10 Trends You Will Continue to See In 2014

Many businesses ask me what do you see happening in the next 12 months. They ask me question like:

What should we expect?

Where should we be investing?

What should we be thinking about to keep ahead of the curve?

The list below is not particular to anyone industry, rather a general overview of the state of the analytics ecosystem at a particular moment in time. For many industries, if they were to focus on a single item below it would perform wonders for their business, and yet others would need to adapt for more.

  1. Data science move to the every-man.
  2. Analytics will drive cloud-based business solutions.
  3. Cloud data warehouses transform the process from months to days.
  4. Business individuals began to have expectation of flexibility and usability in their dashboards.
  5. No longer is retrospective views of the data enough, so the addition of prospective views become important.
  6. Embedded analytics begins to to come into mainstream business.
  7. Dashboards with context become important, hence narrative around the data becomes key.
  8. Business users began to seek information wherever they are and not just at their desktop.
  9. Social media becomes a measures of competitive advantage for organizations.
  10. NoSQL will become increasingly more important as organizations attempt to work with unstructured data.

It is fabulous time to be involved in analytics and organizations of all types. We are at a new frontier of business that we should all be excited by rather than intimidated by.

The Death of the Data Scientist?

There has been a lot of chatter recently around the notion that data scientist are soon to be replaced by a 30/hr specialist from places like Odesk, Freelancer, and Elance.  Before we go down the path of can we replace a data scientist, let us take some time to hone in on exactly what a data scientist does? Being candid, there is a plethora of answers to this question.  If we mean, a person who pull together a data summary or modeling task that has been well-defined before they even encounter the problem, then I think it is absolutely possible to come in at a30/hr price. In truth, I see that time of data scientist being replaced by automated software without having to deal with a freelancer at all. Look to how other scenarios like this have occurred, such as online marketing or site development.

But we need to focus on the concept “the data problem was previously well-defined”.

Data scientist who achieve higher salaries happen to be in either two distinct camps:

1) The Engineer:

This individual knows how to choose the proper tools and infrastructure to solve a specific, technology laden data problem. These individuals usually work on the leading edge of a problem or at times there may be very few examples of this problem being worked in global community. This is markedly different than the well-defined problem of the freelancer situation we defined earlier.

2) The Communicator:

This individual knows the technical side of what data science is and how to get at solutions, but there strength is in the story telling. Many times business leadership is unknowing about what is possible with data science and for that they need a translator of sorts. These types of individuals encounter organizations that know they have a problem to solve, but they do not necessarily know how to frame the question so that it can be satisfied by the data. These business look for someone who is personable and not thousands of miles away to guide  them through what they feel is incredibly difficult and important.

While it is certainly true that there may segments of data science which are automated, there will certainly always be a place for problem solvers – think physicians, attorneys, developers, consultants, etc. Like these roles just mentioned, data scientist is not simple a role.

Not all data scientist are performing rote tasks.

There will always be a place for individuals skilled at solving leveraging technology to solve complex business problems and we will have to invest more than $30/hr to garner their expertise.

R: Numbers

In general, numbers in R are treated as numeric objects.

For example,

 3 # numeric object
[1] 3
 3L # explicitly gives an integer
[1] 3
 Inf # a special number which represents infinity
[1] Inf
 1/0
[1] Inf
 1/Inf # can be used in calculations
[1] 0
 0/0 # NaN ("not a number"); also, seen as a missing number
[1] NaN

 

Numerics are also decimal values in R. This happens by default, so that if you create a decimal value for x that is will be of the numeric type.

 x = 8.3 # create x which a decimal value
 x # print the value of x
[1] 8.3
 class(x) # what is the class of x?
[1] "numeric"

 

Even when assigning an integer to a variable such as N, it is still being retained as a numeric value.

 N = 43
 N #print the value of N
[1] 43
 class(N) # what is the class of N?
[1] "numeric"

 

You can further confirm that N is not an integer by using the is.integer function.

 is.integer(N) # is N an integer?
[1] FALSE
 is.numeric(N) # is N numeric?
[1] TRUE