Putting Your Data to Work is SOLD OUT! Working on the 2nd Edition Now!

I feel so excited to share with you all that we are now completely sold out of my recent book, Putting Your Data To Work: An Analytics Guidebook for the Filipino Professional.

I am currently working on a 2nd addition with additional images, content and an expanded section on predictive analytics.

If you would like to pre-order a copy of the 2nd Edition, send us an e-mail at analytics@dmaiph and we will add you to the waiting list.

My plan is to have copies available by mid March if not sooner.

Thanks so much to all of you who purchased a copy of the 1st edition. I’m proud to say that we’ve gotten some fantastic feedback from readers that I will incorporate into the new edition.

2017 is the year that Analytics will go mainstream across the Philippines. My book is a great guide for ensuring your organization has a solid analytics foundation as the era of Big Data is upon us.

My Analytics Story – My passion is solving problems by bringing together the best talent, cutting edge technology and tried and true methodologies. DMAIPH is all about empowering people towards better Decision-Making through the use Analytics and business Intelligence. This is what I do best. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly for a free consultation about getting more analytics into your career and your business.

Key Analytics Tip – Build A Data Map

One of the keys to being successful with analytics is having a clear view of how all the data flows into and through your business.

Building a data map to show all the entry points, all the places where data is stored, who and how it is accessed and what filters might change your data is one of the things I can help you do.

This is step one towards data integration and is a great exercise for a half day in-house training/seminar for any business or organization that is struggling to get valued out of it’s big data.

Per Wikipedia… Data integration involves combining data residing in different sources and providing users with a unified view of these data. Data integration appears with increasing frequency as the volume and the need to share existing data explodes.

If you can imagine a map of your business in your mind you are half way there. The next step is to build a flow chart like the one below

If you have something like this then you are on ahead of the game.

If you don’t, let us set up some time to discuss how to get started.

Analytics Culture – The key to using analytics in a business is like a secret sauce. It is a unique combination of analytics talent, technology and technique that are brought together to enrich and empower an organization. A successful analytics culture is not easy to create, but DMAIPH can show you how. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can build a strategic plan to turn your company into analytics driven success story.

Big Data Analytics and Business Intelligence

Enabling Your Business to Make Smarter Decisions

Are you tired of being under constant pressure to make the right number-based decisions for your organization?

Are you too often overwhelmed by an out-of-control flood of numerical information, much of it conflicting and confusing?

Big data is booming ast as organizations devote new technology resources to tapping the terabytes (if not petabytes) of data flowing into their organizations.

Today big Data is flooding into the business both through internal processes and externally via social media.

What does this all mean for business intelligence (BI) users and systems?

With all the attention on advanced analytics for big data, what’s the play for BI?

Integrating advanced analytics for big data with BI systems is an important step toward gaining full return on investment.

Advanced analytics and BI can be highly complementary.

Advanced analytics can provide the deeper, exploratory perspective on the data.

BI systems provide a more structured user experience through there richness in dashboard visualization, reporting, performance management metrics, and more can be vital to making advanced analytics actionable.

Recently on December 6, 2016 I was at Astoria Plaza, Ortigas Center, Pasig City for a dynamic and empowering one-day training on Big Data Analytics and Business Intelligence.

Course Description:

Make smarter business decisions using these powerful data analysis techniques

Information is supposed to make us smarter, but more often than not, it simply overwhelms us.

This program is for you if you feel like you’re drowning in data and unsure which data to use to drive your company initiatives.

The truth is that the amount of data available to help run your business is greater than ever before. To effectively use this information, managers must consider the practical side of big data…what matters to you is how do you grow and build a team to make smarter decisions.

Much of the information out there just discusses the promise of the data deluge. The challenge is not the volume of data but rather the judgment needed to use it.

This seminar goes beyond the qualitative side of data analysis to explore proven quantitative techniques and technologies for identifying, inventorying and integrating data, so that more informed and reliable business decisions can be made.

Learning Objectives  

Apply Best Techniques and Cutting Edge Technologies to Organize, Interpret, and Summarize Quantitative Data
Create a Process to Analyze Data and Identify Patterns Not Apparent at First Glance
Reduce “Analysis Paralysis” and Go from Hard Data to Well-Reasoned Conclusions in Less Time

What Was Learned

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

What Was Covered

Using data and statistics effectively in business today
Improper data manipulations and their consequences
Exploring quantitative data collection methods
Improving analysis success by effectively utilizing software
Understanding regression, trend lines, and scenarios in Excel
Utilizing the power of business intelligence software
Finding and analyzing data patterns, trends, and fluctuations
Interpreting and translating data into decisions

Who Attended

Over 80 business professionals who needed to learn more about the basic tools to quantitatively and accurately analyze the mountains of data that come across their desk each minute of every day.

