So Just What is Analytics Anyway?

 

In less than 200 words… Analytics is simply the discover of patterns in data.

Analytics is used by organizations to answer business questions, predict business trends, mitigate risk and provide actionable insights.

Businesses who use analytics are at least 33% more profitable and up to 10x more efficient then ones who don’t.

To be successful with analytics you must have the 3 T’s; talent, technology and technique.

When you have a team of curious people who like to use data in their decision-making, you have the talent. It is not just a matter of training them to be great analysts.

Using tools like business intelligence applications, data visualizations and business dashboards, allows technology super charge your teams ability to analyze data.

Knowing what analytic technique to apply for any specific business need is the third component you need to be awesome with analytics.

Investing in analytics will give you an edge over your competition and optimize your team’s potential.

Make one of your 2017 goals to get more analytics in your business to empower more data-driven decisions!

IMG_6912Follow my blog @ www.dmaiph.com to learn how!

Daniel Meyer

President & Founder of DMAIPH

Decision-Making, Analytics & Intelligence Philippines

News & Events- DMAIPH is a highly engaged leader, sponsor and participant in analytics events across the U.S. and the Philippines. As an Analytics Champion I write, blog, speak and lecture about analytics in a wide variety of forums. I authored several publications on analytics including my latest book, Putting Your Data to Work. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to learn more about where I will be talking about analytics next.

DMAIPH will be at the Techtonic2017 event being put on by PMCM Events Management this coming July!

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Being A Great Analyst > Key Attribute #4 > Be Enchanting

If you are a good analyst or a decision-maker that uses analytics, being enchanting makes your job much, much easier.

One key to using data and analysis effectively is understanding how to enchant people by being likable, trustworthy and using data and analysis to further a great cause.

A few years back, I came across a book by Guy Kawasaki called Enchantment. It is my all-time favorite business book.

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Guy Kawasaki is a marketing expert and used to be Apple’s Chief Evangelist (aka Chief Marketing Officer). At Apple their goal is to convert customers to being loyal to Apple products for life.

In Enchantment, Guy talks about how Apple and other successful companies are able to create enchantment in their customer base that fuels passionate and long lasting relationships.

As an analyst there are many lessons that you can draw from Enchantment to being an incredibly impactful member of your organization.

One of the pillars of Enchantment is being Trustworthy. As an analyst, you have to be trustworthy for people to want to follow the direction your data and analysis point.

Your data has to be clean, valid, and accurate.

Your analysis has to be easy to understand, easy to replicate and easy to boil down into a few bullet points.

When you accomplish these things you are creating trust. Getting decision-makers to listen to what the data is telling them comes when the analysts have their trust.

That’s just one part of Enchantment. I use many examples of how to apply Guy’s concept to data and analysis in my training classes and in my company.

If you are looking for a way to add value to your company, which in turn can make the business more successful then this book is a must read.

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.

Quick Analytics Career Question

Greetings to You My Valued LinkedIn Connection,

I was talking with a young professional just getting started in his analytics career. During our conversation we discussed what is most important to being a great analyst. With that in mind, I’d ask you to share your thoughts.

In your opinion, of the following ways to learn about analytics, which one has been the most important in your career path?

  • Formal Education – A degree or certificate in an analytics related field.
  • Self-Learning – Using trial and error and online resources.
  • Subject Matter Experts – Being trained/mentored by an expert.
  • Seminars/Workshops – Attending events to acquire new knowledge.
  • Technical Training – Attend training on specific technical areas.

Thanks for sharing. As always I will roll up all the replies I get and blog about it.

Dan Meyer, Analytics Champion, http://www.dmaiph.com

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Analytics Survey – DMAIPH conducts quarterly analytics surveys to collect data on current trends in analytics. We specialize in surveys that assess analytics culture and measuring how aligned an organization is to using data and analytics  in its decision-making. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to find out more about how DMAIPH can conduct surveys to help you assess the analytics culture in your business.

The Analytics of Measurement and Evaluation

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

To understand the connection, let’s start with the mission of the Measurement & Evaluation program.

