The Role Of AI In Data Analysis

Artificial Intelligence (AI) is finally bringing a multitude of capabilities to machines which were long thought to belong exclusively to the human realm: processing natural language or visual information, recognising patterns, and decision making. Self-learning machines are the  essence of AI and while the basic concepts date back more than 50 years, only recently have technological advances enabled successful implementation at industrial scale. 

In our ultra fast-paced age of computer-connectivity, businesses produce massive amounts of data that can be challenging to keep up with. But when you learn to analyse data with AI, you can produce results far beyond what humans are capable of, both in terms of speed and accuracy. 

Typical applications of AI include autonomous driving, computer vision, decision making, or natural language processing. AI holds the benefit of being adaptable to very heterogenous contexts just like humans. Well-trained AI is capable of performing certain tasks at the same skill level as humans but with the additional advantages of high scalability and no need for pauses. 

AI can discover patterns in the data that are too complex for human experts to recognise. In some specific applications such as computer vision, AI has already achieved performance levels surpassing that of humans, for instance in skin cancer diagnostics. 

The idea of AI dates back to the 1950s when AI successes were largely limited to the scientific field, however, in the last years, established IT giants like Google, IBM, and nVidia, driven by the abundance of data, algorithmic advances, and the usage of high-performance hardware for parallel processing, have begun bridging the gap between science and business applications. 

Adoption of AI has become easier due to freely available algorithms and libraries, relatively inexpensive cloud-based computing power, and the proliferation of sensors generating data. Hence, not only established firms but also start-ups play a significant role in bringing AI to life, not least because it enables  a much shorter product development cycle - in some cases, as short as three months. 

The Value Proposition Of Analytics

Analytics is the process of taking raw data and applying some form of analytical technique in order to find meaningful patterns in the data. The analytical techniques that we can use vary, although a few of the most popular methods include:

• Applied mathematics 
• Statistical analysis
• Machine learning

With AI, these technologies can communicate among different repositories of variable data, and they can analyse and plan real-time response strategies. Advances in AI can play a significant role in making business processes more efficient and powerful with the help of automation. Analytics, too, is becoming more accessible and automated because of AI.  

Here are a few ways in which AI is contributing to analytics:

• With the help of machine learning algorithms, AI systems can automatically analyse data and uncover hidden trends, patterns, and insights that can be used by employees to make better-informed decisions. 

• AI automates report generation and makes data easy-to-understand by using Natural Language Generation.

• Using Natural Language Query (NLQ), AI enables everyone in the organisation to intuitively find answers and extract insights from data, thereby improving data literacy and freeing time for data scientists.

• AI helps in streamlining BI by automating data analytics and delivering insights and value faster.

We can also use AI to provide the “brains” to our advanced technological systems, which can comprehend and analyse data from a wide range of sensors in great quantities with more accuracy than humans.  As innovative technology expands and IoT devices are connected to the Internet, this trend is likely to continue. 

The Business Use Case AI Analytics

There are several use cases of AI analytics that nearly all businesses can benefit from: 

Forecasting Demand: This is an example of predictive analytics and is one of the most powerful applications of AI. In fact, according to McKinsey Digital, AI-powered forecasting can reduce errors in supply chain networks by 30 to 50%.

Predictive Maintenance: This refers to AI-based techniques that are designed to predict the condition of a company’s equipment so that they can estimate when maintenance will need to be performed.

Business Monitoring: From customer experience monitoring to revenue and cost monitoring, this is an example of diagnostic analytics that nearly every company can make use of to improve customer satisfaction, reduce churn and increase revenue.

The complexity and scale of data being produced and used by businesses across sectors are more than humans alone can handle. Enterprises have started adopting the new wave in AI analytics to tackle data and improve their processes, analyse data faster, optimise operations and make data teams more productive.

McKinsey:     anodot:    MonkeyLearn:      I-HLS:     Dataconomy:     SecurityInfowatch

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