A tale of two employees

a tale of two employees

a tale of two employeesI was recently talking to a CIO friend of mine.  She has a really good team of people working for her and has recently gone through a hiring spell where she has six new-ish employees on her staff. All six have been at the company from anywhere between 1 month to 7 months.

While talking to this CIO, she was relating some stories of a few of these employees. She was telling me of a recent experience that has her rethinking the employment of one of these new people.

The first of these we’ll call Joe for discussion purposes. Joe’s resume is spectacular (I’ve seen it) and his experience over his career seem to be perfect for someone on the ‘fast track’ to move up through a company.  Additionally, the recommendations from previous employers are some of the best that I’ve seen.   He’s a rockstar on paper.

The CIO also told me of another employee. We’ll call him Bill for ease of discussion.  On paper, Bill is an average employee. His resume looks good and his experience over his career shows an employee who goes to work and does his job and goes home. There’s nothing that screams “high achiever” with Bill, but he’s a good employee. An average employee, but a good one.

Apparently, things came to a head over a recent holiday.  As is usual, during this holiday, there were some folks from the IT operations group on call in case something happened at the office.  That’s the life of IT operations.

During this holiday, one of the people on call got an alert about one of their systems. She dutifully logged into the VPN and started reading the logs.  Apparently, the problem was one that required a broader call-out of other team members to resolve, so she sent out there messages to the other on-call team members to start fixing this issue. Neither Joe nor Bill was as part of the on-call team members.

As the team began working through the issue, they realized that they would need to bring someone in from the development team brought to make sure some of the configuration changes they were making wouldn’t affect some of the software platforms. They reached out to the head of development and asked him who they should reach out to.

The director of development reached out to both Bill and Joe via text message to ask for some help. Within a few minutes, he had received responses from both employees. I’ve paraphrased them below.

From Bill:

Happy to help. Should I come into the office? Tell me who to reach out to.

From Joe:

Its a holiday. Why are you asking me to work?

Here we have “Joe the Rockstar” unwilling to put in a little extra time and effort and “Bill the Average” willing to do what he needs to do to get the job done.

Which of these employees has my CIO friend rethinking their employment?  A ‘rockstar’ on paper means little of that person isn’t able or willing to get the job done.

 

You (probably) don’t need Machine Learning

Your company doesn't need Machine Learning (probably)

Your company doesn't need Machine Learning (probably)Statistically speaking, you and/or your company really don’t need machine learning.

By ‘statistically speaking’, I mean that most companies today have no absolutely no need for machine learning (ML). The majority of problems that companies want to throw at machine learning are fairly straightforward problems that can be ‘solved’ with a form of regression.  They may not be the simple linear regression of your Algebra 1 class, but they are probably nonetheless regression problems. Robin Hanson summed up these thoughts recently when he tweeted the following:

Of particular note is the ‘cleaned-up data’ piece.  That’s huge and something that many companies forget (or ignore) when working with their data. Without proper data quality, data governance and data management processes / systems, you’ll most likely fall into the Garbage in / Garbage out trap that has befallen many data projects.

Now, I’m not a data management / data quality guru. Far from it.  For that, you want people like Jim Harris and Dan Power, but I know enough about the topic(s) to know what bad (or non-existent) data management looks like – and I see it often in organizations. In my experiences working with organizations wanting to kick off new data projects (and most today are talking about machine learning and deep learning), the first question I always ask is “tell me about your data management processes.” If they can’t adequately describe these processes, they aren’t ready for machine learning.  Over the last five years, I’d guess that 75% of the time the response to my data management query is “well, we have some of our data stored in a database and other data stored on file shares with proper permissions.”  This isn’t data management…it’s data storage.

If you and/or your organization don’t have good, clean data, you are most definitely not ready for machine learning.  Data management should be your first step before diving into any other data project(s).

What if you have good data management?

A small minority of the organizations I’ve worked with do have proper master data management processes in place. They really understand how important quality, governance and management is to good data and good analysis. If your company understand this importance, congratulations…you’re a few steps ahead of many others.

Let me caution you thought. Just because you have good, clean data doesn’t mean you can or should jump into machine learning. Of course you can jump into it I guess, but you most likely don’t need to.

Out of all the companies I’ve worked with over the last five years, I’d say about 90% of the problems that were initially tagged for machine learning were solved with some fairly standard regression approaches. It always seems to come as a surprise to clients when I recommend simple regression to solve a ‘complex’ problem when they had their heart set on building out multiple machine learning (ML) / deep learning (DL) models.   I always tell them that they could go the machine learning route – and their may be some value in that approach – but wouldn’t it be nice to know what basic modeling / regression can do for you to be able to know whether ML / DL is doing anything better than basic regression?

But…I want to use machine learning!

Go right ahead. There’s nothing stopping you from diving into the deep end of ML / DL. There is a time and a place for machine learning…just don’t go running full-speed toward machine learning before you have a good grasp of your data and what ‘legacy’ approaches can do for the problems you are trying to solve.

Five things the CEO wants to know about Big Data

Five things a CEO needs 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?

