Data is an acute business asset. It’s what drives invention today and enables firms to stay competitive in the global marketplace. Now with the convergence of big data and AI, companies can more easily leverage advanced analytics capabilities like predictive analytics and more efficient surface actionable insights from their vast stores of data. With big data and AI-powered analytics, firms can empower their users with the instinctive tools and robust technologies they need to extract high-value insights from data, fostering data literacy across the organization while reaping the benefits of becoming a truly data-driven organization.
What You’ll Find in this Blog:
- What is AI analytics?
- Differences between AI Analytics & Traditional Analytics
- The Convergence of Big Data and AI
- How is AI Contributing to Analytics Capabilities?
- What are the business benefits of AI analytics?
- Parting Thoughts
What is AI analytics?
AI analytics is the product of automating data analysis – a traditionally time-consuming and people-intensive task, using the power of today’s artificial intelligence, deep learning, and machine learning technologies. In addition to structured data sources, AI is increasingly able to analyze unstructured data, via the use of AI analytics tools such as natural language processing (NLP), speech analytics transcription, and computer vision for image and video analytics.
What’s the Difference between AI Analytics and Traditional Analytics?
To better understand the arena of AI analytics, let’s now review its differences:
Traditional data analytics is generally undertaken by a technical team of data analysts. Here’s an example of how a team of analysts might traditionally attempt to solve a business challenge:
- An event, incident, or trend occurs in the company over a while – for example, sales are down for the quarter.
- Data analysts then form hypotheses about what the potential causes might be for the sales decrease.
- These hypotheses are then tested against the data for that period until they find enough evidence to support a particular hypothesis.
- The analysts then write a report that summarizes their findings and will often present potential next steps for the business to take.
AI analytics, on the other hand, based on machine learning algorithms constantly monitors and analyzes huge amounts of data. The results stand apart from traditional analytics in:
An AI-based anomaly detection solution learns the normal behavior of the data without being explicitly told what to look for. It does it at any granularity: revenues per country, products, channels, etc.
The AI model will identify unusual drops in revenue and alert the appropriate teams in real-time. In addition, an AI-based analytics solution leverages clustering and correlation algorithms to provide a root-cause analysis so that any issues can be remediated as soon as possible.
The accuracy of the ML algorithms depends on how they were designed – they need to autonomously learn many different patterns accurately – which requires the use of multiple types of algorithms. For more details on AI & ML, see our white paper.
The Convergence of Big Data and AI:
Big data and AI have a synergistic relationship. Data is the fuel that powers AI. The massive, complex, and rapidly evolving datasets referred to as big data make it possible for machine learning applications to do what they were built to do: learn and acquire skills. Big data supplies AI algorithms with the information necessary for developing and improving features and pattern recognition capabilities. Without large quantities of high-quality data, it wouldn’t be possible to develop and train the intelligent algorithms, neural networks, and predictive models that make AI a game-changing technology.
AI, in turn, helps users make sense of sprawling, diverse datasets and sort through unstructured data that can’t be organized into neat rows and columns. AI enables firms to use big data for analytics by making advanced analytics tools more powerful and accessible, helping users discover surprising insights in data that was once locked away in enterprise information silos. Leveraging big data, AI, and advanced analytics, companies can provide their decision-makers with greater clarity and understanding of the many factors influencing their business while encouraging creative, intuitive exploration of large-scale, multi-dimensional datasets.
How is AI Contributing to Analytics Capabilities?
Thanks to the latest advances in AI, analytics is becoming:
- More efficient- thanks to automation.
- More accessible- thanks to improved UI. Natural Language Processing enables analytics tools to understand natural language queries.
- More powerful since previously difficult to analyze data such as text and videos are now easily analyzable.
What are the business benefits of AI analytics?
The ability of AI systems to analyze data autonomously has multiple business benefits. The main among them is reducing the labor cost of data scientists and other highly paid and limited-availability analytics professionals. Other benefits of AI in analytics include:
- Risk management- AI analytics can improve the effectiveness of risk management models and create smarter strategies.
- Innovative products- AI analytics tools perform big data analysis that can drive updates to existing products and creating new ones.
- Turbocharged supply chain- Supply chain executives recognize AI in analytics as a disruptor that empowers them to apply data-driven knowledge to solve previously unsolvable challenges.
- Customer engagement- Use AI analytics tools to determine what customers are looking for—acquire them, retain them and cultivate their loyalty.
- Successful marketing campaign- Create focused and targeted campaigns with AI analytics from current customer purchases.
AI and data analytics are often used together because the former boosts the functionalities of the latter. With AI, analytics technology can conduct more in-depth analysis paving the way for micro-targeted insights that are not easily found by human analysts. Complex analysis with several variables can be done quickly and efficiently with AI.
AI in data analytics also makes it easier to clean data – a vital step in the analysis process. It’s important to understand that AI and analytics are not the same and should not be considered as such because AI is part of the analytics ecosystem. Companies must understand the difference and be willing to use the technology if they wish to gain an edge over their competitors.
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