What Is Big Data?

Big data has acquired a popular definition, often called the Three Vs. This means that today’s data comes at more incredible Velocity, in greater Variety and increasing Volume.

Velocity is the speed of data transmission. The highest Velocity of data is usually streamed directly into computer memory rather than written to disk. This makes a rapid evaluation and action response imperative, primarily as some IIoT products function in or near real-time.

Volume is the vast amount of data you have to process. This data is usually unstructured and low-density, often of unknown Value. Data feeds might come from sensor-enabled equipment, a mobile app, clickstreams on a web page or even social media. Volumes can vary from dozens of terabytes to hundreds of petabytes of data.

Variety defines what kind of data you’re getting. Instead of traditional structured data types, data can now be unstructured and semi-structured. These data types include video, audio and text, which have to be preprocessed before they mean anything.

Extra Vs

Two more Vs have recently increased in importance: Veracity and Value. It’s critical, especially in business, that your data is truthful (veracious). You need to be able to rely on it, and in an age of hackers and cybercrime, this is ever more difficult.

As part of this honesty, data has its Value. Big data has become a currency where tech companies trade in reliable and accurate data. Finding Value in big data starts with its analysis, which is often the job of AI nowadays. Since the Volume is so tremendous and real-time decisions must usually be made, AI is much more capable than even the most insightful analysts. Add in ML (machine learning), and even more data is produced, which significantly benefits business.

Where Does All This Big Data Come From?

Large data sets date back at least half a century, when the first data centres and relational databases were developed. Once the internet was well and indeed launched, social media sites and other online services started producing a massive volume of data. This required a method of managing and storing it, most commonly on open-source frameworks that made it easier to handle. 

In the last few years, the IoT and IIoT have astronomically multiplied the Volume of big data. More and more objects and devices are connected, constantly gathering data on product performance and customer usage patterns. New ways of managing big data are increasing its Value with the vast scalability of cloud computing and graphic databases. These can display massive amounts of data for quicker and more comprehensive analysis. 

Big Data Benefits

All sorts of business activities can benefit from big data, including:

Machine Learning

Machine learning means that we now teach machines rather than just programming them. This is made possible by the big data available to train machine learning models.

Predictive Maintenance

Big data, both structured and unstructured, contains indications of potential problems that might cause machine failure. These include a machine’s make, model and age, log entries, error messages, sensor readings, etc. Analysis of this data allows organisations to perform preventive maintenance, maximising the viability of parts and equipment. 

Product Development 

Big companies use big data to gauge customer demand using key performance indicators (KPIs). They measure the commercial success of previous and current products and use the data to create predictive models for new products. They may also use data and analytics from test markets, social media and focus groups. 

Operational Efficiency

Big data’s impact on operational efficiency is valuable. Many aspects of production and customer input are available for analysis and assessment. Big data analytics help to reduce outages, adapt to current and future market demands and improve decision-making. 

Driving Innovation

Big data analysis offers endless possibilities for further innovation. It can highlight the connections and interdependencies among processes, entities, humans and institutions. You can then use those insights to determine new ways to improve your business. Big data can help with decisions on planning and financial matters. It can illustrate market trends and help you understand what customers want.

Customer Experience

This more precise picture of customer needs is facilitated by big data collection. Using information from call logs, web visits, social media, etc., enhances customer interaction knowledge. This helps you deliver more excellent Value, such as personalised offers, proactive handling of problems and reduced customer churn.

Fraud And Compliance

Security environments are constantly evolving, along with compliance requirements. You can find patterns in big data that help identify fraudulent activity. You can also speed up regulatory reporting with the large quantities of information you derive.

Pros And Cons Of Big Data

Pros

• More information allows for more complete answers.

• More complete answers double down to provide more confidence in the data.

• More confidence in the data enables a different approach to problem-solving.

Cons

• Size matters – and big data requires a lot of storage space. 

• Volumes constantly increase, creating storage problems for organisations struggling to keep pace.

• Big data needs processing to be of Value for meaningful analysis – 50-80% of the time spent by data scientists is on the curation and preparation of clean data.

• The rapid evolution of big data technology requires constantly modifying frameworks to handle it. Keeping pace with big data technology is presenting a serious challenge.

Embracing Big Data

Big data is a giant boatload of information. We’ve had data sets and databases for several decades, but big data is much bigger and more complex. New data sources such as live stream feeds increase the Volume of these data sets to such an extent that traditional processing frameworks can’t handle them. On the upside, you can use these colossal volumes of data to address business issues in ways you could never have done in the past.