How can BPS Analytics create Radical Change within Industries in the Era of Data?

Business Process as a Service (BPaaS)

Businesses are constantly chasing demands of customers. Most might think business processes have figured it out, but the reality is far from where businesses want to be. In fact, most businesses are not even aware of the customer’s emotional inputs. Do you know what came to the rescue…?? Offering their esteemed customers Business Process as a Service.

Yes, that’s right!

Business Process as a Service often familiarized as BPaaS is a flexible solution to deploy IT-intensive business processes that blends business process outsourcing with cloud computing technology.

Following this definition of Business Process as a Service (BPaaS) and discussion of cases, you will understand how BPaaS is useful to a firm and its dependence and correlation with data.

Business Analytics Process

Recalling how Nokia approached cell phone designing, where they talked to people, asking them questions, while also observing user behavior to understand the presence of actual need.

This in itself is a huge challenge, as sometimes customers cannot put their desires into words. It is just there but not there, which makes it a complicated scenario to handle. In such cases, we have to look beyond the basics of getting a feedback from the customer and start looking at usage data.

Human behavior data is not something that can be plotted directly or linearly, and it is here where the challenge pops up. However, technology offers a solution and this can be solved using predictions through.

Business Process Analytics that entails BPS analytics and data science, which make it easier to predict the needs and demands of customers.

Let’s define Business Analytics Process in simplified terms. Business Analytics is the process through which companies analyze historical data using statistical methods and technology in order to obtain new insights and enhance strategic decision-making.

The following elements comprise of the lifecycle of a Business Analytics Process:

1. Identify problem/need/area for improvement
2. Collect enterprise data on the subject
3. Cleanse and process the data
4. Analyze and report the data
5. Create predictive analytics
6. Deploy model
7. Evaluate efficiency

Let’s look at a business problem…

Fetching customer feedback, the most important aspect for a business to determine how it runs.

Verbal or survey feedback is usually tainted because of time constraints or the societal need to be polite. Usage behavior then becomes a barometer for measuring the requirements truly.

The measuring of usage is what is going to drive customer experience and customer retention, which should help businesses drive customer’s delight.

As an example, let us look at internet data packs sold by telecom companies:

Customers are given a daily packet of internet data, the consumption of which can be very easily tracked. Now here, customers can be delighted by helping them carry forward the remaining data to the next day, such that they can use more of it without paying extra for the next day (in case the usage increases).

As and when all the above considerations are met (through automation), customers are delighted and go through an incremental upward swing, which makes it possible for the companies to sell more.

Role of BPS Analytics in the Era of Data

Before we look into how can Business Analytics Process create a Radical change within industries in the era of data, let’s identify the elements involved in the process.

What is Data Value Chain?

The evolution of data from collection to analysis, it’s distribution and updation to create an impact on the final decision is termed as data value chain.

The interconnection between each step i.e. right from production to use of data is important to be monitored, to bring the ‘value’.

The data value chain has 3 distinct aspects:

• Big Data (data collection)
• Data Analytics
• Artificial Intelligence (AI)

If Big Data is taken into consideration, it seems like a big challenge. However, it has been solved effectively with Analytics and Data Analysis by breaking data down. In doing so, Big Data and Analytics have found their feet, whereas AI, specifically in environments where there is a human element involved, is still trying to find its pivot point.

The impact of what the customer desires is of great importance to the companies and can cause massive shifts in how marketing should be approached.

Moreover, expensive advertisements can be replaced with customer handouts that drive further sales, while the overall cost remains lower with a higher ROI.

Going forward we can see larger investments in consumer behaviour mapping instead of a celebrity advertisement because there is no greater brand ambassador than a happy customer who will willingly generate positive content for the business with no give and take.

eed for Data-driven Audit to fetch BPS Analytics

Creating and sustaining an AI for the sensor based machines or manufacturing sector is far easier and predictable than creating and sustaining one for human interaction as human nature though predictable, can be irrational and dynamic depending on varied circumstances.

Clearly, the relevance of data in developing a usable and functional AI is seen when you focus on the value chain.

With analytics and Big Data being the two driving forces, data-driven audit plays a crucial role to monitor the data performance. The concept of data-driven auditing revolves around automation and AI.

AI for human interaction has huge sets of challenges. If AI merely uses past data to deal with human beings, then the customer experience can become extremely unpredictable. What makes the AI for human interaction challenging is the Identification and Analysis of tone of a person and instant resolution of the issue that is making the customer agitated.

Predictive Analytics with an outlook to the future will help create AI that will help identify the real needs. It will involve human intervention wherever necessary, to create a wholesome human experience.

We are a long way off from a completely independent AI, but to get there we have to ensure we do not ignore data collection and Analytics.

The tricky part in the above mentioned value chain is the invisible but extremely important factor of a bias free and dispassionate data-driven audit.

Even after much advances in Big Data, it suffers from the challenge of being unstructured. Hence, data-driven audit needs to be considered and turned into a concurrent feature that works on Big Data before it goes into any kind of Analytics.

Data Audit at the initial stages helps support the collection by giving it some kind of a structure. This increases the efficacy at the Analytics stage and Analytics can look at data from a structured point of view, if data driven auditing approach is implemented.

Analytics can then drive this with its learnings into Artificial Intelligence, which effectively makes decision making a data driven, automatic and human intervention free exercise.

Speaking of which, let’s highlight the data category framework which defines the relationship between data availability and data quality in an organization.

From a cost perspective data-driven audit might look expensive. The reality is if you want to build an effective Analytics and Artificial Intelligence platform, Data Audit is an investment that is necessary and pertinent.

In the days to come, data audit may not necessarily be a cost function due to legal requirements and privacy laws, due to which, there could be a huge need to perform data-driven audits at the very first stages of data collection.

For an AI driven world, data-driven audit is extremely imperative because it feeds quality data into AI, which then generates intelligence that supports the creation of a standalone intervention free Artificial Intelligence.

Moreover, we need to accept the fundamental fact that changes done at an AI level will end up looking like a patchwork than a seamless experience until Data Audit is made a fundamental requirement to ensure speed, accuracy and stability of the AI function.

Hence, leveraging upon different BPS analytics solutions is the key to drive better performance for your business process.

BPS Analytics Solutions to opt from:

• Customer Analytics
• Risk Analytics and Compliance
• Portfolio Analytics
• Marketing Analytics
• Safety and Risk Analytics
• Forecasting Analytics
• Operation Analytics
• Workforce Analytics
• Financial Analytics
• Call Centre Analytics and the list is endless…

Therefore, it is better to ask what data needs to be captured, and then build one system around it rather than capturing the data and then deciding what to do with it.