Businesses of all kinds have been paying more and more attention to data and how it can be used to their advantage. This growing interest has led to a booming market for data science platforms and software as well. The data science platform market size and share will grow at a CAGR of 27.6%, reaching around $695.0 billion by 2030. These numbers might be a sign that in the very near future, these data tools and technologies will be an essential part of running a business.
Data science involves employing a variety of methods to extract meaningful insights from large amounts of data. Data analytics is also a similar and related field and practice, but the two deal with data in different ways. Data science is often used to make predictions, identify opportunities, and support strategy – in short, to understand the future while typically involving more technical skills like Python and statistical analysis. On the other hand, data analysis is used to solve problems and spot trends rooted in the present and what exists now while typically using skills like SQL and BI tooling. Data science benefits companies that are looking to grow and improve their revenue and helps them fix urgent issues.
Data Science Applications
Data is used to understand an organization's operations, helping make informed decisions that benefit the company most. Here are some ways data science can be applied in the field of business:
Better customer experience
Data science allows organizations to use the data to offer a better, more personalized experience for their customers, allowing trust to grow and increasing the chances of retaining a stable group of clients. Forbes’s insights on what today’s consumers want highlights that personalization has been driven by social influence, industry trends, and order history. Data science collects customers’ transactional information and preferences, predicts the best content to push, and combines all that knowledge to give customers tailored messaging, valuable promotions, and relevant recommendations. This personalized experience can make it more efficient for people to find what they want on their terms, streamlining the transaction process and increasing sales. Without this data, businesses risk losing the connection with their consumers, who want to feel understood and catered to rather than undergoing a general experience.
Retail companies like clothing businesses use customer surveys to determine the best and most flattering products for each customer, depending on personal style or body type. Streaming services note things like your watch history to offer personalized recommendations for movies or TV shows you might enjoy. These allow businesses to create value for their clients, ensuring everyone is satisfied with the products they receive.
Many companies primarily rely on their people to complete tasks, but having staff do the repetitive work every day eats up precious time, dampens productivity, and impacts efficiency. Automating certain processes can help speed things up, giving your employees more time and resources to focus on other areas of the business. This technology won’t be able to replace human workers entirely, but people can work with it to make tasks less redundant and time-consuming.
Automation through data science can be applied in many sectors of the organization. It can be used in HR management by automating tasks like resume screening for potential employees, distributing payroll, or scheduling and records management. In addition to staff affairs, this technology also helps with inventory planning and management by tracking goods, contacting suppliers, and ensuring that all expenses are correct. It can also be beneficial for data management, as data scientists or analysts can work with tools like Shipyard to automate processes and make it easier to obtain useful and structured datasets that can be utilized for other parts of the business.
Any successful company these days will be making use of digital advertising. This may be through a platform such as Google adverts, or it could be through a PPC (pay-per-click) campaign where you only pay for an advert when someone clicks on it. Automation is at the forefront of a Google Ads or PPC campaign, as it allows companies to control their marketing campaigns through machine learning by pulling levers according to which audience and intent traits drive the best results. Indeed, an article by Ayima on conducting a PPC audit explains how one is leveraging and analyzing data already acquired to fine-tune the outcomes, thus improving the campaign's efficiency. At all stages of digital marketing campaigns, data science has a role to play. Data can even be analyzed to spot future trends, potentially long before industry commentators pick up on them.
Chris Pitt is head of marketing at Vertical Digital, and he outlines how it isn't just PPC and Google ads that use data science to improve outcomes for the business. "The thing with search data is that it’s unbiased and real-time," he said. He then gave a great example of data science predicting a trend that businesses were able to use for future growth. “That might be a grand trend like veganism, which we’ve seen growing for many years now. But what’s interesting is that, if you go to Google Trends, Google knew that this was going to be important many, many years ago. So, if you were looking at that data six years ago, you would have been able to predict this trend and start developing products ahead of the curve.
Data can be an intimidating thing to work with, which is why Shipyard is the best tool to help you with all your data management needs, including running data science workflows. Get started today and automate your organization’s data workflow without the hassle or fuss.