Analytics is the scientific process of discovering and communicating the meaningful patterns which can be found in data. It is concerned with turning raw data into insight for making better decisions. Analytics relies on the application of statistics, computer programming, and operations research in order to quantify and gain insight to the meanings of data. It is especially useful in areas that record a lot of data or information.
Types of Analytics
Analytics can be split into 4 categories. While automating descriptive analytics was possible since the early days of computing, machine learning and AI enable companies to automate issue diagnostics, outcome prediction, and prescription of next actions.
- Descriptive Analytics (What happened?): Showing what is actually happening based on given data; often usually via dashboards and reporting tools.
- Diagnostic Analytics (Why did it happen?): Analyzing past performance to determine not only what happened, but why it happened.
- Predictive Analytics (What could happen?): Describing what scenarios are likely to occur, often in a predictive forecast.
- Prescriptive Analytics (What should we do?): Making suggestions about what should be done and their basis.
History of Analytics
In the past, data storage and processing speed limited analytics. Today, those limitations no longer apply, opening the door to more complex machine learning] and deep learning algorithms that can handle large amounts of data in multiple passes.
As a result, the standard descriptive, prescriptive and predictive capabilities of analytics have been augmented with learning and automation, ushering in the artificial intelligence era.
This means we’ve gone from asking what happened and what should happen to asking our machines to automate and learn on their own from data – and even tell us what questions to ask.
Today most organizations treat analytics as a strategic asset, and analytics is central to many functional roles and skills.
One growing field of analytics powered by machine learning is natural language processing. Computers use NLP to interpret speech and text. Chatbots use NLP to answer customer service questions or offer investment advice in online chat windows. They can also offer scripted suggestions to live call center employees.
Machine learning and artificial intelligence have also brought us useful applications like self-driving cars and recommendation engines, which promise to taxi us around while we binge watch the next recommended TV series based on our tastes.
Of course, analytics shapes more than our leisure time. With faster and more powerful computers, opportunity abounds for the use of analytics and artificial intelligence. Whether it’s determining credit risk, developing new medicines, finding more efficient ways to deliver products and services, preventing fraud, uncovering cyberthreats or retaining the most valuable customers, analytics can help you understand what drives your organization’s success – and how it matters to the world around it.
Business Value of Analytics
- A new way to work: The nature of business is changing, and with that change comes a new way to compete. Keeping up with the demands of today’s tech-savvy workforce means having a method for creating value and running quickly. Deliver speed and simplicity to your users while maintaining the highest standards for data quality and Data Security. A centralized analytics platform where IT plays a pivotal role should be a fundamental part of your business analytics strategy. The combination of both business-led and IT-led initiatives is the sweet spot for innovation.
- Uncover new Opportunities: Advancements in analytics technology are creating new opportunities for you to capitalize on your Data. Modern analytics are predictive, self-learning, and adaptive to help you uncover hidden data patterns. They are intuitive as well, incorporating stunning visualizations that enable you to understand millions of rows and columns of data in an instant. Modern business analytics are mobile and easy to work with. And they connect you to the right data at the right time, with little or no training required.
- Visualize your data: You want to see the data signals before your competitors do. Analytics provides the ability to see a high-definition image of your business landscape. By mashing up personal, corporate, and big data, you can quickly understand the value of the data, share your data story with colleagues, and do it all in a matter of minutes.
Risks and Challenges of Analytics
The main risk for the people is discrimination like price discrimination or statistical discrimination. See Scientific American book review of "Weapons of math destruction"
There is also the risk that a developer could profit from the ideas or work done by users, like this example: Users could write new ideas in a note taking app, which could then be sent as a custom event, and the developers could profit from those ideas. This can happen because the ownership of content is usually unclear in the law.
If a user's identity is not protected, there are more risks; for example, the risk that private information about users is made public on the internet. In the extreme, there is the risk that governments could gather too much private information, now that the governments are giving themselves more powers to access citizens' information.
In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.
The analysis of unstructured data types is another challenge in getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments, and universities. For example, in Britain the discovery that one company was illegally selling fraudulent doctor's notes in order to assist people in defrauding employers and insurance companies, is an opportunity for insurance firms to increase the vigilance of their unstructured data analysis. The McKinsey Global Institute estimates that big data analysis could save the American healthcare system $300 billion per year and the European public sector €250 billion.
These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation. One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set.
Analytics is increasingly used in education, particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student performance, predict graduation likelihood, improve chances of student success, etc. For example, in a study involving districts known for strong data use, 48% of teachers had difficulty posing questions prompted by data, 36% did not comprehend given data, and 52% incorrectly interpreted data. To combat this, some analytics tools for educators adhere to an over-the-counter data format (embedding labels, supplemental documentation, and a help system, and making key package/display and content decisions) to improve educators' understanding and use of the analytics being displayed.
One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. In such cases, cloud computing and open source programming language R can help smaller banks to adopt risk analytics and support branch-level monitoring by applying predictive analytics.