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.

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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.

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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.

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. 

 

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More and More Frequently I Find What I Do Being a Form of Data Evangelist

The concept of a key influencer in a field being called ad Evangelist has been around business for awhile. I remember reading how early on, called their marketing people evangelists. One the comes to mind is Guy Kawasaki, who wrote about this in his book Enchantment.

When you have something that is either new or still young in its overall adoption cycle, you need evangelists to raise awareness and being the process of acceptance.

When it comes to data evangelism, there has definitely been a number of key leaders pushing for more and more adoption of analytics across various organizations. Bernard Marr is one I have followed quite extensively.

Based on the importance so many companies have placed on analytics in recent years, you would expect to find that just about every business leader buys into the concept of using data to drive decision-making.

To be sure, the tech giants and the banks have been on board for a long time and you have seen the adoption of large-scale analytics really start to have a lasting impact among major players in fields like professional sports, the entertainment industry and politics.

And nowadays, most social media platforms have lots of built in analytics that provide instant insight into what’s hot and what’s not.

However, both factual evidence and general observation are showing this is not necessary the case across the board. We still see headlines saying things like “62% of businesses have no data analytics strategy”.

In fact, many small and medium sized companies are still not where they could be when it comes to optimizing their business data.

Three of the biggest challenges they face are not really knowing what question to ask, how to manage the data so that it is well governed and getting the data to decision-makers seamlessly.

Terms like data interpretation, data collection, data governance, and data automation are not concepts easily articulated by many business leaders.

Having a data intelligent business culture is a lot more than just buying a business intelligence tool and putting it on top of Excel.

There needs to be solid foundation in all aspects of the data life cycle, a clean and well governed data lake to house all business data, and the ability to present data impactfully.

I have found that this is often the missing element when organizations are trying to craft an analytics strategy. They focus on technology or they go out and acquire high priced talent, but in the end they struggle because not enough of their decision-makers are on the same page.

As I train groups of professionals, both in public and in in-house trainings, I find most of the attendees do not have a solid foundation in how data is collected, stored and managed. They just know how to run reports, build simple dashboards and share data in ways that do not often influence the audience as intended.

So that is what I have found myself doing hundreds of times the past several years. Helping build that foundation. Connecting the dots between the various phases of the data life cycle and helping define the data value chain. Once an organization has that down, then going out and getting a fancy new tool or bringing on data science makes sense.

Being a data evangelist is all about getting not just 1-2 executives to buy into the power of analytics, but making sure the whole organization is in a place where they can truly optimize their data, become more business intelligent and compete on the same level as the big boys when it comes to making data-driven decisions.

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DMAIPH – Decision-making, Analytics & Intelligence Philippines

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.

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.

Getting Started With Analytics

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.

A general definition of an analyst is a person who analyzes or who is skilled in analysis. Analysts examine things carefully and in detail so as to identify causes, key factors, possible results, etc. generally using a process of identifying, inventorying and integrating data. *http://www.dictionary.com/analyst

I often hear that most 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 most likely clicked to this page because you fit the description above. If that is the case, then you made a good decision. 😉

All kidding aside, I have designed a method to help you look at analytics in a way that will make data and analysis easier to understand and conduct. My trainings and published content will also instruct you on how to share data in a more dynamic and influential way.

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. In fact the quickening demand for analytics talent has made it very hard for most businesses to find good analysts.

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.

Keep in mind that 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. Do you have analysts on your team?

Analysts are skilled in using various technologies and methodologies to identify, inventory and integrate large amounts of data quickly. Are you an analyst yourself?

If you answered yes to either question, but you feel you need more training for yourself or your team, you are not alone.

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 and build business dashboards.

I will cover all these concepts in more in upcoming my training classes. The classes are designed specifcally for people new to analytics and for business leaders looking to upgrade the level of analytics in their business.

For a list of training events, please visit www.sonicanalytics.com

Upcoming Training Dates

· June 5 in Ortigas (Metro Manila, Philippines)

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

· August 14, Rancho Cucamonga (North of Los Angeles, US)

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

My goal with this series is to help you look at analytics in a way that will make data and analysis easier to understand and conduct.

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.

I Need Analytics Training. Where Do I Start?

If you put yourself in the mind of the typical Filipino professional looking for analytics training, it is not easy to figure out where to start.

The ecosystem is not very unified, with a hodge podge of public training solutions available and only dozen or so schools offering analytics.

To someone who is relatively new to using big data to solve business problems, it can all seem very nosebleed inducing as well. Data science, predictive analytics and machine learning can all sound complicated and expensive.

So where do I start? That is a very common question I get asked when I talk about analytics in the Philippines.

The answer comes in three parts. First we need a framework to set certain standards and definitions of what a Data Science and Analytics enabled professional should know.

We base that on the set of 10 DSA competencies as defined by APEC’s Project DARE,

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(Props to my fellow AAP board member Sherwin Pelayo for the awesome images)

Since few people need to know everything about everything, it is best to figure out which competencies you want to focus on first.

Once you have an idea of where to start, then the next step is determine what kinds of job skills match the competencies you are looking to develop.

This can be done by determining where in the data life cycle you are looking have an impact,

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Once you have a firm idea of the skills you want to build and where they fit into the analytics life cycle of your business, then it is a matter of planning out how to level up.

This is where the AAP has take APEC’s competency list  and broken then out across the various job functions along the analytics life cycle by level of skills required.

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This allows us the ability to determine where we are in terms of analytics maturity and design the appropriate plan to level up.

