Why Analytics Projects Fail – #13: Over Reliance on External Help

The final reason I will articulate in this series of why analytics projects fail is an over reliance on external help. Historically the over reliance would happen when a team is “too busy” to learn the ins and outs of the analytics software they using.

An example would no one internally has the training to maintain or update the software themselves. Any fixes, patches or enhancements have to be done with the help of someone not on the company payroll. This has obvious limitations like not being top priority or made to wait longer the necessary, as well the potential slowdown caused be internal review and QA processes. Not having someone on the insides trained to handle external products is a major risk to an analytics project.

Another examples is when internal analyst don’t have the initiative to own the software. Meaning they just do the minimums, never really learn all the things the software can do and do not offer any new idea of solutions. Being totally dependent on a vendor to keep you up to date on all the new possibilities for use of the software is extremely short sighted. This often causes going the long way on a project instead of knowing about short cuts.


A third example is that your team is not empowered to work independently and their schedule is dictated by the availability of the vendor. Important deadlines might be missed or extended because the vendor resource is not available when you need them.

Regardless of the impact, relying too heavily on your analytics software vendor leaves open the risk of what if the external expert leaves. I have seen this happen a number of times, where analytics projects were halted or even cancelled because the expert was outside the company and left the project. The most common outcome of losing your expert is that things stop working and you have to either use workarounds or start over.

The key lesson here, if you are an analyst working with externally supported software, it behooves you to become the expert on it. This will mitigate any the risk of being over reliant on the vendor. It will also assure you of having more control of maintaining, fixing and upgrading your own analytics process yourself, which makes you more valuable to the organization you work for.

Analysts who know why things fail, are proactive, find solutions and become analytics champions are the ones you want to measured by. In the end, the best way to make sure your analytics projects don’t fail is to be awesome at what you do.

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.

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.


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: #2 – Lack of Vision

Lack of vision often accompanies lack of focus when good analytics projects come up short or even fail.

The end product produced by the hard work being put into the analytics project has to be championed from top down. If the top dog is not talking about your project and pushing the merits of its implementation, they people will fight getting on board. Their focus will go astray.

Lack of vision is most often caused by a rush to implement a new analytics tool or a need to quickly upgrade reporting. A good analytics solution needs clearly thought out goals and buy in from all stakeholders.

Vision comes from being on the same page. A good project manager can control the vision message and repeat it like a broken record at every project message. An analyst gifted with good communication skills can keep sharing the vision to remind everyone of the benefits at the end of the project. It takes people dedicated to the project to keep it moving.

Vision can be handled like a marketing campaign. A catchy project name, an engaging tagline, a central theme accompanying communications and updates can all keep people focused on the end goal.

If your project is floundering because of lack of vision, then you need to channel your inner cheerleader. It often takes an analytics champion to produce the a positive outcome. And if you are reading this blog, that champion is probably you.

Nothing helps get a vision across better than good visuals… what does the end state look like for everyone involved. Find ways to motivate them with posters, with slogans, with dashboards, with free food. Just keep reminding them where we are going and how awesome it will be when we get there.


Lack of vision can also be an issue when an analytics projects are not well planned out. Maybe the scope was too narrow or too broad or perhaps the technology we are using is obsolete.

Vision is a glimpse of what the future may look like. If I can’t conceptualize why we are doing what we are doing for this project, then we can’t very well share the vision we are supposed to be seeing.


My final thought on dealing with lack of vision, is that no one will be better at fixing this then you. As a data person, you have to be bold in your use of data to push the vision and you have to be brave in taking the lead on sharing the vision as much as possible.

Analytics should be accessible across your organization.  If you are in a situation where your analytics efforts are being stymied by lack of vision, connect with me and I’ll help you get things straightened out.

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: #1 – Lack of Focus

Lack of focus is common reason analytics projects fail. Keeping a focus on any project can be a challenge for companies that are not well organized. If you are an analyst or trying to champion analytics in your organization and are up against a lack of focus, I have some ideas for you.

