Q9: Can you please describe the concepts of storing data in a data ware house?

Twenty years ago data was mostly stored in databases. These databases housed all the data a business would need to do analytics. Transaction data, sales data, customer data, demographic data was all neatly collected, stored and analyzed in databases.

A surprising number of companies still store most of their data in databases. It works well for business that just need to look at historical data to conduct basic descriptive analytics.

About ten years ago the amount of data captured in a business and the growing diversity in date sources and data storage brought about the mainstream use of data warehouses in the business world.

Data warehouse are often a collection of databases interconnected so that data can be brought together into one place for reporting and analysis.

Whether you are working with a data base or a data warehouse, you should have a basic understanding of how data is stored. It should be in table format, with header columns and data rows.

A good way to quickly assess the analytics culture of a business is to look at how data is shared among management. Does it look table like? Or is it obvious that most of the time spent by the author was put into decorating? If you can’t easy sort something, then you are not dealing with a good data culture.

The best way to have a good data culture is to have well documented data structures. Any dB admin worth a grain of salt has the data hierarchy mapped out and has a knowledge base to help users know what data is in each field.

Like with finding data, being good at storing data starts with knowing the environment. Any good analyst should have a basic understanding of how to use SQL to pull a query for a data table. Even if you cant do hard core coding, know how data is generally stored in a structure is key.

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Another important concept about data warehouses if you have to know how to join or blend data from different sources. When you have multiple data tables in a warehouse you often need to join the data on a common field. Data blending goes on step further as you are often trying to take data that doesn’t have a natural point on common that is easy to join on. Advanced data warehouses and data management tools can blend things easily, but its still important to understand the core concepts of how to join and blend data.

As I mentioned in earlier posts, there is now a new concept taking root that one up data warehouses. Data lakes are being used to address the fact that we have more unstructured data then we have structured data. Data bases and data warehouses were designed only to handle structured data the easily fits into a data able.

Now we have to collect data from images, videos, blogs, comments and other places that are not easily converted to a value. Data blending across both traditional structured data warehouses and new types of data is not easily done in most data warehouses so tools are being developed to bridge this gap.

The lake is no longer a place just to fish, but also to do all the other things a lake can be used for.

So, when it comes to understanding data warehouses, learn who built and/or maintains it and buy them a cup of coffee. Get your hands on the data dictionary, knowledge base, FAQ, metadata.. whatever you can to map out the data environment. If you do that then you can find use the big data stored in a data warehouse to find the right data at the right time.

Q8: Here’s something a lot of us are wondering, what exactly is big data?

Think about some of the things you do in your daily life. You get up, you eat, go to work/school, shop, do something for entertainment, bank, go online and do things on social media. Everything you do generates data. That data is captured in countless ways. And then its stored in countless places. And analyzed by countless numbers of people. And then used in countless ways by businesses to market, design, advertise, build, sell, and so on.

Every time you check your phone to see if there are any updates on Facebook you generate a lot of data for your phone manufacturer, your service provider and Facebook itself. Everything you like or comment on can be turned into a data point. The time, place and length of your connection all provide useful data. Get the point? Its endless.

That’s big data.

In general, big data is thought of as all the data businesses capture and store in a database that they can use for business decision-making.

When you think of data collections that have millions and millions of rows of data like big bank transaction data, or traffic data for major cities, or all the statistics captured everyday across professional sports. Way too much for man to analyze without help from technology. That’s all big data.

Every business defines its big data a little differently. There is no one way to look at how best to manage big data because big data is such a living, evolving, never ending flow of information. It’s like lakes of water that are too big to swim across and too deep to dive to the bottom of without help. And no two lakes are alike.

Data analysts and data scientists are the ones who know the lake and guide you across or build you a submarine to explore the bottom.

As I have mentioned in previous posts, knowing the data environment is key to your success. And big data just adds weight to that statement. If you don’t know where all the data is coming from, can’t be sure if its clean, then you will get lost in the deluge of big data.

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.

Q7: What exactly is data science and why the rapid rise of data scientists?

A year ago I might have found it challenging to really answer this question. The first time I had heard of the term data science and a data scientist wasn’t that long ago. And I have been doing some pretty advanced analytics for close to 20 years now. I know the term has been around in academic and research circles awhile longer, but 2014 is the first time I ever saw a job posting for data scientist in big business.

So what is data science? Besides simply being the study of data, it generally refers to using complex models, machine learning, predictive and prescriptive analytics and powerful technology to analyze business data in much greater volume, velocity and variety then possible a few years ago.

And of course the ones charged with doing the data science are data scientists. They understand math, statistics, and theories that can be applied to business data using new technologies and methodologies.

The biggest challenge to being a true data scientist is that you have to be adapt at both technology and working with people. Being a business data expert, knowing how to code and doing higher math are only half the job. You have to also share your data, communicate it in ways that drive action, share and engage with non-data centric people. It’s hard to find people who are good at both.

Image from Forbes Magazine.

In addition, whole some data scientists are educated to be data scientists, very, very few actually have any kind of degree in data science. That kind of degree really didn’t exist until very recently. Instead most data scientists have advanced degrees is related subjects and have migrated into the business world do to market demand.

That demand has been growing at a staggering rate the past few years as every day we generate more and more data across the planet. President Obama first employed a data scientist for his campaign in 2012. The White House now has a chief data scientist position.

If you were to compare results from job board searches form 2012, you’d see maybe 100 data scientist job postings. Now its easily in the 1000’s. So that’s why the job market for data scientist is one of the hottest around. Lack of training programs, having both tech and people skills, and the booming demand due to unending new data to being analyzed.

Some people ask me if I’m a data scientist I am careful with my answer. True data science is not something I am academically prepared for nor I have never published anything in a scholarly journal. But my real world experience working with data has made me an expert on many aspects of data science.

I guess I feel more like an analyst, but a freakin awesome analyst who can do a lot of things using data that are super important to a business.

Analytics Education – Facilitating a mastery of the fundamentals of analytics is what DMAIPH does best. As a key parnter of the Data Science Philippines Meetup Group, DMAIPH champions the use of using data. All across the world, companies are scrambling to hire analytics talent to optimize the big data they have in their businesses.

We can empower students and their instructors with the knowledge they need to prepare for careers in 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 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.

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.

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