thing they have all done. No matter their age, income, education.. .they have all attended a birthday party at a Jollibee. If you think McDonalds is a pure representation of middle America, than times that by 10 and you get what Jollibee is to the Filipino.

Now that all just on the outisde. On the inside, Jollibee has spent a lot on its internal data processing and decision making. They have crushed McDo in the Philippines based on an old school model of overwhelming numbers. But since then, they have gotten smarter. I saw that in their analysts and the way they were using data to solve business problems. There success is multifaceted, but a key pieces of it is their business culture is much more data-centric than their competitors. They have a deep and wide ranging strategy to keep adding complementary pieces (they bought the Philppines Burger King franchise last year) to the brands they offer… now they have added another potential winner.

Expectedly, Jollibee’s stock took a dive the day after the announcement on the Philippines Stock Exchange. Short term mindset and conservative investors worry its a mistake to get into the coffee business and many are still waiting to see how the Burger King acquisition plays out. But not me. Based on what I’ve seen, inside and out, I think its a shrewd move.

I’m quite bullish on the bee.

Time to invest in some shares.

https://www.cnn.com/2019/07/24/business/jollibee-coffee-bean-tea-leaf/

Seven Years in the Philippines

In January it will be seven years since I left Wells Fargo and moved to the Philippines to set up an analytics training company.

As year seven comes to a close, some reflection on how I got to where I am now would be beneficial to my strategic planning for the next seven plus years.

BPO Elite was my first venture and the idea was to train fresh graduates and young professionals to be analysts. It was a pretty successful first year in terms of developing a training approach to teaching the fundamentals of business analytics. Business wise we made a lot of missteps along the way that ended up dooming the business. But the training method was sound and I started to build up my credibility as an analytics experts in the Philippines.

Year One was all about validating that empowering people to be analysts was indeed something I could do successfully.

Year Two brought DMAIPH, Decision-Making, Analytics & Intelligence Philippines to life. Based on the business lessons learned from the shortcomings of BPO Elite I focused more on building an influencer network. Entering in to business deals where other people would market my trainings, freed me up to focus on meeting with influencers. The goal became to work within existing networks and expanding reach so that more and more people learned about the important of analytics with DMAI top of mind on how to train people to do it.

Year Two was the year I built the foundation that allowed me to become the top analytics training expert in the Philippines.

In 2014, I launched a separate business focused on the outsourcing of analytics and data heavy customer care solutions. For a large part of the year the analytics trainings took a back seat to setting up a team of 100 office and home-based staff for clients in the U.S. It wasn’t the reason why I moved to the Philippines, but the opportunity proved to be quite lucrative and allowed me to keep doing analytics trainings and speaking engagements without having to worry to much about that part of my business being profitable.

Year Three was all about doing what needed to be done to make a profit.

By 2015, the outsourcing business was running smoothly, and I was able to get back to doing a lot of partner trainings and public speaking engagements. I had the good fortune to now be one of the most sought-after public speakers on analytics in the region, speaking at schools, conferences, and tech event I was also able to start getting my message outside of Manila and being asked to do events across the country.

Year Four was the year where I my face (actually the credibility behind it) really started to sell.

2016 was a big year for DMAI. I published my first book, Putting Your Data to Work. My goal was to make a guidebook that Filipino professionals could use a both a companion to my trainings as well as a resource to convince decision-makers to invest in more training. I also upped my game with doing more public trainings and speaking at even bigger events. It was a very profitable and satisfying year.

Year Five was where I perfected training content and my public presentations. The book was really the lynchpin behind all that.

By 2017, I began calling what I do as more an advocacy than a business. I got involved in several large-scale analytics training initiatives not just in the Philippines, but across SE Asia. I helped found an association to further an analytics centric focus in the outsourcing industry and was even invited to give testimony before the Senate of the Philippines.

Year Six was where it all came together. I hit that sweet spot where I was really good at something I loved doing and I got well paid for it.

