Big Data Analytics: A Primer

Published Tue 02 Jan 2018

Big data refers specifically to the management and analysis of very large datasets, which are difficult to process using traditional methods. By examining and analyzing these datasets, businesses can unlock valuable insights, realize new revenue opportunities, improve their marketing strategies and more. The benefits are proven – but how do you go about it?

Data analytics can be daunting. When you're looking to unifying multiple data sources and analyzing that information as one, it can be challenging to figure out a step by step process that works for your company. We've broken the process into steps – extraction, storage, clean-up, analytics, and visualization. Read on to get the lowdown on each stage.

Data Extraction

Most businesses already have a wealth of data ready and waiting to be drawn upon; applications such as Salesforce, or web tracking applications like Google Analytics, are designed to create large amounts of data. However, you most likely have an additional untapped resource in the form of business data within documents such as PDFs and Word documents, as well as on your company’s actual web pages. This information can be tapped into with the help of Data Extraction tools such as Octoparse; increasing your data pool gives you the best opportunity for insight. Web scrapers can also do double duty by gathering competitor data, providing insight from the flip side.

Data Storage

Having a lot of data is not much use if you don't have anywhere practical to put it. Storage for big data analytics should be flexible, powerful and fast. Tools such as Hadoop and Cloudera provide excellent services for storing large datasets, but can be complex for beginners to start working with. Other software such as Talend provide a more accessible front-end to data storage and analytics, but may be less powerful in terms of customization and features.

These specialized data storage tools provide scalability to your data storage, allowing you to store huge amounts of data over multiple servers. Not only that, but such tools tend to offer excellent data loss protection – vital for when your business direction depends on your analytics.

Data Clean-up

Data comes in all shapes and sizes, and not all of it will be valuable to you. Creating usable data sets is critical to finding accurate insights from your data, and is a step that should not be overlooked. ‘Cleaning data’ refers to removing corrupt, inaccurate or irrelevant data. This covers a spectrum of issues – punctuation, number formats and language can all throw a spanner into the works of your data analysis and result in incorrect conclusions being drawn.

OpenRefine is open source software which is designed to handle huge data sets, and given that most data is stored in common, universal storage types (e.g. csv files), it works seamlessly with most data storage tools. It also lets you connect with external web services to extend your data, and create links between datasets, taking the first steps towards your data analytics.

Data Analytics

Data analytics is a slightly misleading term, as there are actually two elements to analyzing your data – mining and analysis. Data mining involves creating predictions and decisions based on your existing data; it allows you to create predictive models and determine trends in your data. Data analysis, on the other hand, is looking for answers to your questions within your data. You can explore the information within your data, and test your hypothesis based on factsStatwing is an excellent tool for data analysis, whilst RapidMiner is fantastic for predictive analysis and data mining. It’s important to remember, though, that your predictions will only be as good as your models. It’s wise to employ the skills of a data analyst to refine your models, and put your data to work fully.

Data Visualization

A picture is worth a thousand words. By putting data into a visual format, it can be easier to digest and pick out trends. Not only that, but visual data can be much more understandable to the rest of your company. Tools like Datawrapper can help put your data into charts and graphs, as well as other visual formats, and convey meaning more effectively than raw data. Utilizing data visualization can also make it easier to check how small differences to your input data or predictive models can change your results, helping you to drill down to accurate conclusions.

Bringing it all together

Big data analysis can be manageable and approachable when you break the process down. Many software solutions will carry out multiple steps of the process for you, while other software solutions specialize in one element of analysis. It's best to explore your options to determine what works best for you and your business. No matter what option you choose, though, if you apply tried and true data analytics principles, you're sure to get some useful insight out of the wealth of data your business has. The important thing is to make sure you utilise the findings effectively to make smarter decisions and improve the way your business operates.

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