Category: Analytics Training

All The Tools And All of The Talent but none of the Technique… Where Good Analytics Intentions Go Bad

I have seen so many examples of this. A majority of companies throw money at analytics in the form of buying new technology, but don’t spend a fraction as much on the people who need to make the technology work.

A good analyst using Excel is much more powerful then a mediocre analyst using a cutting edge BI tool. Without the innate curiosity, knowledge of the business and ability to communicate discoveries that come with a good analyst, your analytics plans will fall short no matter what the sales reps from the analytics companies promise you.

Now we have the 2016 Presidential Election results to analyze. Most predictive models had Clinton winning. Most of the polls had Clinton winning.

So where did the analytics go wrong? Well, its definitely not the technology. And I don’t think it was the talent.

In the coming days, I am pretty sure we will find it was the technique.

It was not getting deep enough data.

It was looking at the data and seeing what you expected to see.

Curiosity was lost.

Finding new perspectives to make sure we have the right data next time.

Hillary Clinton’s campaign will be a case study in where good analytics where not good enough.

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.

 

11 Days Of Analytics Training in Just Over 3 Weeks

By the end of next week, I am on target to have completed at least 4 hours of analytics training in 11 of the past 25 days.

This tells me two things… the need for analytics training here in the Philippines has never been greater and I need to move finding an in-house business analyst who can also do training higher up my priority list.

Ideally, someone with some business dashboard building experience, knows the BPO industry and is passionate about teaching other people how to be good analysts.

Qualified candidates are in short supply, but I know there are some out there who will make a great addition to the DMAI Science Team we are now building.

If you or anyone you know is interested, please connect with me on LinkedIn or send me an email @ danmeyer@dmaiph.com

There is an ever increasing analytics pie out there and the time is ripe to not just be a good analyst, but get into helping create a whole wave of analysts.

Be a creator, not just a user!

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Infusing HR Analytics into Organizational Behavior and Human Resource Management Classes

One of the things I have been working on is helping a top school here in the Philippines develop a strategy to infuse more HR Analytics into their Organizational Behavior and Human Resource Management Classes.

This effort is a precursor to a class specifically on HR Analytics, which is to the best of my knowledge, the first ever here in the Philippines.

So as I put more thought into the syllabus of each class, it occurred to me that a good way to approach analytics is to introduce it slowly over the length of the 3 classes, which follow in a natural progression.

Starting with the OB class, we can focus on how to identify data in an organization that will be useful to a HR team to measure things over time. To help really get at causality of human behavior on a wide scale, you need to have the data to understand context.

In the HR Management class, we will spend more time working on the inventory part of analytics, which is to bring the data into an analysis and reporting structure that helps us discover patterns and trends based on that data.

Then the HR Analytics class, we will then proceed on how to integrate the data and the analysis into tool like a business dashboard.

At a high level, the students will gain an appreciation for the wealth of data HR can access in an organization and how the analysis and reporting of this data can lead to more data-driven decision making.

Its great to have an understanding of why people leave a job, and to have good reporting on attrition patterns, but you also need to have the ability to enable strategic action based on data and not just observation or simple metrics.

That is what our students will be able to do that will separate them from other Psychology grads entering the workforce. They will be ready day one to be HR Analysts who can bring a much needed data centric skills set to a very people driven discipline.

If you are a school administrator or professor and need to get more analytics in your course work so your students are better prepared for the analytics centric jobs, connect with me. I can show you how. I even have a textbook you can use. My new book Putting Your Data to Work is ideal for the nascent analytics learner.

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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.

 

BI Professionals Spend 50-90% of Their Time ‘Cleaning’ Raw Data for Analytics

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BY ON AUGUST 26, 2015 • ( LEAVE A COMMENT )
Last year, the NYT shined a light on big data’s “janitor” problem – that data scientists and business intelligence pros spend too much time cleaning, not evaluating data. But how big of an issue is it, really?

