Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

Analyst Skills are Hot

analytics and business intelligence jobs

BusinessWeek recently reported (listen to pod cast) that there are over 3 million jobs available in the US.  Of that, one of the hottest areas is for analysts. Looking at job posting trends from Indeed, even as the economy has stalled and affected recruitment, analytic and business intelligence jobs are still showing consistent demand.  In fact, even as IBM announced cuts, it has opened up job reqs for analysts to help customers identify opportunities and understand their businesses.  Don’t have all the skills, no problem, if you are an overall good fit IBM will train you.

Which brings up an interesting perspective of the analyst community.  While there are certainly the math and stat majors along with masters and PhD candidates, many of today’s analysts in corporations are self taught and accidentally landed into a data crunching career.  There aren’t many that went to college and said, “Gee, I’d like to be a statistician.”  But, somehow, many analysts have found an affinity toward analyzing data and putting it into context for gaining insight and making business decisions.

Not surprisingly, if you look at barriers in organizations, particularly marketing, and their ability to leverage data to achieve business goals, many feel they don’t have the knowledge to do so.  In fact, they may not know what they need to know to get the right person.  So, these coveted positions continue to remain unfilled, waiting for the right candidates to show up .  How long should your business wait to find the right person and what is that costing you in missed opportunity?

Finding the Right Candidates

When hiring, I’ve typically focused an one’s aptitude and capability to analyze information rather than the tools used or complexity of analysis they have done.  The first reason is that there are very few out there that would fit the bill and if they do it takes a lot of money to bring them in.  The second is that while I want analysts to understand standards and procedures to analyze data, I don’t want individuals with rigid and unimaginative thinking that can constrict their ability to look at information in a new way for better insight.    When it comes to complexity, investing in the proper training/education, and mentoring them through projects works the best.  This way their learning is specific to the business need rather than a broad based approach to statistics and analysis.  Essentially, provide the academic guidance within a relevant corporate environment and application.  Overall, candidates should be inquisitive, creative, and obsessed with data, and self starting.

I know others that have strong relationships with universities and pluck candidates out of programs that have provided applicable experiences in analysis.  This is a favorite of research organizations where they partner with professors on a regular basis.  In addition, there are associations and institutes that offer advanced research courses that up and coming analysts attend and are resources to help find those with a high aptitude for analysis.  Many times professors, leading statisticians, and research professionals teach these courses and can be conduits to finding the right candidates.

How do You Fill Your Analyst Positions?

If you regularly hire analysts, what are you looking for?  What have you found makes an analyst successful in your company?  And, what advice do you have to help those that are having trouble filling analyst positions?

Reblog this post [with Zemanta]

Filed under: Hiring, , , , , , ,

Cool Data Visualization – What is That?

Want to help your organization optimize operations, extract market opportunity, see what customers think?  Provide a visual representation in a single slide that tells your senior executives what is happening and what to do.  That is, if they can understand it in a milli-second.

There are a lot of really great algorithms that are creating interesting visual presentations of behavior, influence, and connections across people and topics.  The problem is you might as well be looking at fractals for all the business insight you gain.  It may provide perspective for the resident math geek, but for the average business executive it is just modern art that needs further interpretation.

I remember in college when I first started programming mathematical equations to model data and played with fractals.  It was exciting, creative, and helped me to link data with a tangible result versus a simplified equation or answer.  I used to put fractals up on my website like works of art.  I even included a link to input random numbers for others to create their own.  It was so cool!  Was it practical?

Don’t get me wrong, I love data visualization obviously.  It can simplify very complex analysis to gain insight faster.  What I’m struggling with is needing the ability to connect data visualization with executive intuitiveness.  Heat maps, network graphs, and the variety of data maps I see being generated today are a far cry from what I would bring into a budget meeting let alone show to board members.  More time is spent explaining what executives are looking at than having conversations about business objectives and investments.  It is also not just executives.  Business managers and directors need to understand the business as well.  Pretty is nice, but value is better.

The other aspect that of today’s data visualization leaps is that it disassociates the business from the information.  If only a small group of geeky mathematicians and programmers understand the data, it creates a mysticism that can lead to distrust of information.  If people don’t understand it, they don’t learn, and they don’t improve.

What I hope we can do as really smart statisticians, data analysts, and programmers is make the connection between information and visualization so that it further democratizes insight and empowers our business rather than mystify.

Reblog this post [with Zemanta]

Filed under: business intelligence, , , , , , , , , , , , , , ,