Impact of Big Data on Process Optimization



Recent advances is the technology to gather, transmit and analyze huge amount of data, referred to as Big Data, in real time has impacted the way business is conducted across domains. Big data promises to disrupt the competitive landscape and is deeply impacting various functions within an organization. This article explores the impact of ‘Big data’ on the process optimization discipline.

Internet of Things

The 21st century concept of “Internet of Things” promises to connect the big data with the processes. As per a recent McKinsey & Co report, Internet of things can have an impact of $2.7 to $ 6.2 trillion by 2025. This presents a huge revenue potential for organizations involved in optimizing processes based on the big data.

Onset of big data

Advances in social media, sensors etc., has made it possible to continuously gather information about people, machines, processes and to transmit the information in real time. It’s astonishing to note that 90% of all the data available in the world today was created in the last two years. Big data comes in high volume, at high velocity and in a variety formats. The measurement of big data has gone quantum. It’s possible to capture minute level data, hitherto unavailable and analyze them. Data is coming in from social media, credit card spending reports, online spending, consumer internet browsing habits etc. Organizations that can make use of huge amount of information at their disposal tend to gain competitive advantage over others. Organizations can make use of various analytics software available to analyze the big data and make changes to their process accordingly.
In their book "Keeping Up with the Quants," professors, and analytics experts Thomas Davenport and Jinho Kim advocate the below steps to effectively make use of big data:
  • Define the problem - identify the problem, review past findings
  • Solve the problem - create the model , collect data, analyze data
  • Communicate the findings

Big Data and Process Optimization

Once big data is analyzed, the natural next step is to act on it by finding the relevant business processes and optimizing them for better results. Big data shortens the life-cycle and provides a microscopic view for measuring the ROI from process improvements.





The following steps could be followed to optimize business processes based on data analytics:
  1. Identify the type of data available - volume, velocity, variety
  2. Combine business understanding with Data Analytics
  3. Identify critical processes 'impacted by/related to' the data
  4. Work with the business SME's to humanize the data by applying business insights and help them make the decisions on required process changes
  5. Collect process metrics
  6. Identify process improvement opportunities based on the data analytics results
  7. Implement the process changes/improvements

Big Data & Process Optimization
•      The data analytics results as starting point for process optimization
Or
•      Identification of critical business processes leading to identification of type of data to be analyzed out of the huge amount of data
•      After process optimization (through elimination of bottlenecks, automation, etc.,) collect  new data set and analyze for any improvement
•      Process Monitoring might overlap with collection of data from outside sources

 

Data Analytics & Process Monitoring


Data Analytics
Process Monitoring
Data Analytics measure the data related to the business entity worked on by the process
Process Metrics measure the attributes related to process execution
Data analytics tools have the capability to display the data surrounding the processed business entity such as the sales, time to market, customer satisfaction.
Process Monitoring tools such as IBM Business Activity Monitor or BIRT can display the status of activities working on the business entity


Application:

E-Commerce

Data on order fulfillment time periods – starting from the product leaving the supplier warehouse through product reaching the retailer’s warehouse, time spent during transportation, time spent in the distribution center to the time stamp on which the product is delivered to customer – can give great insights on the order fulfillment process of e-Commerce retailers. After the bottlenecks, if any, in the order fulfillment process are removed, the new data set on the orders can be analyzed to measure the ROI. The data on order fulfillment can even be compared across regions to identify any best practices that can be replicated and standardize the order fulfillment process across regions.

Retail

Data on merchandize sales across stores – which products are selling, which products are over stocked, which products are occupying more retail space but selling less, what products are the customers looking for – can be used to identify optimization opportunities in the product sourcing process. Data analytics provides an effective method to measure the results of the improvements made in the product sourcing process from suppliers.
Retailers gathering information on consumer spending patterns through loyalty programs can update their process to define relevant product & services to the consumer

HealthCare Insurance

Error resolution time data from the call center of a health care insurance can lead to an analysis of customer issue resolution process – customer information scattered across enrollment, billing, benefit systems and claim processing systems. Improvements in processes could be followed up by tracking the volume of issues raised by customers and issue resolution rates.
Data on the classification of claims could be used to devise intelligent business rules around claim classification and automated settlement.

Manufacturing

Gathering data on the production levels of assembly lines in real time can help modify the process for supplier sourcing and transportation of the finished goods

Banking

Capturing the client’s banking pattern along with their demographics and income profile could help the banks tweak their processes to classify clients – High Priority, At Risk, Maintain, Potential to Grow – and better devote their resources. The new financial product set up processes can be customized and fast tracked for ‘High Priority’ and ‘Potential to Grow’ customers.
Domain
Data Analytics
Processes Impacted
E-Commerce
•       Order Fulfillment Time Period
•       Supplier to Retail Warehouse transfer Time
•       Time spent by Product at Distribution Center
•       Time from Distribution Center to Consumer
•       Order fulfillment process of e-Commerce retailers
•       Compare order fulfillment data across regions
•       Standardize order fulfillment process
Brick & Mortar Retail
•       Products with High Sales
•       Overstocked products
•       Products with high store inventory  but low sales
•       Products capturing consumer attention
•       Consumer spending patterns through loyalty programs
•       Product sourcing process from suppliers
•       Inventory management and Store merchandizing process
•       Process to define relevant product & services to the consumer
HealthCare Insurance
•       Call center Error resolution time
•       Data on classification of claims
•       Customer issue resolution process – customer information scattered across enrollment, billing, benefits and claim processing systems
•       Devise intelligent business rules on claim classification and automated settlement
Manufacturing
•       Real time data on production levels of assembly lines
•       Supplier sourcing process & finished goods transportation
Banking
•       Client’s banking pattern
•       Demographics and income profile
•       Processes to classify clients – High Priority, At Risk, Maintain, Potential to Grow
•       Decisions on devoting resources to clients
•       Customizes product set up process for high priority customers

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