Stop Using Pre-Scripted Questions in an Interview

Stop_sign_pageOver my career, I’ve hired over 50 direct reports and been involved in the hiring process for over 150 other people.  For the most part, all of these hires ended up being good people and great employees. Sure, there were a couple bad hires in there but I’d like to think my track record is pretty good mostly because I approached each interview as a conversation rather than a list of questions.

Many of these hires were technical people that filled roles as engineers, data scientists, trainers, technical support, consulting and sales roles.  I’ve seen (and hired) the gamut of roles throughout my career so I think I’m in a position to say the following:

Please…PLEASE…stop using pre-scripted question forms.

Please stop walking into an interview with a list of questions that are you are going to ask one after another. Please stop going down a list of ‘required’ questions that you got from HR or from the internet.

For example, I was hiring for a position way back in my earliest days of my career. During the phone interview, the hiring manager spent 30 minutes going line by line down a page full of pre-scripted questions (e.g., Tell me a bout a time you were creative, Give me an example of how you’ve been a leader, etc etc).  The interview was a very halting, non-interactive process that was painful.   It was also a process that made me realize I had no interest in every working for that company and that manager.  Side note: My response to the “tell me about a time you were creative…” was “…last time I interviewed someone, I came up with my own questions.”

If you only ask a set of pre-scripted questions, you are doing yourself and your interviewee a huge disservice.  You aren’t allowing them to be themselves and you aren’t allowing your own personality to come out during the interviewing process.  I realize there might some companies that require a certain set of questions to be asked during the hiring process. I’m OK with that as long as the hiring manager takes their time to ask their own questions and have a conversation with the person they are interviewing. That conversation needs to be a real conversation that touches on all aspects of the role you are interviewing for.

For example, take a look at this list of questions for data scientists.  There’s some great questions on there about the technical aspects of ‘doing’ data science but I’d lose my mind if I were sitting through an interview with those questions being asked of me.  Many of those questions are what google and stackexchange is for.  Sure, you want to hire a technically competent data scientist but sitting down with a list of questions like that isn’t the only way (or even the best way) to do it.

Rather than use that list as-is, a good hiring manager would use that list as a baseline to try to build a ‘road’ to try to lead a candidate down.  Rather than ask questions like “What is latent semantic indexing? What is it used for? What are the specific limitations of the method?” why not get the candidate talking about their recent projects and try to lead them down a road that talks about the approaches the took, the analysis methods they used and the decisions they made in how they approached the data?

Pre-scripted interview questions are great to give you ideas on what topics you want to cover, but make the interview your own. Don’t use the list as a crutch…have a conversation with the person you are interviewing…don’t just lob questions at them.

Perfect Takes Work

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

Don’t Ask if you Can’t Act

Don't Ask if You Can't DoIn a recent Harvard Business Review article titled “Don’t Ask for New Ideas If You’re Not Ready to Act on Them”, Ron Ashkenas provides an example of a company wanting to do the ‘right’ thing but not having the processes or systems in place to pull it off.

The example provided by Ashkenas is one that I’ve heard and experienced many times myself.  One of his clients implemented a ‘crowdsourcing’ approach to gathering innovation ideas from people throughout their business. This company received so many responses that it took nearly a month for all of the responses to be analyzed, categorized and reviewed.  It then took a few more weeks for executives to respond to everyone and announce that they were planning on following up on specific ideas to pursue.

As I mentioned earlier, I’ve seen this type of thing happen at other organizations I’ve worked at. The urge to identify and implement innovative ideas is a strong driving force for any organization and crowdsourcing these ideas makes a great deal of sense. That said, taking the step of asking for new ideas is pointless if your organization can’t quickly act upon those ideas.

This is why every organization needs to reimagine (or reinvent) itself as an agile organization. To truly be able to act upon innovative ideas, an organization needs to be able to marshal the necessary resources (e.g., people, systems, data, etc) to be able to analyze and act upon these new ideas.

Organizations need to be able to pivot and turn quickly to address their clients needs and their competitors offerings. This agility requires the utmost agility in all aspects of the business, including the IT group.  The IT group must provide systems and capabilities to allow the business to gather data, analyze that data and act upon that data quickly and easily.

In the example given by Ashkenas, an agile organization with an agile IT group should be able to put the right tools together capture, track, analyze and report on ideas from around the business.  If your IT group can’t put together the right tools at the right time to deliver the right services, you probably need to spend some time rethinking your IT group and its leadership. Additionally, if your IT group can deliver the right tools at the right time but it still takes you weeks or months to analyze and react…you have larger problems than just a non-agile IT group.