Xplenty just wrapped a commissioned study of +200 BI pros and found that a third spend 50-90% of their time just cleaning raw data. This is one of the first reports to tie an actual # to the ETL process.

Source: bigdataanalyticsnews.com

From my days at Wells Fargo being an analyst I know how hard it was to maximize your analysis and communication time and minimize time spent finding and cleaning data. This was especially true for me as I was using more unstructured data to do things like competitive intelligence then structured data.

I see it being even more of a challenge now because the % of unstructured data in any business has exploded the past few years. Being able to mine valuable insights from unstructured data is a time consumer, at least until you get a process in place to extract and refresh the data using some kind of technology.

In addition, businesses continue to find new data points to bring into their data warehouses, dramatically increasing the amount of structured data.

What this means is a lot of analysts are spending a lot more time looking through mountains of data to figure out exactly which data to use. Its not going to get easier.

Good data gathering methodologies and nimble BI tools can help cut down on some of the workload, but in the end we just keep making data faster then we have the ability to truly process it.

There is just no replacing the human factor of someone knowledgeable about the business who can interpret the data and decide what data to use and what not to use.

Which makes life even more challenging, because once we determine what data we want to use, we still often have to take the raw data and clean it up so it is valid and so it will fit nicely into our BI tools.

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If you have having trouble figuring out what data to use in your business and if you find yourself spending far too much time cleaning the data, perhaps DMAI can help. We have a Data Science team ready to assist your organization with just these types of challenges.

Analytics 3.0, Big Data Equals Big Insights: Learning to Use Big Data to Build a Smarter Global Workforce

That was the title of my quick introduction to Big Data for HR to a crowd of about 1,000 HR professionals yesterday. My agenda was to talk about:

  • Using Big Data in HR to empower more Data-Driven Decision-Making
  • Extracting Key Business Insights using Big Data
  • A Big Data Analytics centered approach to building A Smarter Global Workforce

I must say it was pretty awesome as the topic generated a lot of discussion about the biggest challenges facing HR professionals when it comes to Big Data.

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Not surprisingly, one of the top challenges the attendees are facing back at work is getting their managers to support Big Data initiatives. There is such a great need for awareness of what Big Data is all about and how analytics is used to extract the right data to give decision-makers the ability to make smarter decision.

To start off, I suggestesd they think of HR Analytics like a Pyramid

Start with the base and gather all the HR Big Data

Build to the middle of using HR specific Analytics

Strategic Focus comes out ofthe top and you get Actionable Insights

If you can show that a Big Data approach adds value, optimizes processes and provide a strong return on investment. Basically you need to use data to support the use of more data.

Identifying data sources and analytical resources can provide guidance in understanding your organization’s needs and capability to adopt a talent-centric data-driven approach.

Having a data-centric culture is the first step in optimizing the Big Data in your business.

And my final word of advice was that you have to be the one to champion Big Data. You can’t wait for someone else to. As a leader in HR, you need to be the one pushing the issue of how to use Big Data to to the forefront.

The Number Of Solutions Is Just Not Enough

I just came across a couple of companies like DMAI that provide Philippines based analytics outsourcing to overseas clients. I guess that makes about a half dozen companies that I know of that are seriously trying to take advantage of the huge opportunities out there to push the Philippines to the forefront of global analytics solutions.

However, its just not enough. I see more and more Filipinos everyday employed in analytics for a wide range of companies. The number of analysts out there has mushroomed from a few thousand to tens of thousands in just a few years. Yet, a large percentage of these analysts need a lot of help to optimize the analytics in their businesses.

The efforts of big industry, working with the government and higher education to include analytics training within college curriculums is really picking up steam with dozens of schools in the early implementation stages of preparing tomorrows analytics talent.  Yet, the projections are so staggering that even if every schools filled every planned class to the max we will stall have a huge talent shortage.

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I am equally excited to dive deeper into the midst of this opportunity as I am sometimes a little overwhelmed with where to focus most of my energy.

Writing books, teaching courses, training, public speaking, setting up data science teams, taking on more outsourcing clients, the list just keeps getting bigger.

