Ever since companies realized the true potential of data, the need for data science and the people who are experts in it have been growing in leaps and bounds. The reason data science emerged in the first place is its ability to eliminate bias and inequality in data when it comes to decision-making, especially in a social and corporate environment.
It is no news that AI and machine are the catalysts for many companies to take intelligent business decisions and conceptualize future-proofing strategies. However, there is something that has been concerning many organizations and even some governments. How people-centered and regulated are today’s data-driven world? As data science is of paramount importance when building algorithms and datasets used for AI development, it is important to talk about the ways data science can be channeled towards a better use of it. This blog walks you through everything—from challenges to implementing
Human-centered data science, what is it?
As the name suggests, human-centered data science is an interdisciplinary field that enables people make sense out of data using quantitative and qualitative methods. In other words, human-centered data science focuses on paving the way for humans to explore and extract insights from vast data sets. As the world is about to benefit from the inevitable revolution of AI, RPA and machine learning, it is important to ensure that the values of people and ethics are considered the cornerstones of data science.
Why does human-centered approach matter to data science?
Data comes from humans, and humans can be unreasonably biased. It is almost 2023, and everyone will soon be coming up with their new year resolutions and milestones. Enterprises and companies are no different. In 2022, we have seen various breakthroughs and innovations that firmly made us think we live in a world where AI has become more than just a buzzword. Of all the recent AI developments, better understanding of human behavior and algorithm advancements are the most anticipated and talked about.
Today, company need data for almost any kind of business operation. Data science has become an essential component of today’s business world. Among many reasons, its ability to be neutral and unbiased when it comes to building algorithms, which play a vital role in the realm of AI. This means that data is being used to create products, services, and solutions that are tailored to meet the needs of people, rather than just collecting data for its own sake. By focusing on the people at the center of the data science process, data scientists can create solutions that are more effective and inclusive, as well as more ethical and respectful of the people they are designed to serve.
Reasons there is more to data science than just data:
To better understand what human-centered data science is and ways it can be leveraged to serve the betterment of humans in a business environment, here are some of the examples and benefits to using it.
Personalized Customer Experience
Customer retention is one of the classic challenges most companies face, especially in today’s business environment where customers are spoilt for choice. Companies like Amazon and Netflix have been personalizing their customers’ shopping and user experience by recommending content and products based on what they like and not. This way, human-centered data science plays an integral role in understanding the logic and behavior from the data the customers leave behind.
Businesses never deny the fact that marketing is the backbone of any reputed brand. Not only does marketing help companies get the highest ROI from their marketing spend but also enables them to decipher what their customers want or expect when buying a product or service from their brands. When the right ads meet the eyes of the customers, it spreads the message that the brands really care about what their customers need and love as to the products or services the brands offer. Only data with a touch of logic can help advertisers and content creators tailor their marketing campaigns and strategies to appeal to a vast number of new and existing customers.
Automated Customer Service
When it comes to automation, the availability of quality data is of paramount importance than the quantity of data. Customer servicing is among the most crucial area of a business that shouldn’t be overlooked. As automation relies heavily on algorithms to execute tasks that are mundane and repetitive, companies use ethically responsible data science to tasks such as customer servicing. This way, both the consumers and brands can rely on chatbots, which are predominantly AI and RPA-powered.
Predictive Analytics and Optimizing Price Strategy
Have you ever wondered how companies grasp the nettle when there are hundreds and thousands of competitors who are most likely to be similar in terms of the services and products they offer? This is one of the areas where many traditionally run enterprises try to find a possible panacea. Predictive analysis has played a significant role in data science ever since business owners found that they could extrapolate trends and patterns, such as predicting customer behavior, churn rates and a lot more. When data science gets a human-centered approach, it would help data and business analysts understand human psychology behind every single transaction.
Human-centered data science: An instrument to achieve human-centered AI
There comes a myriad of news every day about the latest innovation and advancements in the realm of AI. It stands to reason that many companies and government organizations such as UNO have started integrating ethics, morality and integrity to AI models and systems they program to interact and work with their customers and people in general. If AI and other ground-breaking technologies such as deep learning is about to operate based on data developers and companies feed into them, then the onus is on those developers and enterprises to ensure their AI is ethical, trustworthy, responsible and humane enough to be entrusted with responsibilities such as decision-making.
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