Building a Data Culture

virt_1_346x214Many companies want to ‘do’ big data today. They’re spending money on systems, software, consulting, training and other services to be able to capture, process, analyze and use data. Those are all things that need to be done to be up their data science capabilities and skills. Companies need the right platforms, the right systems, the right people and skills to be able to properly analyze and use their data.

There’s one area that many organizations fail to address when building up their data analytics programs and skills. That area involves the corporate culture. Specifically, it involves the culture around listening, curiosity, investigation and willingness to try and fail.

Corporate culture can play a huge role in the success or failure of data analytics programs. If your company’s culture doesn’t like hearing new data that may provide conflicting information, your big data initiatives may be set up for failure from the very beginning.

In my experiences, the ability to listen and act on new data is one of the most important aspects of corporate culture that leads to success with data analytics and big data. If you don’t have a corporate culture and leadership team willing to listen to new information. For example, if your CEO doesn’t listen to data or arguments that go against her beliefs, you may be in for a very difficult time if your data analysis shows a reality different than the one that she expects or wants.

While listening and accepting competing arguments and data is the top cultural issue that can make or break big data, the other cultural aspects are important as well. For example, if the people who are working with your data aren’t curious about the data and willing to spend plenty of time investigating that data then you may be wasting money giving those people the proper skills to become a data scientist. You may be training them to act as your data scientists, but if they aren’t interested in finding out more about your data and investigation new avenues of analysis, you may not get the move value from them or your big data initiatives.

Lastly, your corporate culture should be willing to accept failure. Now, I’m not saying you should embrace or excuse failure, but many times in the data analysis world you end up finding analyses that don’t match with your expectations. Much of the time spent by data scientists is spent in small analysis projects looking for new ways to look at data. Many of these small projects end in failure with nothing of measurable value to show for the time spent on that project. Even though it may seem like wasted time, these types of projects are what make great data scientists as it allows them to continuously improve on their skills.

Successfully implementing big data initiatives is much more than just buying some software or systems. Successful big data initiatives require working on soft skills as well as organizational culture to ensure that the big data mindset is ingrained throughout the organization.

This post is brought to you by SAS.

Important Skills for the Data Scientist

Blog_SkillsWhat are the important skills for a data scientist?

Most people would say the most important skills are data related. These skills are important. Extremely important. Without the interest or ability to analyze data, it would be hard to be a data scientist.

Many would argue technology skills are important. Data scientists need to be able to use many different types of systems and technologies to analyze, visualize and report on data analysis.

Others would argue that software development skills are important.  Data scientists spend a good deal of time writing code of some sort to help sort, organization, analyze and visualize data.

A few people might argue that data scientists need to be good data administrators. Data must be stored somewhere, so it makes sense that data scientists should have a good feel for managing data.

The skills listed above are the ‘hard’ skills that data scientists need. They are all important and they are all necessary for data scientists to master.

But what about the soft skills?

What about the ability to communicate both in writing and verbally? What about the interpersonal skills needed to discuss business problems and challenges?    How about the ability to research the business and dive into how the business does what it does?

Aren’t those skills important too?

I think they are. What do do you think?

The People Challenge for the Midsize Organization

Smiling Group of ProfessionalsThere are many challenges facing the IT function within the midsize organization. Actually, there are many challenges facing the IT function within all organizations but there’s a particular challenge facing the smaller organizations.

The ultimate challenge for small and midsize organizations is finding the right mix of people and technologies for the things the organization needs to do. For example, for an organization to start exploring big data, they first need to find people and technologies that allow them to do the data collection,data storage and analysis that needs to be done. This particular challenge isn’t all about money, it’s about finding the right people with the right skills to do the work as well as finding the right technologies and systems to deliver the required functionality.

The challenge of finding the right people and technologies isn’t just an SMB challenge. It is faced by every organization, but small and midsized organization can have more of challenge on their hands because they can’t offer the same long career path to new employees.

Over on the Midsize Insider, S. Anthony Iannarino wrote an article titled “You are Hiring for Runway” where he talks about the need to hire for the right skills but also the right “attitude” and growth potential. He writes that organizations should hire those people who can bring a long-term advantage to the business.

That’s the difficulty for many SMB’s – it is often hard to keep people interested for a long career due to limited opportunities. The challenge for the small and midsize organization is, first, to find good people. Then, they need to find ways to keep those people interested and challenged.

WIth the right approach and mindset, small and midsize organizations can provide more opportunities to employees than their larger competitors.

IBMThis post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.

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