Section One

Big Data—It’s Not Just Size

Describe the Importance of Effectively Analyzing Big Data in Business Today
Come up with a Data Map to Analyze the Big Data in your business.
Establish Clear Objectives When Analyzing Big Data
Recognize and Apply Various Data Collection Methods
Identify and Resolve Problems Associated with Data Collection
Discuss the difference between Data Warehouses and Data Lakes
Determine when to use Data Blending in your analysis

Section Two

Analysis—Using Business Intelligence Tools

Assess Your Current Analytics Culture
Describe the Issues and Trends in Today’s Analytics Field
Optimize your use of MS Excel for Big Data analytics
Discuss the concept of Data Visualization
Utilize BI Tools like Tableau Public
Build a Business Dashboard Prototype

Section Three

Interpretation—Assessing Results

Articulate the Importance of Accurately Interpreting Data
Determine and Analyze Risk, Uncertainty, and Probability
Spot Patterns, Trends, and Fluctuations Through Correlation, Regression, and Descriptive Statistics
Understand when to employ Descriptive, Predictive or Prescriptive Analytics
Build Data Models

Section Four

The Art of Presenting Big Data

Apply a Process to Present Big Data Clearly
Select the Appropriate Presentation Format to Communicate Your Findings Effectively to Your Audience
Master the Power of Enchantment
Use Findings from Big Data to Drive Decisions Within Your Organization

Too often people dive into the data only to be lost in haze of data.

This discussion will be pragmatic and immediately applicable to analysts, professional using analytics and managers of analysts across all industries.

Analytics Training – DMAIPH in partnership with Ariva Events Management, 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 on which of our DMAIPH analytics training solutions is best for you.

Measurement and Evaluation: Analytics and Data Driven Decision Making

Recently Worked on a paper for a school… interesting topic…Measurement and Evaluation Analytics and Data Driven Decision Making.

I will also be doing a related webinar this coming Feb 15 entitled Analytics & Data-Driven Decision-Making.

Webinar details:

February 15, 2017

1pm Eastern

Webpage with webinar registration links: http://programs.online.american.edu/msme/webinars

The ability to effectively evaluate projects, programs and processes requires a thorough understanding of analytics.

Analytics is generally defined as the discovery of patterns in data that provides insight and identifies opportunities.

Organizations that invest in analytics generally make much better business decisions then one’s that don’t.

In fact, IBM found that organizations who use analytics are up to 10x more efficient and 33% more profitable the ones who don’t.

A good analytics solution constructs a universal framework for collecting, analyzing and using data to determine project effectiveness and efficiency.

As the amount of data available increases daily, the use of analytics is becoming essential to all levels of an organizational today.

This Big Data allows both deeper analysis but also requires more skill in getting to the right data.

By taking inspiration from the way corporations use business analytics to optimize their Big Data, our program measurement and evaluation processes can be greatly enhanced.

Bringing data together from a variety of sources and integrating the data into the decision-making processes, allows the empowerment of decision-makers to make much more intelligent choices.

When analytics driven leaders possess the practical assessment skills needed to evaluate projects bridging various sectors and industries, they are much more effective then ones that don’t.

In today’s information age, the quick and efficient measurement and evaluation of projects using analytics ensures success with corporate, non-profit and governmental organizations across various sectors and industries.

Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses. We can empower students and their instructors with the knowledge they need to prepare for careers in analytics. Contact DMAIPH now at 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.

Q6: Can you provide some tips on how to manage data?

So you have the data lake, the messy version of the lake or data swamp and then the pristine, well managed version of the data lake called the data reservoir.

Imagine how a reservoir of fresh water is used for multiple purposes… fishing, drinking, watering crops, providing electricity. That’s how your data should be structured. Even if you are working with multiple data sources made up of a lot of unstructured data from social media, you need to be organized with your data.

I’m willing to bet that if you are reading this then you are by nature pretty organized. Analysts tend to be. If you are working in an data swamp and the company culture is not data-driven, the best advice I can give you, no joke, is to find another job.

What to look for in a data-driven company? Are the data warehouses easy to use? Is their documentation on the data architecture? Is there a knowledge base? Are there experts and are they open to helping you?

If you say yes to questions like that, then your data management tasks are generally about optimization, data blending, adding new sources and being a kick ass analyst.

If you say no to questions like that, then your data management tasks are generally about cleaning data, lots of data validation and having your analysis be filled with caveats that you might be missing something.

So a few tips I have for those in good data companies; get your documentation fresh, do a lot of bread crumb dropping, save your queries and models.