First off, size really isn’t a factor when it comes to the organizational culture towards being organized. In some cases size makes the problem more apparent, but size can also mean more resources, so in most cases its really not a serious issue.

There is generally a strong correlation be the way data is handled in a business and how organized the company is in other ways. Lots of paper, manual processes and clearly define process flows may look organized, but it’s highly unlikely they deliver the type of data leadership really needs.

Lack of focus often happens when leaders seem overwhelmed and say they don’t have time to dedicate to things like analytics. The first thing you need to figure out is are people resources really stretched too thin or is it more a cultural issue where being too busy is more of a badge of honor.

The best way to deal with overworked teams is to start putting data around what they do every day and come up with solutions to improve time management and delegation.  Few people can truly say they have extra time, but everyone can say they need to figure out how to manage time better. A good analytics solution does just that.

Few people can rationally explain why their job would be harder if they had more analytics.  So it’s not too hard to get people to buy into the concept of analytics, but to get them to buy into the actual practice you need to be part salesman and part storyteller.

You need to show them the value putting a greater focus on your analytics project will bring to both the business as a whole and to each individual involved in the project. Besides showing data to champion the use of more data, you need to tell stories about how its helped in other places. You need to get them to envision how much better life will be once your project is complete.

Lack of focus can also come when the project is not well thought out of you get scope creep… when additions are made to a project that start distracting people form the original goal of the project. It is hard to stay focused when you don’t see focus in the project itself.

The final point I’ll make is that you also need visible and consistent buy in from the person(s) in charge. If they are not advocating for analytics, then your project will never get the focus from stakeholders and project team members you need then you will fail.


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.

DMAIPH Fuels New Businesses Ready For Launch (3 of 3)

The third type of company we can add value too is one ready to launch. You have a great idea, you have the funding, you have an awesome product or service, but you aren’t 100% sure if you have everything you need to get out there and make money. . We can offer our business intelligence package of demographic profiling, competitive landscaping, social media strategy and market assessment. We can also offer virtual staffing support to assist with marketing and operations. Our hybrid staffing solutions will give you access to a very talented work force that is flexible and affordable.

We have shown marked achievement in helping a couple of new businesses get out of the gate successfully.  One client we helped was a mobile app designer who had a great idea, enough capital tyo get started, but little idea on how to really understand the opportunity to make money. In addition, the client was hesitant to take on all the HR work in hiring a team when he had no idea how many people he would need in the short term. We helped them with both.

Using the same methodology, we used with the chiropractor, we conducted a business intelligence assessment to help figure out how to identify customers and where to find them. We also used our home based, hybrid staffing model to hire six people to help the client have get off the ground. Charged with editing pictures for the mobile app site based on set criteria, 24/7, with as close to real time response as possible, the team quickly became a key part of the business strategy.

Another new business we helped get off the ground was someone who wanted to open a call center business in Manila. She was well funded and motivated, but didn’t really know where to start. The client really needed help knowing how to settle on a location and to set up the business. She also need help designing her marketing and social media strategy. So that is where we started.

We developed a social media strategy for the business for both client marketing and employee recruitment. You would be surprised by the number of businesses who still have not figured out an online branding strategy to sell their business.


In parallel with the social media, we also conducted a business intelligence assessment to help her pin down the location for her to set up the business. Worried about being in a place that was both convenient and not overly saturated by competitor, our data and analysis pointed her to the right place. She has been there for 4 years now, expanded her site twice to accommodate growth, and added several new customers all while having little challenge in recruitment.

If you are thinking of starting a new business, we can help. Let us show you how to use the data around you to make good strategic decisions.

Analytics Consulting – DMAIPH specializes in a variety of analytics consulting solutions designed to empower analysts, managers and leaders with the tools needed for more data-driven decision-making. We have helped dozens of companies get more analytics in their business. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can tailor an analytics solution made just for your unique requirements.

