Eric D. Brown, D.Sc.

Data Science | Entrepreneurship | ..and sometimes Photography

Category: Thinking (page 2 of 9)

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.

Best practices aren’t enough

Trading Psychology 2.0: From Best Practices to Best ProcessesI’m currently reading Brett Steenbarger’s “Trading Psychology 2.0: From Best Practices to Best Processes.” I’m a fan of Dr. Steenbarger’s work and bought his new book as soon as it was released.

While this book is targeted at traders and investors, there’s a great deal of knowledge that can be gleaned from it that can be applied to many other aspects of your personal life as well as business.

For example, the premise of the book is that the four main ‘habits’ a trader should focus on to improve their trading are “adaptability, creativity, productivity, and self-management.”   Those four habits are ideal of any individual and any company to focus on as well.   Just like a trader, a business can have the best strategy in the world but if they can’t execute on that strategy and adapt to changing markets in a creative way, the strategy is (or will become) worthless.

I ran across this nugget in one of the first chapters and thought it worth sharing:

…what makes traders good are best practices—sound methods for deploying capital and managing risk. What makes traders great are best processes: detailed routines that turn best practices into consistent habits. Adaptability, creativity, productivity, and self-management: These aren’t just things that the best traders have. They are what best traders do—routinely.

Emphasis mine.

Interesting concept isn’t it?  Best practices aren’t enough….to be consistent and remain in business as a trader, consultant, person or company, you need best practices and best processes.  But the key to long-term success in anything is how you build, change and manage your best practices and best processes.

A best practice today isn’t going to be a best practice next year.  The same is true for processes. Something that works today may not necessarily work next year.   You need both best practices and best processes but you also need to have the wisdom to know when those need to change to adapt to the changing marketplace.

This is where the four key habits come into play.  If you can adapt your strategy you can survive.  If you can think of new ways to approach your market and client base, you can survive.  If you can be both creative and adaptable while doing the right things and managing yourself and/or your business, you’ll not only survive but you should thrive.

Best practices aren’t enough. You’ve got to add best processes into the mix.  Additionally, you (or your business) need to have the smarts and creativity to know when its time to change those processes and practices to better align yourself and your strategy with existing or future market conditions.

Back to the book…while it is targeted at trading, there are a lot of very valuable insights that can be applied to all aspects of your life…I highly recommend it.

The Dichotomy of Data Access and Data Privacy

yin-yangIn order to use data within your business, you must first collect that data. Seems simple enough right?  You capture some data, store it somewhere and the use that data at a later time in your analysis.

What about data privacy concerns? Where are you collecting your data? How are you protecting that data?  Are you collecting/using social network data or other user-generated data from public sources?   If you are using data from ‘users’, do they know their data is being collected, stored and used for something other than the system it was generated in?

These types of questions are the ones that every organization and data scientist should constantly be asking, especially if a single byte of captured and/or analyzed data is generated by a consumer or user of your systems.

In addition to the data that you might capture within your organization (and perhaps from social media, blogs and other user-generated content), there will be data available from data brokers. It may not be next week or next year, but you can bet that all the data that are captured via wearables and other Internet of Things devices will be made available for a price.

Over on Business Value Exchange, Helen Beckett, in a post titled “Watch Out! The Personal Data Market is Coming”, writes:

Citizens and consumers, who generate thousands of bytes of data every day – switching on devices or utilities, making purchases, boarding transport or just walking down the street in CCTV cities – can celebrate. The data they collectively generate is an asset that is being mined to create value and making companies and even industries rich on the back of it. Now the personal data exchange is coming.

Companies will jump at the chance to buy data from these brokers and exchanges and begin using that data in their analysis. Just think about how powerful it would be for an insurance company to have access to your health data via Apple Health or Fitbit or data from a device in your car that reports on speed, location, distance driven, etc.

From a data science and an organizational perspective, having access to data like this is an enormous advantage for any company looking to better understand their clients.  If you can gather data on individual users daily activities, it makes it much easier to market to those users as well as customize your products/services to those users.