Keep the data architects,database admins and/or IT staff in your circle. Share with them how powerful your analysis is because of their help. And most importantly, show you masterly of the data lake. Tell your story. And teach others how to fish in it.

For those of you not so blessed with good data cultures. You have to start on both ends. Map out the data flow. Try and assess where the data goes bad. Is it the input or capture of the data, is it a loading process, is it filers? Once you get a start on the front end, then go to the back end.

Who needs the data? How much of what data is being provided now is actually usable? Eliminate any unnecessary data. Basically start cleaning up the swamp at the same time you map it. And again tell this story. Don’t make excuses, but you do need to educate. Let people know there is a problem with the data and outline what you will do to correct for it.

In either case, before you go out and request or purchase new tools or start adding new data… make sure you have the architecture figured out. That’s the best tip I can give you about managing data.

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.

Q5: What are some basic strategies an analyst can use to find the right data at the right time? – Part 1

Several years ago I came across a book called the Accidental Analyst. 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.

When I am training newbies, I generally brake finding data into two parts… the process of getting the data and the process of making sure the data is valid.

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 me to deliver something. Knowing what data I would need, where I would find it and how long it would take me 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 off know their data. You have to know how the data is captured, where it is stored and how it makes it 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 you data comes from and how it gets there. Learn from them. Partner with them. Buy them doughnuts.

A few months 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, how to you find the right data at the right time? You know its structure, you understand how its 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. We’ll talk about data validation and data quality in a future post.

Q4: Can you please describe the current state of analytics in the Philippines? – Part 2

So the last blog post gave us the history. Now let’s cast an eye on the future.

Over the past year or so I have started to see a significant effort from data science and analytics professionals come together to address some of the challenges outlined in my last blog post.

In short, the way higher education and the government has approached the need for analytics talent is simply to little to late to meet the needs of many businesses.

Everything they are doing helps, but in the end the world is desperately looking at the Philippines to do with analytics what it did with customer service. To become a center of capable, long-term and affordable talent.

With taking customer service calls, it was a natural fit given that most Filipino college graduates have a foundation in English. With analytics and data science it has not been so easy. While many Filipino have the underlying course work in coding, database management, computer science, etc… they are not getting enough exposure to data-driven decision making, business intelligence tools, and more advanced things like machine learning, prescriptive analytics and blending big data from diverse data sources.

I don’t want to sound too pessimistic, things are moving quickly but it is generally the multinationals driving things forward. They have the clients, they have the need and so they go out and find people and train them. That’s why 3 years ago hardly anyone in the private sector was offering analytics training, now you see more and more options all the time. They are generally expensive and narrow in focus, but they are opening up huge opportunities for data loving Filipinos to get into upwardly mobile and financially rewarding careers.

I belong to a couple of newly founded organizations of data scientists and analysts who meet on a regular basis to share knowledge, support each other’s ideas and build a community with the goal of using data to helping both the Filipino to fill these open jobs and for the Philippines to begin to use more data in decision-making so we can solve the big issue problems important to all of us.

It’s a pretty exciting time.

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So where next?

Given that the Philippines is one of the youngest countries in terms of average age on the plant and the youth are incredibly communal and very tech savvy, I have found great success in training batch of Filipino fresh graduates in basic analytics. Of the 200 or so trainees I have personally trained, most of them now have jobs with analyst in their title.

I have also seen a lot of talent quickly go from novice to expert using applications and doing coding in relatively short periods of training. In many respects the approach to analytics is more vocational then academic allowing for quicker training.

Beyond these strength, you can expect more partnerships between the government, higher education and big business to offer training and career pathing. The success of the BPO industry is really the driving force to add employees who can do the tasks of an analyst. The huge surge in job postings demonstrates this quickening trend.

Finally, the reason I see a bright future for analysts and data scientists in the Philippines is the simple fact that Filipinos gravitate to under filled career paths, they push themselves to get the skills to fill those jobs. You see it in the Middle East oil fields, in sailors and seamen in just about every ship at sea, you see it with overseas workers across the planet, and you saw it happen with call centers.

And that is exactly why I set up my business in the Philippines. Here are some of the analytics solutions we offer:

HR & Recruitment Analytics – The recruitment and retention of top talent is the biggest challenge facing just about every organization. You really have to Think Through The Box to come up with winning solutions to effectively attract, retain and manage talent in the Philippines today. DMAIPH is a leading expert in empowering HR & Recruitment teams with analytics techniques to optimize their talent acquisition and management processes.

Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to learn how to get more analytics in your Business or your HR & Recruitment process so you can rise to the top in the ever quickening demand for top talent.

