Analytics Centric Cultures – Learn More June 5-7, 2018 in Ortigas

Inspired in part by Bernard Marr’s 2010 book, The Intelligent Company, my goal these past several years has been to build and/or be part of data-driven business cultures. The description of the book on Amazon sums it up well, “Today’s most successful companies are Intelligent Companies that use the best available data to inform their decision-making.”

In his book, Bernard advocates for using Evidence-Based Management that is using the best available data to inform decision-makers. In parallel to this, I have been empowering companies and professionals to empower decision-makers to use more data as well. I call it data-driven decision-making, but at their cores, there are very similar approaches to managing success.

The cornerstone of the book is the five steps to more intelligent decision-making, which are:

  • Step 1. More intelligent strategies — by identifying strategic priorities and agreeing your real information needs
  • Step 2. More intelligent data — by creating relevant and meaningful performance indicators and qualitative management information linked back to your strategic information needs
  • Step 3. More intelligent insights — by using good evidence to test and prove ideas and by analyzing the data to gain robust and reliable insights
  • Step 4. More intelligent communication — by creating informative and engaging management information packs and dashboards that provide the essential information, packaged in an easy-to-read way
  • Step 5. More intelligent decision-making — by fostering an evidence-based culture of turning information into actionable knowledge and real decisions.

As information and data volumes grow at explosive rates, the challenges of managing this information is turning into a losing battle for most companies. In the end they find themselves drowning in data while thirsting for insights. Combine this with an increasingly severe shortage of talent with analytics, data visualization and good communication skills, things look bleak for companies not adhering to lessons like those suggested in the Intelligent Company.

In addition, Data-Driven Cultures Do These Things:

  1. They embrace Big Data. They aren’t afraid of it. They relish the addition of new data sources and actively look for more.
  2. Managers use Evidence-Based Management techniques. Just about every choice comes based on data analysis.
  3. Challenges are addressed with Data. When something happens that was unexpected, the challenge is met with a data centric approach.
  4. The right data is being used. A lot of work goes into validating data and keeping it clean and fresh. The concept of having a data lake that supports multiple parts of the business is in place.
  5. They have the right analytics talent. Analysts are empowered to go out and discover not just current challenges, but look for potential ones as well.
  6. They know how to communicate. The sharing of information is done to benefit everyone. You won’t see lots of data trapped in silos. Data has no one true owner.
  7. They take action based on their data and analysis. You don’t see a lot of useless reports that kills a small forest or clog up an inbox with massive files. They keep it smart and simple.

Data-Driven cultures are a lot harder to find than they should be. In this day and age, every company should have a strategy on how to use data to drive more intelligent decisions, but they don’t. Success eludes many companies because they don’t have the 7 qualities listed above in place. If you were to ask what they look like it would be something akin to this:

· Top management is afraid of data. Senior leaders don’t even know how to use MS Excel. There is no analytics champion in the organization to spearhead data projects.

· Decisions are made based on what worked in the past, relying on experience and gut feel. There is little evidence used to go in any certain direction.

· When things don’t work out, data and analysts take the blame. You will hear a lot of “why didn’t you tell me” and “I didn’t see it coming” excuses.

· What data is being used is old, dirty, incomplete, full of errors and doesn’t tell the whole story. Reports are basically useless and just produced to look at what people generally already know. They look for what’s there, oblivious to what’s not.

· They do not share data. They hoard it. They don’t trust anyone else with access to it. The data is stored in unconnected storage places. There is no common understanding how to use data.

· They fail a lot. Success generally happens by hard work as much as luck. It’s impossible to know for sure what caused what to happen.

It’s not easy to take a company that has little or no data-driven decision-making and turn it into an Intelligent Company, but it can be done. I have done it. I have guided transitions from the stone-age to the information age. Let me show you how.

I will cover all these concepts in more in upcoming my training class on June 5-7, 2018 at Discovery Suites in Ortigas. For a list of training events, please visit www.sonicanalytics.com

Dan Meyer heads Sonic Analytics, an analytics training, consulting and outsourcing company with offices in Manila and the San Francisco Bay Area. 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.

