Big Data Is Leading a Greater Demand for STEM Students
Big data, the huge volume of data collected daily from transactions or operations of particular businesses, is becoming more relevant in the contemporary business world. This data has to be processed into meaningful information that’s applied in the explanations and understandings of various business scenarios like market trends, profit and losses, product development and risk analysis in near-real time.
STEM (Science, Technology, Engineering, and Math) is becoming critical in the analyses of big data. Through empirical procedures, the gigantic volume of data can be processed into meaningful information. The corporate world is now turning to this body of scientific insight for a better understanding of big data. This has, in turn, created a growing demand for STEM students. Below we’ll be exploring reasons for this ever-growing demand.
Cost-Benefit of Big Data
In 2010, 13 Exabytes of data was stored by users across various industries of the global economy with an estimated value of $700 billion to the end user. This data is projected to be viable for a decrease of an estimated 50% of production cost. In terms of labor needs, between 140,000-190,000 workers with extensive data analysis skills will be employed to analyze this info in the US. Over 1.5 million managers will have to acquire skills in digital media as well. This transformation will touch across multiple sectors.
Various sectors of the economy have been looked into to ascertain how big data impacts the economic values:
- Healthcare in the US — focused on efficiency and quality, $300 billion could be generated annually.
- Manufacturing and personal location data globally — could tap into an excess $600 billion in consumer surplus.
- The public sector in Europe — operational efficiency enhancements could save an excess of approximately $149 billion.
- Retail in the US — a possible increase in operating margin by more than 60%.
Challenges of Big Data Analysis
To effectively process big data, we have to understand the challenges that exist within the data that prove problematic to the processing stages. They are:
- Heterogeneity – data is all mixed up together in no specific order even for similar industries and for different users.
- Scale – this is the representation of the amount of data collected and stored. Managing and organizing this rapidly exploding data is quite challenging in any field particularly the emerging ones.
- Timeliness – large amounts of mixed up data require a considerable amount of time to sort out, extract, understand and process into meaningful information.
- Human collaboration – despite the commendable efforts that have been put into developing outrageous computing capabilities, there still remains a huge hurdle for computers to process data into information. Human intelligence is still depended upon for identification of minute variations in data patterns that significantly alter the analytical processing.
- Privacy – there is a growing concern directed at the collection, storing and manipulation of private public data. Despite law enforcement policies regulating this aspect, adequate and satisfactory privacy policies will have to be managed through both technical and sociological paradigms.
Processing Big Data
The processing of big data into meaningful information is a multi-phase procedure that employs technical and scientific analysis skills. At every phase, new challenges emerge that have to be addressed for a meaningful conclusion to be reached.
The stages involved in the process are:
In this stage, all the information pertaining to the particular corporate phenomena is captured and stored. All information is generally gathered into a huge data bank with no particular immediate analysis or selection of particular information for storage.
The collected data will not be in a format ready for processing. The data is too general and wide for any meaningful analysis to be conducted. In this stage, you identify which variables you want to examine and apply a code to pull out the particular data.
Heterogeneous data will remain irrelevant if we apply the normal data collection and processing procedures. Metadata will have to be formed for the results from big data to be understood and reused in subsequent analyses.
To query and mine big data, we must apply and rely upon more complex scientific procedures than those used in simple data processing and analysis. Development of coordination between different platforms of databases is vital for effective querying and mining procedures.
For the analysis of Big Data to be meaningful, users have to understand the results. Interpretations of results from big data analysis will have to be made by a human decision maker. Instead of relying on computer analysis, there is a need for human insight into the understanding and verification of results from the computer.