2018 could have easily been a redux of 2017. In fact, we started doing a lot of high paying in-house trainings, bigger and more successful public trainings and I was being invited to take part in all kinds of big picture initiatives across the region. But something changed. 2017 was the year where I had reached the mountain top. As I look back at 2018, I have accomplished everything I had set out to do back in 2012.

That said, I’m not done with the Philippines yet!

Already have 5 trainings booked for the first quarter of 2019. Including ones coming up in January and March. Here are the links to find out more:

https://www.sonicanalytics.com/data-analytics-20

https://www.sonicanalytics.com/data-analytics-30

I will always have a soft spot in my heart for my adopted home in the tropics as I also look to expand my trainings to where I spent much of my youth… the state of Florida. .

Will be laying some seeds the next several months and kick off my first trainings and speaking engagements in the Sunshine state early next year.

Let’s see where the next seven years of championing analytics takes me.

Dan Meyer Quotes 2

DMAIPH – Decision-making, Analytics & Intelligence Philppines

Over the past few years businesses in the Philippines have invested heavily in big data, analytics and data science, but still have not achieved the expected outcomes of data-driven companies.

Based on our learnings from the 100’s of Filipino businesses and 10,000s of Filipinos who have taken part in DMAIPH Analytics trainings all across the country, we have crafted a proven, non-technical approach to upskilling your team in analytics.

In 2019, we will be launching two new training programs: (1) Our DMAIPH Applied Analytics Master Class series for executives, leaders and decision-makers and our (2) DMAIPH Applied Analytics Boot Camp series for practicing analysts.

We will feature case studies of real Filipino run business, exercises based on actual analytics challenges being solved by Filipino analysts, and provide you with a copy of my book, Putting Your Data to Work, an analytics guidebook for the Filipino professional

Connect with us via our marketing partner, http://www.sonicanalytics.com to learn about upcoming analytics trainings and events.

Finding the Right Data at the Right Time

Sir Conan Doyle’s famous fictional detective, Sherlock Holmes, couldn’t form any theories or draw any conclusions until he had sufficient data. Data is the basic building block of everything we do in analytics: the reports we build, the analysis we perform, the decisions we influence, and the optimizations we derive.

resized_99261-basil_85-16636_t300 — Basil Rathbone as Sherlock Holmes

Several years ago I came across a book called the Accidental Analyst (*www.accidentalanalyst.com). The book opens with the questions, “Are you drowning in a sea of data? Would you like to take control of your data and analysis to quickly answer your business questions and make critical decisions? Do you want to confidentially present results and solutions to your managers, colleagues, clients and the public?”

Written by two Stanford professors, the book explores how and why people become good analysts and goes into detail about how to approach analytics successfully. After reading the book I was inspired to come up with a way to teach analytics to college students and fresh graduates.

The core of both the book and my program hinges on the ability of an analyst to find the right data at the right time. The authors suggested that identifying your data is where it all starts. Identifying exactly what you need to address whatever it is that you need to report.

Back at Wells Fargo, the single greatest attribute that I had that made me successful was my ability to size up how long it would take to deliver something. Knowing what data I would need, where I would find it and how long it would take to analyze it to come up with something useful made me somewhat of a wizard in the minds of the team.

Finding the right data at the right time requires one to first know ends and outs of their data. You have to know how the data is captured, where it is stored and how it makes its way to you. Knowing the data architecture in your business is the key.

So you have to get to know the people who know where your data comes from and how it gets there. Learn from them. Partner with them. Buy them doughnuts.

A couple of years ago I came across an analogy being used to describe data in a business. That of a data lake. A data lake is the living, breathing, evolving pool of all the data in a business. If you have a good data architecture, and you can navigate it fairly easily, then you have a data lake. Ideally, your business has data structured in such a way you can live off it. Data to a business is like water to living things… it sustains life

So once you have the lake mapped out, then you have to learn how to fish it. Knowing where the fish are biting is another key. Once you know what data you need, you have to know how to get to it quickly.

