Artificial Intelligence on Hadoop for an Improved Business Ecosystem

The amount of data generated daily by businesses keeps growing, and soon it becomes a concern. As the business generates more data, there is a need to store it and connect its core business directly to clients. Hadoop provides a quick solution to this by taking care of all big data generated.

That is one side of the problem solved because businesses do not just require storage and data-moving solutions. They also need to use the data to study customer behavior, detect fraud, assess risks, predict future outcomes, and so on. AI on Hadoop helps achieve this goal.

Importance of Hadoop in the business environment

Before looking into the importance of Hadoop to a business, it is good to take a quick look at the difference between Hadoop and Spark. They are primarily designed to do the same work of securely moving data from the limited storage capacity in a business to almost unlimited storage. What makes a difference between the two is how each does its work.

Spark processes its data through in-memory but Hadoop processes from disks and writes to disks. Consequently, their data processing approaches create a significant difference in their execution speed. Both Spark and Hadoop work at an exceedingly super speed, but Spark processes at a speed of up to 100 times more.

Having noted that main difference, Hadoop infrastructure helps businesses take better advantage of leveraging data science. Apart from collecting data from a company’s mainframe, Hadoop can also collect more data off the mainframe in unlimited volumes. This data can be accessed anytime needed and used to improve company products, performance, pricing, service, etc.

Challenges of using Hadoop

Deploying Hadoop in the business ecosystem is not without challenges, although there is a solution to every challenge. Its architecture and structure are very complex and consume a lot of time, making it hard for entrepreneurs to gain insights relevant to business or e-commerce success.

Its storage and processing ecosystem is made of different technologies collected together and several open-source projects put together. Because of this, Hadoop requires highly skilled IT experts with experience in analytics capabilities.

Integrating its various technologies is also complex, and more often, businesses find themselves spending more time working on Hadoop architecture instead of spending time focusing on expected value gain for the sake of business progress.

Instead of working on the real data science that Hadoop should offer, data experts find themselves working more on gaining the relevant skills for value extraction from its architecture. Hadoop infrastructure uses over 20 different packages integrated to enhance its function ability. They are packages like Hive, Yarn, HDFS, and Spark.

Tracking all these packages takes time, and specialized skills are needed to understand the entire Hadoop ecosystem. Even after mastering all the skills required, data scientists might still need to do their tasks repetitively to solve the business need. Another avenue, therefore, becomes a necessity to help develop predictive analytics without overly exaggerating the cost. This is where Artificial Intelligence on Hadoop comes in.

Benefits of big data

Businesses have for many years used data to gain insights for producing cost-effective, user-friendly products. They collect data and use it to predict customer behavior and get insights on customer retention strategies.

Today, technology has enabled businesses to collect data faster from various online sources like social media, websites, GPS, CCTV, and many more. Due to more avenues for collecting data fast, companies find themselves with so much data that they face another challenge of processing it.

With proper ways to tap into this data, companies can enjoy several advantages. They are service and product repositioning, evaluate market size opportunities, improve on service and develop new products, improve on security, cushion on fraud, and many more. To help businesses collect big data effectively and process it to add value to the business, the best option is to use AI and Hadoop.

AI on Hadoop – what next

Hadoop remains a complex system and this is a big advantage when businesses think about a highly secure system. Its complex infrastructure and architecture can only be cracked by highly skilled personnel. The system at any one time holds massively huge data but businesses need to use that data to its advantage.

AI technology on Hadoop makes this goal a reality by carrying out several tasks. AI uses various algorithms that imitate the human thinking process. AI is configured to process the data generated from Hadoop to give strategic outcomes. It can generate different feedback from customers, pinpoint trending programs, apps, and sites visited most or often. Businesses in the financial sector can detect fraud loopholes, predict inflation, trade better on currencies, and so on.

Using Hadoop and AI successfully

AI does not produce data but depends on other data-producing sources. Once it’s fed with the right data, AI decodes it, makes connections, churns out relevant insights, and gives results that a business can use. Hadoop helps integrate raw information from varying sources then feeds it to AI to decode and process it. To get the most accurate results, Hadoop ensures there is all-rounded data available.

Impact of AI and big data on businesses

Big data can be obtained from various sources, which are then integrated with Hadoop. Businesses use this data to obtain various results and its impact on businesses is huge. These benefits vary and can range from improved business intelligence, customer-focused service, cost readjustment, and adjusting marketing strategies.

Businesses can save time spent on processing data on Hadoop by incorporating AI into its infrastructure. Business managers can concentrate on the core business other than on understanding the complexity of Hadoop. When data scientists in a business spend too much time extracting and processing data from different Hadoop components, it increases the cost value, which can take a toll on business profitability.

With AI, less time is taken and in return, the entire process will cost far much less. AI helps eliminate the complexities of the Hadoop ecosystem and improves efficiency. The benefits of AI on Hadoop are immense and can be extracted under each unique business environment depending on its need.