Techniques for collecting, processing, and analysing large sets of data

The volume of data we generate has exploded since the internet, social media and data analysis methods transformed our access to information. The explosion is fuelling a revolution in how we collect, process and analyse large datasets.

Estimates suggest over 400 million terabytes of data are generated worldwide each day.  Whether it’s taking photos, creating social media posts, audio or video clips, or generating research or sales data, the bytes add up.

“Every reaction or engagement (like, retweet, share, comment) is a piece data that, if mined, provides valuable insights about your brand and your products, and reveals market trends and customer behaviour,” says Indium, a digital engineering company focused on building modern solutions across applications, data and gaming.

That is a lot of data to collect, process and analyse. Software tools that mine data and analyse it in a myriad of ways, in real-time, are developing at pace using algorithms, machine learning and artificial intelligence to keep us consuming, generating and learning from data, 24/7.

So what is big data? and how are large data sets collected, stored and processed to give us the reliable insights we crave, fast?

What is Big Data?

Size matters and when it comes to ‘big data’ we are talking terabytes or petabytes. But the complexity of the data and other characteristics are also important. Characteristics such as how quickly data is generated factor in when defining large datasets as ‘big data, and also features such as how it can be analysed and used.

According to Google Cloud, ‘big data’ refers to extremely large and diverse collections of structured, unstructured, and semi-structured data formats that continue to grow over time.

Data analysts often use five ‘V’s to assess data and decide whether or not it really is ‘big data’:

• Volume – is the dataset terabytes or larger?
• Variety – is the data collection from various sources, such as social media or the internet of things (IoT)? Does the variety of data include images, text, audio and/or video?
• Velocity – is it real-time data, generated and processed instantly?
• Veracity – is the raw data reliable?
• Value – what value is there in analysing the data in terms of business insights and/or profitability?

Big data analysis may integrate raw data from various sources and in some cases involves transformations of large amounts of unstructured data to structured data.

Structured data tends to be data that can be stored as tables, for example, text, dates and numbers, whereas unstructured data includes audio and video clips and images that are more difficult to analyse without advanced techniques.

However varied the data sources, one thing is certain, many datasets are so huge that traditional data management systems are insufficient to store, process, and analyse them. 

Data collection

Large datasets present real challenges when it comes to collection and storage. The data are often highly variable, with different and changing formats, structure and data sources.  

Automated data collection methods are known collectively as automatic identification and data capture (AIDC) and range from barcodes and QR codes to facial and voice recognition. Data quality varies and the data must be stored in such a way that it can be transformed, mined and analysed to provide the desired insights to support business decisions.

Typically, big data is collected via distributed file systems such as Hadoop, Spark and other open source cloud storage solutions. Rather than keeping all the data stored in one place, distributed file systems store the data across multiple ‘nodes’ allowing data analysis to happen in parallel.

Data storage

Traditional relational (SQL) databases just can’t do the heavy lifting when it comes to large datasets. Non relational NoSQL databases provide more flexibility, storing data in flexible schemas and allowing data scientists and analysts to query large datasets that are less structured and suitable for applications that require flexible data models.

Increasingly data analysts are also using ‘data lakes’ and ‘data warehouses’ to store large datasets.

Data lakes allow structured and unstructured data to be stored together at any scale and remove the need for structuring data. According to Amazon Web Services (AWS), this allows businesses to run different types of analytics – from dashboards and visualisations to big data processing and machine learning – to ensure they can maximise opportunities and gain quantitative insights from their data.

Data warehouses can store historical data as well as new information and are designed specifically for reporting and analytics to generate better business intelligence.

Data processing

Raw data, however big, is not much use as it is. Before useful insights can be gleaned from large datasets, the raw data needs to be cleaned and processed.

This is a tall order for big data. Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. Data processing can help structure the data and arrange it in a database. Rather than relying on one input and one processor, to process large data sets, multiple inputs and processors are used to generate one output.

Data engineers use open-source programming languages such as Java and Python, accompanied by libraries such as Panda, to process and explore large datasets. With Hadoop, the Hadoop Distributed File System (HDFS) stores the data and then a processing model called MapReduce takes care of the processing.

Data analysis

When it comes to large data sets, big data analytics are key. Without effective analytical tools the value of large datasets cannot be realised.

Whether it’s a retail company focussed on developing market-leading products, or healthcare experts searching for new and effective medicines, data-driven insights are essential in businesses’ decision-making.

Data analytics tools are evolving at pace and are designed with specific types of data collected and the desired outputs in mind. Whether it’s sentiment analysis of social media content, data mining to identify patterns and trends, or deep learning to make predictions without extra programming, almost anything is possible. Big data analytics include the following areas.

Descriptive Analytics

Useful if you’re looking to analyse total sales for your company from historical data. Data visualization tools like Tableau aggregate data and provide graphs formats and dashboards that go beyond the graphs and analytics you can generate from an Excel spreadsheet.

Diagnostic Analytics

This type of analysis goes a step further and helps you understand why something happens, such as what has caused a dip in sales. Statistical techniques within tools such as Tableau help identify patterns, repetitions and correlations.

Predictive Analytics

If you want to use your data to make forecasts for future outcomes, predictive analytics tools will help. Statistical analysis and machine learning techniques combine here to mine historical data and make predictions for future events. This type of analysis is highly valuable for business intelligence and can provide meaningful insights for industries from retail to banking to engineering design and more.

Prescriptive Analytics

These artificial intelligence tools, developed by data engineers and data scientists, are highly sophisticated, and use complex algorithms and machine learning to go one step further. They can provide actionable insights, such as pricing recommendations, that can turn predictions into reality. 

Where next for data analytics?

Data science is an exciting and rapidly evolving space. Data scientists, data engineers and data analysts work across a broad spectrum of industries and sectors, from product engineering to healthcare and from design to sales and finance. But more bright minds are needed to take data processing, mining and analysis to the next level.

If you’d like to be part of this fast-moving area, and are considering a career in data science, the 100% online MSc Computer Science with data science at Wolverhampton, may be a good fit for you.

The course is designed to develop agile, flexible and dynamic graduates. It offers a rich blend of computer science and data science training and management skills to set you on the path for a career in data analytics. Find out more about postgraduate computer sciences degrees at Wolverhampton.