“The ability to effectively evaluate projects, programs and processes is becoming increasingly essential to organizational success today. American University’s online Master of Science (MS) in Measurement & Evaluation provides you with the knowledge to lead these evaluation efforts and the technical skills needed for analytically demanding roles in upper management.” 1

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

Likewise, an efficient measurement and evaluation of projects, programs and policies using analytics should ensure success. An analytics centered approach will likely work with corporate, non-profit and governmental organizations across various sectors and industries.

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We can look specifically to two key business analytics concepts I have used in my twenty plus years of analysis work; Key Performance Indicators (KPIs) and Data Visualization. The key to my success was my ability to answer important business questions using analytics.

Analytics is generally defined as the discovery of patterns in data that provides insight and identifies opportunities. As Carly Fiorina, former CEO of HP said about analytics, “The goal is to turn data into information, and information into insight.” 2

Organizations that invest in analytics generally make much better business decisions then one’s that don’t. In fact, IBM found that organizations who use analytics are up to 12x more efficient and 33% more profitable. 3

In the corporate world, business analytics is widely use to track, analyze and report Key Performance Indicators (KPIs).

KPIs are rolled up to senior leadership to drive business strategy, identify and mitigate risk and to optimize operational productivity.

This approach is very similar to the way projects in the Measurement and Evaluation are tracked, analyzed and reported.

So we need to ask ourselves, what are the KPIs for the project, program or process we are measuring? What points of data need to be captured, analyzed and reported to determine success?

A successful analyst is able to remove the noise when analyzing data and isolate what matters most to his or her organization. That is what is at the heart of measurement, knowing what data is important and what is not.

Once we have the right data, we can measure what the data tells us to determine success, causality, impact… whatever the outcome may be.

A quote often attributed to management guru Peter Drucker perfectly sums up why big corporations rely so heavily on analytics when he said “What gets measured, gets managed.”

Similarly, policy decisions can be made based on what is measured. Project funding can be impacted by what is measured. Process optimization can be directed by what is measured.

Once we are able to measure what is truly important to policy-makers, managers and decision-makers, we need to make sure we present the data in a compelling way.

This is where data visualization comes in.

I often make the analogy that if a picture is worth a thousand words, then a good pie chart is worth a thousand rows of data.

We all know that most people learn more by seeing something then by reading or hearing it. Data visualization takes that a step further.

Data visualization is not only important to presenting our insights but also for exploring the data for insights. Most people find it easier to process information when it is in the form of a picture then a collection of data.

Chip & Dan Heath, Authors of Made to Stick, found that, “Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful.”

The ability to take all of the data gathered in the measurement phase and use it in the evaluation phase will make a significant difference in the success of the project, program or process you are working on.

According to the Office of Planning, Research and Evaluation, “Program evaluation is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs, particularly about their effectiveness and efficiency”. 5

Data Visualization can be used to paint a picture of a program, project or policy that influences outcomes based on the KPIs. And by appealing to the basic human fascination with stories, a persuasive graph, chart or infographic can make all the difference in the world.

By adopting the business analytics concepts of KPIs and Data Visualization, and applying them to the world of programs, policies and projects, you can find the same level of success I found in the corporate world.

  1. American University, “Certificate in Measurement & Evaluation” http://programs.online.american.edu/online-graduate-certificates/project-monitorin Accessed October 20, 2016
  2. Carly Fiorina Speech from December 6, 2004 http://www.hp.com/hpinfo/execteam/speeches/fiorina/04openworld.html . Accessed October 20, 2016
  3. Simon Thomas, Senior Analytics Consultant for IBM https://youtu.be/Zi8jTbXnamY . Viewed October 20, 2016
  4. Chip & Dan Heath, Authors of Made to Stick, http://heathbrothers.com. Accessed October 20, 2016
  5. OPRE, http://www.acf.hhs.gov/opre/resource/the-program-managers-guide-to-evaluation-second-edition. Accessed October 20, 2016

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.

The Analytics of Project Evaluation

When looking at how to use more analytics in program evaluation, let’s start by getting a standard definition.

Per Wikipedia, Program evaluation is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs,[1] particularly about their effectiveness and efficiency”.

This is very much like business analytics in how business leaders look at the analysis of business data to answer questions, identify opportunities and mitigate risks.

Program effectiveness can be measured many ways. Like how a cost-benefit analysis or market penetration report could be used by a company to assess the success of a new product or service.

