Advanced analytics has been a prominent tool for businesses to escalate to analyze humongous volumes of data to extract incisive insights. Analytics is an immense field with many subfields, so it is important to understand its prominence for your business. Analytics refers to the skills, technologies, applications, and practices for continuous growth.
Advanced Analytics is the autonomous examination of data (both in structured and unstructured formats) using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover delve insights, make predictions, forecast trends or generate recommendations. Advanced analytic techniques include; data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network, and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, and neural networks. In this article, we will help you to discover how advanced analytics can optimize your business outcomes.
Traditional Analytics vs Advanced Analytics
Analytics is the old-school process, which consists of two major areas; Business Intelligence and Advanced Analytics. Business intelligence traditionally focuses on using consistent set metrics to measure the past performance of the business and drive new strategies for future achievements. Where advanced analytics goes beyond business intelligence by using state-of-the-art technologies and machine learning algorithms to predict future outcomes and set a defined set of strategies to achieve them. A sophisticated advanced analytical approach helps businesses in answering;
1. Why something is happening?
2. What if these trends continue?
3. What will happen next? (forecasting)
4. What is the best way to make your prediction happen?
5. What is the best that can happen? (optimization)
Traditional analytics is focused on reporting and querying whereas advanced analytics is completely about the optimization of current and future opportunities of the business.
How Advanced Analytics can scale business data?
As discussed earlier advanced analytics is all about predicting the future, a business can leverage this technique to channel both structured and unstructured data volumes. And below mentioned are a few ways in which advanced analytics can provide the utmost value to a business.
1. Predictive modeling
Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. It is one of the most known mathematical techniques, often used to discover market trends, changing customer behavior, market price fluctuations, demand and supply analysis, etc. This mathematical modeling technique tries to define future trends based on past insights.
2. Data mining
Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. Data mining is also known as knowledge discovery in data. By discovering trends, patterns, and themes in big data, data mining helps in building reliable data-based decision models.
3. Text mining
Text mining or text analytics is an artificial intelligence technique that leverages natural language processing to transform the unstructured text or data in documents and databases into a structured format where machine learning algorithms can be further applied to drive incisive insights. Text mining is mostly used to read call center transcripts, online reviews, customer surveys, etc.
4. Multimedia mining
Multimedia mining is another form of data mining, where computer vision and artificial intelligence integrated with NLP techniques are leveraged to read, collect, and analyze data from images, videos, pictographs, etc. A few successful applications of Multimedia mining are sentiment analysis, digital documentation, image classification, and image tagging.
5. Descriptive modeling
Descriptive modeling is an advanced mathematical modeling technique or a process to describe real-world events and the relationship between the factors responsible for them. For instance, a descriptive model can help businesses in identifying real-time market trends and why these trends are occurring based on past events.
The true potential of data is untouched in many organizations. And integrating advanced analytics techniques at your operational level can help in experiencing it. Particularly predictive and prescriptive analytics can optimize future operations to gain positive ROI.
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