Big data is a big deal for big business. Without the right talent, however, all that data is just a constant stream of unstructured information – it’s just white noise. Data Analysts and Data Scientists convert this complicated data into useful information, helping companies grow in directions they might not otherwise have imagined.
Data on its own are outputs gathered by operations on a computer that may be stored or transferred elsewhere. More than 2 quintillion (that’s 2,000,000,000,000,000!) bytes of data are created every day. Whether that’s through phone sensors, cameras, satellite information, personal health trackers – you name it – the amount of data that exists in the world is growing every day.
The term “Big Data” is used to describe exceptionally large data sets that grow exponentially over time. This includes information about the production of goods, customer feedback, and consumer behaviour. Businesses can use this data to improve operations, provide better customer service, and ultimately, increase their revenue and profits.
Big data is appealing because it operates on the premise that, the more information you have about something or a situation, the more accurate predictions you can make about the future. Think of customer engagement on a website. By sifting through the data collected from each user clicking around on the site, you can use the data to predict behaviour. This helps with product development and marketing, to name a few things.
In understanding how to use big data properly, businesses can benefit in a wide variety of ways. These include:
Data scientists and analysts are able to translate big data into actionable insights that yield positive results for businesses across many industries. Most leading companies increasingly rely on such people in data jobs to find out information about their customers. This in turn helps them to increase their company’s efficiency and improve their project management flows.
McKinsey reports that data-driven organisations are 23 times more likely to acquire customers than businesses that aren’t data-focused. This is largely due to the fact that data-driven companies are closely monitoring their audience and are better at responding to their needs.
For example, let’s say your company makes blankets. On your company’s website, a data scientist sets up a framework for collecting user data based on where they click on the website. The data analyst will then take a look at that data and report on their findings. Let’s say that the blue blanket gets a lot of clicks, while the green one gets very few. The analyst might suggest increasing the stock of the blue blanket, or pushing out a more robust marketing campaign to sell the green one.
When different types of data are compared and analysed, relationships that were previously concealed are revealed. This is relatively simple with smaller data sets. But with data that comes in at such a high volume and that is so complex, traditional data management tools and systems struggle to store and process it properly.
This is where jobs in data science and data analytics come in. In simple terms, data scientists build algorithms that help model data, while data analysts examine data sets to identify trends. Both help businesses make strategic decisions using collected evidence.
As big data continues to get bigger, so too does the data analytics market. It’s expected to continue growing as companies try to leverage both data scientists and analysts to gain valuable insights. By 2027, the worldwide big data & analytics industry is expected to reach $146.71 billion in market value. This is projected to create an estimated 11.5 million new jobs in data analytics and data science by 2026.
Analysts are like statisticians – they find patterns in existing data sets. They are the storytellers of data. Their role is to summarise fascinating facts and trends in the data that is collected. These outcomes can be used by a company to help them make the right decisions that will ultimately increase profits and reduce financial losses.
Importantly, they help companies better understand and target their audience, come up with new innovations for their products, and cut costs all around. They are problem-solvers.
Data scientists are the pioneers of data. Using their knowledge, they create algorithms that collect and organise data. Through experiments that they design, they can help a business gain valuable insights to help them achieve sustainable growth.
Their main goal is to ask questions in order to locate potential avenues of study. They take the analysis one step further and use that data to develop new processes for data modelling and production, using tools like algorithms and machine learning along the way.
Big data is everywhere – not just in tech companies. Nowadays, data science and data analytics are necessary in most industries. The adoption of big data analytics appears highest, however, in telecommunications, insurance, and advertising industries, followed by financial services, healthcare, and general technology.
With the amount of data growing every day, data science and data analytics jobs are among the most in-demand in the job market. As organisations grow their data collection scope and sophistication, they will inevitably need scientists to help build the infrastructure and analysts to help them make sense of the data.
Becoming a data scientist or analyst also comes with some personal perks, too. For both positions, salaries tend to hover quite comfortably around $70,000 per year, even in junior positions. For senior or specialised positions, you could expect salaries of $100,000 per year or more. It really is a great investment to not only improve your skills, but your salary, too!
Big data is only getting bigger. This means that careers in data science and data analytics aren’t going anywhere anytime soon. Now is the time to start learning the skills necessary to tap into this market, land a secure job, and increase your salary.