Recipes don’t always work

recipes dont work

How many times have you followed a recipe while cooking something in the kitchen and had the result turn out not to be quite what you expected?     If you’re like me, more times than not. Sure, the result is edible (usually) but isn’t quite what you expected or what was described in the recipe.    Recipes don’t always work.

Recipes are great starting points for cooking great food, but a good cook will tweak that recipe to create something that is perfect for them / their family. They tweak whatever recipe they have to match their own tastes and their own inclinations.

The same is true of good leaders.

A good leader isn’t going to take something they read in a book like Good to Great and implement the idea(s) without tweaking the ‘recipe’ for their own organization, people and culture.  Just because something worked for one organization doesn’t mean it will work for another without changes. Just because Jim Collins provides insight into how a company was successful in the paste doesn’t mean that the thing that company did will work for your organization today.

Stephen Covey made a lot of money by telling people there are seven habits that successful people follow but I guarantee there are more than seven habits that successful people can follow and plenty of successful people that do things other than his seven habits.  The same is true of systems for time and task management. Sure, the systems work but you have to ask whether they will work for you. If you don’t sit down and actually look at your task list, that task management system isn’t going to work at all for you.

I hear people say that you’ve got to write 1000 words a day (or 500 words a day…or X words a day) in order to ‘be creative.  I hear people say you have to meditate or get up at 4AM or get 10 hours of sleep or exercise every day.  I hear people say all sorts of things that have worked for them but that same process may or may not work for you and/or your company.

Recipes are great starting points and they can help create starting points for what you’re trying to do. That said, they aren’t the ‘end-all-be-all’.  When you’re working with people, recipes generally don’t work as they are written down. They require some tweaking and some changes to fit into your personal approach and/or your organization’s culture.

Next time you’re trying to do something and you think some ‘recipe’ you’ve found is the way to go, just remember – recipes don’t always work.

11 years, 1400 blog posts…and a pretty graph

network map of keywords extracted from

As part of a tutorial on Text Analytics and Visualization I just finished  over  on technical blog called Python Data (where I blog about using python for data analysis), I took a look at all 1400 posts over the last 11 years (11 years!).  The findings weren’t surprising but they are interesting (at least to me).

First, a visualization. This is a network map of the top three keywords from each of my posts and their association with other keywords. To create this, I took each post and performed some natural language processing on it to find the keywords for each post and then created a matrix that describes the relationship between keywords in different posts.

network map of keywords extracted from

Its hard to tell from this image, but the large clusters revolve around ‘business’, ‘cio’, ‘data’, ‘people’, ‘project’ (which are all shown in the large cluster in the middle of the graph) and photography (the top right cluster).  While not a bit surprised (I mean…this is the stuff I write about), it is really cool to see it all layed out like this and to see how things are connected together. For example, ‘business’ and ‘data’ are connected together a number of times as is ‘cio’ and ‘business’…which means I must be writing about the ‘right’ stuff because those things go together quite well.

Here’s am much more readable version of the above graph with some filtering and node adjustments.

filtered network map of keywords extracted from

The top 10 keywords on the blog over the last 11 years are:


Note: If I do this analysis again, I think I’d remove ‘quote’ from the text before doing any analysis since it really doesn’t add much value, but I left it in simply for completeness.

If you are technically inclined (and a python developer) you can jump over and read how I did this analysis on my Text Analytics and Visualization post.

What digital projects should you be chasing?

Chasing Digital ProjectsI was talking to a CMO today about their current and future plans for digital projects. We were talking about data analytics, customer experience, technology, social media and other topics when the CMO asked what the ‘next’ project or technology that she and her team should be chasing.

She asked:

We’ve talked about all data, social, digital transformation, the cloud and everything else…but what should I really be focused on? What projects should my team be chasing for the future?

I couldn’t give exact types of projects that her team should be focused on, but I did share my thoughts on the only area that I think make sense for marketing teams to focus on.

That’s right…just one area.  If you are going to chase digital, you should chase it in this one area.

The only area marketing teams should be focused on (and chasing) is in improving the experience for your customers. That might be SEO projects, data analytics or a new application, but by focusing on the customer experience, the marketing team is focused on one of the most important aspects of a business. Customer experience is the key to driving engagement and growth for a business and has been called ‘the next competitive battleground.’