We need to look at the data (analytics), plan a course of action (strategy) and share our data-driven viewpoints (presentation). So he has started an internship program under Sonic Analytics to empower the youth the use Analytic, plan Strategy and Present their views… ASP!

Sonic Analytics(www.sonicanalytics.com) brings big data analytics solutions like business intelligence, business dashboards and data storytelling to small and medium sized business looking to enhance their data-driven decision-making capabilities.

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Finding the Right Data

“Data! Data! Data! I can’t make bricks without clay!”

-Sir Arthur Conan Doyle

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.

Finding the Right Data at the Right Time

Back at Wells Fargo, the single greatest attribute 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 few 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.

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.

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.

In the end, whatever you do, make sure you have the right data.

I will cover all these concepts in more in upcoming my training classes. For a list of training events, please visit www.sonicanalytics.com

I’ll be conducting the following business analytics trainings over the next few months:

· June 5 in Ortigas (Metro Manila, Philippines)

· July 17, in Pleasant Hlll, CA (San Francisco Bay Area, US)

· August 22, in Bonifacio Global City (Metro Manila, Philippines)

Dan Meyer heads Sonic Analytics, an analytics training, consulting and outsourcing company with offices in Manila and the San Francisco Bay Area. 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.

We need to look at the data (analytics), plan a course of action (strategy) and share our data-driven viewpoints (presentation). So he has started an internship program under Sonic Analytics to empower the youth the use Analytic, plan Strategy and Present their views… ASP!

Sonic Analytics(www.sonicanalytics.com) brings big data analytics solutions like business intelligence, business dashboards and data storytelling to small and medium sized business looking to enhance their data-driven decision-making capabilities.

The AAP Analytics Internship Matching Program

Innovating the Data Science & Analytics Internship Experience in the Philippines.

The Analytics Association of the Philippines will offer a Data Science and Analytics Internship matching program for Filipino students and employers. The AAP will serve as a conduit and catalyst bridging theory and application to ensure value to the company as well.

Our program has been developed to address 3 current challenges facing students and employers when it comes to data science and analytics themed internships:

  • students are often given menial tasks that do not apply knowledge / learned theory
  • mechanisms do not address disconnect between areas of interest and areas of need
  • companies are not able to effectively identify parts of the value chain that can be assigned to interns while causing no substantial risk to the business’ operations

By enrolling in the AAP DSA Internship Matching Program, students will be matched with employers that provide opportunities to learn and practice DSA skills that are in high demand in the workforce. Students can also be fast tracked for employment post-graduation by their match in a more continuous process.

By enrolling in the AAP DSA Internship Matching Program, employers will be matched with students that are committed to learning DSA skills that can add value to the employer’s business as well as provide a more seamless path to career placement.

Employers will also be encouraged to take a more active role in providing, business cases, data sets and resource speakers for the programs the students they are matched with come from. By being more involved with their students before and after the internship, the bridge between academia and industry will be optimized.

To this end we have developed the following process that will kick off on January 15,2018:

  • Students apply for OJT matching with AAP
  • Employers apply for OJT matching with AAP
  • AAP Matches students and employers based on profiles
  • AAP conducts orientation for students and employers
  • AAP provides online resources to both students and employers

The AAP will assess partner schools and their respective tracks and courses that could work on analytics (end-to-end of value chain) The AAP will also define areas of expertise of each program based on the APEC DSA Competencies and the AAP DSA Framework. Students will be interviewed and vetted.

Additionally, the AAP will provide a matrix of industry partners and corresponding needs (with parts of the value chain, doesn’t have to be siloed, can cover multiple parts)

As for the employer, they will:

  • Define problems/needs (could be something students can work on parallel to an existing team effort)
  • Define final output (paper/study, running program, a presentation, proposal, working product, solution)

As for the schools, they will:

  • Provide 1-2 professors to assist in mentorship
  • Conduct processing of internship experience to give feedback to AAP

Overall our goal is to offer a unique value proposition by facilitating internships with a view of work as an end-to-end process that involves deep-diving into a specific problem or project of the company. The companies enrolled in our program get real value out of internships besides serving as a marketing tool.