And that will lead you to one of the AAP member companies for the appropriate type of corporate training or to one of the AAP member schools for the right higher education solutions.

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And that is how you can get a better idea of what type of analytics training is best for you and your business to get started with.

Analytics Leadership – 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.

 

 

Augmented Analytics: A.I., Machine Learning & Predictive Analytics

I recently spoke about Augmented Analytics: Trends in Artificial Intelligence, Machine Learning and Predictive Analytics.

Much has been made about the business implications of recent, rapid advancements in cognitive computing.

It’s no longer just a pair of eyes scanning data.

We now face the reality of advanced analytics tools to help human knowledge workers glean actionable insight from vast and deep lakes of historical, transactional and machine-generated information.

This is augmented analytics and it includes concepts like predictive analytics, machine learning and A.I. If these are terms you have a general familiarity with, but haven’t spent much time thinking about how the related to big data, analytics and data science, then you should.

Business analytics today can be done with MS Excel and maybe a good BI tool, but business analytics tomorrow will require augmented analytics skills.

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Analytics in the Philippines – The Philippines is at the center of the action when it comes to solutions to the global need for analytics. Blessed with a solid foundation of young, educated and English speaking workforce, companies around the world are look for Filipino analytics talent to fill analytics positions.

DMAIPH was set up to facilitate these solutions and bring the talent and the business together. And that is exactly why I wrote Putting Your Data to Work, the first analytics guidebook designed specifically for the Philippines. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can help you take advantage of this unique global opportunity.

Data Science and Big Data Analytics For Finance

Was recently asked to do a one hour talk on data science and big data analytics for Finance… so I created A Step By Step Process To Get More Value Out Of Your Finance and Accounting Data.

To get started, we will discuss at a high level what is analytics, big data and data science and how it can be used in Finance and Accounting to get more value out of all the numbers you have in your business.

Per Deloitte, “In today’s highly competitive business environment, companies need more from Finance than accurate financial statements and reports. They need forward-looking, predictive insights that can help shape tomorrow’s business strategy and improve day-to-day decision-making in real time. “

New IT applications and infrastructure such as big data technologies, predictive analytics, as well as modern mathematical methods are opening up new possibilities for gathering and processing large amounts of data and opportunities for generating value.

They keys to a sound data science and analytics approach to Finance include the following:

  • A Process for Using Big Data to Answer Business Questions
  • A Well Mapped Data Lake of all the Data Finance Needs
  • The Right Mix of Analytics Talent, Technique and Technology
  • A Top Down Embrace of an Analytics Centric Culture

By translating data into insights around financial statements and operations, the finance team can unlock and create new value. Being able to identify unrealized and often unexpected potential as well as quickly and decisively mitigating risk, data science and analytics can take your team to a new level of insight and performance.

This in turn supports the finance function to make better decisions by being able to understand what has happened and why, and then predict what may happen next. The end result is a strategy built on data and one with a much higher rate of success then ones based on intuition or gut feel.

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

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.

APEC Data Science & Analytics Key Competency #4: Domain Knowledge and Application

According to the APEC (Asia Pacific Economic Cooperation) Advisory Group, Domain Knowledge and Application is one of the key competencies of a Data Science & Analytics professional working in the region.

By definition, a DSA professional can apply domain-related knowledge and insights to effectively contextualize data, achieved by practical experience (e.g. apprenticeships) and exposure to emerging innovations.

In my own experience, I knew Wells Fargo data like the back of my hand, but my domain knowledge would have easily allowed me to the same great things with other big banks. When I toyed with the idea of moving into the health services industry, it was obvious my skills would be useful but I had a lot ot learn about the domain knowledge of healthcare data.

Since, domain knowledge represents knowledge and insight that is unique to the organization or industry and that analysts need to consider when conducting any data project. Without this knowledge, analytics solutions may not entirely address the real business problem.

In my experience, domain knowledge about the data being analyzed can sometimes be acquired through exploration of the raw data.  Often, good analysts become subject experts just by playing with the data and asking questions to domain experts about the data.

Given the dearth of analytics talent in many areas, reality will dictate that a lot of data projects will have to be done without sufficient domain knowledge. However, most experts would agree the best results come when the ones using the data, know the data.

So, it behooves companies to invest more in educating and enabling internal resources then looking outside for DSA talent. My solution to this is to introduce apprenticeship programs where subject matter experts train current staff with high DSA affinity who are currently working in other roles.

As an example, there are likely thousands of current call center agents who have the aptitude to be analysts an data scientists, but never had the opportunity to of into DSA. Given they are already employees with proven track records of success, they would be much more likely to have the domain knowledge needed.

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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.

Top 5 Reasons to Attend the Digital Transformation Summit 2017

From the Global Chamber® Manila Newsletter

May 9, 2017

  1. Be inspired by an all-star lineup of speakers, who are already gaining significant benefits from adapting new technologies
  2. Learn about various technological platforms such as AI, IOT, Big Data, Analytics, and how it’s disrupting businesses
  3. Reflect, reexamine your company’s issues and goals, and set your vision and new strategy for the next decade
  4. Engage and network with executives and fellow thought leaders
  5. Empower the culture of innovation within you!

Not registered yet? It’s not too late to sign up!

I will be one of the speakers, talking about Data Storytelling. It is quite an honor to part of this awesome event! 🙂

Attend one or both days of the Digital Transformation Summit this May 24 and 25 at SMX Aura Convention Center.