BPM Analytics

Big data analytics and BPM – A complete guide

Business Process Management (BPM)

Streamlined workflow, efficient resource allocation, assured quality, accountable staff, handling hurdles, maintained delivery timelines, measured and improvised efficiency, and enhanced agility — are just a few benefits of following a process.

Within an organisation, a business process orchestrates individuals, systems, information, and equipment to facilitate the achievement of specific objectives. These objectives can encompass various activities such as product assembly, delivery, personnel recruitment, or invoice payment.

Typically, a business process is broken down into multiple tasks and assigned to a team. Therefore, it is labour-intensive and needs to be managed for smooth functioning.

The discipline of managing a business process is called Business Process Management (BPM). It entails modelling, analysing and optimising end-to-end business processes, and is concerned with establishing a uniform procedure for routine and repetitive transactions that follow a predictable pattern or process.

Where patterns and repetitions are discussed, Big Data Analytics must be mentioned.

Big data analytics

Big data analytics deals with examining large data sets for patterns and correlation, and extracting meaningful insights.

It has empowered many industries with insights into customer behaviour, market trend predictions, pinpointing bottlenecks in processes, detecting fraud and misuse, and so on. Big data analytics offers endless possibilities because of its wide range of capabilities that include descriptive, diagnostic, predictive and prescriptive analyses.

BPM meets big data

If leveraged for BPM, big data analytics can help businesses in multiple ways, some of which are as follows:

Process optimisation:

Business processes generate large volumes of data. Big data analytics can analyse them to identify hindrances and inefficiencies. This enables businesses to tweak their processes and make data-driven improvements. This streamlines workflows and enhances operational efficiency.

Example: A retail company generates data from sales, inventory and customer transactions. Big data analyses these datasets and spots bottlenecks in the supply chain or delays in inventory replenishment that cause stockouts. This insight helps the company to adjust order quantities and delivery schedules. This ensures better inventory management and optimises the procurement process.

Business outcome projection:

Predictive analysis, a type of big data analytics, can analyse historical data that helps businesses make informed decisions and set up practices that benefit customers and businesses alike.

Example: Say, an insurance company leverages big data analytics to analyse its historical claims data. The algorithm establishes a correlation between the claims and external factors such as weather patterns and demographics. This helps them identify areas prone to accidents or natural disasters. This insight enables the company to allocate resources, adjust insurance premiums and offer risk mitigation solutions accordingly.

Real-time monitoring:

Big data analytics helps organisations monitor business processes in real time for process performance. This enables them to identify and address issues promptly. Real-time monitoring allows rapid response to changing conditions, proactive decision-making and corrective actions to optimise processes on the fly.


Example: A logistics company using big data analytics can monitor its fleet in real-time. By collecting data from GPS trackers, fuel consumption sensors and traffic reports, they can identify deviations from planned routes and optimise delivery routes in real time. They can also detect potential maintenance needs and improve fuel efficiency. Real-time monitoring allows them to promptly address such deviations and disruptions.

Customer insights:

Having a better understanding of customers can greatly benefit businesses. Customer data analysis gives organisations valuable information regarding customer preferences, behaviours and needs. This information can be used to tailor business processes and offerings to meet customer expectations, enhance customer experience and drive customer loyalty.

Example: An e-commerce company analyses big data from customer browsing behaviour, purchase history and social media interactions. By using advanced analytics techniques, they identify specific customer preferences and purchasing patterns. This allows them to personalise product recommendations, offer targeted promotions and improve customer segmentation strategies, all of which are sure to enhance the overall customer experience.

Risk management:

While risks in business processes are undesirable, they are inevitable. Big data analytics is a huge help here. It can identify potential risks, fraud and compliance issues. This allows businesses to implement appropriate controls and mitigation strategies.

Example: A financial institution employs big data analytics to detect unusual spending, account access from a different location and other such irregularities indicative of fraudulent behaviour. This enables organisations to promptly flag and investigate suspicious activities, thereby protecting both themselves and their customers.

A wrap-up

In modern business, the synergy between big data analytics and BPM unveils an exciting paradigm of possibilities. By harnessing data-driven insights, organisations can optimise processes, unlock hidden efficiencies, and chalk out successful strategies.

From process optimisation to real-time monitoring, predictive analytics to customer-centricity, and risk management to transformative decision-making, adopting big data analytics in BPM illuminates the path to enhanced operational excellence.
By embracing this dynamic duo of innovation and intelligence, businesses can navigate the ever-evolving landscape with confidence, charting a course toward a future where success knows no bounds.

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