DMAIPH is Good at Helping Companies Looking to Expand (1 of 3)

The first type of company we generally get involved with is companies looking to expand. Huge growth is just around the corner and there is a need to staff up quickly,    but given some uncertainty as to how much growth will be needed how soon, you want a little more flexibility in staffing. Our virtual, hybrid staffing solutions will give you access to a very talented work force that is flexible and affordable. We can also assist with developing the expansion strategy with our business intelligence package of demographic profiling, competitive landscaping and market assessment.


Let me tell you about one of our clients who came to us in expansion mode. We started with six work from home staff who were tasked with adding key words to online auction items. Starting with a small team who would just do a small percentage of the key wording for our client. We quickly found our talent pool was very large and very deep. So we quickly built the team up to a dozen and then 25 and ultimately 50 people. We worked closely with the client at first but over the first six months also built up our own expertise to the point, the client became very hands off.

Work from home positions make a lot of sense for this work as its very independent and easy to assign, track and review. We also got past a lot of challenges with work from home team, but really building the team using social media to connect them and make them feel like part of the larger organization.  This hybrid approach where the employees feel both independent and connected has led to extremely low attrition.

We also found that in the Philippines, there are literally a million former call center employees, with college degrees, good English and the drive to do a good job who left the call center industry to spend more time with family. So our work from home jobs are right in line with their needs.

Things have worked so well, the client has had us add a graphic design team, an email-marketing team, a desktop support team and a customer care team in addition to the key word team. The key word team has added a QA team and a 2nd level support team as well.

From a financial standpoint we helped the client save a lot of money by staffing in multiple locations, added the ability to do 24/7 customer care and never miss a beat with meeting our SLA.

This is the kind of thing we can do for any company looking to expand both rapidly and strategically using our home based, virtual team solution. Just connect with me and I will explain how.


Analytics Consulting – As a founding member of Gloabl Chamber Manila, DMAIPH specializes in a variety of analytics consulting solutions designed to empower analysts, managers and leaders with the tools needed for more data-driven decision-making.

We have helped dozens of companies get more analytics in their business. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can tailor an analytics solution made just for your unique requirements.

Q18: Can you please talk about recent developments in higher education on how to train more analysts?

The past couple of years have seen some remarkable developments in higher education in regards to analytics. Just a few years ago there were only a handful of colleges and universities in the U.S. that offered any kind of degree in something akin to data science. However, now you can find dozens of schools offering graduate degrees in analytics and/or data science. These changes in higher ed were preceded by several vocational schools and certificate programs. All in, if you do a google search on data science or analytics degree program you will get 100’s of schools in your results.

Besides the U.S., I have seen a few program in the UK and several in India getting more into analytics education. In the Philippines several schools have already started implementing the CHED (Commission on Higher Education) memo requiring schools to offer a business analytics elective series of classes. We have come a long way in a short time, but what is best for you?

If you are thinking about getting some formal education you will need to determine where you are currently with your analytics skills and where you want to be long term. Because of the crazy growth in the field, it can be pretty hard to tell what is the best bang for your buck.

Without pointing to any specific institution or program, I can give you some broad difference to consider. In a latter blog I will actually review some of the best programs and talk about them in on my blog site.

So here are the differences as I see them:

  1. Accidental Analysts. People who are doing a lot of analytics and have for some time, but have no formal training in analytics. These are accidental analysts who still make up a huge % of people doing analytics every day. For people at this level, going back to school full time to get a formal degree is not generally an option. For people in this bracket short term training programs and certifications in specific tools are the best bet to stay on the cutting edge.
  2. Legitimate Data Scientists. Few and far between, people with both the academic credentials and the business experience to do significant data science generally look upwards to getting a masters or even doctorate in a specialized field from a top school. There are a lot of programs out there to do that, but they tend to be pretty expensive and difficult to get into.
  3. Aspiring Data Scientists. If you are still young in your career and/or not finished with college you can consider getting your undergraduate degree in a related field and then progressing on to post graduate work. This is a recent development that poses an opportunity to those just starting out. In the near future these kinds of analysts will replace the accidental analysts for the most part. That is if there are ever enough.
  4. Part Time Analysts. People who do analytics or are part of a data science team, but have already established a career path in a different discipline. For those like you, training programs and certifications abound. It is pretty easy to find one that fits your unique situation and give you the added data muscle you need in your job.
  5. Managers of Analysts. If you are not really the one doing the heavy data lifting, but have team members that do. You need to be able to understand them, but not all the things they do, then you might be looking for a more generalist overview of analytics. Trying to optimize your analytics business culture and lead big data projects are skills you might want to improve on. There are training programs popping up for this need as well.


