Q5:What are some basic strategies an analyst can use to find the right data at the right time? – Part 2

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 everyday 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 you 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 get the data, do some analysis and then 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.

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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 as become a data swamp, clogged with bad or unusable data. As the % of unstructured data increases daily, its easy to see how data swamps have become the norm. Even he most robust data collection and mining can run afoul if the data is not trustworthy.
So getting back to the last post… know how the data is populated. Who, when, why, how, how often, with what filters… things like that. 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.

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.

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.

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.

Q4: Can you please describe the current state of analytics in the Philippines? – Part 1

Let me tackle this question in two parts. The history major in me demands we look at how we got to where we are now before we talk too much about where we are going.

To start, both the appreciation for and the use of analytics has grown tremendously over the past few years. When I first started thinking about setting up a business in the Philippines back in 2011, hardly anyone knew much about analytics. Big banks, large call centers, multinational corporations and only the top schools were even talking the concept.

It was a challenge to fill my initial training classes due to lack of general awareness. Even at industry events and conferences it was rare to hear much about the idea of using data to drive business decisions.

Doing a search on the top job board in the Philippines back in 2012 for the jobs with analyst in the title netted about 1,000 job postings on any given day. The average salary was some here around 30,000 PHP a month. It was a challenge to find good talent and those who could do analytics were all gainfully employed.

It wasn’t until 2013 that I stated seeing other analytics training options and those were just ones being done by IBM to meet the CHED (Commission on Higher Education) requiring the implementation of a six class elective tract in business analytics. The was accompanied by the launching of Analytica, and IBM backed effort to push the Philippines towards being more a viable option for analytics outsourcing.

At this time a job search for analyst would bring back about 1,500 jobs. Salaries were starting to rise for analysts as well with the market average getting closer to 50,000 PHP. Still not a lot of public training or analytics centric organizations around then.

About the same time I started getting invited to schools on a regular basis to lecture about analytics to IT, CompSci and Management students. For the most part they had no idea of the career opportunities out there for those with analytics talent. I consulted with several schools on how to implement the CHED memo and how to prepare their students for analytics careers.

In 2014, an analyst job search was yielding closer 2,000 open jobs. The average salary climbed north of 50,000 Pesos for an experience analyst. I did a lot more trainings, being able to routinely fill a class of people hungry to learn more about analytics and how it could help them in their jobs.

The most in demand analytics skills up to this point where many centered on management reporting, production analysis and workforce management. Most analysts used some kind on proprietary database to store data and did just about all their analysis in Excel.

By 2015, analytics was finally in the mainstream. Job posting now routinely called for specific skills sets in programming languages and business intelligence tools. Multiple organizations made up of analytics professionals started coming together. The number of jobs open hit 2,500 on any given day and salaries for really good analysts hit 70,000 PHP a year. By this time, many outsourcing companies focused on setting up team of analysts to offer analytics as an outsourcing option. Big data jobs and even data scientist positions started showing up in large numbers.

So here, we are now in early 2016. The sky is the limit when it comes to Filipinos with analytics talent being able to enjoy good career growth and make substantial salaries. The schools are now starting to churn out talent with analytics careers in mind. Things look great on the supply side of analytics talent and the market growth opportunity for businesses offering analytics is huge.

An additional complexity in the analytics world is the vast number of tools out there to gather, store, analyze and present data. Although IBM is by far the biggest player in training people, they are not the universal solution when it comes to the methodologies and technologies people use every day.

The biggest challenge today is that the demand for analytic talent dwarves the actual current and near term talent supply. The global need for not just analysts, but also data scientists has quickened to a point where catching up for the Philippines seems almost impossible.

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HR & Recruitment 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.

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 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.

20 Questions with Dan Meyer about the Fundamentals of Analytics.

Recently I was asked to put together an FAQ about analytics. Based on my experiences from training people how to better use analytics, these 20 questions are the ones I most commonly get asked.

To start can you provide us with a basic overview of what is analytics?
Can you tell us what makes you an analytics guru?
What are some of the current trends in analytics?
Can you please describe the current state of analytics in the Philippines?
What are some basic strategies an analyst can use to find the right data at the right time?
Can you provide some tips on how to manage data?
What exactly is data science and why the rapid rise of data scientists?
Here something a lot of us are wondering, what exactly is big data and how can we use it?
Can you please describe the concepts of storing data in a data ware house?
Please talk about how, when and why we use should descriptive analytics?
Can you next describe how to best use predictive analytics?
Next please explain when and how we can use prescriptive analytics?
A lot of us want to know what is business intelligence and how does it add value to analytics?
What is data visualization and how does it help drive better decision-making?
What is a business dashboard and how is it used in a business?
Can you tell us more about current trends and hot new tools in social media analytics?
Many of us work in recruitment or HR. What are some best practices and technologies used in HR and recruiting?
Can you please talk about recent developments in higher education on how to train more analysts?
How would you describe your approach to teaching analytics?
So in conclusion can you explain a little more about your own method for using data to drive better decision making?

Each day for the next several days, I will take each question and elaborate and share with you my own personal FAQ on the Fundamentals of Analytics.

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The Fundamentals 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.

The Best Bunch of Analytics Interns Ever

2011 – Year Zero – The Best Bunch of Analytics Interns Ever

So this is simply the story behind one of the coolest things I have ever done.

Earlier in 2011, before I set up BPO Elite, I was chatting with a friend who was attending a local community college. She was trying to find a speaker for a business club she was an officer in. I offered to come in and talk about remittances. It was a lot of fun. After the talk and lots of Q+A, one of the students approached me and asked if we had internships.

Hadn’t really thought of that before, but it made a lot of sense. I had worked with several interns while with Wells, and generally introduced them to how we did analytics. So it was a natural progression.