If a project doesn’t touch the customer experience, there needs to be a very thorough discussion of whether that project is worth taking time and money away from your customer facing digital projects.  There are times when marketing teams need to take on non-customer facing projects, but you shouldn’t be out there looking for those projects or chasing those technologies. Let those technologies and projects come to you.

Chase the projects that are focused on improving the customer experience.

Whether that is engaging your clients better, improve customer service or eliminating a pain point for clients, those projects will improve your customer experience.

Beyond the customer experience, there are other projects that CMO’s can focus on and chase, but I’d argue that anytime you are working on these types of projects, you are not directly improving the customer experience.    There are always going to be knew digital projects and new technologies, but for the CMO and the marketing team, the customer experience should top of mind and a major filter for all new projects and technologies.

Finally, when a new technology or buzzword comes along, take a step back from all the buzz and ask yourself and your team(s) how that technology or approach will improve the customer experience and build competitive advantage for your organization. If that new tech or buzzword doesn’t drive customer experience, you probably shouldn’t chase it.

Can you really win if you aren’t the best?

Can you really win if you aren’t the best?

Can you really win if you aren’t the best?People often say that if you work hard and apply yourself, you’ll succeed. But lets be realistic….that isn’t always true, especially because everybody has a different definition of ‘succeed.’   Sure, you can work hard and apply yourself and become better than you were, but it doesn’t mean you’ll become the ‘best’ at something.  That’s just not how life works.

Let’s say, for example, that you decide to become the world’s fastest runner in the 100m race. That’s a lofty goal, but unless you are born with a very specific set of genes and start training very young, the probability of meeting that goal is pretty low.  That said, there’s nothing stopping you from pushing yourself to become faster than you were yesterday or last week.

If you aren’t the ‘best’ at something, does that mean you can’t win at that something (or at life)? Not at all. Even the absolute best have bad days.  Underdogs win all the time, which is why you should always continue to improve and become better than you were because you never know when your chance might come to be ready when the ‘best’ falters.

If you’ve not seen or read Moneyball (the book is here, the movie is here), you are missing out. The book talks about how the Oakland A’s baseball organization took the ‘B’ players and built a baseball franchise around them.  I don’t recall if there were any of baseball’s “best” players on the Oakland team at the time, but I do recall that there were a lot of the ‘also rans’ that many teams didn’t think were good enough for their team.

Many people will argue that the real story behind Moneyball is how statistics and data analysis can play a really important role in running a business. These people are correct…these things are important and they were an important part of the Moneyball / Oakland A’s story, but the part of the story that many miss is that these ‘B’ players also worked really hard to become better at what they did.  They didn’t just relegate themselves to be also-rans…they kept pushing harder and harder to become better than they were.

The same is true for corporations.  Maybe you don’t have a team comprised of the most talented and skilled employees, but if you and your leadership team continue to push yourselves and your people, you (and they) can do wonderful things.  If you build a culture of improvement where the smallest failures aren’t punished and show your team(s) that you are constantly improving yourself – and expect the same from them – your team and company will be able to compete. You may not win every time, but you’ll be around for the long-haul.

You can win if you aren’t the best. Anyone can. You may never be considered the best, but if you continue to try to get better, you’ll always be better than you were.

That said, just imagine if you don’t push yourself or your team to constantly improve? If you and your team are OK with being average, you’ll never have the chance to win.

Perfect Takes Work

Alan McFadyen's "Perfect" Kingfisher shot

Every so often I run across a story that leaves me in awe. A story out of Scotland about a photography who has spent years trying to make the ‘perfect’ picture is one of those stories.

In “A Perfect Photo of a Kingfisher, 720K Pictures in the Making“, the story of Alan McFadyen’s attempt to capture the ‘perfect’ photo of a kingfisher diving a mirror-like water surface is described. From the article:

Thus began an obsessive quest for the perfect shot, a quest McFadyen estimates took some 4,200 hours and 720,000 exposures. He tried many angles and compositions before landing on the idea of a mirror image.

4200 hours and 720,000 images.  Can you imagine? Sure…its much easier to take that many photos with a digital camera these days and with the speed of modern professional gear, you can rattle off 10 images a second but still…4200 hours (175 days if worked straight through) is a long time looking for that ‘perfect’ image over the course of 6 years.

