Why Data Science and Big Data Analytics Could be a great


Rare is the organization of today that does not gather data. Not just this – the amount and the range of data being collected are both growing. Unlike in previous times, there are now no concerns about storing and managing such voluminous data; the focus, instead, is on mining this data as a potentially great source of useful insights to guide the future course of action.


It is thus no surprise to see the upswing in data science and big data analytics. Extracting actionable answers from data is a skill much in demand across diverse sectors, such as ecommerce, marketing, manufacturing, and healthcare. Firms in these domains look to data analytics as a source of better processes and higher profits.


However, there are a few things to consider before jumping into the field:


There is no one definition for a role.


For an IT analyst working on creating reports and dashboards, data analytics might appear to be somewhat similar. However, this is not true, and analytics is so much more than just report generation. The concerned department could be named Big Data, Business Intelligence, Data Analytics, or Data Science, and the work is more than just report generation.


Analytics as such includes:


  • Data analysis: Synthesizing raw data and manipulating it to extract useful insights
  • Data modeling: Grouping data points to create better strategies
  • Data foundations: Identify possible new data points and features that can improve data and analysis models
  • Dashboarding and reporting


You must love numbers!


This is self-explanatory, but bears reemphasis – a love for all things numerical is intrinsic to analytics. Working on data science and big data analytics needs strong knowledge about accurately using statistical techniques. Data is a mystery, a puzzle from which something useful for the organization must be discovered and pulled out.


The other part of this, which is equally important, is communicating the insights. The purpose is to influence others, so data and analytics must tell a story. The work must be descriptive, and it helps to communicate visually the results gleaned from the analysis – using charts and graphs, for instance. Getting a usable, actionable answer from a position of not comprehending the solution is the goal.


You must be able to manipulate data and know basic statistics.


Aspiring big data professionals from IT backgrounds sometimes do not realize that problem-solving skills without basic statistical knowledge (as explained earlier) are not enough. Candidates not from mathematical or statistical backgrounds would do well to get familiar with:


  • Descriptive analytics: Describing and summarizing data sets, and studying their frequency, central tendency, dispersion, and relationship with other data sets
  • Predictive analytics: Predicting potential outcomes from historical data


It is important to know how to work with data manipulation tools, of which R and Python are very useful.


Coding knowledge is necessary.


A candidate need not be a computer scientist, but he or she must know how to code. This is essential for reproducibility, so others can build on what has already been done. You should be able to write a program that does what you are doing, or you might need to choose between doing it forever or teaching others how to do it. Get comfortable with SQL, Python, R, Hadoop, cloud-native and desktop analytics platforms (Looker, Tableau, and Microsoft Power BI), and of course Excel. Also, bear in mind that business or regulatory concerns might preclude the usage of cloud data warehouses for data analytics, so the analyst must know how to process big data through a Hadoop cluster or an equivalent solution.


Slice and dice data the right way.


Analysts and decision-makers tend to have preemptive hypotheses on the insights that will be generated from the data, and a belief in their accuracy. However, working on the data, descriptively exploring it, and testing it statistically might prove the hypotheses to be inaccurate or might go against initial assumptions. To avoid a circle of rechecking, re-slicing and re-dicing data sets without any additional insights or results, big data professionals must clearly define objectives and boundaries for exploration, along with considering other factors like changing trends or limited data features and sample groups.


Strong communication skills are a must.


Aside from technical skills, data science and big data analytics requires at least a minimum level of communication to explain the results of a project or to promote the project itself. This includes a design sense to create visualizations, and the ability to communicate with non-technical colleagues. The candidate also needs a curious attitude, to be able to keep pace with constantly-evolving concepts and new algorithms. Finally, there must be a portfolio of data projects, with accompanying writeups explaining the work done.


Be comfortable with rapid changes.


Presenting insights or deploying models is not the end of data analytics. There will be follow-up questions, new data sets, and additional questions. It is important to be able to make the analysis reproducible (it may need to be re-run for validation at a later date). Also, the candidate must be adaptive and should understand how new product features or external affairs might change results.


The job description is not always fully explanatory.


Intuitive ideas about work as big data professionals do not always line up with the reality of how time is spent. Most of the time will be spent in thinking about how to use data to solve a problem, performing exploratory data analysis, and writing code for the model, graph, and derived statistic. The biggest chunk of effort is likely to be in data cleaning, a prerequisite for modeling.


Getting into data science and big data analytics is not a quick step! Evaluate your skills and what you truly are good at and wish to do, upskill yourself suitably, and you are all set to become a big data professional.


I just find myself happy with the simple things. Appreciating the blessings God gave me.