“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway” -by Geoffrey Moore.
Big data normally refers to datasets so huge & complex which includes structured, Unstructured & semi-structured data, they create remarkable challenges for traditional data management & analysis tools in a practical timespan. The sources of big data analytics tools are countless & rising.
Think about chats on social media networks, transactions from financial markets & e-commerce sites, surveillance cameras, signals from RFID tags, browsing patterns, cell phone conversations, urban traffic cameras, web search & even weather satellites. Big data analytics courses wrap all these stores of information & more. For industries such as banking, telecom & media, big data collection is already table stakes.
Big Data Analytics utilizes advanced analytic techniques. Big data has several characteristics: high velocity, high volume, or high variety. Internet of Things (IoT), mobile, social & Artificial intelligence (AI), are driving data complexity through new sources & forms of data. Big data is obtained from transactional applications, sensors, networks, devices, video/audio, web, log files & social media — much of it produced in real-timebig data analytics tutorial.
Big data analytics tutorial allows analysts, business users & researchers to make better & quicker decisions using data that was previously unreachable or unusable. Businesses can utilize advanced analytics techniques like predictive analytics, text analytics, data mining, machine learning, statistics & natural language processing to obtain new vision from previously unutilized data sources independently.
Big data, integrated with analytics, can provide organizations impressive opportunities for enhancing efficiency & operations. Yet the possibilities for using big data to ask new business questions and meet market requirements can be even more fascinating. How can big data provide the means for businesses to be more energetic?
Future of Data Analytics
Data Science & Biga Data Analytics space is placed to reach over $273 Billion by 2023 & companies like Amazon, Microsoft & Google are so soundly invested in not only gathering data but enabling data for the enterprise.
As machine learning & AI continue to develop, the way we use analytics also resumes to rising & change. While in the past, businesses concentrated on collecting descriptive data about their customers and products, more and more, they’re about pulling both predictive & prescriptive learnings from the information they gather. So—what is the difference between descriptive, predictive analytics & prescriptive analytics? And which one you need in your company?
If you’re new to the data analytics field, let’s do a swift overview:
1. Descriptive Analytics
Data that offers information about what has happened in your company. Just think about web hit numbers, a monthly sales report, marketing campaign rates, etc. They provide you a vision of how a project is performed during Digital Transformation.
2. Predictive Analytics
Data that gives information about what will happen in your company. Drawing more complex machine learning & AI processes and algorithms, predictive analytics help you determine what will happen—how well a product will sell, which marketing to use for the highest impact, who is likely to buy it.
3. Prescriptive Analytics
Data that gives information on not just what will happen in your company, but how it could happen better if you did a, b, or c. Far off giving information, prescriptive analytics goes even one step further to suggest actions you should take to optimize a process or service to the highest degree.
Anyhow, descriptive, predictive, and prescriptive big data analytics projects all play important roles in our organizations today. We don’t always require complex algorithms running on our data. Sometimes we just want to know how much traffic our social media pages are getting or where our financials stand. However, in those instances where we do want to improve efficiencies & optimize performance, prescriptive analytics is playing a progressively important role.
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Prescriptive Analytics Makes Marketing Serene
When we switch to predictive big data analytics tools, things get a bit transparent. AI & machine learning can tell us more particularly which groups of customers to target, and which products or discounts to offer to maximize impact. They can even tell you what medium & what time of day to use to target them. But the results are quite descriptive. It will not suggest to you what you should be doing to better your outcomes.
Prescriptive data science and big data analytics takes three main shapes—guided marketing, guided selling & guided pricing. It uses AI & machine learning to guide buyers with less human interaction—advising the right buyer, at the right time, with the right content—telling salespeople which product to offer using what words—telling you what price to use at what time in which situation. This information allows you to maximize not just sales but price & benefit overall during transformation.
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Different types of big data analytics, when automated, can permit you to make real-time decisions—something chemical & gasoline companies prefer, for example, modifying prices throughout the day to maximize profit. Achieving the benefits of data & more particularly prescriptive analytics comes down to having the technology, systems & processes to maximize available data.
If you want to move up the food chain to hold the power of prescriptive and big data analytics tools, it is important to have the right infrastructure & software to power your data. It’s because prescriptive big data analytics is about believing that the AI will do the work to escalate sales on your behalf, based on the calculations it’s performing in the background (which is lead by your systems of record, tools & infrastructure). It also requires hand-over control. But the data it generates from these exchanges is also magnificently insightful, proving that often AI can optimize sales & marketing like humans never could.
To know which type of analytics your company should be investing in, you need to start with the big question: what do you want to achieve? According to my, prescriptive big data analytics are strong, but they won’t be mandatory for every company. They also will need a lot of twisting. No algorithm was designed perfectly the first time. It takes effort, time, & focus to make prescriptive analytics work effectively. But if you are in a competitive marketplace—managing anything from products to people—prescriptive analytics could mean a great uplift to profit & productivity.
How is the use of Data Impacting Financial Services?
Financial services will continue to progress entirely with advancement in Big data use. The forces which are leading the evolution are:
- Entirely Changing Customer.
- Fragmented Ecosystem
- Speed of Technology change
- Reduction in Trust
Which Data is used?
The study below shows that Articficial Intellingence (AI) in the financial services (FS) market uses a large variety of data, including publicly available information such as the weather, vision from payment providers & even customers’ social media.
The real-world impact of Big Data & Advanced Analytics
No matter where you are on your journey—whether you are just beginning, developing a data strategy, or boosting your existing investments, we help you generate a step-change in your return on data during the process of Digital Transformation. Here are some examples:
1. Travel & Tourism-
By executing optimized ticket pricing based on advanced analytics, a travel & tourism company was able to amplify earnings before taxes by up to 20 %.
2. Telecommunications
Through analytics, a telecommunications company amplified new business by $30 million.
3. Shipping
Through finer management of shipping data, a company was able to upgrade the income by $500 million.
Final Word
When used productively, big data analytics do far more than support your company’s pathway to success. If you aren’t opened to utilizing them to their fullest, you may as well sit out of the game—just be ready to get outgalloped!
Change isn’t just coming, change is here—whether we like it or not. Big data analytics tools has the power to disrupt almost every core of today’s economy, modifying everything from how we run our businesses to the type of businesses we run. Big data is here to stay. Squeeze its potential! It could lead your company to unbelievable levels of success.
Frequently Asked Questions-
1) What is Big Data Analytics Example?
Big Data Analytics utilizes advanced analytic techniques. Think about chats on social media networks, transactions from financial markets & e-commerce sites, surveillance cameras, signals from RFID tags, browsing patterns, cell phone conversations, urban traffic cameras, web search & even weather satellites.
2) What are the five types of Big Data Analytics?
- Descriptive Analytics
- PredictiveAnalytics
- Prescriptive Analytics
- Diagnostics Analytics
- Cyber Analytics
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