Why data science is essential to optimize business efficiency

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Nielsen, the global gold standard-bearers in market research, recently found that 81% of consumers strongly feel that the businesses they patronize should focus strenuous efforts on protecting the environment. They want purchases that help reduce their impact on the natural world – and growing numbers of the savviest companies are taking heed of this trend and are offering sustainable products to these conscientious consumers. But how do they know what products will appeal most to what demographics? The answer can be summed up in two words: data science.

Business today is conducted in a world that has become supersaturated with data. In 2020, the total volume of data generated in our digitalized world hit 44 trillion gigabytes – and it’s still climbing.

Quantities of data as vast as this defy the computational powers of even the most mathematically and statistically brilliant human brains. And that’s where data science steps into the breach, applying verifiable scientific methods, procedures, algorithmic protocols, and systems to glean valuable knowledge from the mountains of data that surround us.

Business efficiency today requires an ability to find the most valuable insights buried in those immense oceans of “bits” we’re immersed in all the time. Since most of it is invisible to us, it only becomes discernible through specialized data science methods. Methods that organize and “clean” it so that it can be transformed into actionable information.

Here are a few reasons why data science has become crucial to business efficiency.

Improved actions based on quantified data

Without data, decision-makers are left shooting their assorted business ideas in the dark. Given that 80% of data is unstructured and effectively invisible (the so-called “80% blind spot”), those decision-makers will gain a significant advantage if someone can supply them with scientific procedures for filtering that amorphous mass to capture real trends.

Data scientists can develop predictive models for such purposes by pushing the unstructured data through their specialized filters. The upshot is a range of data-based simulations outlining various possible scenarios – a lot better than a shot in the dark. From these, best-case scenarios can be selected and implemented, forming a new, highly efficient business protocol ready to deploy on recurring trends.

Enhancing your product’s relevance

Data science methods are capable of probing your company’s history of competitive hits and misses, comparing them with current market analyses, and making data-driven recommendations on where your products and services will stand the best chance of selling optimally. This creates a new understanding in the company about who is helped most by its products or services and what business processes turn out to be redundant.

Benevolent feedback loops can be set up in this way, while destructive or ineffective ones can be retired. Businesses can become more laser-focused on providing the solutions their customers are seeking.

Identifying and honing your target audience

Current estimates put the total volume of data we generate every day at a staggering 2.5 billion gigabytes. Putting it mildly, that’s a lot of data. And it poses a big challenge to businesses: how to identify what parts of it are important to the company and its customers.

Businesses are continually collecting data from customers – from visits to websites to what ends up in shopping carts to social media likes to the results of email surveys. And all that means one thing: if it can be accurately and effectively analyzed, it can yield a hugely valuable understanding of what customers need.

Data scientists can sift this data to lift new insights from it to help businesses target their customer base more efficaciously. Services and products can be removed from a scattergun approach and placed instead within more targeted strategies that tailor them to the demographics most inclined to want them.

For example, suppose your data scientist provides you with insights pinpointing correlations between age and income. In that case, you’ll be better equipped to focus promotions or offers on potential paying customers that you might have overlooked or inadvertently excluded before.

Data science: a great career to move into

To become a data scientist, you’ll need to get the required qualification. But with the insights you’ll be capable of delivering armed with that specialized knowledge, you can be sure of one thing: you’re going to possess coveted skills and know-how that will, in turn, make you highly sought-after by employers.

If you have an analytical mind and a penchant for creating solutions out of apparent impasses, and especially if you have a background in science, mathematics, engineering, or computer science, gaining an advanced data science degree may be more within your reach than you imagine,

A growing number of prestigious and highly rated universities, such as Kettering, offer rigorous data science programs for working adults. That means people who don’t have the option of walking away from family and work obligations to attend a campus-based course for a few years.

Kettering’s Masters Degree in Data Science can be completed on a fully online basis within just 24 months of starting. And the skills you emerge with will place you in an enviable position in the professional jobs market, with a very handsomely remunerated and endlessly fascinating career ahead of you.

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