Will marketing continue to grow their share of the technology budget?

marketing technology budget

marketing technology budgetIf you are involved with marketing and/or technology in any large organization, you most likely are hearing a lot about marketing technology (MarTech) and the ‘explosion’ of MarTech people, technology, projects….and budgets. There are some folks out there who claim that within a few years, marketing will be spending more on IT than the CIO but many IT professionals I speak with just have a hard time accepting that marketing will ever drive more spend on tech than IT does/will.

My response to these folks is simple: It is already here.

Let’s take a second to think about some of the higher priority items for the IT group. According to the 2016 State of the CIO report from IDG, the top 3 priority items for CIO’s in the coming year are:

  • Complete a major enterprise project
  • Help reach a specific goal for corporate growth
  • Upgrade IT security

Those first two items are pretty broad and specific to each organization, but they could easily relate to MarTech projects. For the last item on IT security upgrades, the report says that about 12% of the IT budget will be spent on upgrading IT security (which seems low to me, but I’m not as close to security as I am to other parts of IT).

Another nice little nugget of knowledge from the 2016 State of the CIO report is the following:

57% of the total dollars invested in tech is now directly controlled by IT (and is expected to grow to 59% in the next 3 years).

One thing to notice with this particular stat – while the majority of budget is controlled by IT, there’s no real detail in the report on where or how that money is spent other than to say that 33% of marketing’s budget is currently set aside for technology.

All the numbers and data points mean very little without some context, which is what many IT professionals (and non-marketing people) lack.  The spend on MarTech five years ago was generally small. Marketing groups would spend money on content management systems, web analytics platforms and e-commerce systems but very few were investing money in large, enterprise-level systems. That has changed in most medium to large organizations. Today, marketing organizations are spending money are those enterprise systems because they now understand how important an integrated, enterprise platform can be for driving engagement and revenue.

Will marketing continue to grow their technology budget? I think so.  Will IT professionals continue to complain about that? Probably….but the majority will realize how important MarTech spend is to the future of the organization.

The three roadblocks to big data success

Three roadblocks to big data success

Three roadblocks to big data successAnne Fisher just published a nice piece titled “Why Big Data Isn’t Paying Off for Companies (Yet)” where she describes research from the American Institute of CPA’s (AICPA) regarding big data initiatives within organizations around the world. You can jump over to the study itself but Anne does a very good job describing the findings in her article.

One of the key findings from that study is that here are three main roadblocks to big data success. They are:

  • Being in too big of a hurry
  • Trying to start too ‘big’
  • Not incorporating corporate culture into big data initiatives

In my work with clients on big data, that third roadblock is the one that tends to cause the most problems.  Sure, many companies want to through big data at all their problems (or is it throw all their problems at big data?) but it doesn’t take long for them to realize they need to start small and go slow to make sure they know what they’re doing within the big data world.

Roadblock #3 is the one that gets most companies into trouble, especially those that are very bureaucratic.  These bureaucratic companies tend to attract people that like to hold onto information because it makes them feel more ‘powerful’ or in control.  If you implement big data systems and processes correctly, your organization should become much more information rich, which is something that bureaucrats don’t really like.  Therefore, it is imperative to big data success that organizations remove the bureaucrats and bring in a more open, sharing culture.  Fisher and the AICPA agree:

Busting up the bureaucracy, so information can flow quickly to the right people, requires a kind of manager that the AICPA study refers to as “integrative thinkers.” The relatively few organizations that are making profitable use of big data have hired or cultivated executives Thomas describes as “collaborative leaders who can see horizontally across their whole organization and connect the dots.”

Are you thinking about corporate culture in your big data initiatives?

Innovation and the CIO – Survey Results

Michael Krigsman just published a piece titled “What do business people think of their CIO?” that shares some output of a research report put out by Tech Pro Research regarding the perception of the CIO within the business,  including some results related to innovation and the CIO.

I’ll leave you to go read Michael’s write-up on the subject…he does a good job pointing out the highlights.   I did want to point out one of the results from the report that discusses innovation and the CIO within organizations.  From the report:

…there is a sizeable gap of 32 percent between CIOs and non-CIOs regarding whether the role has a strong impact on technological innovation and creativity. Nearly one-third of non-CIOs feel their CIO has little or no impact, but only 6 percent of the CIOs feel they have little impact, and none feel they have no impact. The low percentages of those reporting little or no impact across both groups show a solid footprint on the part of the CIO.

This particular passage has the following chart to accompany it:

Innovation and the CIO

From the chart, you can see that 47% of all respondents believe the CIO/CTO has ‘some impact’ and 30% believe they have a strong impact. That said, if you pull out the CIO/CTO responses, its a 49% and 20% split. Still, that’s a majority of people that believe the CIO/CTO have some impact on innovation and creativity when it comes to using technology.

Based on these responses, it is clear that most respondents believe the CIO / CTO role is vital to innovation within the organization. That’s a good sign for the CIO/CTO role.  Jump over and read the rest of the results from Tech Pro Research and/or  Michael’s “What do business people think of their CIO?” article.