Companies and enterprises from around the world are quickly adopting the data-driven decision-making process in their workflow. As the entire process of collecting and collating data to perform the analysis is becoming much simpler and easier. The process of churning out actionable insights from your data becomes a difficult and complex process when this data becomes Big Data.
Before we understand how Artificial Intelligence (AI) and Big Data help in business transformation, let’s understand the role of both these domains in extracting the value from the collected data. AI and Big Data are integral to the entire process. When AI and Big Data are combined it helps in offering accurate analysis through clear insights from a large data set.
Although it may seem that terms like AI and Big Data are just buzzwords without any practical application. But thanks to Cloud and IoT (Internet of Things), there are numerous use cases in today’s data-driven economy. To be able to understand the role of Big Data and AI in various use cases, let’s first understand what these technologies are and then discuss ways in which organizations can adopt these technologies.
What is Big Data?
Big Data represents a large amount of information in data sets that are produced from several different types of sources in multiple formats. Now, these formats could be software applications, customer feedback surveys, IoT sensors, videos, images, etc.
Big Data is created by collecting a large volume of data from real-time data streams, legacy datasets, or databases. Since the market and economic conditions are changing at a rapid rate, it’s important to sort, secure, and analyze data for both short and long-term use.
Enterprises often develop a specific architecture for the analytics process that may use a mix of cloud-based applications and data warehousing tools that will help them in collecting data, collating it, and implementing data visualization techniques on it. AI-powered tools today are playing a central tool in orchestrating many processes and integrating them together to offer comprehensive insights to help in the decision-making process.
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Let’s understand what AI or Artificial Intelligence is.
The term Artificial Intelligence or AI was first coined by John McCarthy, a computer scientist, in 1950. He used it to describe AI as the term that denotes the study and development of intelligent machines.
AI is a program or algorithm designed to help complete tasks that require human capabilities. In simple words, the technology imitates the human brain and its functionalities while adopting human-level intelligence that will help in performing tasks without needing any human intervention.
How do they work, you may wonder? Well, largely they identify patterns within the data and then offer a response based on a logic that has already been defined. There are numerous applications and use cases for AI. It could be handling customer service through a chatbot or even driving an autonomous car.
AI has been many times misunderstood by the general public because of the grand movie plots and overpromising news headlines. Finally, after decades of research and technological advancements in developing hardware capable of handling such a high volume of data and computations. The other biggest push for AI was the invention of Cloud Computing which enabled businesses to access one of the world’s best computational resources within a few clicks.
Today AI has developed into a tangible business tool, and tech companies like Microsoft, Amazon, Google, and IBM are leading the race in implementing AI into their services.
What is Machine Learning?
Machine Learning is one of the most trending technologies today and has a wide range of applications in today’s scenario. ML could be described as a subset of AI wherein the algorithm is trained in such a way that it can self-learn itself in scenarios without the need to explicitly code every situation and its responses.
There are basically two types of ML, one being supervised and the other being unsupervised. The Supervised model requires data to be trained that will cover detailed scenarios. These datasets must contain past information that algorithms will compare with new information and learn themselves. The Unsupervised model doesn’t require datasets that cover every piece of information. This type of model is instead developed using unlabelled data so that it could be developed without any human interference. This way the Unsupervised model develops itself over time able to comprehend patterns. That means that the system doesn’t require any more coding other than from the initial phase.
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How do Big Data and Artificial Intelligence support businesses?
Data collection was always an important core business practice for many years now. Today’s digital tools help make data collection an easier process than before. With the amount of data produced each second, it’s becoming more difficult to handle the data or efficiently use it for making a decision.
AI tools help in speeding up the process irrespective of the fact that this data is collected in real-time or from a database. That’s why businesses are deploying these AI tools to increase efficiency, design personalized experiences, and cut costs. Companies use Artificial Intelligence for automation and analytics purposes. It helps in transforming business operations.
There are many tools out there in the market that help organizations identify and predict trends helping in the process of decision-making along the entire workflow. On top of this, data will be properly organized using visualizations, charts, reports, and graphs for better readability. Several business processes are being automated using data-driven technologies with AI.
The AI technology is still in its nascent phase even then the technology is much easier in adopting and implementing in many business practices as there are many ways how technology interprets visual, speech, or text formats.
To better understand what AI can do and couldn’t, we first have to understand how AI is being integrated with businesses in various fields like Vision, Speech, Search, Knowledge, and Language.
The AI algorithms are trained to process, discern and search images using Computer Vision technology. This technology uses algorithms that help in understanding and determining images and videos. The Computer Vision algorithms help in a wide variety of applications from storing, and captioning documents to supporting sensor arrays. Many different industries are using this technology for tracking purposes, it directly helps them increase productivity and efficiency.