Business Intelligence tools help us here. As does coding languages to extract data from a database. These are your fishing tools. You have to practice using them to be good at getting the right data at the right time.

Another way to optimize your data search is to save your work. Of as I call it leave yourself breadcrumbs. Save the query. Cut and paste the code into a document and save it. Write down the steps. Whatever you need to do to replicate what you just did so you can do it again in the future without starting over from scratch.

So to recap, if you know data structure, you understand how data is stored and you leave yourself clues to do things faster next time.
Now the other part of the equation is knowing if the data you are using is the right data. Finding data quickly doesn’t do you any good if you bring back the wrong data.

So, how do you know if the data you are using is the right data to be using?
I can’t count the number of times I asked myself that question. In general, just about every new analysis or project or research or whatever it is you are using data for, you have to ask that question at some point.

Even data you have used a hundred times and comes from a highly trusted source needs to be scrutinized.

Now if you work with data every day in a familiar format, from the same source and with no changes to the data gathering and storage process you don’t have to spend much time validating it. Usually you will see problems when something just doesn’t look right when you are doing the analysis.

On the other hand, things get a whole lot trickier when you are using data from a source you don’t use often, or something has changed in the way the data is populated or if it’s the first time you are using the data.

When this happens, I have a few suggestions on how to validate the data.

First off, pull the data, do your analysis and draw some conclusions. If it passed the eye test and it feels ok to you, then your job is just to validate it.
One simple way to do this is pull the data again the exact same way to make sure you get the exact same data. Or change one parameter like the dates used in the query. See if that significantly alters the way the data looks and feels.
Another option is to have someone else do the same thing independently. See if they get the same results you do. You can also find someone who knows the data to look over your work to see if it makes sense to them.
Whatever you do, the best way to prevent publishing or using bad data is to involve someone else. Not always possible, I know, but it’s the best way to go.

Another suggestion is to (1) get the data, (2) do some analysis, and then (3) step away for a while. Come back to it with fresh eyes. Don’t let our minds play tricks on us by making us see what we want to see and not what is really there.

I have seen several articles showing research that most time doing data analysis is actually spent cleaning data. In a lot of businesses, the data lake has become a data swamp, clogged with bad or unusable data. As the % of unstructured data increases daily, it’s easy to see how data swamps have become the norm. Even the most robust data collection and mining can run afoul if the data is not trustworthy.

I can’t stress this enough. No matter how good you are at analysis, or what tool you are using to do the analysis, if you don’t have an understanding of what happens to the data before it gets to you then you are probably not drinking from a clean lake.

DMAIPH – Decision-making, Analytics & Intelligence Philppines

Over the past few years businesses in the Philippines have invested heavily in big data, analytics and data science, but still have not achieved the expected outcomes of data-driven companies.

Connect with us via our marketing partner, http://www.sonicanalytics.com to learn about upcoming analytics trainings and events.

Staying Current with Analytics

Every few months I devote a day to discover what the current trends in analytics are. 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 at least once a month and I follow dozens of blogs, web sites and social media groups. Being well versed on what is current in analytics is a key to success.

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 writing about analytics, data visualization is near the top. Business dashboards continue to be a big need. Business Intelligence (BI) tools evolve and new ones’ pop up, but Tableau continues to be a market leader.

That said, we are still squarely in an MS Excel dominated world. Upwards of 80% of Filipino professionals I recently surveyed still use Excel as their primary tool for data analysis. And even the ones who have dedicated BI tools, still use Excel for 75% of their analytics work. The adoption of BI tools is trending upward, but the curve is still very step.

Another trend that has been on the upswing is how 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 just a few short years as mankind generates unprecedented amounts of data not easily captured in a database every day.

