DataPillar

The Future of AI

The Future Of AI: The Impacts on Data Analysis and Business Intelligence

Artificial intelligence has been disrupting many industries in the past decade and will continue to do so in the future of AI By the end of this blog you will have an idea of:

1 – Will AI take over data analytics?

3 – AI Impact on Data Analytics

2 – How can AI be used in data analytics and business intelligence?

4 – How will the future of AI impact business intelligence?

Now the real question is:

What does the future of AI hold for Data Analytics and Business Intelligence? Many people around the globe also seem to be confused about how AI will impact human labor in the fields of data analytics and business intelligence.

1 – Will AI take over Data Analytics?

Well technically, AI will at some point reduce the amount of human input in lots of processes but at the same time, it will create lots of other opportunities too for those who will befriend it. AI is a continuously evolving field, but at the same time, it still needs extra help from human experts in the relevant field.

2 – AI Impact on Data Analytics

As data keeps getting bigger and more complex, data scientists have been facing difficulties in analysing data more accurately in less time. Fortunately, AI has been like a friend and a helper to these data scientists in processing, characterising, and labeling huge amounts of data. In doing so, they will require AI processes that can handle large amounts of data while consuming less time.

These are some of the ways AI can help data analysts monitor data quickly.

Automated Data Preprocessing (ADP):

One of the most crucial and typically time-consuming tasks in any project is getting the data ready for analysis. ADP takes care of the process by assessing data and identifying fixes, eliminating fields that are troublesome or unlikely to be useful, generating new attributes where necessary, and enhancing performance using intelligent screening techniques. AI is still mastering data preprocessing tasks, but shortly, it will surely excel in preprocessing real-time data and generating valuable outcomes. 

Real-time Analytics:

Handling data that was available before starting analysis is surely complex but not more than handling real-time data. Imagine you have a continuous stream of inputs that are constantly throwing raw data without any preemptive measures. AI can now analyse this type of data in real-time and provide businesses with insights into customer behavior, current trends in the industry, and decision-making.

Data Security:

Data is the backbone of any business or organisation, and the protection of data is the main goal of any organisation. Machine learning and AI can be used to “keep up with the bad guys,” automating threat detection and responding more effectively than conventional software-driven approaches, with today’s constantly developing cyber-attacks and the proliferation of gadgets.

Scalability: 

Data Scientists and Data Analysts are attempting to develop new techniques for handling and interpreting data due to the diversity and scale of data continuing to expand quickly. It’s crucial for individuals handling the data to combine a variety of languages, hardware architectures, frameworks, and tools to manage the data store because AI workflows are so different.

Data analytics has been revolutionising commercial data management for years. More than ever, businesses are coming up with innovative ways to examine data more thoroughly to increase productivity and make money. They use a variety of methods to achieve their targeted business goals, with machine learning and AI deployment being only two of them.

Natural Language Processing (NLP):

NLP or Natural Language Processing has been one of the major developments in AI in the past decade, and with the rise of ChatGPT and other language tools, it expanded its capabilities by outshining every language-related query. With the rise of this trend, businesses are also using large language data sets to automate their customer experience, user engagement, and query generation. The future of AI will surely take these experiences to the next level and help businesses generate good leads and customer satisfaction.

3 – How can AI be used in Data Analytics and Business Intelligence?

A branch of business intelligence known as AI analytics uses machine learning methods to unearth new patterns, correlations, and insights in data. AI analytics is the process of automating a lot of the tasks that a data analyst would typically complete. Businesses rely on data, and AI is the tool that can convert data into valuable resources by extracting valuable information from it. AI is now using its decision-making skills along with better customer engagement to keep up with the latest trends. Now let’s discuss what are some of those areas where AI will impact shortly in the field of data analytics and business intelligence.

4 – How the future of AI will change Business Intelligence:

Data analytics have long been considered the main factor in the success or failure of any business. As AI continues to expand, it is changing the way we analyse data and extract our desired results. Here are some of those processes AI will automate shortly to make businesses more profitable.

AI change Business Intelligence

Increased Automation:

Automation is one of the many things modern businesses are opting to enhance their responsiveness and increase their customer experience and they are doing this with the help of AI processes and techniques. AI is expected to automate many of the tedious and time-consuming tasks involved in data analytics, such as data cleaning, data preprocessing, and report generation.

Improved Accuracy:

As businesses started to expand, their data quantity increased multiple folds as compared to the last decade. And handling that data means dealing with redundancy and missed points which in turn results in less accurate outcomes, which will further down the road lead to decreased customer engagement.

Integration with IoT and Edge Computing:

Artificial intelligence (AI), edge computing (EC), and the internet of things (IoT) are developing rapidly. These devices are generating more and more data at increasing speed, and information technology (IT) professionals are facing a torrent of IoT data. To handle this type of data, we need a mechanism that is able to accurately give the desired result. And in near future, AI will be intelligent enough to process all this data.

Greater Personalisation:

A report from Epsilon revealed that 80% of customers are more likely to do business with a brand when the brand provides them with a personalised experience. Additionally, a report from Accenture showed that 91% of those polled said that they are more likely to purchase from a brand that knows them and provides them with relevant recommendations and offers. In short, customers will prefer products or services which will give them a personalised result to their queries.

Better Decision-Making:

AI is to business, what telescopes are to star-gasing. It enhances sight, clarifies confusion, and makes decision-making easier and faster. With growing data and customer base, businesses are now facing the problem of managing both while not compromising the quality they are producing. The future of AI holds a lot of new ways to incorporate decision-making and detecting malware and hackers is now less time-consuming.