Technology, as we know it, is moving at an incredible pace and has found applications in virtually every facet of human endeavor.
With so much buzz around big data, Machine Learning (ML), and Artificial Intelligence (AI), it’s obvious that the technology drive that’s propelling the world is not stepping on the brakes any time soon. Hence the need for individuals and businesses to adapt.
What is big data?
Because there’s so much information being churned out of different industries and other data-driven quarters, you’ve probably wondered how all that data are collected and how are they stored. These are all valid questions and every ICT enthusiast should care to know how such ocean of data is being managed.
And this brings us to what big data entail.
The term, big data, is used to describe a large amount of data (either structured or unstructured) that is seemingly overwhelming to process using traditional methods.
And because large data are generated every second and the size of data growing exponentially, there is a need for businesses to devise reliable and efficient means of processing and storing them.
Statistics show that over five hundred terabytes of new data are pumped into the Facebook database every day. And the bulk of this data includes videos and photo uploads, message exchanges, and comments.
Now, that’s how much data is being generated from one out of several social media platforms. Another example of big data is the amount of data generated by the New York Stock Exchange — which amounts to one terabyte per new trade data daily.
What are the uses of big data?
Contrary to popular misconceptions about the complexity and size of big data, the sheer size of big data is it much of a challenge or something to lose sleep over. The question is, what do organizations do with such voluminous data — how are they been analyzed and what are they used for.
The is a myriad of uses and applications of big data. And because data is readily available, one can easily collect and analyze them — And ultimately, use them to proffer solutions to industry problems.
Big data can be used to reduce cost, save time, develop new products and make smart decisions. With the right data analytics, you will be surprised at how much you can accomplish in your business or organization.
Common applications of big data include but not limited to:
- Determining the causes of failure and defects in real-time
- Generate personalized coupons at the point of sales for customers based on their buying habit/behavior
- Detect and track fraudulent behavior before they materialize
- Recalculate risk portfolios in split second.
These are just a few of the several applications of big data across different industries and the uptick in IoT and connected devices has propelled big data into the limelight and made it gain more traction.
Today, it’s almost impossible to discuss innovation, operational efficiency, and improved customer experience without mentioning big data, AI and machine learning. Why? We live in a data-driven world.
Here is how big data work
To make the most of big data (regardless of your industry), you must make time to understand how it works. You also need the different types of big data — structured, unstructured and semi-structured.
Before we discuss, how big data works, let’s quickly touch on the different types of data and waalk our way up from there.
- Structured data
A data is said to be structured if it can be processed, accessed, and stored in a fixed format. This kind of data is easy to manipulate and derive value from.
- Unstructured data
This type of big data is exactly on the opposite side of structured data. It has an unknown form and structure. In addition to its huge size, this type of big data poses challenges in terms of processing and deriving value from them.
Think of a data source that returns a combination of images, videos, and text. It provides organizations with a heterogeneous mix of information. However, the challenge is that they can’t derive value from it because of its raw and unstructured format.
- Semi-structured data
As the name implies, this type of data contains both structured and unstructured data. We could go on to discuss the characteristics of big data — volume, variety (the heterogeneous sources of big data,) velocity (the speed at which big data is generated) rated, and variety but that would take us away from the scope of this piece.
Tips on how to use big data
Here are simple steps and tips on how to use make the most of big data
- Develop a big data strategy
- Identify big data sources
- Collect, manage and store the data
- Analyze the data
- Use the data at your disposal to make data-driven decisions
How does Artificial intelligence fit into the big data climate?
Data they say is the new currency. And it has become invaluable in today’s digital economy. If you are wondering what role artificial intelligence has to play in the big data economy, the answer is not far-fetched.
The only way one can derive value from big data is if you can make sense out of it. Speaking of making the most of big data, you can leverage artificial intelligence to process large amounts of data in real-time.
Considering the potentials of leveraging big data in business and across virtually every industry, AI can provide accurate projections of future outcomes based on historical data.
As such, making it much easier for big data users to achieve their objectives faster such as improving customer services, making SMART decisions, and improving operational agility and efficiency.
What are the benefits of AI
Whether machines are more efficient than humans is not a subject for debate. Computers can work around the clock without making errors or becoming fatigued. And of course, they don’t get bored too.
It is these qualities of technology that make machines and technology-driven inventions like AI the best option for analyzing large amounts of data. And they will do it without blinking an eye. No wonder, AI is fast replacing humans in positions and operations that are repetitive and considered arduous.
In straightforward words, AI helps businesses to saves time and money. They also come in handy for generating business insights and reducing errors.
But that’s not all.
AI-driven technology can also be used to enhance customer experience and ramp up revenue ( think chatbots on websites). Their applications and benefits are endless and limitless. If they are properly deployed, AI can boost sales opportunities, classify customers based on their buying habits and behavior, detect fraudulent transactions, review documents, and do research, among several other possible applications.
When it comes to deploying AI tech in your business, it’s always best to think of the value it can add to your business and channel your resources in that direction.
What is machine learning?
Not to bore you with all the technical jargon needed to define or describe machine learning (ML), you can think of ML as the practice of learning from data and using them to make a determination or prediction.
Hopefully, we didn’t lose you with that definition. The idea of machine learning is the ability to learn from data without relying on codes or commands from programming.
What are the benefits of machine learning?
Like AI and big data, the benefits of ML is are endless. The sheer size of available data, affordable data storage, and increasing demand for more efficient and sophisticated data processing has led to the high demand for machine learning.
Today, virtually all industries and businesses have realized the role of data in making strategic decisions. As such, they have turned to machine learning models to help them analyze data faster and identify profitable opportunities and potential risks quickly.
Machine learning has found applications in virtually every industry including marketing and sales, e-commerce and social media, transportation, healthcare, and finance.
In conclusion
Machine learning and artificial intelligence are definitely worth the hype they are receiving and they are here to stay. If you are looking to build a formidable business and spike up your ROI, AI and ML are the way to go.
