The importance of Big Data for your business

I'm sure you've heard the term Big Data thrown around a lot, as we now call large or complex data sets too large to be analyzed by traditional processing systems. 

Every second, a huge amount of data is generated around the world via cell phones and other technological devices that we use on a daily basis.

Processing and cross-referencing all this information can be of great value to your business. Through it, you can better understand the market, your customers and improve your company's production, logistics and sales processes. 

The 3 Vs of big data

Systems that process and store big data have become a common component of data management architectures in organizations, combined with tools that support the use of big data analytics. Big data is often characterized by the three Vs:

• the large VOLUME of data in many environments;
• the great VARIETY of data types often stored in big data systems; and
• the SPEED at which much data is generated, collected and processed.

These characteristics were first identified in 2001 by the American Doug Laney, then an analyst at the consulting firm Meta Group Inc. More recently, several other Vs have been added to the different descriptions of big data, including veracity, value and variability.

Although big data does not equal any specific volume of data, big data deployments generally involve terabytes, petabytes and even exabytes of data generated and collected over time.

Big data in science and business

Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns and carry out other actions that can increase revenue and profits. Companies that use it effectively have a potential competitive advantage over those that don't, because they are able to make faster and more informed business decisions.

For example, big data provides valuable information about customers that companies can use to refine their marketing, advertising and promotions in order to increase customer engagement and conversion rates. Historical and real-time data can be analyzed to assess the evolution of consumer or corporate buyer preferences, allowing companies to become more responsive to customer wants and needs.

Big data is also used by researchers and scientists to identify signs of disease and risk factors, and by doctors to help diagnose diseases and medical conditions in patients. In addition, a combination of data from electronic health records, social media sites, the web and other sources provides health organizations and government agencies with up-to-date information on threats or outbreaks of infectious diseases.

In the energy sector, big data helps oil and gas companies identify potential drilling sites and monitor pipeline operations; similarly, utilities use it to track electricity grids.

The origin of big data

Big data comes from a myriad of sources, including transaction processing systems, customer databases, documents, emails, medical records, Internet click stream logs, mobile applications and social networks. It also includes machine-generated data, such as network and server log files and data from sensors in manufacturing machines, industrial equipment and Internet of Things devices.

In addition to data from internal systems, big data environments often incorporate external data on consumers, financial markets, weather and traffic conditions, geographical information, scientific research and much more. Images, videos and audio files are also forms of big data, and many big data applications involve streaming data that is processed and collected continuously.

How big data analysis works

To obtain valid and relevant results from big data analytics applications, data scientists and other analysts must have a detailed understanding of the available data and a sense of what they are looking for in it. 

Once the data has been collected and prepared for analysis, various data science disciplines and advanced analytics can be applied, including machine learning, deep learning, predictive modeling, data mining, statistical analysis, streaming analysis, etc.

Using customer data as an example, the different branches of analysis that can be done with big data sets can include:

Comparative analysis - examines customer behavior metrics and customer engagement in real time to compare a company's products, services and brand with those of its competitors.

Social media segmentation - analyzes what people are saying on social media about a business or product, which can help identify potential problems and target audiences for marketing campaigns.

Marketing analysis - provides information that can be used to improve marketing campaigns and promotional offers for products, services and business initiatives.

Sentiment analysis - data collected on customers can be analyzed to reveal how they feel about a company or brand, customer satisfaction levels, potential problems and how customer service can be improved.

The best strategy for your company

If you want to develop an effective big data strategy for your company, count on the services of 4KST. We specialize in machine learning and data analysis for the B2B segment.

Through our own algorithm and the application of data stream mining, we offer our clients the best up-to-date predictive analysis to boost their management. With real-time updates, our algorithm guarantees effective predictions over time and a competitive advantage for our clients.