Data science has been a prominent part of the digital economy for several decades with the help of applied science. Big data firms have already saturated the landscape with their terminology and their approaches to making money. Companies often bring in big data consultants to show them how to collect more data and analyze that data in order to improve their bottom line. The leading companies of the world are now fluent in many of the tenets of data science. However, data science will only continue to grow until it has impacted nearly every technology. The implications for applied statistics on business, privacy, and the growth of technology are vast. Only the most advanced and forward-thinking companies will be able to incorporate the tenets and best practices of data science in order to survive without a great deal of consternation.
What is Data Science?
In order to understand the implications of applied statistics, one must better understand the concept in general. Data science refers to the study and usage of data in a number of different contexts. These contexts all revolve around the attempt to make patterns and predictions out of a dataset. The broader term for the practice is the field of statistics. It is a more applicable version of statistics that can be used in a multitude of different fields.
Data science involves using mathematical patterns and scientific tests to determine greater understandings about the world at large. A machine learning program may be helpful in determining the opinions of a large group when only looking at a small sample size. It also might excel at packaging, sorting, and sharing data in a way that turns that data into a valuable product.
Statistics Data-driven Decision Making
One major implication of data science is the growing importance of statistics to every aspect of the modern economy. Data science has become a basis for a number of different industries. Companies spend billions of dollars each year paying for data analysis firms and big data sections of their own firms. They use big data software to analyze data that has been collected and to find new sources of data that can be analyzed for trends and patterns.
Trends and Patterns
Those trends and patterns can then be the basis of new business decisions that both save the company money and help add more data and make systems run more efficiently. The implication for business is that applied statistics has to be a critical part of business today. It needs to be added into the milieu of issues that a company has to look at on a daily basis. This process appears much easier than it will be for the thousands of companies around the world that take to change very slowly.
Data Science in the Workplace
In most companies, managers and employees already have experience in payroll, operations, and the management of different accounts. These systems are often transported to different departments with multiple individuals working in them. Practically all companies have transferred these procedures over to online-based systems. Now, these companies have to bring on an entirely new procedure. They have to add a requirement to bring in data and consider data throughout their operations.
New individuals may have to be hired and new consultants will have to provide guidance on the best way forward. If the companies of the world fail to apply machine learning, they have a chance of falling behind their competitors. Companies that utilize applied statistics have lower expenses and higher profit margins in most instances. They are able to invest more in research and advertise their products and services to a wider array of people. Companies need to be able to work on this higher level in order to survive.
Applied Statistics and Research
A foundation of applied statistics is polling and popularity. Data science is the secret to crafting effective polls. These polls can help gauge the popularity and significance of a number of different concepts. A poll can ascertain if a population is interested in a particular topic. It can make sense of user reviews that show specific complaints about a product or service. Applied statistics can break apart the different opinions and words used when writing those reviews. The practice of machine learning is not only effective at crafting a poll. It is also key to understanding polls and utilizing those polls to great effect.
Effective Machine Learning
An effective machine learning operation can utilize polls and feedback in order to change the amounts of funding that a company spends on different aspects of their business. Data science can help make these decisions automatically and without spending a considerable amount of time and energy from employees. The polling industry is also the secret to massive organizations such as the Amazon Mechanical Turk system. Thousands of people are currently being put to work in order to fuel the machine learning industry. A key implication, therefore, is shifting the global economy a step further towards a technological basis.
Supercomputers and Applied Statistics
Technology will be powered and improved by the growth of applied statistics. Data science provides yet another reason for individuals and companies to spend more money on technology. More money on technology means that individuals will press companies to spend more money on research and development. Faster computers help to create a positive feedback loop where companies spend more on applied statistics and receive more effective applied statistics systems as a result.
Stronger computers are also helpful in solving many of the other problems in society that do not have as clear of a profit motive. Nonprofits that help protect individuals and cure diseases will be helped by the incredibly profitable applied statistics industry provoking tech companies into building stronger, faster computers. The implications for this seemingly small part of the economy can spread to the rest of the world.
Privacy in Applied Statistics
Perhaps the most important implication of applied statistics is a significant shift in views of privacy. In previous decades, individuals took their privacy for granted. They knew that their personal information was not being collected and used by other groups. People had an understanding of privacy that extended to all aspects of their day-to-day lives. Shopping at stores meant handing over money or a credit card and that was all. The rise of the internet and applied statistics created another avenue for these companies.
At the beginning of the internet’s history, the many aspects of the internet were created and offered by individuals who had a passion for technological creation. They started out being free to those individuals. The lack of a profit source helped lead to the dot-com bubble bursting in the early 2000s. Data science changed this problematic system entirely. Companies realized that they could view website users as sources of data that could then be sold to advertisers. More users meant more money and a steady stream of income. Individuals started to trade their data for the use of otherwise free services. This system has created a problem for anyone wanting to use internet services such as social media without paying a monthly fee.
Applied Statistics Impact on Data Science
Data science is not as significant as the rise of the internet. The internet has virtually created a new economy and a new way of engaging in every industry. But applied statistics has helped to validate and make a number of industries profitable. Companies have to be careful in order to ensure that they will be able to keep up with this rising trend of applied statistics. Those that ignore data science do so at their own financial peril.