We call it the big data era, which is powering many technologies like artificial intelligence, machine learning, business analytics, and more. By the time big data came into the mainstream itself, it had already accumulated a massive volume of information. If you utilize it properly, itcan give really valuable and actionable insights to anything you look at. All types of businesses and all industries now make use of these massive data stores for data-backed decision-making.
Artificial Intelligence with big data
As we have seen above, big data is here to stay and is growing as a big thing for the future. Artificial Intelligence is there already in high demand for its capabilities in making human tasks easier and more accurate. IT has a foreseeable future, where big data and AI intersect at many points for a synergistic association. As we can imagine, Ai or machine learning may be meaningless without data, and on the other hand, mastering data management may be impossible without the use of AI.
The data scientists and IT people all have realized their job is to sift through all these data and parse it to be easily understood and interpreted. For business decision-makers, it is important to analyze huge stores of data and improve their decision-making than humans can do on their own. AI algorithms are written to accomplish enormously complicated tasks in a structured manner by deriving insights from big data stores. By combining both AI with big data, you can see the trends in technology, business, entertainment, e-commerce, everything that comes under this combination in the future.
The data professions and business analytics experts are in high demand now as industrial corporates are now trying to broaden their AI and big data capabilities. The basic objective is to catch up with this growth and leverage the growing volume of data being produced by various sources. Another promising technology that leverages this data with Ai and machine learning capabilities is IoT, which will rule the future world.
Use of AI in big data
The Internet offers enormous data about consumer likes, habits, dislikes, behaviors, and personal choices, which are impossible to assess that way a few years ago. It is possible to get a lot of data through social media and online profiles, product reviews, interests tagged, likes and dislikes, etc. One can track the shared and liked content, rewards programs and loyalty apps, CRM systems, etc., which all can fetch insightful data from the consumers and add to the big data stores.
Regardless of the business or industry, the greatest catch of artificial intelligence on the backdrop of Ai is its learning skills. Ai can recognize the hidden data trends, which is highly useful if it can adapt to the fluctuations and changes based on the trends. By identifying data outliers, AI knows what kinds of customer feedback can be considered the most significant and make the adjustments as needed. AI also can work expertly with data analytics, and this is the basic reason big data remains inseparable from AI. Artificial Intelligence-based deep learning and machine learning are now pulling out every piece of data input and using those to generate new interconnections for business analytics. However, there may be problems if the data used is not of good quality.
Here comes the relevance of a quality data management practice in place. Organizations need to focus on error-free data collection, storage, and processing. The use of a quality data storage system and unrelenting support to DBMS is a necessity for enterprises. RemoteDBA.com can offer reliable consulting in all aspects of enterprise database management.
Ai for business analytics
As per Forbes research studies, it is found that a fine combination of big data and Ai will help to automate about 80% of manual work and about 70% of the human involvement in data processing work as well as reduce the overhead for data collection by 64%. This further suggests that, when combined, AI and big data have the potential to affect future workplaces tremendously in addition to their standalone contributions to the business and marketing specialties.
Say, for example, supply chain and fulfillment operations are largely reliable on quality data. These businesses are now turning largely to AI-based technologies that can offer reliable real-time insights based on customer needs. As the time to market and customer response are critical in these industries, it is important to come up with real-time solutions for data management, and AI-powered business decision-making becomes critical. These businesses can build their marketing strategies and financial planning around the real-time flow of information by using these.
To ensure data quality for big data and Ai, there should be an agreed-upon and proven methodology for data collection and storage. This must be ensured before feeding the data through many machine learning algorithms or deep learning applications. Professional support and expert supervision are needed for this process. The companies which plan to adopt these strategies should deploy qualified and skilled professionals to handle these operations.
The merger of big data and AI
As we have seen, AI and big data can effectively work together for bigger achievements. The raw but quality data is first fed into the AI-based engines, which will make the analytics smarter. We only need minimal human intervention for the AI system to run its way at the next stage. Going further, even lesser human intervention is required to run AI, and this is when we may come to realize the fullest potential of the ongoing AI + big data cycle.
A real-time study done by XenonStack shows the overall objective of AI as:
- Logical reasoning.
- Automated scheduling and self-learning.
- Machine learning and deep learning.
- NLP (natural language processing), which is the ability to understand normal human conversations.)
- Computer vision, which is the capacity to draw out the most accurate information from images.
- Practice general intelligence.
All these AI utilities are largely immature now, and these AI algorithms may require a huge volume of data to proceed further. For example, if we consider NLP, it will not be possible in a full-fledged way without billions of real-life human speech samples broken down into bits that the AI engines can process. In every possibility, we may expect big data to grow exponentially and AI to become a more viable daily option for the automation of tasks.