The photograph that took 720K images to make:

Alan McFadyen's "Perfect" Kingfisher shot
Alan McFadyen’s “Perfect” Kingfisher shot

Perfect takes Work

I’ve been known to say ‘perfect destroys good’ and ‘done is better than perfect’, but in this case, I have to agree that ‘perfect’ is perfect.  Sometimes, it is worth the effort for perfect.

The takeaway from this (other than an absolutely stunning image) is that perfect takes work. You don’t show up to your first day on the job or project and do things perfectly. You don’t pick up a camera and take the perfect image on your first attempt.

Perfect takes work. Are you willing to do the work?

Check out Alan’s work on flickr and his Scottish Photography Blinds website for more info on him, his photography and services.

Image credit: Photograph by Alan McFadyen and used with permission

To have a great analytics culture, you need a great communications culture

Employee-communicationWhen you read about big data and/or data analytics projects and systems, it is rare that you also read bout communicating the outcome of those projects. Without the ability to communicate the results of any analysis to the broader business, most big data / analytics projects are doomed to mediocrity…or even failure.

The quantitative mind is a great one. It is one that I’m very familiar with and one that I wholeheartedly support.  The ability to take a data set, analyze that data and create new information and knowledge from that data is an extremely important skill for people and organizations to have.

Just as important is the skill to be able to convert the outcome of any quantitative analysis into something that is easily digestible by people throughout an organization.

Take, for example, the world of academia.  There are many really smart people performing research within universities and research facilities. These people conduct research and then publish the outcomes of that research in academic journals to share their new-found knowledge with others.

Have you ever picked up an academic journal/article? These articles are generally well-written and delivered in formal academic styles but they aren’t exactly ‘easy reading’.   They are meant to be used for academic reporting within academic circles. They are also used within industry but most practitioners that read these journals and articles are usually people with similar education and experience as those folks who are writing / publishing these articles.

What happens when a finance manager picks up the Journal of Finance paper titled “Determinants of Corporate Borrowing?” Will they easily understand what the paper is trying to communicate?  Let’s take a look at a portion of the abstract of the paper:

Many corporate assets, particularly growth opportunities, can be viewed as call options. The value of such ‘real options’ depends on discretionary future investment by the firm. Issuing risky debt reduces the present market value of a firm holding real options by inducing a suboptimal investment strategy or by forcing the firm and its creditors to bear the costs of avoiding the suboptimal strategy. The paper predicts that corporate borrowing is inversely related to the proportion of market value accounted for by real options. It also rationalizes other aspects of corporate borrowing behavior, for example the practice of matching maturities of assets and debt liabilities.

I would argue that anyone – given enough time – could understand what that paragraph is trying to communicate, but in the fast-paced world of business, does anyone really have time to sit down and study this paper?  I doubt it.  Most will call up a consultant and ask to help better understand the optimal approach to corporate debt.  What is that consultant going to do?  She will take her experience as a consultant (and in finance/banking), study the business, literature and best practices and then make a recommendation to the business on what they should do. If the consultant is any good, these recommendations will be provided in an easy to understand document that can be implemented effectively within the organization.

The same approach needs to be taken with data analytics.  We can’t just throw a spreadsheet or chart over the wall at the business and expect them to understand what the data is telling them or what they should with that data. I see a lot of this these days though. A company will implement a new big data project, perform some analysis of the data and then provide the output of the analysis in pretty charts and tables but very rarely are there deep, meaningful discussions and analysis about what that data is really telling the business and/or what the business should do based on the data analysis.

Now, you may say that good data scientists / analysts already do this…and you’d be right. But, not everyone is a great analyst nor is it a skill set that most organization’s are hiring for these days. When I talk to clients about big data, they talk about the need to get the best hardware, software and analytical skills…but they rarely talk about the need to find great communicators.

Companies regularly spend millions of dollars on the ‘hard’ costs for big data and data analytics. They’ve even begun spending a good deal of money on the ‘soft’ costs to get their people the best training available so they can be the best data analysts available but it is rare that they spend much money on communications training.

The funny thing about this particular topic is that most data scientists consider themselves to be good communicators.   In my experience, the really good ones are…but the majority of the ‘new’ data scientists struggle with this aspect of their job.

If you want to be a great data scientist, become a great communicator and storyteller. As a data scientist, if you can’t communicate in a way that is informative and useful to the business, the work you do in the ‘quant’ world isn’t that valuable to the company.  The same can be said to the business in general – if you want a great data analytics culture, build a great communications culture. You can’t have one without the other.