By Insight Editor / 28 May 2021 / Topics: Data analytics Artificial Intelligence (AI) Data management
The intersection of big data and Artificial Intelligence (AI) is central to finding value in the information you’re collecting. When applied to big data, AI eliminates analysis paralysis while avoiding inaccurate interpretations by providing clear and actionable direction fast from large data sets.
While the two concepts are often seen as nebulous buzzwords, big data and AI use cases are increasingly common thanks to cloud and Internet of Things (IoT) sensor adoption.
Applying AI to your data will help you uncover trends, evaluate existing programs and understand customer behavior — shining light on previously hidden opportunities. You can also pursue new customer engagement and automation practices that increase productivity, create new revenue opportunities and lower existing costs.
To get a better understanding of how big data and AI work together, let’s start with the basics, so you can adopt big data and AI solutions at your organization.
Big data describes diverse information sets that are produced from a variety of sources in multiple formats — software applications, IoT sensors, customer feedback surveys, video, images and more.
Big data sets are created by collecting large volumes of information from real-time data streams, established databases or legacy datasets. Since the environment is ever-changing and growing, powerful software is required to sort, secure and interpret the information for short- and long-term use.
Organizations often develop an analytics architecture that uses a mix of cloud-based applications and data warehousing tools to collect, organize and visualize the data. AI-powered tools are central to orchestrating these many moving pieces into cohesive insights that inform decision-making.
AI was introduced in the 1950s by a computer scientist named John McCarthy who used the term to describe the study and development of intelligent machines.
While AI has spent much of its lifetime misunderstood because of overpromising news headlines and grandiose movie plots, the field of study has grown rapidly from theory to tangible business tool. Today, technology leaders like Microsoft, Google and IBM have made strides in AI development to support cloud analytics, customer engagement and more.
Practically speaking, AI is a program, or algorithm, that’s designed to complete tasks that would normally require human capabilities or input. You can further distill AI’s definition to technology that resembles or has some form of human intelligence, whether that’s recognizing speech, reviewing images or holding a conversation.
AI largely does this by identifying patterns within the data and then providing a response based on pre-defined logic. An AI can be as straightforward as a chatbot that handles custom service or as complicated as a self-driving car.
No AI definition is complete without defining machine learning — a complementary technology to data and AI with similar business application.
Machine learning is a subtype of AI where an algorithm is trained to respond to data without having been explicitly programmed in that way. Machine learning is often the mechanism by which an AI functions.
When you’re developing AI, you often use machine learning to continually improve the AI by learning from previous examples. There are two approaches to building a machine learning algorithm: providing it supervised or unsupervised data.
Supervised machine learning involves a data training set of past information that the algorithm then compares new data against.
For instance, an algorithm used to identify human faces is fed a data training set that includes many different images of humans and non-humans. Each image is labeled for the algorithm to understand that image A is a human and image B is not. Based on that training set, the algorithm slowly learns to identify human faces.
The unsupervised approach provides the machine learning algorithm unlabeled data so that it can develop a deeper understanding of how to identify patterns. Unsupervised data trains itself without much outside influence after it’s initially coded.
Data collection has been a central business practice for decades, but today’s digital tools make it easier than ever. With data sets growing by the second, it’s nearly impossible for any one individual or business to efficiently use the data they’re collecting.
AI-enabled applications can make short work of any data set, whether it’s collected in real-time or from an established database. Businesses are using AI tools to increase efficiency, develop personalized experiences, inform decision-making and reduce costs.
Data and AI are typically applied to analytics and automation, helping businesses transform their operations in the process.
Analytics tools, like Microsoft® Azure® Synapse, help organizations predict or identify trends that inform decision-making around product development, service delivery, workflows and more. Additionally, your data will be organized into dashboard visualizations, reports, charts and graphs for readability.
Meanwhile, business processes can be automated when data and AI solutions are developed. For instance, in the manufacturing industry, inventory tracking, predictive maintenance and safety reviews can be supported with AI. Any business can use AI to review and search through documents and field customer service requests.
Although AI hasn’t matched or surpassed human intelligence, the technology is becoming increasingly easy to adopt and implement into many business practices, due to how the technology interprets visual, text and speech formats.
While it may sound like the possibilities are limitless with AI, the technology still has its limits. To help you develop a complete picture of how you can integrate AI into your business, let’s review five areas where AI excels:
AI can be trained to process, identify and search images using computer vision, a type of algorithm that is trained to understand and respond to images and video.
Vision-trained AI capabilities range from storing and captioning documents to supporting IoT sensor arrays. Many industries are using visual tracking to increase productivity and efficiency.
Some computer vision AI industry applications include:
Customer engagement is a popular use case for AI technology by converting speech to text and text to speech. When analyzing customer behavior or training staff, you can easily review recorded customer interactions with annotated transcripts. Additionally, you can develop speech-based assistants, like Alexa or Siri, within your applications.
AI can be trained to map information, provide recommendations and support semantic search These tools will help improve your digital products’ user experience by offering helpful information that meets their requests.
Additionally, you can maximize the utility of existing and future data, as your application AI will continue to refine its capabilities based on past data.
Consumers expect the accuracy and speed of modern search engines, yet those high bars can be quite difficult to achieve in your own tools. AI can be used to enhance your digital tool’s search functionality, while also enabling it to analyze webpages, images, videos and more to give users precisely what they’re looking for.
Talking to our technology in full sentences, the way humans naturally communicate, and getting meaningful responses is achievable through Natural Language Processing (NLP). You can integrate NLP into your applications or bots to better meet user needs or develop customer service tools that can hold voice or text conversations. Additionally, these AI capabilities can detect and translate languages.
Language understanding is also impacting analytics. The AI can detect text, phrases, names and even sentiment, which can be helpful with user moderation and tracking the overall user experience.
These AI capabilities are changing the way businesses develop products and services, while also having a major impact on how customers engage with an organization. AI creates a world of possibilities for how your organization can start using its data, increasing its value and the many ways your business can refine its products and services.
But with great data comes great responsibility — you need to have a clear understanding of how to collect, manage and secure your information to protect your business, employees and customers.
As your data environment grows, you’ll want to deploy a comprehensive data management strategy that establishes rules regarding:
Establishing how your business will handle these domains will help ensure your information is readily available, supporting not only your AI-enabled solutions, but also day-to-day business operations.
Data management and governance should be viewed as an organization-wide initiative, so your teams can continually benefit from the data they need. Erwin has created a data governance tool that helps you discover, understand, distribute and manage data assets across individual business units and departments.
Cybercriminals will target any business’s information, especially personal identifiable information, like emails, addresses, phone numbers and account details. This is a concern for every business — regardless of size or industry. Every organization is a target to cybercriminals.
From lone cybercriminals to state-sponsored groups, cybersecurity risks are growing — making it more important than ever to implement an ironclad data protection plan.
Adopting a security strategy that aligns to your data environment’s needs will ensure consistent availability, while protecting your business’s reputation. Most importantly, you should fully understand and adhere to any government or industry regulations that apply to your stored data. If there aren’t any applicable regulations or compliance needs, your business should follow security best practices.
Implementing security measures will bolster threat detection and reduce risk. You’ll also be more effective at counteracting an ongoing breach when leveraging tools that reduce response times and increase visibility across servers, networks and end-user devices.
Your data security strategy should involve various factors, including:
Responsible AI refers to a set of frameworks that ensure your business acts transparently and ethically with its AI-based tool.
Using AI shouldn’t be taken lightly. As mentioned, maintaining governance and security strategies plays an important role, but you’ll also want to guide staff and executives on how they should use AI.
Before your business adopts AI, you should also:
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