5 Uses of Big Data Analytics in Business Process Management

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Data analytics is a game changer. Companies that have already realized its potential and adopted it are reaping big profits. Big data analytics involves the review of large data sets to establish associations, trends, and patterns. On the other side, business processes are the methods used to produce and deliver work.  Big data analytics in business process management marries the two disciplines into a powerful organization improvement methodology.

 

Business processes involve input and output. Examples of business processes are employee recruitment, manufacturing, and customer engagement. This article discusses how data analytics can improve business performance and repair broken business processes. It is a valuable resource for a business that is looking to introduce or widen the application of big data analytics in business process management.

 

Five Uses of Big Data Analytics in Business Process Management.

 

 

Waste Management

 

Wastage eats up a large chunk of business resources. This can be as much as 20-30 percent. With data analytics, companies can come up with efficient waste management strategies. The main advantage of data analytics compared to other methods of business intelligence is precision. The precision helps your business make informed waste management decisions.

 

Measurement is at the center of data analytics for waste management. With it, it becomes easy to identify the business processes that generate most waste. Goals and methods of reducing the waste are then developed. If you are planning to use data analytics for waste management, the following tips will help you gain maximum benefit from it.

 

  • Decide what you want to measure. This can be time, fuel consumption, raw materials, or other things depending on your business.
  • Take measurements at various stages of the business process. The more the measurement points the greater the quality of data you will get.
  • Analyze the data with experts and specialized software to determine its implications.
  • Make adjustments to reduce the wastage. You can then install measurement devices that alert you whenever there is inefficiency.

 

 

Talent recruitment and management

 

Human resource is one the most critical components of a business. With that, talent recruitment and management should be handled thoroughly and accurately. The HR department has a lot to gain from data analytics. For example, predictive data models can be used to assess an employee’s performance. The sad news is that most businesses make their talent management decisions based on inadequate data, and this is costing businesses a handsome fortune.

 

The kind of data that you can use to develop a better talent management strategy for your firm includes

 

  • Production and delivery delays
  • Employees profiles
  • Employees error rates and work output data
  • Staffing levels and employees workload
  • Performance appraisals and employee rewards
  • Employee absenteeism
  • Revenue per employee evaluation
  • Six sigma data
  • Employee training data

 

The use of data analytics in talent management comes with the following rewards

 

  • It helps the business management identify where there are problems with productivity.
  • It helps the company acquire talent that suits the business needs and values.
  • It helps the management predict when there will be changes in turnover due to staffing issues.
  • It encourages innovation within the business.
  • It helps the management understand the needs and abilities of different employees. This can help the business retain skilled employees for longer periods.

 

 

Customer Management

 

This is one of the areas where data analytics is extensively used. Data models are used to analyze customer data. The results of the analysis are then used to streamline business decisions. You can use data analytics in your firm to

 

  • Gauge the effectiveness of customer-related processes
  • Evaluate the quality of your customer service and customer satisfaction
  • Provide accurate customer categorization
  • Acquire new customers and retain existing ones
  • Verify customer data
  • Predict customer behavior
  • Improve supply chain management
  • Carry out accurate predictability analysis
  • Develop favorable pricing policies
  • Maximize customer value

 

 

Product Development

 

Historically, product development involves a lot of data collection and analysis. Maybe that explains why one of the major uses of data analytics in business processes is product development. Before a product is developed and released to the market, the developers have to collect and examine data regarding the product specifications, customer experience, competition, and pricing. The following questions are some of the questions that have to be answered

 

  • What are competitors offering and at what price
  • What problems do the competitors’ products solve
  • What are the strengths and weaknesses of the competitors’ products
  • What are the markets trends
  • Which specifications will impress the customers

 

For such business processes that involve analysis of large sets of data, data analytics comes in handy. Compared to traditional methods of business intelligence, data analytics offers accuracy and comprehensiveness in product development. This ensures that the kind of product developed is what the market needs. If you are planning to launch a new product, you can use data analytics to extract and use data from sources such as

 

  • Online product reviews
  • Marketing blogs
  • Customer surveys
  • Retailer catalogs
  • Manufacturer and crowdfunding sites
  • Products associations

 

 

Manufacturing

 

Manufacturers are using data analytics to enhance accuracy and efficiency in manufacturing processes. The adoption of data analytics and sensor technology are the primary drivers of the Industrial Internet of Things (IIoT), towards which many modern businesses are moving. Data analytics is being used to solve specific manufacturing problems rather than being implemented in wholesale.

 

As expected, manufacturers whose processes involve large data sets are leading the pack in the adoption of data analytics. For example, previously, before a computer chip was released to the market, it had to undergo around 19,000 tests. Today, through the use of predictive data models, technology companies have reduced the number of tests required thus saving roughly $3 million in manufacturing costs.

 

You should not presume that data analytics is for big companies only. Small-scale manufacturers are also using data analytics to streamline their processes. Manufacturing industries can use data analytics in the following ways

 

  • Tracking defects in the manufacturing process
  • Increasing energy efficiency
  • Tracking product qualities and abnormalities
  • Assessing the quality of raw materials and parts supplied by third parties
  • Evaluating the performance of different suppliers
  • Forecasting changes in output
  • Testing and simulation of new products
  • Managing risks in the supply chain
  • Customization of business products

 

 

Big Data Analytics in Business Process Management

 

Data analytics has not been around for long. Despite that, it has increased the profit margins of businesses that have adopted it by enhancing efficiency and cutting costs. Now that the world is moving towards data analytics and Internet of Things (IoT), you should integrate big data analytics in business process management before it’s too late.

 

This is a guest blog contributed by Accelerate

 

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