Program efficiency can be measured using elements of Six Sigma or Lean. Looking for waste or defects in the end results of a project can lead to discoveries of poor implementation or biased data collection.

Another primary goal of project evaluation in both the public and private sectors, is providing stakeholders with information on “whether the programs they are funding, implementing, voting for, receiving or objecting to are producing the intended effect.”

To achieve this goal, you need a system to gather, analyze and report data. Like in any analytics project, the key is finding the right data and using it to answer questions, educate your audience and provide meaningful insight.

Answering questions like, “how much the program costs per participant, how the program could be improved, whether the program is worthwhile, whether there are better alternatives, if there are unintended outcomes, and whether the program goals are appropriate and useful.[2] will indicate the level of success the program achieved.

There are many analytics techniques like data blending to bring in supporting data form outside the program. Predictive models can show where the project would go if it continues to get funding. Data visualization can also be used to help illustrate findings that can be useful in program evaluation.

Just off the top of my head, I can see a lot of opportunity for the use of a business analytics approach to Project Evaluation. There is a lot of common ground in methodology and reporting, but I think bringing in some cutting edge business analytics to the mix would allow even more insightful and actionable project evaluation.

Let’s find out.

1, 2  https://en.wikipedia.org/wiki/Program_evaluation

Evaluators can learn from the ways that the corporate sector uses business analytics to understand, interpret, and display Big Data. Key aspects from the corporate sector that are useful for monitoring and evaluation include identifying what data is important, and finding ways to visualize it for consumption. In my upcoming webinar with American University on analytics solutions, I will be talking about how analytics is relevant to measurement and evaluation.

Webinar details:

February 15, 2017

1pm Eastern

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

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.

Let Your Data Tell You When It’s Time To Say Goodbye – Habitual Tardiness

Came across this blog post the other day and it inspired me to write about how use analytics to know when to let go of troublesome employees.

The first type I’ll blog about is ones who are habitually tardy.

“Handling employees who are constantly tardy for work is one of the difficulties of being a manager — no matter the industry. Simply firing them isn’t always the best policy when you consider the effort spent trying to hire their replacement. On the other hand, if your organization thrives on teamwork, having one team member not pulling their weight is bad for office morale.” Wise words form the blog I read.

The best way to deal with tardy employees is to look at the various data points that are generated by their behavior.  This allows you to be unbiased in your decision-making when it’s time to say goodbye. The 5 data points I suggest you focus on are:

  1. Total Down Time. What % of their shift did they miss plus what time it takes for them to get ready to work (logging in, opening systems, etc.) plus any time out of production you use to counsel them. Take this number and compare it to someone who comes in early, is ready to go when the clock starts and you never have to pull out of production to give warnings too. You will see a surprising difference of how much less time habitually late employees are contributing for the same pay
  2. Distance To Work. Look at how far they have to travel every day to get to the office. I am betting its further than most. There is generally a strong correlation between schedule adherence and distance to work. Not always, but a high % of the time.
  3. Difficulty of Commute. Look at the commute they have every day. How much time do they spend in traffic? Do they have to switch transportation modes? Is their route full of unpredictable impediments? It’s likely that challenges in their commute also have something to do with their consistent tardiness.
  4. Quality Scores. Again, as a general rule, employees who have trouble getting to work on time also have lower than average quality scores.
  5. Primary Production Metrics. Likewise, you generally see lower production metrics from employees who don’t start their shift ready to go.

“When simply walking by their desk to acknowledge a late arrival doesn’t stop the issue, it is probably time for a one-on-one meeting with a frank discussion.” Use this one-on-one time to review these metrics. Share with the employee some insights into why they might be late so often as well as how it effects the business.

It’s my experience that when you show them the data, it generally has a much more profound impact then just talking about things in a general sense. The power of your total down time is the highest on the team. You have the longest and most challenging commute. Your QA scores and production metrics are in the bottom 25% of the entire team. All of these can either be more motivating to the employee or they can provide a good reality check.

