With the rapid advancement of Big Data, its power and influence are increasing very rapidly. Likewise, technologies, applications, and opinions based on Big Data are swiftly rising. Big Data may be the next big thing or utterly dead; a panacea or menace; the key to all future innovation or just a hollow branding term. Between these extremes, Big Data is an important area of focus for consumer finance. It has the potential to support and scale consumer financial health.
Big Data’s Evolution in Consumer Finance
Big data is a set of tools that can be used for creating, refining, and scaling financial solutions. It is sewn into the consumer financial services marketplace, in sophisticated ways. It is instructive to examine the greatest potential areas for the further development of big data. Also, the ways to foster its use in a safe, responsible, and beneficial manner on a large scale.
Big data is now a fundamental element of risk-profiling for the banks. Analysts can study the impact of geopolitical escalations on different market segments. Now, banks can map out market-shaping events in the past to predict future patterns.
Investment banks are using big data to analyse the effectiveness of their deals. They do this by studying the insights of trades they did or did not win on a client-by-client basis.
The data systems at most banks are not like retail giants or startups or fin-tech companies. They were not constructed to analyse structured and unstructured data. Remodeling the entire IT and data systems needed a deep analysis of a bank’s data. Updating is very time-consuming and costly.
Some banks have merged or acquired other banks or financial services businesses. These are facing even more complex issues while incorporating and updating IT systems. This is where big data can prove to be a game changer.
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Surge in hiring of big data analytics specialists
The competition between banks and fund managers to hire big data specialists is heating up. Banks are actively recruiting to fill two main, but different roles: Big Data Engineers and Data Scientists/Analyst.
Big Data Engineers are coming from a strong IT background. They have development or coding experience and are responsible for designing data platforms and applications.
Data Scientists, in contrast, are bridging the gap between data analytics and business decision making. They’re capable of translating complex data into key strategic insight. Data scientists are also known as analytics and insights manager or director of data science. They should have sharp technical and quantitative skills.
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Organisations working with Big Data, like Investment Banks usually follow this hierarchical structure:
Junior Associate –
A big data developer mainly working on Hadoop, Spark, Sqoop, Pig, Hive, HDFS, HBase. They’d have 5-6 years of industry experience in basic Java/Python/Scala programming.
Salary Range: INR 12-18 Lakhs per annum
Senior Associate –
A big data senior developer working on Hadoop, Spark, Sqoop, Pig, Hive, HDFS, HBase. They’d have an industry experience of 7 to 10 years in advanced Java/Python/Scala programming.
Salary Range: INR 18-25 Lakhs per annum
Vice President –
A big data architect with architecture experience in Hadoop, Spark, Hive, Pig, Sqoop, HDFS, HBase. They’d have expert programming knowledge in Java/Python/Scala with 10 to 15 years of experience.
Salary Range: INR 25-50 Lakhs per annum
The salaries of Big Data Engineers/Architects are 15-20% higher than other technologies in the current market scenario.
Combining massive data sets thoughtfully can lead to greater accuracy and granularity. Financially underserved consumers often have unique combinations of needs. Thus, tools allowing scalable tailored services at low costs are vital to the mutual success of consumers and providers.
However, the Big Data mosaic effect has also often raised concerns about its potential risk to consumer privacy, combining large data results in overly sensitive insights.
From my experience, a career in Big Data is extremely rewarding in the present scenario, especially in the financial sector. Huge volumes of data are threatening technologies like data warehousing. I have shifted in my own career from being a data warehouse architect into big data and data science as that is the need of the hour.
What do you think will be the impact of Big Data and other data technologies in the near future? Comment below and let us know.
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What is data warehousing?
Data warehousing is a place where data collected from multiple sources is stored. The data in a data warehouse comes from data marts that collect data after interaction with the users, operating systems, and flat files. It consists of raw data, metadata, and summary data. Data from various databases are integrated using a query-driven and update driven approach. It is non-volatile, subject-oriented, and time-variant. The functions of a data warehouse are data extraction, cleaning, transformation, and loading. OLTP and OLAP operations on the data are performed for processing. Data warehouse architecture is of 3 types: single-tier, two-tier, and three-tier.
What are the skills and responsibilities of a Big Data Junior Associate?
A Junior Associate should know various data analysis tools and data sources. They should know how to clean, integrate, and present the data in a graphical format. Logical thinking, decision-making skills, and time management skills are required to succeed in this field. They should observe small details and infer information from them. They should have some experience using statistical tools like SQL, SAS, Excel, etc. and data analysis tools like Hadoop, Spark, Hive, etc. Junior Associates are expected to manage large scale data, model, design, and process large datasets. They are expected to write complex procedures to handle statistical data and clean, integrate, and extract valuable information from data using data analysis tools.
Where is Big Data used other than in finance?
Big Data has a wide range of applications. It is used in healthcare, manufacturing, media and entertainment, IoT, governments, etc. In healthcare, it helps in the analysis of the data and helps in providing personalised medication, identifying patterns in side effects caused by drugs, and helping in better diagnosis. The manufacturing sector forecasts output, increases energy efficiency, analyses product quality, etc. In entertainment, it helps in schedule optimisation, ad targeting, content monetisation, and product development. The data generated from numerous devices in an IoT network is stored efficiently. Governments use Big Data for cyber security, scientific research, tax compliance, weather forecasting, etc.