From an individual perspective, it is a little frightening to know that every aspect of my driving or my fitness routines (or lack thereof) could find its way into the hands of my insurance company. Likewise, it is disconcerting to know that said data could also make its way into the hands of companies who want to market their services or products to me based on where I’ve driven or how far (or how little) I’ve walked in the last few weeks.

As data scientists and organizations, we want to be able to access and analyze as much data as possible and we want data that is as granular as possible.  With personal data available today (or available in the near future), we have very granular data.

As individuals, we are (or should be) concerned with how companies are using our own data.  We at least want to know how that data might be used and when it is being used.

This is the dichotomy we face today. We want to use as much data as possible but we also worry about data privacy of our own data.  The challenge for any organization or data scientist is to find the right balance between using the right data with the right granularity with necessary privacy issues that consumers need and want.

How is your organization balancing data privacy and data access?

This post is brought to you by HP’s Business Value Exchange.

More isn’t always better

More is always better?More is always better right?

More feedback from clients can help you improve your service.  More money can help you build better products and teams. More data can help you make better decisions.  More resolution can make your photos better.

More is always better isn’t it?

Well. No. More isn’t always better.

Seth Godin recently said that “Too much resolution stops giving you information and becomes merely noise, which actually gets in the way of the accuracy you seek.”

This is very true. Anyone that’s ever worked with data will tell you that more data just means more work. Sure, you may find a great nugget in that additional data, but that extra data doesn’t always equate to more knowledge but it always equates to more work.

To Seth Godin’s point, more ‘resolution’ isn’t always the answer either. I can go buy a $50,000 camera with the highest resolution possible and still make terrible photos. Just because I have the resolution available to me doesn’t mean I have the lenses available to take advantage of that resolution nor does it mean I have the talent to utilize the high resolution.

More isn’t always better.  Adding more data to your already large data set isn’t going to find the answer for you. It might help you find more questions to ask, but it doesn’t guarantee that an answer will be found.

Rather than go spend $50K on the ‘best’ camera, spend $500 on an OK camera and learn the skills and methods  needed to make the most of what you have. When you’ve mastered your ‘art’, then move up to something more expensive with more functions.

Rather than focus on gathering more data, you need to be focused on using the data you have in the most optimal way possible. Make sure you have the tools and skills in place to analyze / use what you have before you go and add ‘more’ to the mix.

Normalcy Bias and You

Normalcy BiasIf you aren’t familiar with the term “normalcy bias”, here’s a quick definition:

Normalcy bias is…the phenomenon of disbelieving one’s situation when faced with grave and imminent danger and/or catastrophe. As in overfocusing on the actual phenomenon instead of taking evasive action, a state of paralysis.

Another way to think about normalcy bias is the often used term in project management circles of ‘analysis paralysis.’ A much simpler way to think about normalcy bias is that it is an assumption that nothing bad will happen in the future because nothing bad has happened in the past.

I hope everyone reading this can see how dangerous normalcy bias can be, especially for people or organizations going through difficult times.  People are smart and can generally see that things aren’t working out for them or their company…but very few people take action to make a change.

I can provide a real-world example from my past to illustrate this point.

An Example

While in grad school for my MSEE, I worked in the telecom industry and did some contracting work at a major telecom player in the mid to late 1990’s. This company was a leader in the telecom space and was growing by leaps and bounds but just about everyone I spoke with at the company was worried about the future of the business.  They didn’t know how the business could continue to grow at the pace they had been growing at and were always amazed that growth continued at a record pace.   I was a dumb kid with little life experience and couldn’t quite understand how the business was growing so rapidly but I just figured there were people way smarter than me making decisions and leading this company so I just shrugged my shoulders and went about my work.

Things were apparently great for this particular company throughout the 1990’s. They were setting records for revenue and growing rapidly. Everyone I spoke with at the company was concerned that the company was going to implode ‘soon’ because they just didn’t understand how the growth could continue.  There was a real fear of a major catastrophe ‘soon’ but nobody seemed to be doing anything about it.  Everyone was focused on an implosion because they didn’t feel like things were going that great but nobody seemed to be taking any steps to ‘fix’ the problems.  I remember speaking with some folks back then about the issues and can clearly remember someone saying “well…nothing bad has happened yet so I doubt it will.”