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“Being proactive as a manager while showing you understand and even relate to their personal situation might enhance that employee’s performance over the long haul. It is vital you take the steps to get to the bottom of the issue before contemplating further discipline.” Using these data points in your verbal, written and final warnings add much more weight to your counseling. And when/if they finally hit the 3rd strike, you have a lot more data-based rationale behind your final decision. See the original article here:

How long until You Give Up on an Employee Who Keeps Showing Up Late?

If you need help in coming up with a way to build more analytics in your schedule adherence and discipline process, just let me know. I am happy to help.

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.

Using Analytics To Validate A Candidates Primary Motivation For Work

I am often asked how to better use analytics in recruitment. Besides the fairly obvious ways like analyzing your pipeline for trends or looking at candidate demographics, I sometimes suggest coming up with a way to correlate candidate answers to items in their resume.

For example, what motivates the candidate to work. The question can be asked a number of ways. Here in the Philippines, it is pretty standard to ask a candidate why do they want to work. This is a different question then the more universal why did you apply for this job question.

The rationale for the question is generally to learn more about the candidate as it applies to commitment and work ethic. Common answers are I need to help meet my family’s financial needs, support my children, pay for a younger sibling’s education. The problem I with this question is that it set’s up a situation where the interviewer can feel sympathetic to the candidate.

As a counter to this, I train my team to look for a few queues in the resume to help validate the genuineness of the candidate’s answer. In short to do some analysis, and record some data for future analysis.

Most resumes here have a biodata section that includes things like parent’s occupation. This is a good place to probe more if the reply to the what is your primary motivation to work is family financial needs. You should also notate this and start compiling data on each candidate response that can then be used down the road in looking at their success as an employee if hired. You can also get a sense for what types of people are being attracted to apply for open jobs. Both can be very valuable insights when building some predictive analytics into candidate screening.

You can also look for employment gaps. If they are working to serve an overarching financial hardship, then there should not be significant gaps in their work experience and/or job hopping. This is a great insight into dependability and work ethic. Make sure to capture this data as well.

You can also ask them specifically how working will meet their primary motivation. Do they have specific costs and amounts at hand, or is the answer more general or even vague? Have they really thought through the cost versus their compensation? Probe to see if they have done analysis themselves on their own needs.

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SO you get the idea, one simple questions with a short reply can actually lead you to much deeper analysis both during the interview and when looking at trending over time.

Ideally, every question you ask is something you can use to generate data. Every answer they give should be validated against the data in their application or resume. And here you create and capture more data.

In the end you will amass a wealth of information on candidates that you can analyze to look for patterns showing you who to hire and who not too. It can also help you determine that if you do hire them, what kind of employee will they become. Adding some simple analytics at the front end opens things up to a whole new level of data-driven decisions making in your talent acquisition process.

If you need a little help in adding or enhancing analytics in your recruitment process, let me know. Happy to help!

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.

Why Analytics Projects Fail – #10: Key People Leave

One of the toughest analytics challenges to fix is when key people leave. This reason is another people problem, but with a technology bent. Depending on the importance of the person(s) who leaves, you can experience anything from a minor hiccup to a total meltdown of your project.

One example of this is when the one who built the database leaves. Often they take their unique knowledge of the data structure with them.  Another example is when the systems architect who knows the ins and outs of where the data flows departs. This can make it difficult to track down errors and bugs. Lastly,  the database admin who wrote the code might be the one who quits, taking with them all their coding work. I can even be worse if they leave on bad terms and take a key piece of your development work with them or even destroy it.

In general, the best outcome you can hope for is to is build workarounds that allow you to keep the project going, however sometimes you are better off just starting over or worst case you just live with what you have. So step one is seeing where you are in the process and then determining what it would take to replace that person.

If you are able to continue, then you need to start doing a better job of documenting and making sure information is shared so this won’t happen again. I learned this lesson early in my career. Learn all you can about all aspects of the data environment and document them. A lot of times a clear understanding and documentation will be required by management to assure funding and resources.

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If you have to stop the project until you can find a replacement, then you should also learn, document and share everything so that the new person can pick things up as soon as possible.

In this case, the new person will likely be dependent on you to learn the ropes so use that opportunity to change your culture to be more open.

A final point to add, make sure you understand why the person left.