So where does this take higher education? Some schools and programs are very broad based and offer generalist solutions. Others are quite specific and are geared to producing specialists. Knowing which education option is best for you is the challenge.

Higher Education across the globe is evolving to incorporate more analytics and data science into its curriculums. The need is there and is growing at a break neck pace. Where we are now is lights years from where we were two years ago, but where we need to be is far down the road.

More on that next blog post. In the meantime, if you are trying to figure out how to up your analytics game, drop me a note and I’d be happy to help you figure out what path you should take.

Q15: What is a business dashboard and how is it used in a business?

Much like a driver uses a car’s dashboard to make lots of decisions before and during a trip, a business dashboard helps a business decision-maker to plan for his business.

Wikipedia’s definition of a business dashboard is quite long. A business dashboard is  “An easy to read, often single page, real-time user interface, showing a graphical presentation of the current status (snapshot) and historical trends of an organization’s Key Performance Indicators (KPIs) to enable instantaneous and informed decisions to be made at a glance.”

That is a mouthful. But lets break it down to help us understand how a business can use dashboards to make better decisions.

  • Single Page – You need to be able to see everything you need to know at a glance. If you need to scroll or click to get data it really lessens that power of the dashboard.
  • Real Time – If the data isn’t current, then you really are limited to being able to take action. With technology today, not having a way to feed real time data in your dashboard is pretty old school. Plus this can help you set up some useful predictive models that feed into the dashboard.
  • Graphical Presentation – People pick up data much quicker from visual queues like charts and graphs then they do a table full of numbers. There are a lot of great visualization tools out there to add a lot of both style and substance to analyzing business data.
  • Current Status – Besides being furnished with real time data, you should be able to look at where things stand right now. Like how a speedometer keeps you within the speed limit, real time status can help you know where to focus your energy most.
  • Historical Trends – The priority is real time, current status all in one view. That said, having the ability to switch to historical trends is also something to look for in an awesome dashboard.
  • KPIs – One of the keys to getting the most bang for your buck with a dashboard is to make sure you are feeding the right KPIs into it. The audience will gravitate to what is most important to them and if its not available at first glance they wont use the dashboard. So knowing the business well enough to know the key KPIs for the power users is super important.
  • Make Decisions – The bottom line is that if a dashboard improves the speed and the accuracy in which decisions are made then its working. Companies with really good analytics cultures use dashboards at staff meetings and conference calls and have pretty much killed the use of power point for most discussions.

When you walk into a company and you see business dashboards on the wall monitor and/or on desktops you are in the kind of place we should all be. The technology is there, its more a matter of culture to make it useful.


Hope that helps shed some light on how business dashboards can help a business. They just give you much more relevant and useful data summarized and offered in easy to use and understand bites.

My team is very adept at setting up business dashboards using Tableau Public. Let me know if you’d like to know more.

Q14: What is data visualization and how does it help drive better decision-making?

Most of us are well aware that people generally learn best visually. A simple pie chat can turn a 1,000 row excel spreadsheet from a headache inducing overload of data into something one is able to make decisions on in a few seconds.

Of all the things that have made me a successful analyst, one of my greatest skills is knowing which visual to use in my presentations and reporting.

To demonstrate how data visualization can drive better decision-making, I will borrow from analytics guru Bernard Marr’s 7 Key Ingredients for Knock-out Data Visualizations.

