Data is one of the most valuable resources in the world today, and that’s why businesses like Facebook that are built entirely on their ability to collect and provide access to data are able to make so much money. In fact, big data and data analytics are both beginning to transform the tech industry in ways that no one previously thought possible – from helping businesses to make more informed decisions about their strategies, to gaining the kind of market insights that would completely fly by most people unnoticed.
Data Analytics and Data Science
Data analytics is one of the most important areas of current scientific research. We have only just begun to scratch the surface of what is possible with data analytics; from machine learning, to making predictions about the future, data analytics is enabling us to harness the true power of data and turn it towards solving previously unsolvable problems. In order to apply data analytics and data science techniques to data and make it useful, we need very large data sets to work with.
The more useful data we have, the more powerful the predictions that we are able to make as a result. There are a variety of places that all of this data can come from. In some cases, this data will come from scraping freely available online data sources, and in other cases, it is data that a business gathers during the normal course of its operations.
Once sufficient useful data has been gathered, analytics can be used to mine that data for useful information and patterns. These analytics are increasingly being done by using machine learning algorithms. These algorithms are able to pick out patterns from data that would be impossible for a human to discern. It is by training algorithms to spot these patterns that we are able to train them to accomplish things that were previously impossible.
You’ve probably encountered a CAPTCHA while browsing the internet. These are the simple verification tests that are designed to weed out bots and prevent them from automatically connecting to online services and executing commands like a person would. Many of these CAPTCHAs involve picking out cars, bikes, road signs, and other road-related items from a series of images. It might surprise you to learn that these tests are part of training machine learning algorithms.
It’s no coincidence that this type of CAPTCHA first appeared when Google began working on their self-driving cars. The answers that people give to these tests are used to both test the capabilities of the existing algorithms and to train them to get better at identifying the right solutions. But how does this work?
This branch of data science seems insanely complicated, but it actually works on relatively simple principles. In order to teach an algorithm how to identify whether an image is of a particular item or not, it needs to be supplied with a large number of examples. For example, if you want a machine-learning algorithm to learn how to spot bananas, then you would feed the algorithm a database consisting of many thousands of images of bananas. The algorithm can then ‘learn’ what makes a banana a banana, and will eventually be able to decide for itself whether it is looking at a banana or not.
One of the key implementations of data analytics today is within the business domain. There are a number of ways that businesses can make use of big data to their advantage – it can be used to inform decisions and alter strategies as well as make powerful predictions about future market behaviours and business fortunes.
Businesses who want to utilise data analytics within their organization will need to identify both a suitable source of data and the appropriate tools to analyse it with. The best methods for gathering data will depend on what the business wants to achieve – if you want to gain more insights into how your business is currently performing and how it can be improved, you should aim to collect as much useful internal data as you can.
On the other hand, if you want to understand the market that you are operating in and how it is expected to evolve and develop in the near future, you need to focus on gathering appropriate data about the market instead. In order to ensure that you are gathering the right data and analysing it in the right way, you might need the services of a qualified data scientist. You can either hire data science and analytics services that focus on business, or you can hire a Chief Data Scientist to work on your staff full-time.
There are a number of reasons that it is worth considering bringing a data scientist onto your team on a permanent basis; not only will you be able to unlock the power of our current data analytics techniques, but you will also be able to position your business perfectly for future developments. Data analytics is a fast-moving field and the number of ways that we can apply big data analysis techniques to the business arena is growing by the day.
Many of the businesses that analyse big data sets will analyse data that represents a snapshot in time. That is to say, the data has been collected and collated into a database before being fed into the data analytics software. However, there is a growing number of situations whereby the data that is being analysed is being generated and recorded live in real-time. This enables various organizations to make decisions and analyse situations using the most up-to-date information available.
For example, the banking and insurance sectors have now begun to use data analytics as a way of producing more accurate risk assessments. Instead of just looking at someone’s financial history, these businesses are now able to evaluate a larger number of variables and make a more informed final decision.
Similarly, retail businesses can utilise big data in order to gain deeper insights about what trends are emerging and how they can get ahead of them. It is notoriously difficult for a person to predict what the next big thing is going to be, but by looking at variables beyond those that people can easily understand, big data algorithms are able to get ahead of the curve in a way that a person never will.
Driver of Growth
One of the reasons that the modern big data industry is so important to the technology sector more widely is because it is continuing to prove to be one of the biggest sources of new technology jobs today. As the potential applications of big data become clear, and access to the necessary software and hardware to take advantage of it becomes more widespread, businesses for whom data analytics capabilities would have once been a pipe dream are now looking for ways of bringing data scientists onto their staff.
Data analytics is a hugely important discipline for the modern tech industry. Within the tech sector, data analytics underpin the latest advances in machine learning and autonomous vehicles. All those self-driving cars being prototyped by Uber and Google are dependent upon advanced machine-learning algorithms to analyse a camera feed and discern obstacles, road signs, pedestrians, and other obstacles that self-driving cars need to be careful of.
Outside of the tech industry, businesses across the board have been benefiting from our advanced understanding of big data and its capabilities. Businesses in a range of industries and sectors are now applying data analytics to the data that they collect as part of their routine operations in order to gain deep insights about the state of their business and where they are headed.