How Big Data Drives Career Success

The original term for the first incarnation of Information Technology was “Data Processing.”

Even though that description has now been discarded, it shows how important data was (and remains) to the core raison d’être of the industry.

What Is “Big Data”?

The widespread use of information technology in today’s world means that collecting vast amounts of potentially useful data has never been easier.  For most organizations that fact is a huge plus, however, it also brings with it a major challenge: how to leverage that data to drive career benefit?

That challenge is platform-independent.  It applies equally to Cloud-based applications, local apps, vast corporate databases, and even relatively humble small business database systems.

Transforming data into meaningful information suitable for business exploitation is a challenge that can be faced by data scientists. It involves a range of disciplines covering various IT areas and Business Analysis.

Data is growing at a considerable rate and it will continue in the future too. Most of the collected data is unstructured (photos, audios, videos, etc.) and it becomes tough to analyze it. Companies are in continuous search of possible solutions that could be adopted to manage this massive data.

The exploitation of large volumes of data to assist in business decision making and strategic development is referred to generically as “Big Data.”

Top 5 Employment Sectors Using Big Data

At one time, many of the above concepts and concerns would only have applied to the big “Blue Chips” with their vast mainframes and IT infrastructures.  While that continues to be a major sector generating demand for these skills, today it’s far from being alone in doing so.

Employers and industries seeking these skills include:

• Financial services covering things such as banking, finance, and insurance
• Market Researchers
• Local and State government
• Retailing
• Manufacturing

Let’s consider a few illustrative examples of how Big Data and Analytics are applied in the real world.

Retail and proposition development 

A retailer records individual customer purchase transactions – resulting in vast amounts of transaction-level data.

That information can be rolled up into a composite view for an individual or even a household, showing buying preferences and average spend levels by time. Business Analytics can then be applied to that to indicate probable individual, household and disposable income levels, the age of the individual and other factors.

Predictive behavior algorithms can then be applied to assist in the development of customized sale propositions for the individual that can be delivered at the right time to maximize potential future sales in other domains. This is called “targeted share-of-wallet” cross-selling.

Credit Scoring

Huge amounts of data are available at granular level relating to our financial transactions – credit card use, spending levels, where the spending is taking place and on what, etc. However, at a low detail level, there is too much data to be meaningful.

Bringing that data up to a composite whole though can be very beneficial in understanding the risks associated with, for example, lending to an individual.

Customer Intelligence

A website might receive very large numbers of hits, but understanding what visitors are doing on the site once there can require the interpretation of data which is both voluminous and complex.

Transform that, from data into information though, and it’s invaluable in terms of knowing what pages people are visiting, how long they’re staying on each page, where they originate from geographically – and how that all relates to subsequent conversions. That intelligence can shape future site development investment to prioritize those domains that are clearly of interest to most visitors.

Opportunities

To lead the big data initiatives, organizations need professionals who have in-depth knowledge of Big Data and how to use this technology to achieve their business goals. Companies are in continuous search for data scientists and are ready to offer higher salary packages with an average salary for data scientists being $116,000. But there is a lack of talent in this domain and has become a challenge for the recruiters.

Employers are prepared to pay premium salaries to people possessing the skills required to drive benefit out of raw data.

Here are just some of the aspects of Big Data that the employees need to master to stand out from the crowd when applying for positions in these types of areas:

• Hadoop And Spark
• Apache Spark & Scala
• MongoDB
• Apache Storm
• Apache Kafka
• Apache Cassandra

To gain expertise in the above-mentioned areas, an all-in-one Big Data Architect Course can be considered to take bigger leaps in the career.

Summary

It can be clearly understood how Big Data can transform businesses right from the financial sector to manufacturing industries. The applications of Big Data cannot be ignored in this advancing technology space. Small as well as large scale industries can harness the power of Big Data to transform their processes and lead their business to achieve great heights of success.