Prescriptive analytics is the most sophisticated type of business analytics and can bring the greatest intelligence and value to businesses. It aims at suggesting (prescribing) the best decision options in order to take advantage of the predicted future utilizing large amounts of data.
The past few years have seen an explosion in the business use of analytics. Corporations around the world are using analytical tools to gain a better understanding of their customer’s needs and wants.
Business analytics refers to the extensive use of data, acquired by diverse sources, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions to proper stakeholders.
Business analytics is categorized into three main stages characterized by different levels of difficulty, value, and intelligence: “Descriptive analytics”, “Predictive analytics”, “Prescriptive analytics”.
Currently, the vast majority of business analytics efforts are spent on descriptive analytics and predictive analytics with typical methodologies including data mining, machine learning, artificial intelligence, and simulation.
Compared to descriptive and predictive, prescriptive analytics is still less mature. It has been considered as the next step towards increasing data analytics maturity and leading to optimized decision making, ahead of time, for business performance improvement.
Prescriptive analytics is the most sophisticated type of business analytics and can bring the greatest intelligence and value to businesses. It aims at suggesting (prescribing) the best decision options in order to take advantage of the predicted future utilizing large amounts of data
Predictive Analytics is an extension of Data Mining technology. Both are based on a huge amount of mathematical theory dating back to several decades. Data mining technology helps to examine large amounts of data. One can sift through all the chaotic and repetitive noise in data to discover patterns and use that information to assess likely outcomes, and then make informed decisions.
Now, let’s move quickly and see how data analytics is playing a vital role in different industries.
1. Data Analytics in Agriculture
In the agriculture sector, data analytics can be applied to the machinery and farming data collected in order to reduce loss, improve efficiency, and lower costs under the condition of unchanged physical properties.
This enables a modern farming concept called precision agriculture (PA) or satellite farming. This kind of farming helps the farmer to recognize the variations in the farming land and how to adjust input for different parts of the land to optimize the output.
Data-driven decision-making has been extended from the business sector to the agricultural sector. Many large enterprises in the agribusiness are becoming involved in data analytics research and development. They are providing solutions for PA and for a variety of other issues in agriculture. Fierce competition between companies has already begun.
Deere.com is a platform for data analytics, which provides the possibility to store, analyze, and visualize results on a web-porta. With the help of such platforms, farmers can figure out when and where to plant which kind of crops, when to plow and when to harvest, and which optimized path should be followed during the work.
2. Data Analytics in Healthcare
Similar to agriculture, data analytics is playing a vital role in the advancement of the healthcare sector. With the easy availability of smart devices including smartwatches, smartphones, and smart wristbands), a new dimension of healthcare has emerged – Smart Healthcare.
Now doctors can remotely examine their patients and suggest treatments on the go. Smart healthcare offers many new possibilities for patients too. Patients can keep updated with their health and fitness data all the time, find other patients suffering from the same disease to discuss various treatments, and easily track the post-surgical needs.
SAP Real-Time Analytics is a complete solution for patient care, human resources, finance, care collaboration, and healthcare analytics
The big health data collected from electronic health records, research, physician notes, insurance claims, and social media data are used by SAP Real-Time Analytics to reduce cost and improve the quality of care.
3. Data Analytics in Manufacturing
Data analytics provides a granular approach to diagnose and improve the whole manufacturing flaws. It is always in the manufacturers’ interest to improve their production processes, product quality, production cycle, and the amount of output per unit of input.
Manufacturers can use data analytics to leverage the data collected from on-the-floor factory machinery alongside other traditional (factory logs) and social data. Some of the advantages of using data analytics in manufacturing are to
- Get unexpected insights into different processes
- Increase accuracy, quality, and yield (amount of output per unit of input)
- Improve the forecast of product supply and demand
- Enhance the understanding of plant performance across multiple metrics
- Boost product quality
- Track all products with defective components,
- Predict machine failure,
- Quantify how daily production impacts financial performance,
- Provide preemptive maintenance and service by continuously monitoring a product instead of fixed-term maintenance
- Identify the root cause of a failure
IBM Analytics provides a complete analytics solution to be used in automotive, defense, chemical, petroleum, energy, aerospace, electronics, and other industries to uncover deeper insights into operations, inventory, market demands, supply chain, and performance.
General Electric (GE) Brilliant Manufacturing is a software suite, which connects people, machines, materials, and processes in IoT. This suite maximizes manufacturing production performance and optimizes operations through advanced real-time analytics.
Manufacturing Analytics by BOSCH is a solution for analyzing production data. Different types of data such as test, process, and machine data from different sources can be used to improve the production process and product quality while reducing the cost with the help of this suite.
4. Data Analytics in Connected Vehicles
A connected vehicle is a vehicle designed with the capability of connecting to the internet and other connected devices including smartphones, traffic lights, other vehicles on the road, smart home appliances, etc.
Data analytics provides car manufacturers with crucial insights into the vehicle system, behavior of the vehicles in certain conditions, and drivers’ patterns.
Ford and IBM are working together to develop a platform that analyzes data collected from a vehicle. Based on data from different sources, their vehicles are able to operate on an anticipatory basis in which they can foresee different things that the human eye cannot see.
BMW Group is also using IBM Big Data and Analytics technology to optimize their products, repairs, and maintenance processes. IBM SPSS predictive analytics software is used to combine and analyze data from different sources like pre-production sensor data, workshop notes, and numerous test drives of prototypes.
Tesla car manufacturers are collecting data from their connected cars and using telematics to batch stream key data points to the backend big data pool.
Caterpillar, Inc. is the world’s leading manufacturer of construction and mining equipment. They have created a new organizational division called Analytics and Innovation (AI) to form a broad and connected analytics ecosystem.
The data collected from gigantic machines are used to develop predictive and prescriptive information. By using data analytics, they are able to point out inefficiencies in the operation of a particular machine by comparing its operational data with that machine’s benchmark data.
5. Data Analytics in the Dairy Market
Nowadays, not only these industries can benefit from IoT, but one of the oldest sectors of mankind, i.eMilk production is also taking advantage of smart technologies.
Farmers are suffering most from the extremely sharp fall in prices. The welfare of cows is of enormous importance for farmers because only healthy and happy cows give the maximum amount of milk. The farmers are able to determine the health of their cows themselves, but this is only true for small herd sizes.
This is why farmers are making more frequent use of tracking systems and data analytics for the automatic health monitoring of their herd. These racking systems do have sensors with the help of which, you can predict the activity and health of the cows.
Europe product named Heatime and Lely product named Qwes-H also integrates rumination detection. It tells the farmer how much time each cow spends on ruminating which is an essential indicator for their health.
Now farmers are able to access data about the health and movement behaviors of their herd from their PC, notebook, or smartphone anywhere and at any time.
Final Words
I hope after going through the whole blog, you can properly decide and analyze how Data analytics is playing an important role in every industry. I just can say that there is hardly any industry left that is not making use of Data analytics.
So, if you are planning to invest in any of these businesses or already running these businesses, they do not stretch time to contact the best software consultant company to discuss and implement this technology in your business as this will give it a definite boost.
Good luck!