With our network of industry partners, prestigious academic institutions and analytics thoughts leaders, the AAP is well positioned to facilitate significant change in the way analysts and data scientists are born.

Our Analytics Internship Matching Program will go a long way in providing tomorrow’s workforce with in demand skills that employers covet, which in turn will allow the Philippines to be a world leader in analytics talent.

Most of the credit for the content of this post goes to Mel Awit, the AAP Analytics Manager. 

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DMAIPH is a founding member of the Analytics Association of the Philippines (AAP.PH) and specializes in arming the Data-Driven Leader with the tools and techniques they need to build and empower an analytics centric organization. Analytics leadership requires a mastery of not just analytics skill, but also of nurturing an analytics culture. We have guided thousands of Filipino professionals to become better analytics leaders. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to discuss a uniquely tailored strategy to ensure you are the top of your game when it comes to Analytics Leadership.

 

 

Analytics for Team Leads: Optimize Analytics & Data Science for More Efficient Operations and Engaged Employees

Join DMAIPH, Augment BPO and Sonic Analytics in a two-day analytics training for Team Leads on August 22-23, 2017 in Ortigas! 

Learning Session Description

Every  organization is looking for a way to better understand what’s working and what’s not working in their operations. By using meaningful Big Data Analytics techniques, your leadership efforts can be greatly enhanced.

Learning Session Outline

In the past few years, we have seen the importance of big data, analytics and data science grow at a dizzying pace.

With real-time operations metrics & reporting, we can finally know what’s happening in our business, with our employees and with our customers.

New technologies like social networks, data rich information systems and business intelligence applications are fundamentally changing the entire operations process.

The pressure to deliver results has never been greater. Team Leaders are now more than ever required to demonstrate the return on investment of their efforts are contributing to the bottom line.

Building analytics centric teams and using techniques taught in this training session will empower more data-driven decision making. This will result in both process efficiency and better return in investment in the operations of your business.

With the global demand for analytics-enabled talent booming and the coming threat of A.I. to the BPO industry, Team Leaders need a deep understanding of analytics.

Learning Session Objectives

  1. Apply Best Practice Techniques and Cutting Edge Technologies to Organize, Interpret, and Summarize Quantitative Data
  2. Create a Process to Analyze Data and Identify Patterns Not Apparent at First Glance
  3. Reduce “Analysis Paralysis” and Go from Hard Data to Well-Reasoned Conclusions in Less Time
  4. Understand the implications of Artificial Intelligence and Machine Learning in regards to the future of work in the Philippines.

Who Should Attend

This session is suitable to a wide range of professionals but will greatly benefit:

  • Managers, Supervisors and Team Leads
  • Business Analysts working as part of an Operations Team
  • Leaders who oversee business operations

Learning Session Process

Based on a Set of Key Data Science and Analytics Competencies developed by the Asia Pacific Economic Cooperation (APEC), our learning sessions are designed for Team Leaders and Managers to use both in the Philippines and across the region.

Day One – Doc Ligot, Cirrolytix

Session One – Data Analytics Methods & Algorithms: Capture, clean and inspect data. Evaluate and implement data analytics to derive insights for decision making.

  • Data Warehouses and Data Lakes
  • Blending Data from Across the Organization
  • Cloud Computing and 24/7 Data Access

Session Two – Data Science Engineering Principles for Business Operations: Use analytics software and system engineering principles and modern computer technologies to share findings and tell data stories. Develop analytic processes to improve HR operations.

  • Analytics with Lean and Six Sigma
  • Getting IT: the 3’s I and the 3 T’s of Data
  • Data Science 101: How to Build a Data Science Team

Session Three: Computing Principles for Team Leads: Apply information technology, computational thinking and utilize programming languages to design and develop data analysis processes and techniques.

  • Optimizing the use of MS Excel for Operations
  • Mangement Reporting
  • Working with the IT Team: Buy them Doughnuts

Session Four – Statistical Techniques for Data Analytics: Apply and/or direct the application of statistical concepts and methodologies for data analysis including predictive analytics.