As October 2018, if I had to pick 5 current trends in analytics to talk about it would be:

(1) How to Conduct Impactful Data Storytelling,

(2) The Analytics and Data Science Talent Shortage,

(3) Using Big Data Analytics for Digital Transformation,

(4) Optimizing Data Warehousing and Data Lakes,

(5) Which Tool Is Best; Tableau or Power BI, R vs Python, etc

And thats is not even touching topics that are on the cutting edge like machine learning, artificial intelligence and augmented analyst. Although those are super important to an overall understanding of how we can optimize data, these topics generally are several steps down the road from where my audience sits. They are still trying to master the fundamentals of business analytics and introductory data science.

So I spend a fair amount of time looking for YouTube videos or TED Talks on these topics to add to what i read.

The amount of information available to consume if immense. I guess as we have more and more data and more and more tools to analyze data, we will have more and more people writing about how to use data.

Its a fun time to be the Data Guy.

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DMAIPH – Decision-making, Analytics & Intelligence Philppines

Over the past few years businesses in the Philippines have invested heavily in big data, analytics and data science, but still have not achieved the expected outcomes of data-driven companies.

Connect with us via our marketing partner, http://www.sonicanalytics.com to learn about upcoming analytics trainings and events.

From Putting Your Data to Work… A Basic Overview of Analytics

Analytics is about looking for patterns in data to help answer questions. Most businesses use analytics to help ensure more data-driven decision-making.

The primary people responsible for conducting analytics on the massive amounts of data we have today are analysts. Analysts are skilled in using various technologies and methodologies to identify, inventory and integrate large amounts of data quickly.

Many Analysts today feel like they are drowning in a sea of data. They need to know how to take control of their data and analysis to quickly answer business questions and make critical decisions. They want to confidently present results and solutions to their managers, colleagues and clients.

You should get started by building a baseline understanding of analytics. The term analytics can often be used interchangeably with statistics and data science. What separates analytics from disciplines like statistics and data science is generally the speed of the analysis and the focus on solving business problems.

The most common form of analytics is business analytics, which is usually used by senior leaders and decision-makers to investigate problems, validate assumptions and to guide strategic planning.

Business analysts are therefore the most common type of analyst. If you do a job search on the title analyst, as many as half the posting will likely be business analysts. However, analytics can be used in an almost limitless number of business functions in specific areas like HR, recruitment, marketing, finance, and so on. Each one can have its very own analyst.

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Analysts have been around a long time, but recent technological advances have both allowed us to produce and capture more data as well as give us the ability to analyze immense data sets quickly. Thus we are amidst a huge boom in the applications of analytics and the need for analytics talent across the globe. Analytics is something just about every business leader is trying to figure out how to use more effectively in their business. To add to our challenge, the demand for good analysts is booming just as fast as the explosion in big data.

As a result, there is a huge shortage of people who are skilled in working with data to answer questions and solve problems. This is why you have seen the number of analyst job postings increasing at an amazing rate.

If you are a business leader, manager, owner, and/or executive are not actively trying to surround yourself with analysts and if you are not infusing an analytics centric culture in your business, you will most likely soon see your business fail.

A business needs analysts to make sense of big data, manage the storage of the data, and know when to use which of the 4 types of analytics (descriptive, diagnostic, predictive, and prescriptive). To be effective, analysts need to have business intelligence tools to create data visualizations, build business dashboards and tell stories with data.

So whether you are an analyst or someone who oversees analysts, Putting Your Data to Work is designed as guidebook to help you get the most out of your business data.

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DMAIPH – Decision-making, Analytics & Intelligence Philppines

Over the past few years businesses in the Philippines have invested heavily in big data, analytics and data science, but still have not achieved the expected outcomes of data-driven companies.

TRAINING IN DATA ANALYTICS

By Maureen Andrei Lepatan

INTRODUCTION

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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EXPERIENCE

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:

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!
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.
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.
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.
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: FIRST TRAINING, FIRST INTERNSHIP

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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ABOUT THE AUTHOR:

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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