Advanced analytics is known as the process of examining content by utilizing tools and techniques in order to discover insights and make predictions for the future. There are different approaches to conducting advanced data analytics.
These different types of advanced analytics are used to drive businesses to make better decisions and solve problems related to data and the effect of different factors. For advanced analytics, there are firms who help businesses with adopting modern analytics solutions in order to analyze and understand their data coming from different sources.
Organizations and companies are capitalizing on the growth afforded by the use of advanced analytics in their business decisions. Using the best solutions designed for your business, you can empower your decisions with the knowledge of advanced analytics.
Add value to your business with advanced analytics:
When it comes to decision making, advanced data analytics are crucial because they help with data interpretation, using various methods and techniques, businesses can now understand their data. Gripped with this knowledge of the causal relationship between factors, organizations are gauging trends and behaviors of the market. Another important side to this is making predictions. Businesses must also be able to make predictions for the future of their business. This is helped by the estimation of the likelihood of events and what if calculations.
The importance of advanced data analytics:
In order to make your business data driven towards the future of BI, you need the services of consulting firms who can help you organize data and automate processing via machine learning. From user behavior, logistics, delivery, to marketing, all departments of your business can be run with the data driven, fact, and agile support of the advanced analytics approach. Advanced analytics approaches also help businesses remove the risk of failure in any processes as well. Predicting the cause and effect of such risks also helps in managing crises. Devising proper strategy for crisis management, damage control and reputation management are also some of the ways businesses are employing advanced data analytics in their daily functioning.
Advanced analytics approaches help decision makers with accurate and precise forecasting without the element of ambiguity. It also shares how fast decisions are made. When predictions are backed by convincing and logical data visualizations linking all factors in causal relationships, executives can achieve better results. Moreover, statistical models also help users with improving the growth of the business by gauging any possible problems that might arise in the future.
Different approaches to advanced analytics:
Following is the guide to understanding the functions, use case and importance of different advanced analytics approaches:
Predictive analytics
This form of advanced data analytics is used to predict what will happen in the future. This is done by forcast gauging and finding out why and what happened in the first place. This form of advanced analytics approach is most commonly used in businesses to make future decisions and keep an eye on the rising trends. In order to gain competitive advantage over their competition and industry leaders, businesses use this type of predictive analytics to get ahead of rising trends and changing shape of the market.
To avoid unstructured customer relationship management and lack of unified visibility, employ the power of data driven decision allowing your business to: ensure data visibility for all, efficient decision making, and avoiding risks. Consulting firms specializing in predictive analytics are helping businesses to streamline their functioning for maximum efficiency everyday.
This is also important when businesses are launching new products. Before launching their services or products in the market, businesses test for possible outcomes like conversion rate, customer impression, their target audience, areas of expansion and response from the adversaries. On the basis of this analysis, marketing strategies are also designed to better engage the audience.
The best use case of predictive analytics is in the healthcare industry where the future of disease spread or virus behavior is studied to guide medicine research in order to prevent it. This is essentially also involved in pharmaceutical research as well where the possible impacts of the medicine or vaccine are also studied. Moreover, weather forecasts are also done with predictive analysis where seven day forecasts, weather and climate change can be gauged properly. The accuracy of the predictive analytics ensures that proper action can be taken against calamities and control the impact of any disaster on the population of an area.
Descriptive analytics
Descriptive data analytics is used for leveraging the data processing to understand trends and relationships between different factors. When it comes to advanced analytics approaches, this type of advanced analytics allows users to gauge what exactly happened. However, this is considered simpler than Prescriptive analytics due to its precise and concise nature of data analysis.
Descriptive analysis is the most readily used advanced data analytics which includes use of data visualization tools and excel sheets to understand and quantify data.
The most common example of descriptive analysis is to understand the customer preferences by companies like Hulu or Amazon Prime. This helps these streaming services to cater to the changing market trends and provide better quality of service. Another common example is the use of descriptive analytics for gauging survey results in order to understand the relationship between different variables and trends. Other examples include developing KPI reports to keep an eye on the performance of businesses by their executives so business goals are achieved on time.
Big data analytics
Big data analytics is the most prominent form of advanced data analytics approach. The main reason for this is the amount of data being produced every day and the need to analyze it. Essentially, Big data has enabled businesses and people overall, to process unstructured raw data into useful information.
The type of information and data being processed by Big data analytics cannot be analyzed using only conventional tools. This is where big data comes in. This has enabled processing of unstructured information like video, images and audio content. The best example of high data analytics is the finance industry where quantitative data is used to gauge future trends and make decisions that reduce risks in the future.
Big data analytics are used in marketing by organizations who want to create effective marketing strategies and get their desired results. Optimizing the supply chain management of warehouses and industries who want to handle their delivery and logistics processes effectively to catch up with the customer demands.
Prescriptive analytics
As compared to predictive analysis, Prescriptive analysis helps users in understanding what next steps to take. It presents possible actions to be taken based on the data results from the processing. This form of advanced analytics approach has the most business potential since it offers tangible solutions and steps to be taken in the future. It essentially affects the decision making process of the business, making it more data and result driven. This form of analytics has removed the shadow of ambiguity for corporations, reducing chances of failure.
In Prescriptive analysis, computer vision, machine learning, statistics and algorithms to provide better data insights and visualization. This type of advanced data analytics approach uses structured and unstructured data for better understanding of the whole picture.
However, one drawback of using Prescriptive analytics is the cost, as many businesses cannot afford huge tech support and expertise needed for this type of extensive research. This is why many businesses are only sticking to the use of predictive analytics. Nonetheless, the future of big data and business intelligence might be able to change the tide in the future.
Data mining
Data mining is another important advanced data analytics approach which automatically looks for patterns and trends through large amounts of data. The main goal of data mining is simple. It is to simplify, analyze and purify raw data into useful and insightful information. When looking for examples of data mining, one will find that this type of advanced analytics is most commonly and widely used with email filtering for spam, removing risks and crisis management.
Other subsections of data mining include, text mining which specifically deals with unstructured data like customer reviews or emails. It also uses semantic analysis which enables computers to understand and interpret flow of sentences and entire files. This helps the machine to understand the correlation between two or more words.
Other more niche forms of advanced data analytics approaches include sentiment analysis which uses Natural Language Processing in order to understand the emotion behind the text. Cluster analysis uses chunks of data to understand the commonalities between. On the other hand, complex event processing uses low level factors to understand high stakes events that might take place.
The future of business in advanced analytics:
Business Intelligence helped grow the trend of using data driven tools to make business decisions but companies are now using even more advanced data analytics to guide their path. The use cases of advanced analytics show that this movement of advanced analytics approach is not going to slow down. From marketing strategies to logistics management, companies are shaping their future with advanced analytics. So, can you.
With the help of embracing data analytics in your business, businesses are utilizing the power of machine learning and artificial intelligence to utilize their daily generated data. With these agile approaches, you can choose the one which suits your business goals the best and voila! You have the power of big data at your expense. Business survival and success in today’s age now depends on the competitive advantage which only advanced analytics approaches can give. Join the wave of extracting meaning from the bulk of information with automated tools of today to ensure business success tomorrow.