  • Predictive Analytics Case Study: Google’s Top Performer Model
  • Tying Management Reporting to Predictive Models
  • Group Exercise: Build a Top Performer Model

 

Day 2 – Dan Meyer, DMAIPH

Session Five – Domain Knowledge & Application:  Apply domain-related knowledge and insights to effectively contextualize data, achieved by practical experience and exposure to emerging innovations.

  • Overview of Big Data, Analytics & Data Science in the Philippines
  • Cutting Edge Trends in Big Data
  • How to Apply an Analytics Process to Solving Business Problems

Session Six – Data Management & Governance: Develop and implement data management strategies and governance, incorporating privacy, data security, polices and regulations, and ethical considerations.

  • The 5 V’s of Big Data
  • The 3 Tenants of Data Governance
  • Information Security Guidelines for Filipino Businesses

Session Seven – Operational Analytics: Use data analytics and specialized business intelligence techniques for the investigation of all relevant HR data to derive insight in support of decision-making.

  • Competitor Landscapes and Demographic Profiles
  • BI Tools Demo: Tableau Public
  • Social Media Data

Session Eight – Data Visualization & Presentation: Ability to create and communicate compelling and actionable insights from data using visualization and presentation tools and technologies. Build a Business Dashboard prototype.

  • Data Visualization Guidelines
  • Group Exercise: Build a Business Dashboard Prototype
  • The Concept of Enchantment
  • Data Storytelling Case Study: The Best NBA Team of All Time

 

 

Case Studies and Exercises

We 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.

Training Investment 

Early Bird Rate: P12,000+ VAT (till July 21)

Group Rate: P13,000+ VAT

Regular Rate: P14,600+VAT (starting July 22)

Investment Includes: 

Two-day Analytics for Team Leads Training, Training Materials, AM/PM Snacks, Lunch and Certificate of Completion.

To inquire about this training or to register, please send an email to analytics@dmaiph.com (event organizer) or visit http://www.sonicanalytics.com (marketing partner).

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Augment BPO

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

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DMAIPH

Analytics Training – DMAIPH offers a wide range of analytics centric training solutions for professionals and students via public, in-house, on-site, and academic settings. We tailor each training event to meet the unique needs of the audience. If you need empowerment and skills enhancement to optimize the use of analytics in your organization, we are here to help. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to set up a free consultation to learn which of our DMAIPH analytics training solutions is best for you.

Working on an Analytics Internship/OJT program…

400 Hour DMAIPH Data Science & Analytics OJT/Internship Program

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

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

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

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

 Week 1 – Fundamentals of DSA

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

Exercise: LinkedIn Profile

Company Deliverable: Company/Organization DSA Profiles

Week 2 – DSA in the Philippines

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

Exercise: Glossary of Data

Company Deliverable: Defining Where the Cutting Edge Is

Week 3 – Data Management & Governance

  • Data Management Macro View
  • Data Governance
  • Information Security

Exercise: Data Survey

Company Deliverable: Info Security Risk Assessment

Week 4 – Data Analytics Methods & Algorithms

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

Exercise: Who’s Who of Data in the Business

Company Deliverable: Data MVPs

Week 5 – Data Science Engineering Principles

  • Data Map
  • Identify Right App
  • Feedback Loop

Exercise: A Visio Data Map

Company Deliverable: Map of Business Data Lake

Week 6 – Computing and Computational Thinking

  • MS Excel
  • Query Data
  • Programming Languages

Exercise: Top 10 Excel Tips Video

Company Deliverable: Top Ten Data Tips

Week 7 – Statistical Techniques

  • Getting IT
  • Analytics Maturity Model
  • Predictive Analytics Model

Exercise: Flight Risk Model

Company Deliverable: Results of Maturity Assessment

Week 8 – Operational Analytics

  • Management Reporting
  • Public Big Data
  • Business Dashboards

Exercise: Tableau Public Mock Up

Company Deliverable: Business Dashboard Prototype

Week 9: Data Visualization & Presentation

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

Week 10 – Final Project/DSA Strategy Presentation

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

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

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

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

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

Teaching Analytics: APEC List of Competencies

Teaching Analytics: An Instructor’s Guide to implementing the recommendations of Project DARE (Data Analytics Raising Employment).

