How Big Data drives innovation

What is Big Data

It is this environment of fierce competition which is driving companies to generate more data than ever. Big Data was born out of this race. In fact, many industry experts advise leaders that Big Data drives innovation. Even the most advanced technology is not as beneficial as the knowledge of how to apply it. Generating actionable insights that allow management to make statistically beneficial decisions is the current strategic bedrock of organizations.

Big Data is information that contains a greater variety arriving in increasing volumes and at an accelerating velocity. These three Vs are common terms to help define Big Data. Simply put, Big Data is typically larger, more complex data. Data can be generated from a variety of data sources. Part of the definition is that data sets are so voluminous that traditional data processing doesn’t work well. But this type of data gets transformed to resolve business problems or to expose new opportunities. Previously these items were unable to be seen and why Big Data drives innovation.

Starting with Big Data

In a matter of decades, the information age is has changed the business world completely. That is because Big Data can work to address a broad range of business activities. Everything from managing customer experience, marketing, and operations. Big Data gives insights that open up new business models. Getting started is comprised of three key steps:

Big Data gives you new insights that lead to new opportunities and business models. Remember three key actions to get started: 

Collect & Integrate

The first step is to bring together data from many disparate sources. Souces can include transactional data, analytics, and social media. Traditional data integration mechanisms, such as ETL (extract, transform, and load) ordinarily are not adequate for parsing and reporting. New approaches are required to analyze Big Data sets at the terabyte, or even petabyte scale. During integration, you collect data, process it, and make sure it’s formatted so that your business analysts can utilize it.

Analyze

Big Data pays off only when you analyze and act on your data. Get increased clarity by generating a visual analysis or dashboard. Have experienced data scientists explore the data further to make new discoveries. Share findings with all decision makers. This way, appropriate data models are built. Make Big Data work for your company in a way that drives innovation while also improving the bottom line.

Manage

Big Data requires a robust storage solution. The cloud offers ultimate scalability, but if you have a data center, on-premise could be an option. The cloud is increasingly more popular because it enables administrators to add resources as needed quickly. Many enterprises choose their storage solution based on where data is created.

Practical Examples

According to economic analysts, Big Data is the next frontier for innovation, competition, and productivity gains. Big Data has become a critical factor in production levels, matching labor, and capital assets. By using Big Data, companies can gain real competitive advantages. Useful for solving the following tasks:

• market forecasting
• marketing and sales optimization
• product development
• managerial decision making
• labor productivity increase
• efficient logistics
• monitoring the status of fixed assets 8.9

At manufacturing enterprises, Big Data also expands as a result of the implementation of the Industrial Internet of Things (IoT) technologies. During this process, the main components and assemblies of machine tools and machines are equipped with sensors, actuators, controllers, and, sometimes, inexpensive processors capable of performing boundary (foggy) calculations. During the manufacturing process, data is continuously collected. Analytical platforms then process these data sets in real-time. 

Decision Time

Reports need to be shown in the most convenient way for critical thinking. Based on the analysis of the data obtained, conclusions are drawn about the condition of the equipment, peak performance, maintenance schedules, and the quality of its products. Thanks to real-time information monitoring, enterprise personnel can:

• reduce downtime
• increase equipment performance
• reduce equipment operating costs
• prevent accidents

For example, in the chemical manufacturing industry, operations control technicians receive an average of about 1,500 alerts per day. That is more than one alert per minute. The alarms lead to increased employee fatigue by having to make on-the-spot decisions constantly. But when a suitable analytical platform filters out unimportant information, the operators have the opportunity to concentrate on critical jobs. Now they identify ways to prevent accidents. 

Also, based on the analysis of Big Data, it is possible to do things like calculating the payback period of the equipment. Knowing ROI makes the possibilities for making capital investments and hiring personnel with confidence.   

Data-Based Control

Data collection initiatives must be managed. It uses resources. There are costs. But, regardless of any setbacks, the trend to leverage more data for business, government, and society continues. The reason for this is simple. Decisions and actions made based on analytical methods continue to surpass those based on intuition and experience. 

The largest companies are actively investing in Big Data. They are hiring more Data Scientist. In other words, the demand for data-driven strategies is expanding. Small and medium sized companies should pay close attention to this trend. The leaders of SMBs are in a good position to observe what works. Successful SMBs will partner with experts, optimize collection practices, and leverage information. This way, smaller companies are also shaping their own organization’s future based on reliable information.