Everyone that I spoke with was guilty of falling into the normalcy bias trap. I fell into that trap as well.  The normalcy bias trap is an easy one to fall into…its ‘easier’ to expect nothing bad to happen even while being presented with bad news.  People (and entire companies) can (and do) tend to ignore the bad news and expect things to ‘work out.’

By the way…that company I used in the above example WorldCom. They filed the largest bankruptcy ever filed (at the time) in 2002.  There was plenty of reasons for people to be worried about their operations and business model but instead of trying to do something about it, people ignored it.  That said, there are many times that things can’t be done because people at the top don’t want things to change…but that’s a different issue.

Normalcy Bias and You

The above example isn’t an ideal one because there are plenty of extenuating circumstances that could have kept people from doing anything about the impending ‘disaster’.  As it turns out, there were plenty of things wrong at WorldCom but there were people at the top of the company that were actively working to hide any wrongdoing and any issues. That said, there were plenty of people suffering from normalcy bias because they assumed things would be fine in the future because they had always been fine even with all of the issues that existed.

Normalcy bias is a very dangerous thing.  By assuming things will always be fine because they have always been fine, you are putting blinders on to issues and not preparing yourself or your organization for change that is most likely coming.

Normalcy bias can be your downfall and the downfall of your business.  Don’t let yourself fall into the trap of focusing on the outcome to the extent that you paralyze yourself from doing anything. Also, don’t make the assumption that nothing is going to happen because it hasn’t happened yet…there are too many examples of failure of families, companies and societies to allow yourself to assume nothing bad will ever happen to you or your business.

To overcome normalcy bias, identify the issues that might happen and be prepared if they do. Don’t go overboard of course but have a plan of action to address the issues you see, minimize the risk that those issues might cause and then have a plan to deal with the fallout from if the issue(s) are ever realized.

Don’t let normalcy bias destroy your life, career or company.  Identify the risks in your life/business, mimimize or remove those risks and then create a plan for how you will deal with those risks if they become reality.

The most critical skill?

large-iconI’m an avid reader. I tend to read a few books at a time (I read one depending on my ‘mood’ at the time).

The other day, I was scrolling through the various sections in Amazon’s Kindle Unlimited program and somehow ended up in the ‘self-help’ section.   Now, I’m not a fan of self-help books because they tend to try to tell people that they can follow a ‘recipe’ and then they’ll be ‘all better.’  Life doesn’t work this way…but self-help guru’s sure want readers to think that way.

While scrolling through the self-help section, I saw a ton of ‘learn to be wealthy…’, ‘love yourself…’, ‘follow these rules to be X..’ (substitute anything for X) and other ‘standard’ self-help books that, in my opinion, really won’t help many people.

In the middle of all these books, I found one that actually would help just about anyone that read it.   I don’t remember the actual title of the book but it was something like ‘critical thinking – the only skill you’ll ever need.’  If you (or anyone) only reads one self-help book, it should be one on how to think critically and apply the skill to your life.

I’ve written about critical thinking before. I believe its a skill that we all need to continually practice and apply to our lives and its a skill that is lacking these days.  Thinking critically can help anyone in any situation.

Need to get out of debt?  Sit down and think critically about why you are in debt and then come up with a plan to get out of it. Want to make more money?  Sit down and think critically about why you want to make more money and then come up with a plan to do it.  Maybe you won’t become a millionaire but you could get a second job to pay off that debt or send your kids to college.

Maybe you need to reorganize the entire IT group.  Do you go out and buy a book about how to organize an IT group or do you sit down and think critically (and impartially) about how your team needs to be organized to deliver the services that your organization needs?  I know some CIO’s do the former but most successful CIO’s take the latter approach.

Critical thinking is extremely important for a person and for an organization.  If you feel the need to buy a self-help book any time soon, try finding one on the topic of ‘critical thinking.’


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