APEC (Asia Pacific Economic Cooperation) hosted an event on May 4-5 bringing together over 50 analytics experts and visionaries from over 14 counties across the Asia-Pacific region to form an advisory group.

The APEC Project DARE (Data Analytics Raising Awareness) Advisory Group started with an agenda set to develop recommended “APEC Data Analytics Competencies.

There are still being finalized, but as of last week here is a high level of the competencies that we came up with:

Business and Organizational Skills

  1. Operational Analytics: Use data analytics and specialized business analytics (i.e. business intelligence) techniques for the investigation of all relevant data to derive insight in support of decision-making.
  2. Data Visualization and Presentation: Ability to create and communicate compelling and actionable insights from data using visualization and presentation tools and technologies.
  3. Data Management and Governance: Develop and implement data management strategies and governance, incorporating privacy, data security, polices and regulations, and ethical considerations.
  4. Domain Knowledge and Application: Apply domain-related knowledge and insights to effectively contextualize data, achieved by practical experience (e.g. apprenticeships) and exposure to emerging innovations.

Technical Skills

  1. Statistical Techniques: Apply statistical concepts and methodologies for data analysis.
  2. Computing: Apply information technology, computational thinking and utilize programming languages and software and hardware solutions design and development for data analysis.
  3. Data Analytics Methods and Algorithms: Capture, clean and inspect data. Evaluate and implement data analytics and machine learning methods and algorithms on the data to derive insights for decision making.
  4. Research Methods: Utilize the scientific and engineering methods to discover and create new knowledge and insights.
  5. Data Science Engineering Principles: Use software and system engineering principles and modern computer technologies, incorporating a data feedback loop, to research, design, and prototype data analytics applications. Develop structures, instruments, machines, experiments, processes, and systems to support the data lifecycle.

Workplace Skills

  1. 21st Century Skills: Exhibit crosscutting skills essential for DSA at all levels including, but not limited too; collaboration, customer focus, communication and storytelling, organizational awareness, critical thinking, planning and organizing, problem solving, decision making, business fundamentals, awareness of social and societal awareness, intelligibility, cross cultural awareness, dynamic (self) re-skilling, professional networking, ethical mindset and entrepreneurship.

Once this list is finalized I will update you all.

My end goal is two fold, (1) to help craft a version of the competencies into an academic discipline that the Analytics Council can present to CHED for adoption and (2) to design a vocational education track to address the basic skills needed for entry level DSA work here in the Philippines.

Stay Tuned!

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Analytics Training – DMAIPH offers a wide range of analytics centric training solutions for professionals and students via public, in-house, on-site, and academic settings. We tailor each training event to meet the unique needs of the audience. If you need empowerment and skills enhancement to optimize the use of analytics in your organization, we are here to help. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to set up a free consultation to learn which of our DMAIPH analytics training solutions is best for you.

Data Science Philippines – Feb 2017 Meet Up

My talk is the 2nd one, a little over half way through the video. Listen first to some real data scientists talking about their trade, then it’s my turn where I talk about just how many data scientists there are in the  Philippines right now. Thanks to DataSeer for asking us to sponsor and for setting up the event. Looking forward to many more to come! 🙂

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

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:

https://www.surveymonkey.com/r/WKG9VJ5

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 analytics@dmaiph.com or connect with me directly to find out more about how DMAIPH can conduct surveys to help you assess the analytics culture in your business.

Learn Analytics Leadership From The Analytics Council of the Philippines

The Analytics Council of the Philippines was founded in December 2016.

Our mission is to serve as a forum for furthering the training, teaching and use of analytics across the Philippines.

Our mission statement and upcoming events will be posted soon.

outsourcing-seo-services-in-the-philippines-forward-solutions-seo-and-web-design-company

DMAIPH is a founding member of the Analytics Council of the Philippines and specializes in arming the Data-Driven Leader with the tools and techniques they need to build and empower an analytics centric organization. Analytics leadership requires a mastery of not just analytics skill, but also of nurturing an analytics culture.

We have guided thousands of Filipino professionals to become better analytics leaders. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to discuss a uniquely tailored strategy to ensure you are the top of your game when it comes to Analytics Leadership.