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AI in Analytics AdvisoryKONNECT An AdvisoryKONNECT Paper Impact of Artificial Intelligence on Analytics March 2021 AdvisoryKONNECT An AdvisoryKONNECT Paper Introduction 0 2 The rapid movement of the world combined with the enhanced role in technology has led to an increase in information generation for analytics and valuable insights. Whilst the world is experiencing digital transformation, there is an insight gap on data, given that data collected pre-pandemic is no longer a useful predictor of future behaviour. Due to the rise in data, both useful and not useful, organisations and systems must integrate artificial intelligence (AI). AI is the “science of making machines smart” (Denis Hassabis, CEO of DeepMind). AI systems are able to make decisions autonomously, accommodating for large data sets that humans are unable to. AI has the ability to enhance data analytics processes by recognising patterns and making predictions at a speed that leads to actionable business insights, such as predicting equipment maintenances and failures before they occur. By 2023, more than 33% of large organisations will have analysts practicing decision intelligence (PWC) AI’s impact on marketing predicted to reach $40 billion by 2025 (Forbes) By 2022, 70% of customer interaction will involve emerging technologies like AI (PWC) In order for enterprises to be data driven, and truly harness the value of data analytics and prediction capabilities, intelligent systems and software such as AI, machine learning (ML), natural language generation (NLG) and decision intelligence (DI), must be integrated. As such, it is predicted that by the end of 2024, 75% of enterprises will shift from piloting AI to operationalising AI, increasing the streamline analytics infrastructure. The following paper examines the power of AI and added value it brings to data analytics. Impact of Artificial Intelligence on Analytics March 2021 AdvisoryKONNECT An AdvisoryKONNECT Paper The Importance of Data Analytics 0 3 As aforementioned, as the value of technology enhances, so does the value of data. “A business is only as fast as its data. What you know and how well you use that knowledge – fuels your competitiveness and growth” (Forrester, The Data Management Playbook 2020). Analytics is about enhancing decisions, harnesses data and identifying new opportunities leading to smarter business moves, efficient operations, cost advantages and fast decision making. There are different types of data analytics that enterprises use Data mining breaks down large sets of data into smaller usable information, identifying anomalies, correlations and determining behavioural patterns. Text analytics processes big chunks of unstructured texts to develop algorithms. Data visualisation lays out data visually in graphs and charts, making complex data understandable to the human eye. Business intelligence turns data into actionable insights which are used to develop strategies and operational plans Analytics helps organisations make sense of large information for growth and development. It extracts useful data from unnecessary information and analyses them to come up with patterns. Such patterns are used to make profitable changes and evidence backed decisions. Business leaders will leverage this new data to develop strategies and deliver an enhanced customer experience – a top priority for enterprises. Investing in analytics is the difference between success and failure. Without data companies are unable to make informed decisions that lead to profit maximisation Impact of Artificial Intelligence on Analytics March 2021 AdvisoryKONNECT An AdvisoryKONNECT Paper The Power of AI 0 4 Digital transformation has witnessed AI being used and implemented across all business processes from production, to finance, to marketing, enhancing the functionalities of organisations. AI automatically analyses data and uncovers hidden pattern, allowing for better decision making, as seen in figure 1. AI techniques such as ML and NLG automated data analysis and visualisation, with ML gathering insights and NGL converting them into human readable formats. The ability to improve data literacy, deliver insights and create value quickly is essential in today’s world. AI proved its usefulness during the pandemic, combing through 1000s of data, social media posts, and papers to predict the spread of COVID and identify vulnerable populations and find treatments. Given that human decision making is more flawed and vulnerable to bias, AI systems remove bias by understanding concepts objectively. AI systems incorporate human elements such as intuition and culture into decision making, ensuring that businesses have a better, unbiased understanding of customers. Figure 1: Percentage of executives who cite the following as benefits of AI (Deloitte, 2017) Impact of Artificial Intelligence on Analytics March 2021 AdvisoryKONNECT An AdvisoryKONNECT Paper Continued... 0 5 AI based models learn from data, the more data they’re fed, the more accurate findings are. As such, AI enhances analytics tools and makes them work more intelligently, accelerating data preparation, automating insight generation and allowing for the querying of data. AI allows for users to interact with data, creating a two-way conversation where businesses can ask questions to data and get answers in real time. AI can help analyse customer data such as credit score and income, deciding eligibility for goods and services. Such systems can also be used to decide how much to invest in marketing and other business decisions with information like projected growth and customer intelligence. The added value AI brings to business intelligence, allows for enterprises to predict outcomes before they happen – a vital investment in volatile business situations. Impact of Artificial Intelligence on Analytics March 2021 AdvisoryKONNECT An AdvisoryKONNECT Paper AI & Analytics: A Power Duo 0 6 AI powered analytics, also known as automated analytics, is a business disruptor, optimising operations. The demand for AI analytics is growing due to IoT, Big Data and the need to understand data. Automated analytics is a data intensive technology, also known as “analytics on steroids”, providing cognitive insights. All AI needs is data and to familiarise itself with information, leading to autonomy. The more data AI has access to, the greater its analytical capability. Automated AI identifies hidden patterns in large data sets, enabling companies to internalise data-driven decision making. In breaking down data into valuable insights in real time, automated analytics expands the functionality of business intelligence, increasing its value. Automated analytics closes the gap between human and machine communication, solving various business problems. 54% of executives say that AI plays a significant role in improving decision making processes. Automated analytics is used for fraud detection, customer and market insights, as well as insights on product performance. Automated analytics gives customers what they want – personalisation. As customer needs are constantly changing, automated analytics has the ability to process new data with speed, giving customers what they want. Data scientists spend 80% of their time cleaning and preparing data for analysis. 79% of executives say that AI adoption makes work easier and more efficient. AI automates the data analytics process, scrubbing raw data for valuable parts, making the data preparation process easier. AI analytics is an asset as NLG and ML aspects allows the platform to understand the data more organically, without human intervention. ML is the most impressive AI capabilities, identifying patterns based on large sets of data, and deriving predictions from them. Automated analytics is more accessible given its use of ML and NGL to find answers and value easily. 54% of executives say that they have boosted their productivity with AI. Automated analytics streamlines the entire data preparation process, dedicating more time to generating valuable insights. In doing so, mundane work in automated, opening the way to more advanced techniques that makes the data analytics process more efficient. Impact of Artificial Intelligence on Analytics March 2021 AdvisoryKONNECT An AdvisoryKONNECT Paper X-Analytics: 0 7 With COVID-19 disrupting the normalities of everyday life, enterprises are finding themselves using different types of analytics. X-analytics, a term coined by Gartner, is a type of analytics that businesses use to thrive in unusual economies, solving tough challenges like climate change and pandemics. X-analytics is about analysing any data in any format, giving business intelligence analysts the opportunity to extract value from all data, both new and old, to understand how behaviour has changed, and how to capitalise on these shifts to drive new business. X-analytics combined with AI is a powerful took for predicting and planning for crises and disasters in the future. Combined, this software is a future proof solution about timeliness and speed, extracting maximum value as soon as possible. X-analytics is a transformative access to data and a new way of thinking, predicting new customer behaviour, detecting hacking and training models on detection. Powered with AI, X-analytics is able to interact with multiple data sets in multiple places. Predictive Analytics: AI powered systems are able to conduct analytics and make decisions autonomously. The need for AI has enhanced due to the increased use of predictive analytics. Predictive analytic tools are
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