The number of solutions is just not enough.. .talk about being at the right place at the right time. No wonder I am having the time of my life.

Big Data Analyst > The Guy Making Sure We Have The Data We Need

If you don’t know where that information is coming from and whether you can trust it, then it’s useless.

Imagine your data as water.

The same idea applies to big data analytics. If you don’t know where the data is coming from, your data lake will quickly start to resemble a swamp instead of what it should resemble: a reservoir, something that guarantees access, quality, and provenance.

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The role of the DMAI big data analyst is at the guy managing the dam at the mouth of a big river. Data analysts constitute the foundation of a data science project and they are trusted with the responsibility of capturing, storing and processing the relevant data. Data Collection, Data Warehousing, Data Transformation and Data Analysis – these are typical tasks of a data analyst.

They are the professionals who play with the tools and frameworks, like Hadoop or HBase, in a distributed environment to ensure that all the raw data points are captured and processed correctly. The processed data is then handed over to the next group of people, the machine learning experts, for taking it further.

In order to call your data a true “reservoir” or “lake,” you big data analyst needs to be able to provide the business-level guarantees that one comes to expect from a data warehouse.

If you are able to create this type of environment the you should have no problem using data analytics in your business, then you are the ideal Big Data Analyst candidate. You are a pro with apps Hadoop, MapReduce or HBase and have the analytical skills required to become a successful data analyst.

A data analyst should be flexible to learn new tools according to the changing business needs and always be willing to upgrade to specialized techniques related to data analysis. Just like the guy controlling the flow of water from a lake to the community that lives off it.

Once we have the guy who makes sure we have the data we need, when we need it, then the DMAI Data Science Team will be complete.

What Is Data Science and Who are Data Scientists?

Per Wikipedia, Data Science is the extraction of knowledge from large volumes of data that are structured or unstructured, which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD).

Does anyone know  a “data scientist”? Data scientists work with large data sets, analysis models, and technological solutions to help businesses drive more data-driven decisions. This is known as data science. Data scientists should have these six skill sets:

Tech Skills

  • Programmer
  • Statistician
  • Domain SME

People Skills

  • Artist
  • Client Facing
  • Communicator

As you can imagine, it is very difficult to find people who have expertise in all 6 skills sets.

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The unique blend of skills required for a role on a data science team is being debated and almost everyone around the globe who is associated with Big Data, Analytics and Visualization has opinion on this topic.

DMAI has determined that the best lineup for our clients in the Philippines is a veteran business analyast, a big data analyst and a data modeling expert.

Ask me how you can get a data science team set up in your business.

Calling All Analysts! It’s Time To Step Up And Do More With Your Skills. Join The DMAI Data Science Team.

The DMAI Data Science Team

The DMAI Data Science Team is being assembled to offer companies and schools with the training and consulting they need to implement analytics strategies in their organizations.

Headed by analytics guru Daniel Meyer, this team of analytics professionals with diversified skill-sets will guide organizations as they build analytics teams, design analytics programs and empower the use of analytics to drive more data-driven decisions.

For your data science project to be on the right track, you need to ensure that the team has skilled professionals capable of playing three essential roles – Big Data Analyst, Data Modeling Analyst and a seasoned Business Analyst. The presence of these three types of analytics professionals, working together for a common goal, will result in proper analysis of relevant information for predicting the behavior of consumers, in line with the business objective.

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With this end goal in mind, we are looking for three super analysts to join our team and fill each of the components. Here are the roles:

Big Data Analyst:

The role of big data analyst is at the base of the pyramid. Data analysts constitute the foundation of a data science project and they are trusted with the responsibility of capturing, storing and processing the relevant data. Data Collection, Data Warehousing, Data Transformation and Data Analysis – these are typical tasks of a data analyst.

They are the professionals who play with the tools and frameworks, like Hadoop or HBase, in a distributed environment to ensure that all the raw data points are captured and processed correctly. The processed data is then handed over to the next group of people, the machine learning experts, for taking it further.

