Multitasking Is A Productivity Killer

Multitasking is a productivity Killer. Picked this up at a HR conference I was at last week. It was a theme during a couple of the presentations.

Multitasking as a competency is not the same as multitasking across projects and tasks during a day. That is good time management and the ability to prioritize what you work on.

The myth of multitasking, that you can do multiple things at the same time is a just that… a myth. Less than 10% of the world’s population is actually able to carry out two distinct tasks at the same time.

One of the speakers listed the Pros and Cons of multitasking:

  • Pros of Multitasking: Reduce Cost by having one person do the jobs of many people.
  • Cons of Multitasking: sense of being overburdened, stressed out, loss of focus, poor quality, high attrition, inflated sense of importance and value

As you can see not much comes from trying to force someone to do too much.

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Back when I was an analyst with Wells Fargo, one of the keys to my success was that I didnt multitask when I had to focus on high priority projects. I would put my headphones on and block out the world. Often I would even close my Outlook and my browser. Giving 100% to a project for a few hours always led to a better finished product. And it feels awesome to have a sense of accomplishment.

I still do this. When Im focused in on a project I tell people I’m busy. I get away from distractions. And I focus in like a laser.

Multitasking is indeed a productivity killer and not falling prey to it is one of the reasons Im as successful today as I am.

The Average Keeps Getting Lower And I Refuse To Tolerate This – Updated

Updated on 10/27/16

http://sethgodin.typepad.com/seths_blog/2015/08/the-average.html

The average

 Everything you do is either going to raise your average or lower it.

 The next hire.

 The quality of the chickpeas you serve.

 The service experience on register 4.

 Each interaction is a choice. A choice to raise your average or lower it.

 Progress is almost always a series of choices, an inexorable move toward mediocrity, or its opposite.

I can totally relate to this. We are a society more and more inclined to settling for the average, and are even ok with it when the average trends lower.

One place I see it happening more than most is in talent management. The demand so far outweighs the supply of good talent; we keep lowering the bar.

Frist it was 4 year degree required. Then it was some college. Now its high school grad.

In just a few years we have gone from a high bar to also most no bar.

Same day hiring. No interview required. No test or assessment. Just how up and get a job.

I hate this.

This new reality taking hold across the Philippines  is deeply concerning to me.

It is unacceptable to me to be involved with anything that is just average, and I just get crazy when I see people doing things to lower the average on purpose.

There is another way.

If you have good analytics, you can be better at setting a realistic bar and not just going lower to meet requirements.

No more mediocrity. No more playing to the average and definitely purposely lowering the average.

I just refuse to tolerate it anymore!

Let me show you how to use the data in your business to turn things around.

Stop the insanity of fueling high turnover and low employee engagement that is lowering the quality of service to a dangerous place.

Who is with me?

If you are, the you will might enjoy reading my new book, Putting Your Data to Work. I can help you use your data.

HR & Recruitment Analytics – The recruitment and retention of top talent is the biggest challenge facing just about every organization. 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.

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.

DMAI Data Science > Where Dreams and Demand Meet

Building a data science team tasked with helping other organizations build data science teams is equal parts dream and demand.

There is a quickly growing need for data science capabilities in the Philippines, but there are few ways for Filipinos to learn how to be data scientists. Almost over night it seems that people are posting job requirements for high powered analytics talent with very little idea of what data science is all about.

Business analytics is just now taking root in academia and being offered as a series of elective classes. Big data is just one class. Predictive and prescriptive analytics are also just one 3-5 month class. Its just not enough.

The big companies who are committed to building their own team are scrambling to find talent in the already hyper competitive BPO industry.

That’s the demand.

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Data science as a discipline is still quite new. In the U.S. and India you are starting to see a significant number of degree programs in analytics and data science. I learned a lot about data science before it even had a name. Analytics is deeply rooted at Wells Fargo and I benefited from being in the right place at the right time to get exposed to some pretty awesome analytics efforts.

This experience unlocked an opportunity to become one of top analytic minds in my adopted home, the Philippines. The opportunity of a life time really. Now I am at a point in the evolution of my business, DMAI, where I need to find 3 people like me to join me in my quest. My quest to help organizations in the Philippines set up data science teams.

I need a dream team. Like the Eath’s Mightiest Heroes the Avengers or the NBA Champion Golden State Warriors. the DMAI Data Science Team needs the best of the best who excel in complimenting each other.

We need a big data analyst strong man in the paint, we need a visionary data modeling expert who can create great data models and pass them off to the shooter of the team, the business analyst.

That’s the dream!

It’s time to join the right and be at the forefront of spreading data science across this great island nation so full of potential.

If you feel the call that I feel and are interested then connect with me on LinkedIn and/or send me you resume at danmeyer@dmaiph.com ,

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.

Data Modeling Analyst > The DMAI Data Science Team Middle Man

The person is the middle is often the most important one. When it comes to data science, the person who takes the data provided by the big data analyst and then gives the output of refined data to the business analyst is often the data science team MVP.

As modeling experts play the role of a link between the data analyst and the business analysts.They have to know both the business and the data and then also know which type of analytics to apply.

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Modeling experts 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 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 not only descriptive analytics, but also both predictive and prescriptive analytics is a plus.

  • Descriptive Analytics looks at the past to explain the present.
  • Predictive Analytics uses past data to model potential futures.
  • Prescriptive Analytics use past data to direct variable present and future options.

If you know someone looking to join the DMAI Data Science team to help businesses and schools around the Philippines set-up and/or build out data science capabilities then please tell them about this post.

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.

DMAI Data Science Team Member #1: The Business Analyst

“We know that one of the first things lead business analysts need to do is to uncover the real issue, problem or business need. And then make sure that whatever requirements or ideas are suggested align with the thing we were trying to address in the first place” – The Business Alchemist

The first person to be recruited for the DMAI Data Science Team will most likely be a business analyst.

Some of they key personality traits for the DMAI BA include understanding how to use data to tell stories that elicit action. The BA has to be a great communicator who also understands data architecture and big data. Experience working in the BPO industry is a plus.

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 like Tableau as 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.

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Ideal business analyst candidates 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 using Tableau for representing business data in a structured manner.

The DMAI Data Science Team works with businesses and schools in the Philippines to build data science teams, empower data science cultures and become magnets for analytics talent.

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.