Other applications of this algorithm include:
- In manufacturing processes, inspections could be automated, and maintenance could be predicted.
- At retail stores, the store conditions could be monitored and also offer real-time alerts.
- In healthcare organizations, workflows could be tracked, administrators could be supported.
It’s a big field where AI and Big Data come together. AI algorithms could be used to convert speech to text and vice versa. It’s a very popular use case of AI and Big Data. One of the most popular applications could be in analyzing your employee’s behavior during customer interactions that will come with automated transcripts. You could also incorporate speech-based assistants like Alexa, and Siri to help you in the job.
AI algorithms help you map information, offer recommendations and support semantic search functionalities. The Google search is one such example, it helps you offer additional information regarding the searched term thereby enhancing the customer experience.
Every customer looks for a Google-like lightning-fast searching experience in their search tools. These businesses can utilize AI’s potential in enhancing search functionality to incorporate images, and videos, apart from web pages to help users offer precisely what they searched for.
NLP or Natural Language Processes is one of the most promising and cutting edge technologies that are transforming the way humans are interacting with machines. NLP makes it possible to have full sentence conversations with machines similar to what humans have been doing for ages. Businesses can integrate NLP technology within the application or bots to help out the customers in meeting their needs through voice or text conversations.
This capability helps you detect and translate languages. Understanding the language spoken completes the puzzle that companies are trying to solve to enhance machines’ capability in understanding human language, and their context, and reply back appropriately. AI is already capable of detecting text, phrases, and even sentiment, which further helps companies with user moderation and determining the overall user experience.
How is Data in general is connected with AI?
With the introduction of AI to businesses, they have changed their strategy and started developing products/services while the customers also are engaging with companies and businesses in a totally different way. AI is generating countless possibilities for organizations in using data, generating value, and refining their products/services.
Each business must possess a clear understanding of how data is collected, managed, and secured to help protect businesses, their employees, and customers.
How to manage AI and data?
The scenario of how to manage AI and data happens with every type of company across various industries. Initially, you may not need a data management strategy, but with an increase in the volume of data sets, there needs to be a comprehensive data management strategy that will help you establish rules regarding various fields like Authentication, Privacy, Access, Creation, Processing, Storage, and Security.
If a business is capable of handling various domains aforementioned will help in making the data readily available. It will help in supporting AI-enabled solutions but also day-to-day business operations. Today, Data Management and governance must be viewed as an organization-wide problem so that each team within an organization could benefit from the data they rely on. These are tools that help businesses in discovering, understanding, distributing, and managing data assets across various individual business units and departments.
The security characteristics
Businesses are targeted by cybercriminals for their data. These data could be confidential information like phone numbers, emails, account details, etc. This threat is omnipresent and there is no one solution that will fix each of these issues. Every business maintaining customer data is prone to a cyberattack. With each day, the hackers are using more sophisticated methods to secure their data, systems, etc.
Being an organization, it must adopt a security strategy that aligns with its data environment’s needs. This ensures consistent availability of services meanwhile ensuring that the business’s reputation is intact. The most important point is to understand and abide by the government or any other regulations for data that they possess. Even in the absence of regulations or compliance requirements, businesses must follow the best security best practices.
Deploying these industry-grade security measures will help in tightening security measures and reduces the risk of cyberattacks. If you have a proper Cybersecurity strategy in place you will be in good shape to counter any data breach attempt, with the help of AI-enabled teams reducing your response times, and increasing visibility across networks, servers, and other end-user devices.
There are several factors that you must include within a comprehensive data security strategy and they are:
- Data Governance, complying with set standards and conducting regular audits.
- Encryption policies
- Regular testing and assessments to find vulnerabilities and threats before its late
- Implementing security controls for handling processes, identifying and validating users, deploying firewalls, etc.
Final Note on AI with Big Data
AI is on its way to becoming the foundation of all the future technology in the coming years. With increased customer demand, companies need to scale up operations, and so they must deploy AI systems to achieve this feat. With the digital revolution and emerging technologies like IoT (Internet of Things) devices on the rise, a huge amount of data is generated at a very fast rate.
Digital transformation is being helped by modern-day technologies like Machine Learning and Artificial Intelligence which are working on these Big Data and churning out actionable insights. These technologies, techniques, and algorithms are helping in generating insights that help transform business processes and operations.
The theme with the introduction of AI is that more data is better. Data helps in figuring out the patterns and trends that will help in shaping the future course of actions that will help in boosting efficiency.