You’d have to be living under a rock to have not seen the boom in specialized techniques that are involved with processing big data.
These large sets of data are often confusing and confounding – enough so that many entrepreneurs have skipped it completely, only to watch their competitors fly past them at lightning speeds.
Big data can comprise hundreds (or even thousands) of terabytes of storage. Alone, Facebook contains at least 100 petabytes of data. I’m not trying to scare you.
It’s just important that you understand these people give up because they did not know how to solve their problems. We’re going to solve that problem now by showing you a few techniques that you can use for big data analytics.
Machine learning is going to become increasingly important throughout 2018. This includes but is not limited to creating programs that can learn from data provided.
Netflix uses a system like this to judge the type of content that customers are most likely to watch by learning from their viewing history.
So, the first solution you should look into is creating software that can automatically go through data provided to your business and learn from it.
Here are a few examples:
- Email list building programs to separate spam messages from non-spam.
- Programs that learn user preferences (like Netflix and Hulu).
- Determine the right type of content to post for a target market by looking at browsing history on your website.
Learning through association
The association rule is used all throughout the education system to help students learn and retain new information. This proven technique can also work with big data. The Developed app that discovers correlations between the different types of data you receive.
Read more: AI machine learning humanize app development
We see this system in full effect on Amazon. They use your browsing and purchase history to find associative patterns. Then they make recommendations based on those patterns.
Here are a couple more examples:
- Retail stores place items in specific patterns to increase sales.
- Extraction of information about web visitors to find patterns.
- Medical professionals compare biological data to find new relationships to help prevent disease.
Generic algorithms revolve around using mechanisms that are based on evolution – in this case, the evolution of business. Problems are optimized in a way that evolves solutions. Simply put, genetic algorithms use a process that mimics biological evolution.
Here are a few examples to showcase genetic algorithms in action:
- Used to schedule which doctors will be working in emergency rooms at any given time.
- Used to develop content like jokes and puns from artificially creative sources.
- Create business processes that mimic the way a buyer moves through every step of the buying process.
No matter how much data you are dealing with, you can put systems in place to automatically organize, categorize, and discover correlations. The solution is to merge artificial intelligence with your current data collection techniques through the use of software.
Big Data analysis is an important aspect of any business. Regardless of whether you are offering business-to-business, or business-to-community solutions, knowing certain patterns will definitely come in handy.
Since analysis results have such a huge impact on your future decisions, and how you can improve your product, it would be useful to know some solutions for big data analysis.
Here, we will cover a couple of existing techniques you can use to process big data, and depending on the type of business you are running, you can choose the one that is the most adequate for you.
A/B split testing
A very useful technique for those who are web designers, for example. It can give you insight on how to improve certain features of your product, so that your consumers respond more positively to it.
It is a technique that compares a control group with a variety of test groups, in order to get best results.
It can be used to determine which treatments are the best, or what kind of layouts, images or colors to use on a website or product package.
Since we are talking about Big Data, the results you receive are really meaningful and statistically significant. Also, if there are more variables that are simultaneously manipulated during testing, it can be often referred to as "A/B/N testing".
Association rule learning
It is a great technique for increasing sales, or forming customer incentive programs. Association rule learning consists of using various techniques for the purpose of discovering interesting relationships, or to be more precise “association rules”.
When you have a large database of purchase histories, you can ascertain which of the products are most commonly purchased together. One of the surprising discoveries made using this technique is that shoppers who buy diapers also tend to purchase beer.
Edge analytics is relatively new and it is still developing, but once it is perfected it will revolutionize the way we process big data. Basically, the data is analyzed the moment it is collected, so you immediately have a complete analysis. This can be really useful for security cameras, so that irrelevant data is no longer stored, or for navigation devices, etc.
Moreover, large retailers will be able to analyze points of sale, and therefore they could either cross sell or up-sell immediately. Also, repairs will be far easier, since malfunction data is immediately available.
Outsourcing is another viable technique for obtaining the necessary business results. If you are a small business owner, and need a market analysis, hiring more people will only drain your budget. You can find people who already have the necessary equipment, and who can analyze the market or media for you. It is a KPO type of outsourcing, so make sure that you find someone who is really competent in the requested field.
There you have it, these are the techniques you can use to break down big data into meaningful results that can improve the efficiency and revenue of your business.
To process a very large set of data we need specialized techniques and technologies. From the above blog description, you and your business can discover interesting correlations, schedule resources in a more optimized way, and categorize the big data into related groups more effectively.
The above-discussed techniques will help you to collect, organize and interpret the large and complex data more accurately.