When they say, “AI will transform our future”, I believe that DATA will. Yes, from the scientific innovations driven by Google such as accurate searches, translation models, and voice assistant to the autonomous Tesla cars- data is the critical internal part.
This drastically increasing importance of the data can be easily seen in organisations demanding for the players with expertise in data science and machine learning like technologies.
From SMEs to the large technology giants are in the race of gathering valuable data figures to empower their business. Till now, only a small fraction of valuable insights have been uncovered from the analytical approaches.
Data Science in Python, Machine Learning R and other programming languages have emerged as a powerful source to unlock the power of data. Efforts with the regression, classification, predictions etc., is leading towards an environment where organisations can make accurate decisions, estimations and many other tasks to make them stand tall among competitors.
Let’s take the example of manufacturing industries where thousands of connected IoT devices collect data every second. These premises are lashed with the sensors, actuators, PLCs and many other gadgets to send this collected information to the administrator panel. This data is used to acknowledge the ongoing operations.
Now, by crunching and analysing this massive unstructured data, manufacturers can increase their throughput, streamline operations, optimize tasks and achieve many more useful outcomes.
Also, the data collected from various other sources such as supply and distribution, retail, finance, surveys, customer sources etc., a company can make critical decisions, generate leads, increase revenue, and make future predictions achieving the best of it.
You must be aware of edge computing technology where the information collected by a gadget is analysed in the real-time at the network node- is also the result of data-driven algorithms. Thus, an algorithm can eliminate the raw information in seconds saving the extra effort for storing it and analysing it. Big giants are investing a significant amount on this technology to achieve maximized result.
The race for maintaining a good CRM among organizations is on the hype after the introduction of the modern data-driven CRM tools like Salesforce, Einstein Analytics, automated Chat bots etc.
Unlike in traditional customer support where companies have to spent a large amount on establishing infrastructure and availing workforce to provide all-time support, these data enabled intelligent bots have potential to deliver 24/7 support, recommendations, personalized experience, pattern analysis etc.
These self-learning models are created with the complex ML algorithms taking conversational and other data sources as input. In this digital trend where most of the organizations are targeting their customers over the internet through websites, relevant contents, advertisements, videos, images, mobile apps, messaging services etc., the integration of these chatbots is not a challenging task. Most of the businesses are using this technology to provide the best user experience and support to their customers.
You may have heard about the IBM Watson transforming the modern healthcare with its powerful approach for analysing massive data sets. This intelligent machine with complex AI algorithms has emerged as the best tool to face healthcare challenges.
Now, doctors can provide the historical data from the past research, patient information, trends and various other relevant data sources to this model which will further fetch useful insights from them assisting doctors in diagnosis, patient care, critical decision making etc.
Things are getting automated. From companies work culture to the manufacturing units all are leveraging AI and other data-oriented technologies to run along with this trend. In the marketing sector, one can see the real potential of a data.
This consumer-centric market where a customer looks through the relevant information sources before buying any product or service, companies are working on the concept of “attract, convert, close and delight.” With the help of analytics, a company can target their customers geographically and provide them with the relevant sources to answer their queries. Thus, they can crack a deal in an incredible way.
“The global data science market is expected to reach 128.21 billion USD by the end of 2022. This market is exploding at the CAGR of 36.5 percent.”
In the e-commerce industry, the role of data is significant. They are using it in recommending users with the best product, pattern analysis, providing a personalized experience, automated support, order management, fraudulent activities or transactions detection etc.
After collecting data from multiple corners such as a users’ behaviour, survey, trends, activity involving searches, supply and distribution etc., a company can avail the best outcome to a user.
The completely automated Amazon Go store has already provided us with the glimpses of the retail future market. This store is the first retail market innovation where a customer doesn’t have to wait for billing in large queues.
They can take whatever they need and go away. The used sensors, actuators and other AI-powered resources will perform automated billing and the amount will be automatically deducted from a customers’ credit card.
This achievement by Amazon is not possible without the accurate data gathered from the app. Without them, the company would have faced challenges in billing and gathering customers information.
One can observe this data explosion with the increasing data engineers bringing AI at the forefront for an organisation. Till now, the demand for the data scientists was on the peak. Organisations spent a big amount on their hiring and empowering growth through advanced innovations and machine learning models development.
But now, the data engineers who are specialized in playing with AI and data through algorithms and application solutions are becoming a priority of the businesses.