Q3: What are some of the current trends in analytics?

Every few months I devote a day to discover what are the current trends in analytics. 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 about once a month and I have well over 200 blogs, web sites and social media groups cataloged. So I like to think I’m pretty well versed on what is current.

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 doing this, data visualization is near the top. Business dashboards continue to be a big need. Business intelligence tools evolve and new ones’ pop up, but Tableau continues to be a market leader. 90% of us still use Excel for 90% of our analytics work.

Still a lot has changed. When I started this just 5 years ago no one was really talking about Big Data or Data Science. People just stared discussing using predictive analytics and now its all about prescriptive, even though most of us are still just doing descriptive analytics. For the newbie, descriptive = historical, predictive = forecast models, and prescriptive = really complicated models with a lot of variables to not just predict the future but to show a lot of alternatives as well.

Now if you talk to experts they make think nothing I have mentioned so far is new. But to the novice analyst or to the manager who really doesn’t care what’s it called, she just want’s results… its all new to them.

So I try each time to really find something really new not just to me but truly new to analytics. Six months ago that was the idea of using a data lake instead of a data warehouse. For those still unsure what a data warehouse is, it’s a collection of databases stored and/or connected centrally. Data lakes are used to describe the reality that 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 in just a few short years as mankind generates unprecedented amounts of data not easily captured in a database every day.

As of today, if I had to pick 5 topics to talk about it would be (1) Hiring Data Science and Analytics Talent, (2) Big Data Analytics, (3) Data Warehousing and Data Lakes, (4) Data Blending and (5) Mining Public Unstructured Data

Check back with me in a few weeks and this list will change.

Q2: Can you tell us what makes you an Analytics Champion?

Well, the first thing you should know about analytics is that there is no one right way to do things. Analytics is in many ways a new profession and up until very recently few people have seen being an analyst as career path. In fact the majority of analysts became so by accident.

Like in my case, most analyst are drawn to analytics because they like to solve problems, have an affinity for working with data, are tech savvy and above all else… insatiably curious. By the time I first had analyst in my title, I had already been doing analytics for several years.

Right out of college I found my novice skills with Excel, my interest in sharing knowledge and my ability to solve problems leading labeling me the data guy. There is nothing specific in my background that would suggest I’d become an analytics guru someday.

Majored in History with a plan to be a teacher. Obtained my Master’s Degree in Education. Started to teach. The school I was working at went bankrupt. Took a job with Wells Fargo Bank just to pay the bills and 15 years later I had amassed a wide range of analytics skills.

If you ask anyone with analyst in their job title, most of them have similar stories. Until recently you could not even get a degree in analytics as schools are just now offering analytics focused courses and degrees.

In 1998, I had the good fortune of being hired by Wells Fargo. The factors that contributed most to my success with the bank were two things inherit in the culture; its progressive use of data in decision-making and its accepted practice of moving up the corporate ladder by moving between departments.

If I had to pick one thing above all others that had made me a good analyst, it is my ability to quickly assess a problem and then identify the data needed to solve the problem. For my money, finding the right data is the most important trait to have and also the hardest to teach. It comes out of being curious and letting that curiosity drive you to find answers.

For 15 years that drive lead me to add new skills, learn new technologies, and develop new methods to become a proverbial jack of all trades when it comes to analytics. I often describe myself as a super hero, analytics being my super power and the wide range of skills I’ve picked up being items on my utility belt.

I am far from an expert on most of the ever increasing number of analytics tools out there, but I know what they can do and what they are good at. There are definitely a lot of people who are better at different aspects of analytics and no one can know it all. But in the end, I have become in many ways a guru of analytics.

I love talking about analytics, explaining it in layman’s terms, empowering people new to the concept, turning on the light in a dark room. I also love talking about prescriptive analytics models, using SQL code to write a complex join between data tables or figuring out what tool would be best use to build a business dashboard.

Providing people with the fundamentals of analytics is what I have been destined to do.

Q1: To start can you provide us with a basic overview of what is analytics?

Analytics is simply about looking for patterns in data to help answer questions. Most people use analytics within a business to help ensure more data-driven decision making. Businesses that use analytics are generally much more efficient and much more profitable then ones that don’t.

Analytics is generally employed by analysts who are skilled in using certain technologies and methodologies to identify, inventory and integrate large amounts of data quickly. What separates analytics from 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 general business analytics that are 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. However, analytics can be used in an almost limitless number of business functions in specific areas like HR, recruitment, marketing, finance, and so on.

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

If you 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.

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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 extrating inights and discovering opportuniites. DMAIPH specializes in empowering organizations, schools, and busiensses 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.

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