You must be wondering – Data Professionals are high in demand and getting popular each day but what makes them so special? What are the perks of being in a Data specific career and how can you get there?
Let’s understand it all:
What exactly is Big Data?
On a normal day, we generate about 10GB data individually by our calls, social media usage, pictures, location traces, shopping bills and much more. Accumulating all that for every individual who has access to technology, we are looking at a billion GB of data generated across the world. If that sounds big enough to you, we can simply call it “Big Data”.
Ok, there is lots of data. So what?
Researchers and tech giants understood the importance of this data and started hunting people who can handle, explore and utilise this data. It gave birth to three new and crazy in-demand titles Data Engineer, Data Analyst and Data Scientist, respectively.
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|Role||Clean, organise and generate insights from (big) data||Manage, protect, centralise and integrate data systems and source||Collect, process and perform statistical analysis on data|
|Mindset||Create AI using big data||Easily design data architecture||Derive insights from data|
|Skills||Machine Learning, Distributed Computing and Data Visualisation||Data Warehousing, Database Architectures, Extract-Transform-Load (ETL) jobs and system management||Communication and Visualisation, Spreadsheet tools and Business driven intelligence|
|Tools and Languages||Python, R, SQL, Spark, Map-Reduce||Hadoop, Spark, Hive, PIG, SQL||SAS, VBA, R, Excel, Tableau|
Let’s take a look at some success stories
Xerox: After going for the Big Data solution, the company shifted its approach resulting in Xerox reorganising their hiring paradigm and lowering their support personnel attrition rates by 20%, saving the company millions of dollars in the long-term.
IBM: Acquiring ‘The Weather Company’ and harnessing the Big Data collected from more than 100,000 weather monitoring sensors, specialised aircrafts, apps in gadgets and various other devices, IBM Watson benefits from more than 2.2 billion unique data gathering points enabling constant weather monitoring. The losses and damages caused by the weather account for nearly $500,000,000 annually in the US alone.
Paypal: Developed an automated fraud detection system analysing billions of records achieving industry-leading loss rate of 0.5%.
Tesla: Analysing enormous data generated by onboard computers on each car, engineers can predict part failures, potential safety issues and automated control and emergency lockdowns.
You may think these are giants which can afford billions of dollars!
What about small-scale businesses? Is Big Data Investment worth it for them?
There are quite a few readymade solutions available in the market, as well as services from cloud providers like AWS, Google Cloud or Azure. All of these make Big Data analytics tools quite affordable. Regular mid-sized startups benefit from these services, some popular Indian companies utilising cloud services are Freshdesk, Sigtuple, Paralleldots, etc.
Startups turned tech giants like Google, Facebook, Apple, Microsoft, etc. have set up their AI labs only to advance research on handling Big Data and creating wonders out of it.
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Why are professionals in data so special?
Let’s dive deeper into the mindset of a data professional, the most challenging and unique skill that separates a great data professional from other DSA (Data Science and Analytics) roles is the ability to use big data. With such huge amounts of data flowing in every second, it becomes hard to process and extract meaningful information on the fly.
Data professionals solve this big data problem by using a combination of advanced algorithms and technology popularly called machine learning or deep learning which is increasingly pacing its way into our daily lives.
Google assistant, Siri, Alexa, Cortana, Prisma, Snapchat filters, Facebook location, face tagging, etc. are all examples of machine learning. At the core of all these technologies lies the seed of big data providing fuel and life for deep learning or Artificial Intelligence.
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What makes Big Data the New Oil
Big data in its enormity is a vast ocean of untapped opportunities. Tech giants are investing billions of dollars to drill down and extract the vital information hidden deep in this big data. In a direct comparison to a very similar vital resource in the modern economy: crude oil, big data indeed is the new oil/fuel for the future.
“Artificial Intelligence is the new electricity”,
Says Andrew Ng (AI scientist) explaining the power Artificial Intelligence brings in providing modern-day solutions. He further assures the importance of big data and probably steps its value parallel to oil making it the “new oil” of the 21st century.
Moment of truth:
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“Data professionals see big data as oil (for its value) and develop expertise to extract it, process it and convert it into insights/solutions that cater not only to companies but everyone!”
Recently, Google’s Deepmind processed millions of petabytes and found 2 new exoplanets hidden to the eyes of interstellar researchers. Various startups have developed image solutions that are better in detecting cancer than the best radiologists in the world. Autonomous vehicles are getting popular and Google launched earphones that allow live translation of any language to what you can understand. All these breakthroughs were only possible due to big data.
Now, let’s answer the golden question:
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How can you transform into a data professional?
To become a better data professional, be it an analyst, data scientist or data engineer, one needs to understand the power and potential of big data. It is advantageous for an aspiring data professional to get their hands dirty with big data techniques. Although, a deep understanding of machine learning algorithms is a must!
Feel free to review the following courses precisely aimed at advancing knowledge in the data domain:
- PG Program in Big Data Engineering with BITS Pilani
- PG Diploma in Data Science with IIIT-Bangalore
- PG Diploma in Machine Learning and Artificial Intelligence
Got it! What are my options?
Get acquainted with big data technologies and familiarise yourself with tools like Spark, Hive, Hadoop, YARN, HBase and Map-Reduce. You can decide whether to proceed as a data scientist, data analyst or data engineer. You will find endless opportunities and high-paying jobs across the globe under tech giants and other MNCs harnessing the power of big data.
Data is being called the new oil. It’s changing the meaning of analytics and advancing the Artificial Intelligence revolution every day. Data is fueling the future as we speak and getting onboard a long sailing ship is a good idea.
Why is Data valuable and important?
Data can be used to speed up corporate operations, eliminate errors in production process and decision-making, detect and reduce risks before they occur, and so on. As a result, any industry may benefit from turning data into value. Companies that place data at the centre of their operations and allow it to drive every action, empowering users and speeding up decision-making across the organisation, will be the winners in the future. They’ll become more adaptable, disruptive, and innovative. Others will only follow them as they discern their route and craft their own course.
How are the three major Data roles – the Data Engineer, Data Analyst and Data Scientist different from each other?
There are a variety of roles that are responsible for collecting, organising, and analysing data in order for a corporation to get serious about dealing with data. Given the broad span of this process, which includes everything from raw data to actionable insights, there might be some misconception about what each data job is responsible for.
Data engineers, data analysts, and data scientists are the three key data roles that firms hire for their data teams. Here are the key differences:
1. Data Engineer - A data engineer's job is to figure out how to collect, organise, and preserve data, therefore they're an important part of any data team. In-demand skills to become a Data Engineer are SQL, Python, AWS, Kafka, Hadoop, interpersonal communication, Time management, etc.
2. Data Analyst - A good data analyst would have the curiosity and ability to look into the data from several perspectives, cleansing and transforming the data to seek for trends. They might discover new avenues for the organisation to pursue, such as locations where additional data could be collected for more in-depth research. The requirements for a Data Analyst are SQL, Statistical Programming, Microsoft Excel, Critical Thinking, Data Visualization, Data presentation, etc.
3. Data Scientists – Data Scientists construct algorithms and prediction models to extract the data the business needs, and they assist in data analysis and sharing findings with peers. The core Data Science skills are statistical analysis, machine learning, computer science, programming, communication and soft skills.