So I took on the intern and before I knew it I had 5 of his classmates on board as well. As I was putting the business plan for BPO Elite together I came up with a list of things we needed to understand the competitive landscape around the new business.

I divided them up into 3 types of analytics interns based on their interests; business analyst, marketing analyst and data analyst. I gave each one a research topic, gave them in a crash course in Tableau and turned them loose on doing some public data mining and analytics for BPO Elite.

About the same time we got our first two clients. A small shipping company that specialized in shipping things to the Philippines and a local chiropractor. Both business owners were at a point where they needed help understanding some of the reasons why there businesses were not a successful as they thought they should be. They knew they needed help, but didn’t know where to turn . Fortunately I had the answer… they need to bring some analytics into their businesses.

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So before we go one, let’s level set on what exactly analytics is. In its simplest form it is a the discovery of patterns in data with an eye towards using these discoveries to help a business be successful. If you ask any 10 professionals who work with analytics you will get 10 different answers. It’s a broad topic with just about every business using analytics differently than the next one. And most small businesses don’t even use analytics. Its more in the realm of the corporate world.

So after explaining to my clients what I could do to help them using analytics and getting a good idea of their challenges, I came up with some plans and turned my interns loose.

We did some good in both cases. Mainly focusing on building demographic profiles of their ideal customers and mapping where they lived, we came up with some targeted marketing materials. We used US Cenus data, Google and Tableau to demonstrate the opportunity around them.

We also spent some time building a competitive landscape for each buseiness as well so the clients could see where they stacked up against them. And finally we added some customer insights, mining data from their social media sites and places like Yelp. All in all, we gave each business owner a sample of the things I used to do at Wells. In both cases it was a big help.

And the best part, the kids learned tons of things they wouldn’t likely have learned in a traditional corporate internship. They got their hands dirty with data and they made a difference in the success of a business.

Today, they are all employed in good jobs, mostly working in position with analyst in the title.

Pretty awesome stuff.

My Analytics Story – 2011 – Year Zero – Past as Prologue

This is the first in a series of blog posts to I have planned to share My Analytics Story – Teaching Analytics in the Philippines.

I first got the idea to do analytics training and outsourcing in the Philippines in early 2011.

A little historical perspective first. For most of its history Wells Fargo was not very big into outsourcing, but was very big into analytics. I had been working as a senior analytics consultant with the bank for several years and doing some pretty amazing things with data blending and data visualization for our management team.

Then Wells Fargo acquired Wachoiva and all of a sudden my team was given the challenge to help set up some new positions in the Philippines. Wachovia had a long and successful history with doing back office operations in Manila.

For those not familiar with Wells Fargo and/or Wachovia:

The first team to be set up across the Pacific was a back office, new account fulfillment team. A fairly routine series of tasks, easy to capture and validate data. My role was to provide benchmark data and then management reporting as the transition progressed. While doing this I was paired up with several business partners in Manila. And quickly I discovered that their analytics tools were not very advance and they really didn’t use much predictive analytics.

The light bulb turned on.

I could do this. I could go to the Philippines and get involved with training people to do more analytics, to bring more data-driven decision making to the outsourcing industry!

Given I was married to a Filipina, most of my friends are Filipino-American and I grew up in a city with one of the largest Filipino communities in California, this was a perfect chance to grow my affinity for the Philippines.

And of course I could get back to doing more teaching in the form of training people to use analytics. I had been thinking about getting back to teaching for a while, but the bottom line is Wells just paid better. Plus, I really love working with data. So then next light bulb went off.

I can take what I am good at analytics, and merge it with my passion, teaching and get into the business of training analytics.

As this plan was starting to evolve in my mind, things at work where also coming to a head. I had been trying for a while to find a new job that would get me closer to being involved in both analytics and outsourcing equally with no luck. Lots of interviews within the bank, but nothing came to fruition. Which in hind sight was a total blessing in disguise.

One day I was chatting with a good friend about my growing frustration of not being able to find the right job at Wells and he said, well why don’t you set up your own business then? Light bulb number 3.

But then how? He suggested we talk with a friend of his who was ironically enough looking to set up his own call center in the Philippines. We had several meetings and decided the three of us would set up a new business both in the U.S. to find clients and in the Philippines to train talent to do work for the clients. I came up with the name BPO Elite and the tag line, making data-driven decisions.

And then we got around to talking about who would run the company. And they both immediately said it would have to be me. Up to this point, never in my life had I contemplated such a thing.

Me being the boss.

The final light bulb burned bright. Now it would just a matter of planning the launch of the new business.

This was around June 2011. We set up BPO Elite legally, built the web site, started doing some marketing.

One thing I needed to validate though was can I actually train people to do the analytics we would be offering as a service. I needed to do a pilot here in the U.S. before moving to the Philippines. I had always thought college students/fresh graduates would be the best ones to hire to work in our business. They are open minded and highly energetic and I could fill their minds with the technologies and methodologies I had used at Wells to be a great analyst.

So thanks I brought on a team of interns over the summer of 2011 with the idea of teaching them analytics and turning them loose on some local small business clients to see if we can drive some results that would be turned into a training model.

It was a huge success. Great pupils. Happy Clients. Lots of Data. More on the how I did it later. For the sake of the narrative, it worked. So it was time to leave Wells Fargo and set in motion the plan to move across the Pacific.

And one more key point I will get to later, one of my parners had a connection at one of the top schools in Manila. So lets not just train fresh grads to do analysts, I should also tie up with the school to teach a class on analytics. More access to talent and a good way to build our brand credibility.

The moral of the story… the reason behind my posts… dream a dream, validate it with data, take calculated risks to seize opportunities and then just persevere.

More to come.

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