Ideal Candidate for the Big Data Analyst role: A Big Data Analyst is predominantly a technical role. The ideal candidate does not need to be very academic but must possess technical competency on the back-end frameworks and tools used for capturing the data points. If you are pro with Hadoop, MapReduce or HBase, then the role of a data analyst would perfectly match your profile. Besides technical acumen, analytical skills are also required to become a successful data analyst. A data analyst should be flexible to learn new tools according to the changing business needs and always be willing to upgrade to specialized techniques related to data analysis.

Component 2 – Data Modeling Analyst

Analytics modeling experts play the role of a link between the data analyst and the business analysts. They are primarily responsible for building data models and developing algorithms to draw conclusive information. Their job is to ensure that the derived information is well researched, accurate, easy to understand and unbiased.

Ideal Candidate for the Data Modeling Analyst role: Candidates with statistical background, having a deep interest in quantitative topics, and are usually preferred for the role of machine learning experts. The ideal professional must have a solid understanding of data algorithms and data structures in specific, and software engineering concepts in general. Knowledge and experience with both predictive and prescriptive analytics is a plus. Capability of handling computational complexity can be considered as an added bonus.

Component 3 – Business Analyst:

Data exploration and data visualization are the two most important responsibilities associated with the role of a business analyst. Business analysts work with front-end tools related to the core business and interact with the higher management of an organization. They further analyze business-level data provided by the data modeling analyst to find out insights related to the organization’s core business interests.

Another important responsibility of a business analyst is to coordinate with the big data analyst and the data modeling analyst to make them understand the business objectives and identify possible focus areas. The ultimate responsibility of a business analyst is to produce actionable insights based on the processed data and help the company leadership in their decision making process.

Ideal Candidate for the Business Analyst role: Business analysts should have expert level knowledge on the underlying business data and source systems. The ideal candidate should have an eye for details and must possess exceptional analytical skills. Moreover, solid understanding of the organization’s business model and the ability to think out of the box are two important qualities that all business analysts should definitely have. It is also important to have sufficient technical skills to come up with precise dashboards for representing business data in a structured manner. Experience with Tableau a plus.

If you are interested in any of these roles with DMAI, please email me directly @ danmeyer@dmaiph.com

Compensation packages will be negotiated based on experience and availability. A part-time arrangement is possible for a pre-defined time period as we build out the capabilities in the team. Potential ownership in a spin-off of DMAI is also a possible form of compensation.

The primary job functions of the team will be related to consulting and training organizations on areas of expertise as well as working together on analytics projects for clients.

Our end goal is to come into an organization and empower those in the organization to address needs in their analytics usage and to grow more competent analytics teams. We will do this for both companies using analytics and schools teaching people to be analysts.

Training Analysts: And The Tasks Keep Getting Bigger

Wrote this over two years ago… its still relevant!

When I first came to the Philippines in 2012 to set up an analytics training business I was ahead of my time. No one was really talking about analytics and most people didnt really get what I was trying to do.

I saw  a huge opportunity to be at the forefront of a shift in services that would propel the Philippines forward as a place where analytics outsourcing would be successful.

After a few years of doing seminars, speaking engagements and training manily to build awareness, things are really start pick up steam.

Attendance is way up in our public training offerings, I am getting invited to more and more schools and companies are starting to really look for analytics training to both enhance their own decision-making as well as exploring offering analytics as a service.

This goes hand in hand with a memo by CHED (Commission on Higher Education) published two years ago that schools are now trying to figure out how to implement.

I have worked with a few schools already by doing a one day overview of how to meet some of the course objectives outlined in this memo, and now I am looking to expand that to a five day training. Here is what it might look like.

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This five day training will need to be eventually expanded into a semester/trimester long class.

Which is precisely what I had in mind when I did my very first Introduction to Analyitics training back in May 2012.

And now that dozens of schools need this, so my tasks keep getting bigger. I couldn’t be happier.

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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.