How Big Data Improves Business Processes

How Big Data Improves Business Processes

August 29, 2019 Uncategorized 0
consulting big data

“Futuristic research from International Data Corporation (IDC) indicates by 2022 organizations will devote approximately $1.97 trillion to enable Digital Transformation”

Introduction

Technology is continuously evolving, and with every new turn, results in the consumption of large amounts of data. Researches from firms such as IBM show we create approximately 2.5 quintillion bytes of data per day.

In effect, Big Data, is not a trend, it’s a reality. While handling this much data influx can be challenging, Big Data offers enormous benefits to businesses spawning through similar technologies like user behavior and predictive analytics. This aspect of technology comes with the potential to lead transformation in developing effective business leaders and faster data-driven decision making.

How Big Data Improves Business

“Big Data”, has for some time now made waves across industries. Every executive in General Mills to Mcdonalds has been implementing new strategies to reap the rewards of big data. In truth, the concept of Big Data is constantly evolving and remains a driving force for several digital transformations, like Artificial Intelligence (AI), Data Science, and the Internet of Things (IoT). But what exactly does Big Data offers is the business world? Here are a few examples we wanted to share to inspire your teams for their future projects.

Customer Care and Management

Proper handling of customers is one area where big data is widely implemented. A variety of data models have been developed to analyze customer data yielding excellent results. The outcomes from the analysis are effectively streamlined to enhance business decisions. Data analytics can be applied in:

  • Developing satisfactory pricing strategies
  • Evaluating the quality of customer satisfaction and services
  • Gauging the effectiveness of customer-related methods
  • Improving supply chain management and Maximize customer value
  • New customer acquisition and holding existing ones
  • Perform accurate predictability analysis and Verify customer data
  • Providing and Predicting correct customer behavior and categorization

 

Effective Waste Management

Wastage consumes large portion of business resources. With Big Data and proper analytics, businesses can improve their waste management processes efficiently. The key benefit of using big data analytics is the precision it offers to business intelligence. This precision aids businesses make knowledgeable waste management decisions. At the center is the measurement, which becomes easier to recognize the business processes that produce the most waste. So, if you plan to use big data in managing waste, the tips below would aid you to gain maximum benefit:

  • Decide what data your business want to measure (for instance fuel usage, raw materials, time, etc.)
  • Take measurements at different stages of the selected process (more measurement points imply the better quality of data).
  • Use experts and specialized software to analyze data alongside its implications.
  • Make necessary adjustments to reducing waste (could include installing measurement devices for inefficiency alert).

Manufacturing Processes

The use of big data analytics enhances accuracy and efficiency in manufacturing methods. With the Industrial Internet of Things (IIoT) already driven by data analytics and sensor technology, several modern manufacturing firms are embracing the concept. As expected, manufacturers with processes involving large data sets are at the forefront of the adoption race. For instance, before computer chips are released, it usually undergoes 15,000+ tests. Nowadays, the use of predictive data models has reduced the required number of tests, thus saving millions of dollars in manufacturing costs. Small-scale manufacturing firms are also making use of data analytics to restructuring their processes. Big data in the Manufacturing sector can be used in:

  • Customization of products
  • Evaluating the quality of raw materials and parts
  • Forecasting, Testing, and simulation of new products
  • Improve energy efficiency
  • Performance Evaluation of suppliers
  • Risks Management in supply chain
  • Tracing defects and Tracking product qualities

 

Product Development and Management

Historically, developing any product comprises of several data collection and analysis. It largely explains why Big Data is of significant benefit in the business processes of preparing a product. Before any product release to the marketplace, developers are expected to assemble and examine data as regards competition, customer experience, pricing, and product specifications. The following questions that also need to be answered include:

  • What are the leading market trends?
  • What are the offers and pricing offered by competitors?
  • What strengths and limitation do competitors’ products have?
  • What problems do our products aim to solve?
  • Which product services would impress the customers?

To completely answer the questions above involves more process analysis of extensive data. In comparison to traditional approaches, data analytics delivers better accuracy and all-inclusive approach in product development. This approach ensures all product developed are suited to solving a market need. Data analytics can be used in extracting data from a variety of sources including Customer surveys, crowdfunding and Manufacturer sites, Marketing blogs, Online product reviews, Products associations, Retailer catalogs, Social Media Platforms, and more.

Talent Management and Recruitment

Human Resource (HR) is amongst the essential business components. With HR, managing and recruiting talent can be done accurately and thoroughly using big data analytics. For instance, predictive data models can be useful in assessing a worker’s performance. But still, most businesses make such decisions based on inadequate data, thus costing an attractive fortune in the long run. Data types useful in developing better talent management strategy based on big data include for:

  • Delivery and Production delays
  • Employee absenteeism
  • Employees error rates, work output data, training data, and profiles data
  • Employee’s workload and Staffing levels
  • Employee Rewards and Performance Appraisals
  • Revenue per Employee Evaluation
  • Six sigma data

The use of Big Data in talent management comes with several rewards, including helping business management recognize productivity problems, acquiring talent with suitable needs and values. It also helps management predictions, encourages innovation, and in understanding the abilities and needs of various employees.

CONCLUSION

Thanks to Big Data, businesses are discovering and learning new approaches to assemble actionable and smart insights towards helping them improve as a business and stay ahead of the competition. Big Data is not just assisting businesses to understand their clients better, it is also about improving business processes. The efficiency of Big Data help accelerates better decision making.

What can our consulting team do for you?

Still not sure about how Big Data can improve your business process? Are you looking for an experienced team to guide and implement optimized Big data service for your business process?

Cheron Analytics comprises of an experienced team of data scientist and network engineers ready to help your business process attain its full potential. We offer a flexible variety of Data services. Click here to learn more.

FAQ’s

What is Big Data?

The term “Big Data” defines a large volume of data (both unstructured and structured) that engulfs a business daily.

Who needs Big Data?

Everyone who uses data. From small firms to big firms, suppliers, and customers as well.

What is the benefit of Big Data in waste management?

With Big Data applied to waste management processes, proper analytics of a business waste can improve its efficiency.

Does human resource need Big Data?

Yes, Human Resource can employ predictive data models to accurately and thoroughly manage talent recruitment alongside assessing a worker’s performance.