Five things the CEO wants to know about Big Data

Five things a CEO needs to know about big dataI spend a lot of time talking to companies about big data and data science. Many conversations are with people at the CxO level (CEO’s, COO’s, CFO’s, etc etc) and usually revolve around basic discussions of big data and data analytics.   One of the things that has surprised me a little from these discussions is that these CxO level people have the same basic questions about big data.

Those of us who are consultants and practitioners within the big data space like to wax poetic about big data and data science like to think that ‘this time is different’ and that big data is really going to change things for the better for any company.   While that may be the case, there are still some very basic questions that need to be answered within every organization before any major investment is made. The questions that I hear most from CxO level people can be categorized into the following types of questions:

  1. What is it?
  2. Why do we care?
  3. How is this different than {insert name of previous approach here}?
  4. What is this going to cost?
  5. Who is going to manage this?

All valid questions and all questions that should be expected when any major initiatives are being discussed. Additionally, these questions shouldn’t come as any surprise to anyone that’s been around CxO level folks before…but they often come as a surprise to many technical people because many think that big data ‘just makes sense’ and should be implemented immediately. The problem with this line of thinking is that it is the exact same type of thinking that has led organizations down many other non-fruitful paths in the past.

For example, I can think back to my early days in telecom and remember my very first job out of college. I was a software tester working on a new hardware platform that was being designed / built to offload data traffic from the public telephone network (PSTN) onto an ATM network. This was cutting edge stuff at the time during the late 1990’s when getting online meant to connect your modem to the PSTN.   The market research had been performed to show that a need existed for this and many discussions where held with technical people at many different telecom service companies. Everything looked great for this particular company until the time came to sell the product.  The CxO level people at these telecom companies were basically asking the questions I’ve listed above…and the answers weren’t compelling enough to warrant an investment in this new, unproven technology.  Sadly, the company I worked for shut down this particular product line after finding no real interest in the product.

Some of you may be thinking that my example is quite different than big data, Sure, there are proven examples of big data initiatives bringing fantastic rewards for organizations – but there are also many other examples of big data initiative failures so it makes sense that companies are cautious when it comes to new technology /initiatives.

When it comes to your big data initiatives, can you answer the above five questions for your organization?

Digital Transformation – Are CEO’s and CIO’s aligned?

Digital TransformationI just read “Digital transformation will shape 2016” over on CIO.com and was a a passage caught my eye.

Before I give you the passage, let me put the sentence into context.  The CIO article is discussing a recent IDC report titled ‘IDC FutureScape: Worldside CIO Agenda 2016 Predictions.’ This report provides some predictions on what the CIO and IT  will be focused on for the coming year(s).

The passage that caught my eye was this one:

According to IDC, the biggest issues in IT leadership will center on business needs, capabilities and availability related to digital transformation. The data shows that two-thirds of CEOs plan to focus on digital transformation strategies for 2016 and that CIOs will be major players in leading every department through this shift. In terms of capabilities, only 25 percent of CIOs report feeling confident in how they are driving new digital revenue streams.

Emphasis mine.

When I read that paragraph, I was a bit perplexed as to how two-thirds of CEO’s will be focused on digital transformation and the CIO will a ‘major player’ in leading these initiatives while at the same time only one-quarter of CIO’s felt confident in how they and their teams are driving new digital initiatives and revenue streams.

Now – before anyone skewers me, I do realize that digital transformation is about much more than finding new revenue streams. Digital transformation covers all aspects of the business from finding new revenue streams to reducing costs throughout the business via technology.  CIO’s know how to do the latter…but as we see from the survey, not many of them are confident they can do the former.

If CEO’s are going to be focused on digital transformation in 2016 and CIO’s are going to be one of the leaders of those initiatives, one would think that those CIO’s would be much more confident in their capabilities (and abilities) to drive revenue. Right?

Sure, CIO’s can help to drive digital transformation without ‘knowing’ how they are doing with digital revenue streams, but if I were a CEO, I’d want to know my CIO had a real handle on all things digital, including how digital is driving revenue.  Alternatively, if I were a CIO and I knew the CEO was focusing on digital transformation, I’d be doing everything in my power to make sure I (and my team) were fully up to speed on what we were doing, planning to do and could potentially do in the coming year.

Based on the responses in this survey, I worry that the the CEO and CIO aren’t quite on the same page when it comes to digital transformation going into 2016.

Don’t focus on the data, focus on what the data tells you

Should we stop talking about big data, and start talking about insights?In a recent post on O’Reilly Radar titled “Turning big data into actionable insights“, Ben Lorica provides some highlights of an interview with Evangelos Simoudis regarding big data.

One of the highlights is extremely important. It relates to the importance of finding actionable insights from big data and sharing those insights in a way that the organization can use.  The quote is:

I think you need three ingredients. You need data, you need the right ways to combine the data and extract features from that data, and then the third ingredient is the ability to analyze the data and bring together the analysis results in a way that provides these insights and these measurable actions. … [They] need to be able to know what actions [they] need to execute in response to these analytics….

Emphasis mine.

So many times when talking about big data, we get ourselves wrapped up in the technologies, processes and systems that we forget to think about the real reason we are even working with data in the first place.  Data is near worthless until it is analyzed.  Sure, you can put some ‘value’ on data but unless you turn that data into information (and then into knowledge) your doing nothing more than being a data hoarder.

So…to my data hoarding friends I say: Don’t focus on the data, focus on what the data tells you.  I don’t care if you have 1 GB of data or 1 PB of data, if you can’t turn that data into information and then knowledge, your data initiatives aren’t going to succeed.  Additionally, if you can’t communicate the insights gained from your analysis, your missing out on the real value in data analysis.

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.

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.

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