|Janus, in ancient Roman religion
is a two faced god and he has
two faces because he is looking
at both the past and the future. In
common parlance, showing two
faces means deception, no where
near where it started
We, analytics experts are always supposed to be looking into the past to learn and predict the future. If we are not going to use it for the next event or time period, we do not need the decision rule. So inherently, analytics is connected to the future event. We look at both past and future and provide the best rules for implementation of strategic judgement to business people or policy makers. That is the advanced ability of an analytics expert (AE).
In one of my earlier note, I was sharing with you how you can be a great BI analyst and still a lot you can contribute; do not have to be concerned about the true application of the predictive part.
I will be using this phrase, AE (analytics expert or analytics engineer) because some how the word analyst is getting disassociated from the current trendy activities of data capture, management, and associated analytics along with the uncanny ability to be an AE, as one personality.
It is understandable that not every one can do all those tasks, but disassociation is hard to understand.
People like to be called as Data Scientist nowadays, because of the phenomenon of BIG data. But the true data scientists who coined the term, using it around the big data companies are actually automation smart computer scientists whose main job is capturing, managing, and automating the parsing of gazillion and gazillions of bits of information every day. With the help of the democratization of analytics tools and machine learning methods, these smart people are also becoming well versed in the sciences of decision making, to manage the ‘data sciences’ processes, so that definition just does not stop with data side alone, but also includes the ability to become more and more an AE.
|A split Personality Janus?
On the other hand, analysts (smart data intelligence creators) have been just AE and they lost the ability to connect to the needs of the speed at which they have to access the data, process the data ,and provide the intelligence almost in real time. Because of that they are, or at least the term ‘analyst’ is, slowly but steadily loosing its importance in the data intelligence world, it seems.
I may be cautioning or unnecessarily creating a divide here for some. I do feel that previously people who were happy to be called as analyst (a highly respected term) are dropping them like hot potato and want to be tagged as ‘data scientist’, to be relevant for the time.
Data Scientist can access, manage, analyze large amounts of data and in real time and with abilities to apply machine learning methods by the practical definition of the usage of that term. The processes and applications followed mostly do not have to worry about Type I error and Type II error or any other best practices of statistics, econometrics, and other behavioral sciences. Every thing seems to work with the simple most powerful techniques of ranking, grouping, and associative rules for almost all problems one faces. And, ada boosting along with random forest and decision trees opened up the next big thing, which is supervised learning. With that it completes the most sophisticated level that can be achieved as of now bringing data scientist closer to analytics expert.
Analysts are interpretive intelligent people but can not access, manage, analyze large amounts of data in real time. They are not updating their skills for the latest trends. With intelligent data management and real time decision reports, computer scientists are becoming data scientists, while the intelligent AEs are not becoming computer scientists (note, you do not need everything in computer science).
With web and mobile becoming hot, definitely there are more opportunities to become a data scientist.
So it looks like a Janus has been created by the current trends and there are going to be hot mails writing about this. The good thing is it does not have to be like that.
When Does an Analyst Become a Data Scientist and More:
It looks to me that the core aspect of data intelligence has not changed. Only thing is that the traditional analytics experts have to become skilled in key programming languages such as Python and some of the Hadoop technologies and associated programming languages so that he or she will always be a Janus looking into the past and at the same time follow the principles of predicting the future so that they provide the right advice to business people and policy makers with powerful abilities of automation.
If Python can get away with its implementation as an alternative to Hadoop then you just need to be an expert user of Python, besides SAS, R, and SPSS.
Some discussions of such developments are here:
|Complementary Skills Representation
So I like the complementary Janus figure which is on the right.
The picture says it well. Don’t ask me which side is data scientist and which side is traditional data intelligence person. That is how I started with the dichotomy to reflect what is going on in the market place. However, this Janus is the symbol of complementary skills needed in the new world so that the Janus can also have the dual duty done well, that is look in the past and predict the future.
With yesterday’s discussion on who is a BI analyst vs. who is a predictive analyst, now it looks like the functions of super AE are the following.
– Be a master of data access, management, decision rule creator in real time – BIG data or small data
– Do powerful analytics (advanced or not does not matter)
– See the past (Powerful BI analytics)
– Apply to the future (True Predictive Analyst)
So when in discussions, your triaging should ask for such details.
Keep up good cheer…will talk to you again.
Fun question for aspiring students, which program provides training needed to become a balanced Janus – AKA – a modern much sought after predictive analyst. Note that this can be an unfair expectation, because there is a lot to learn on the job. However, it is good to think about these if you are aspiring to become predictive analytics students.
From Data Monster & Insight Monster