Do you really know your customer? Your operations? Your business? Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

​

Organizations can use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.

Organizations now have a treasure of large amounts of insights extracted from big data analytics that can transform the decision-making process to a different level. Using big data analytics can support new product development and shortens the gap, and close it in some situations, between what customer wants and what your product delivers. In fact, big data analytics can lead to significant improvements to your current products making them leaner, cheaper, and much more satisfying for current and future customers. Going with the flow, big data analytics will lead to a better environment as it supports the elimination of waste, and unnecessary manufacturing steps, and of course enables big picture decisions related to environmental sustainability.

​

Manufacturers always face an important challenge which is balancing economies of scope and economies of scale. Both present competitive opportunities and advancement, yet each has its own challenges such as standardization of design for economies of scale and limitation of cost saving in economies of scope. Big data analytics enabled manufacturers to establish a better balance between both worlds through the ability to dramatically improve market demand forecasting in size and requirements leading to better planning, supply chain, production, and customization decisions. 

 

​

​

​

​

 

Data analysts, data scientists, predictive modelers, statisticians, and other analytics professionals collect, process, clean and analyze growing volumes of structured transaction data as well as other forms of data not used by conventional BI and analytics programs. Here is an overview of the four steps of the big data analytics process:

Collect: Data professionals collect data from a variety of different sources. Often, it is a mix of semi-structured and unstructured data. While each organization will use different data streams, some common sources include internet clickstream data, web server logs, cloud applications, mobile applications, social media content, text from customer emails, survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT). Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information service providers.

Data is prepared and processed: After data is collected and stored in a data warehouse or data lake, data professionals must organize, configure, and partition the data properly for analytical queries. Thorough data preparation and processing make for higher performance from analytical queries.

Data is cleansed to improve its quality. Data professionals scrub the data using scripting tools or data quality software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.

The collected, processed, and cleaned data is analyzed with analytics software. This includes tools for data mining (sifts through data sets in search of patterns and relationships), predictive analytics (which builds models to forecast customer behavior and other future actions), scenarios and trends, and machine learning, which taps various algorithms to analyze large data sets deep learning, which is a more advanced offshoot of machine learning text mining and statistical analysis software, artificial intelligence (AI), mainstream business intelligence software, and data visualization tools.

The keys to a successful big data analytics program:

• Have a clear objective and direction

• Know what you want to learn from the data

• Understand your sources and the possibilities they present

• Using cyber security to protect your data that became a critical asset

• Set clear uses of the resultant information

Big data analytics uses and examples

Here are some examples of how big data analytics can be used to help organizations:

​