Fostering Big Data Challenges with AI Applications

Big data is the confidential information storage of a company. Like how a flight's black box contains everything that happens in the journey, big data collects all the information about an organisation and stores it together.

Big data describes the high volume of data that are both structured and unstructured which inundates a business on a day-to-day basis. The big data storage is spread across various computers as a single system can't manage such huge data. Big data is considered as a credible and useful source because it can be analysed with AI applications. The analysis can do predictions and make decisions that could lift the company's revenue.

A record suggests that per day, humans create 2.5 quintillion bytes of data every day. If all these data are put to good use, they will surely help humans get a better view of the future. However, data plays a vital role in the business sector. To be clear, data is valued to assets which will help companies get a mind tree of the future through analysis. The World Economic Forum in 2016 estimated an increase of US$100 trillion in global business and social value by 2030.

But thanks to the intrusion of AI technologies. Now, PwC and McKinsey estimate an increase of US$15.7 trillion and US$13 trillion in annual GDP by 2030. The ship of technology is in the mid-way sailing between applications of AI lake big data, deep learning, machine learning, IoT, cloud computing, etc.

With the implication of emerging solutions through AI technologies, a business that faces challenges in maintaining credible sources get a solution. However, there are some major challenges that companies face while talking about big data which could be resolved if they are ready to go through a technological change.

Challenges faced by companies on account of big data

Multiplicity in IT source system

Storing data is a complicated process. The complication gets added while maintaining it. The average Fortune 500 enterprises have a few hundred enterprises IT systems. Most of them are at chaos because of different formats, mismatched references across data sources and duplication.

Managing the high-frequency data

Data flow on a real-time basis. There are issues like censoring of data which remains as an unspoken topic. For example, reading of the gas exhaust temperature for an offshore low-pressure compressor is only of limited value in of itself. But combined with ambient temperature, wind speed, compressor pump speed, history of previous maintenance actions and maintenance logs can create a valuable alarm system for offshore rig operators.

Functioning with data lakes

A data lake is a centralised repository that allows storing structured and unstructured data at any scale. Putting all the data of an organisation at a single-window brings no good. It stirs the data complexity any more than letting data sit in siloed enterprise systems.

Organising diverse data content

There is no assurance that data comes in a single format. A company gathers data through images, files, videos, documents, etc. However, they are put under the same roof called big data. So it is difficult and involves a lot of mechanisms just to differentiate them and put them on diverse channels before doing analysis. One more added trouble is the clarity of the data. Some files don't even comply with the minimum clarity bar.

Adopting emerging AI tools

AI tools sprout from time to time. They are extremely useful when it comes to managing big data. An enterprise IT and analytics team need to provide tools that enable employees with different levels of data science proficiency to work with large data sets and perform predictive analytics using a unified image.

Some ways to demystify big data issues

The first step is to filter data and put them as diverse files of relevant data sets. These involve neglecting the duplicate data files and filling the gap of unavailable data.

Signalling or labelling the outcome is the data analysis is important. For example, if AI-based predictive maintenance applies an analysis, source data sets rarely identify actual failure labels and practitioner have to infer failure points based on a combination of factors such as fault codes and technician work orders.

Big data can seek help from data science which is an expert in analysis and prediction making. Numerous algorithm libraries are available to data scientists today. They can come for aid.

Machine learning algorithms are also of great use as it facilitates the need to receive new data, generate outputs and have some actions or decisions be made based on the outputs.

Big data issues like bad clarity content, unstructured data, data lakes, etc are troubles that could be demystified with the help of machine learning and data science applications. There are many ways that data could be arranged in a proper format and be utilised. It is up to the company to adopt a suitable technique.