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What is AI Analytics
The age of Artificial intelligence has arrived and is causing a lot of excitement. AI for data analytics is not to be left behind. AI Analytics involves the extensive use of machine learning and natural language processing for data analysis. Complex algorithms are used to learn from data patterns, identify the trends, anomalies, and data set correlations. AI analytics is the ideal tool to get data-driven insights for making informed business decisions. It is widely different when compared to traditional analytics. While traditional analytics rely on statistical methods to analyze data, AI analytics uses machine learning to learn and adapt over time, ensuring the insights derived are more relevant and reliable to present needs. Traditional analytics use fixed statistical models and might miss certain insights which might be decisive. Custom AI solutions are capable of processing unstructured data (text/images), which traditional analytics might find difficult to analyze. Custom AI solutions can automate data analysis by classifying and tagging data enabling faster identification of trends and patterns in data. To start using AI analytics, know the business problem you are trying to solve and the data needed to resolve it. Select the right custom AI solutions, tools, techniques, and algorithms for data analysis. Use custom AI solutions to derive insights and have a plan to implement and integrate them into the business process. Ensure the data is high quality and chosen algorithms are appropriate for the task.
How AI Analytics benefits your business
The best benefit of AI based analytics is the automation of data analysis tasks, which will invariably reduce time and the need to invest in resources. This also results in a faster turnaround of tasks helping businesses to make quicker decisions, resulting in increased efficiency. It also enables more accurate predictions than traditional analytics solutions. The insights can be used to create better products for customers in specific target segments or to drive personalized marketing campaigns. There is a reduced risk of cyber threats as AI based analytics can identify them before they occur, ensuring enhanced compliance and risk management. Operational inefficiencies can be fixed, new revenue streams and growth opportunities can be identified for driving profitability and sustaining a competitive edge. Customer service can be personalized and made more responsive by using insights from customer data and preferences. Effective pricing strategies can be developed using insights to know market trends and customer behaviors. Supply chain operations can be optimized, including logistics and transportation to reduce costs, marketing channels that can enable the best possible outcomes can be identified, and the type of messaging needed to influence the target audience. In manufacturing, AI analytics can optimize the use of equipment and machines through predictive maintenance for improved productivity. Defects or any quality issues can be identified by analyzing data from production lines, enabling the improvement of quality control processes. Accurate forecasts on sales or customer demands can be made, helping businesses to remain agile to changing market needs. Factors leading to customer churn can be identified, and steps can be taken quickly to retain them.
How AI is used in Data Analysis
Here are a few answers on how to use AI in data analytics. AI has transformed data analysis with its ability to analyze complex datasets to identify patterns and trends. Analytical artificial intelligence is used for data analysis. Structured and unstructured data can be collected and processed in an automated manner. Automation is also applied for data classification based on data attributes such as customer demographics, purchase history, etc., and predictive modeling to forecast futuristic trends and patterns and derive insights to draft business strategies. Natural language processing techniques can be used to analyze data from social media posts and customer reviews. Customer feedback can be used for sentiment analysis to understand their attitudes and preferences. Insights from analytical artificial intelligence can be used for personalizing products or content recommendations, especially in self-service digital platforms. Similar data points can be grouped to do a cluster analysis and gain insights into specific customer segments or product categories. Stock prices or web traffic time series data can be analyzed using AI algorithms to find any patterns of trends, including identifying anomalies such as fraudulent behaviors. Financial companies can use AI to simulate complex financial market behaviors or supply chains to test multiple scenarios. Such simulations can be used to optimize fleet transportation networks and manufacturing processes. One key highlight of using AI for data analysis is that it can auto-clean and prepare data for analysis, select the important features in a dataset to reduce the complexity of analysis, and identify relationships between variables to predict outcomes with a high level of efficiency.