If there are things you can do to make sure the same thing does not happen again then it is on you to do just that. If it is a cultural thing, then you can be a catalyst for change. If its a compensation thing, then you can help define the expected scope of work and help in the compensation planning. If they left because of a personality conflict, then you can help find someone who will fit in better. Analysts have so much power to shape conversations. Use it.

Analytics Culture – The key to using analytics in a business is like a secret sauce that fuels Data-Driven Decison-Making. 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.

Why Analytics Projects Fail – #6: Lack of Funding

Of all the reasons an analytics project can fail, one of the hardest to fix is lack of funding.

There are numerous causes for funding issues with an analytics project, 3 of the most common being unexpected budget cuts, shift in strategy, and lack of understanding.

When you are faced by unexpected budget cuts, which has happened to me several times, the best thing you can do is try and reconfigure your project so that as least pieces of it can still be completed. The idea here is to do what you can until more money is made available.

Having a well thought out plan that is scalable will help you tremendously. One time when I had a million-dollar dashboard project cut because of budget cuts, I peeled back some features and redesigned others to come up with a new plan for 10% of the original cost. That was approved. And over the next year I had pretty much added everything cut back piece by piece. Bottom line, if the company needs a new analytics tool, its up to the analyst to make sure they get it by being flexible and smart.

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A shift in strategy happens a lot in business. So many internal and external forces are at play, a lot of times what once seemed a priority, can quickly become an afterthought. With analytics this can happen a lot when people fall back the we can just get by with what we have for now mentality. In today’s business world where success is driven by data, this can be crazy but it still happens everyday.

The best way to react to strategy shifts are for you to adapt your project to the new strategy and keep it both relevant and necessary. A good analyst can always find a way to offer analytics solutions for any part of the business. Use this adaptability to show your project can evolve with the needs of the business and you will likely still get funding, albeit for a new set of users.

The third reason lack of funding can happen, is actually a lack of understanding. Often finance decisions are made based on assumptions and predictive modeling… highly susceptible to being wrong if some important variables are missed. This has happened to me a number of times. But after conversations and educational moments with the finance team, the true value and ultimate savings of my analytics projects led to the lack of funding being mitigated.

Some things you can try when your project is impacted by a lack of understanding will take us back to the concept of enchantment. Make sure they like you and understand what value you and your analysis adds to the team. Often this can be a hard thing to quantify in a budget. Make sure you are showing how this project benefits others and helps the business as a whole… build trust. Third, make sure the project you are championing will make a difference, show that difference and educate on the need for that difference, in short show them you are doing this for a great cause.

There are countless reasons for lack of funding to become a roadblock for your analytics project, and countless ways to remedy this. If you are faced with one and need some help getting things back on track, connect with me and we can come up with a way to get your project funded again.

Analytics Culture – The key to using analytics in a business is like a secret sauce that fuels Data-Driven Decison-Making. 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.

Why Analytics Projects Fail: #4 – Lack of a Champion

A lot of time analytics project fail because there is no designated champion for the project.

I see a lot of money wasted on analytics technology because there is no one in the business who masters the technology. Who knows how to use it better than anyone else and knows what more can be done if other people become experts.

Good analysts are curious above all else. In the right place, they can do amazing things to drive innovation, increase profit, optimize processes and build market share. When you don’t have a a champion the outcome of any analytics project will be in doubt.

The most curious person in the organization should be the analytics champion because they love to go out and find the data to answer any business question that comes up.

If your analytics project doesn’t have a champion, then you most likely see a general lack of focus, an unclear vision and an uninterested leadership. Can you be that champion? If you think you can then do the Moneyball and Enchantment things from my last blog. They will help you gain your champion’s belt.

When you read Enchantment, you will start to understand that an analytics champion does as much influencing with their analysis as they do reporting.

Another way t5.5o be seen as the champion, is to make friends with people. Dropping off a box of donuts with the IT developers or sending thank you notes to project team members who went above and beyond is just as important as mastering the coding language used by your new analytics tools.

I keep a lot of analytics books on my desk. I make it obvious that I am always thinking about data and how to use it to improve what we do. I share a lot of content about analytics on social media. People know me as the data guy. You want to be like that if you want to be crowned Analytics Champion.

Analytics Culture – The key to using analytics in a business is like a secret sauce that fuels Data-Driven Decison-Making. 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.