How to unravel the data dilemma: Human-centric machine learning
This is the arena of drawing a balance between the “human” element required in the organizational decision support system. An automated system in itself can never eliminate certain human tasks such as conceptualizing and forming an inherent association.
Tackling the data dilemma with human-centric Machine Learning
As per a report by IDC, the worldwide data will continue to grow exponentially at a staggering 61{4f8b3ed9fef9ea84dcab9ecab3909d1faac4098bcd4a30a2275db8b2fbfea39e} leading to 175 zettabytes of data by 2025, with most of it will be generated from real-time businesses operations. Is it possible to effectively leverage such humongous data in improving business processes as well as customer service? The core solution in optimizing business operations lies in better organizational decision-making. Human-centric machine learning is the only viable option in solving this data dilemma.
A lot of companies today grapple with the challenge of extracting value from the volumes of data generated almost daily. The problem is faced by the decision makers in where to put their faith in terms of future investments based on strategy as well as resources.
Most companies employ myriad of analytic tools to understand the data trends with statistical methods and visualizations. However, translating mere numbers into actual decisions is rather a complicated and lengthy path.
Machine learning is the buzzword of today’s times with its exceptional data processing and predictive modeling capabilities with speed. The primary opportunity it presents is to leverage the domain knowledge to correlate the generated data and ongoing business challenges.
Converting the niche into an accessible resource
Machine learning is considered to require specialized technical skills for which, there is a huge talent gap and the demand surpasses supply in most cases. Furthermore, most data scientists lack a business perspective to be able to envisage the operational impact of the data-driven scenarios.
Here comes the role of human-centric machine learning systems where professionals who do not have the in-depth technical know-how of data science are able to draw meaning from the organizational data through easy to use machine learning models. With a more humanized approach to technology, the value of data can be extracted with speed and ease by employees without significant investment in technical resources and time.
This approach resonates with the data scientists as well as it can open new avenues for them to learn business problems and opportunities in depth with more available time – a possibility which presents a potential for creativity and innovation.
Augmented skills with data-backed insights
A human-centric machine learning system should primarily provide employees with capabilities to prepare models for data visualizations as well as perform predictive modeling with ease. This may involve suggesting optimum actionable insights to accurate forecasting to assist with decision making.
This approach can also reduce manual data labor such as repetitive pre-processing and analysis. Take, for example, resource planning of a company. Instead of arduous tasks of analysis, research and optimization, a forecasting machine learning solution can assist the managerial staff with creating reusable and customizable process models, which may save a great deal of time and efforts.
Strengthening human capabilities – machine learning for everyone
Machine learning systems play a pivotal role in automating the mundane and time-consuming tasks, building predictive models for process optimization however the final decision making control still falls in the realm of managerial staff in most companies. This is because artificial intelligence today still lacks human judgment, creativity, and intuitive sense which are necessarily elements for making effective decisions.
This is the arena of drawing a balance between the “human” element required in the organizational decision support system. An automated system in itself can never eliminate certain human tasks such as conceptualizing and forming an inherent association.
With the human element, applying machine learning will no longer be a complicated and time-consuming endeavor. Rather, this approach will give rise the never before explored opportunities in terms of business growth and data-driven learning.