AI Analytics: Top use cases
Here are common use cases of AI and analytics. The most important is predictive maintenance, where equipment or machinery downtime can be predicted, and proactive steps can be taken to avoid downtime. In financial services, fraudulent activities can be identified quickly to mitigate risks of any breach or compromise. Credit risks for loans or any financial products can be assessed, helping banks make more informed decisions on lending. In healthcare, patient data and medical records can be analyzed to improve diagnoses, find health risks and enhance treatment outcomes. In customer service, it can power up chatbots and virtual assistants and reduce dependencies on support staff. Customer behaviors and preferences can be analyzed to develop custom products and services. Customer voice data can be analyzed to enhance speech recognition accuracy for analyzing customer feedback and sentiments. Customer segmentation is another area of application where AI can be used to categorize customer demographics and preferences. Social media data can be analyzed to understand customer views and preferences. Sales trends and seasonal buying trends of customers can be identified to forecast sales revenue and develop sales strategies. Inventories can be optimized based on customer demand patterns, and supply chain efficiencies can be improved. Logistics operations can be optimized to reduce costs. In this digital landscape, cyber threats cannot be ignored. AI analytics can identify insider threats or external threat vectors to proactively mitigate these risks and prevent a breach.
Top AI tools for Data Analytics
IBM Watson Studio: A data analytics AI platform best used for prescriptive analytics. It is good for preparing data, building ML models and deploying them. Ideal for use in operations, finance, and sales.
Amazon SageMaker: It has pre-built algorithms, AutoML, Jupyter notebooks, managed infrastructure, and model hosting. ML models can be built, trained, and deployed to scale. This tool is used for its ease of use and scalability.
DataRobot: Good for automating end-to-end ML workflows. ML models can be built and deployed at scale. Features include automated machine learning, model deployment, and monitoring. It has an intuitive UI and an ideal for any business.
Microsoft Azure Machine Learning: It has a range of AI tools for data analysis and building and deploying ML models. With features such as automated machine learning, model management, and deployment options, it can easily be integrated with Azure Cognitive Services to build end-to-end AI solutions.
H2O.ai: A good open-source platform to build, deploy and manage ML models. It can be used with Python and R, Hadoop, and Spark. Its UI is intuitive, making it easy for data scientists and developers to work together.
Google Cloud AI Platform: This AI analytics platform offers many AI tools for data analysis and building and deploying ML models. It can be integrated with other Google Cloud services, such as BigQuery and Cloud Storage, for building AI solutions.
RapidMiner: Widely used in finance, healthcare, and marketing sectors, it is used along with Python and R, Hadoop, and Spark for quickly developing and deploying ML models at scale.
How Ness Can help you Get Started with your AI Analytics journey
Ness data & analytics services can rapidly transform your business by helping you realize value from machine learning initiatives faster. Our customized data & analytics services can convert your data into intelligence to enable risk management, preventive maintenance, portfolio analytics, fraud detection, personalized promotions, portfolio analytics, data intelligence, churn analysis, fraud detection, inventory optimization, and predictive maintenance. Our data & analytics services cover end-to-end MLOps and DataOps process design, workflow, and implementation capabilities. We also design and deliver AI governance through Responsible AI and Ethical AI lenses to ensure your AI investments are free of bias, auditable, traceable, and explainable. We can offer expertise in AWS and Azure. Our data scientists and engineers are trained and certified in Sagemaker, Azure ML, Databricks, mlflow, Kubeflow, and open-source Python libraries like TensorFlow, Keras, PyTorch, scikit-learn and Theano. Ness services can evolve any organization’s AI analytics initiatives by enabling more AI powered insights. With Ness AI driven analytics expertise, businesses can gain deeper insights into their data, reveal hidden patterns, and make better decisions. Ness AI powered analytics solutions can empower businesses to respond to changing customer needs or market trends. Our AI analytics services can reduce operational costs by automating data processing and data analysis. By leveraging our AI powered analytics solutions, companies can enhance forecasting accuracies and sustain their competitive edge. Ness AI driven analytics offerings can unlock the full potential of organizational data, which was previously not achievable by using legacy analytics tools and methods.