Even the best analytics will amount to nothing if you don’t report the results properly to the right people in the right way. Make sure you report the results effectively by following these 7 steps:

  1. Identify your target audience. What do they need to know and want to know? And what will they do with the information?
  2. Customize the data visualization. Be prepared to customize your data visualization to meet the specific requirements of each decision maker.
  3. Use Clear Titles and Labels. Don’t be cryptic or clever. Just explain what the graphic does. This helps to immediately put the visualization into context.
  4. Link the data visualization to your strategy. As a result, they are much more likely to engage and use the information wisely.
  5. Choose your graphics carefully. Use whatever type of graphic best conveys the story as simply and succinctly as possible.
  6. Use headings to make the important points stand out. This allows the reader to scan the document and get the crux of the story very quickly.
  7. Add a short narrative where appropriate. Narrative helps to explain the data in words and adds depth to the story while contextualizing the graphics.

So there you have it. Data Visualizations allow the analyst to inform and empower the audience of the report/presentation to use the data to make good decisions.


It sounds easy, but a lot of people really struggle with this concept. Most presentations I see are either too wordy or include visuals the audience can’t see easily. Most reports are formatted in a way that may look good, but have little functionality.

Nothing prohibits good analysis like an excel spreadsheet full of data but not formatted in a way that allows a pivot table to be built.

Likewise a lot of reports are just summaries, with the original data hidden or absent. When you take away the power of an end user to do their own analysis, you really diminish the value of what you are doing.

So besides everything that Bernard said above, I would add make sure you provide the ability for your audience to use and analyze your data.

If you are having challenges with coming up with engaging and actionable data visualizations, let me know. I can definitely help.

Q10: Please talk about how, when and why we use should descriptive analytics?

Going back to our previous definition of descriptive analytics, it is used to answer questions about what has happened in a business. It is primary use is to look at the current business situation with an eye towards looking for cause and effect. It helps one to understand how to manage in the present based on what happened in the past.

The vast majority that have attended my trainings on analytics, are looking for help with descriptive analytics challenges. Using unstructured big data for predictive analytics modeling is not really something they are concerned with.

I have found that people who are really engaged with analytics are very driven to self-educate. They are driven by curiosity to make use of cutting edge stuff to tackle bigger and bigger challenges. For data scientists and really good analysts, descriptive analytics is easy and kinda boring.

But that is a small percentage of people who use analytics every day.  To most of my attendees, its more about how to cut down on the time it takes for them to prepare the reports they have to make and how to make them more useful to their bosses. That’s where most of my descriptive analytics training has an impact.

How to make a better report? How to build and maintain a simple business dashboard? How to have more impactful power point slides. How to streamline the reporting process? This is one way to look at descriptive analytics… its not just taking historical data and using it for reports, but also how to make the reports better.


So how can we use descriptive analytics? Well, we probably already are. Inventory control, payroll, performance management, quality assurance, sales reports, marketing results… all use forms of descriptive analytics. They take what happen, they look at it and then they make decisions.

For the most part this can and is done in Excel. If you want to supercharge what you do in Excel, then you can use a business intelligence tool to build dashboards and publish dynamic reports. This is where most people doing reports need help. How to better visualize the data so it has more power and how to use BI tools to do things faster than can be done in Excel.

In many, many companies a lot of time and energy has been devoted to building reporting tools in house. And this is generally the problem. The reports are static and hard to change. If you are in a company like this, then descriptive analytics can be a bear.

To make the most of it, I suggest using free tools like Tableau Public, which is free, to demonstrate new ways to analyze and report data, to get the boss interested in updating the way you company reports.

Another big challenge facing analysts doing mostly descriptive analytics in the form of reporting, is blending data. Taking data from different data sources and combining them. This can often be very manual and general done in excel if you company hasn’t invested in a way to centrally store enterprise wide data and make it easily accessible. There are some applications out there that can help you with this, Alteryx and Qlikview being ones I have used and they both have a free demo.

If you are already doing predictive analytics, then you probably have your descriptive analytics figured out.

So, if you need help super charging your reporting, are looking to get started using business intelligence and data blending tools, and/or need to build a business case to invest more into analytics, let me know. I’m happy to help you come up with a much better way to build reports that have real impact and don’t take up all your time.