Dealing with data overload

An expert explains the complexities and opportunities in business technology, what’s available and what it can (and can’t) do

By Lisa Woodley

I’ve spent most of my career spearheading, building and driving change with electronic insurance distribution platforms, as well as broker and insurer back office systems. With the advent of these and so many other technologies businesses are facing the problem of data overload.

Just think about how much data your business collects and how many processes your staff execute each and every day:

• Email

• Text messages

• Voice messages

• Websites

• Social media, LinkedIn, Instagram, Facebook and the myriad of other platforms

• Accounting systems

• Policy management system

• Claims management systems

• Customer relationship management (although some CRMs interlink and share data with the policy management system) • Data exchanges with insurers, funders, banks, surveyors, assessors, etc

• Application forms, claim forms, contracts, invoices, policy schedules, word documents, PDFs…

Over the years, I’ve seen new technologies help us better process and manage our data. Each generation of technology helps us process and manage our data more efficiently.

But there is so much data now that it is becoming difficult to know what you have and how to use it effectively.

This generation of technology includes “AI”, “machine learning”, “RPA” and “bots”.

• What are they?

• How can they help your business deal with the volume of data

• How do you measure the value they bring to your business?

• What will be the impact on your business? So let’s first understand what some of them are:

Artificial Intelligence (AI)

Siri and Alexa are prime everyday examples of Artificial Intelligence.

AI is a collection of many different technologies working together to enable machines to comprehend, act, and learn with human-like levels of intelligence. Maybe that’s why it seems as though everyone’s defi nition of artificial intelligence is different.

Technologies like machine learning, image recognition and natural language processing are all part of the AI landscape.

Each one is evolving along its own path and, when applied in combination with data, analytics and automation, can help businesses achieve their goals. (Source: Accenture)

Machine learning

At a very high level, machine learning is the process of teaching a computer how to make accurate predictions using historical data. Those predictions may be answering:

• whether a photo contains a car or a truck,

• whether the use of the word book in a sentence relates to a paperback or a hotel reservation, or

• whether an email is spam.

The key difference from traditional computer software is that a human software engineer hasn’t written the software code that instructs the system how to tell the difference between the car and the truck. Rather, the machine looks at all the data and develops its own algorithm based on correlation of data from past events.

A machine-learning model has to be taught how to reliably distinguish between the different types of data by being trained on extremely large amounts of data. For a machine to tell the difference between a car and truck it needs anywhere between 10,000 to 1 million images containing cars and trucks (and different types of cars and trucks) in order to train it. The more images you provide, the more accurate the machine learning is likely to be.

AI in your office

It is very likely that your business is using AI and machine learning today. If you use Microsoft Office, you may have experienced its intelligent writing assistant, Microsoft Editor. This is a day-to-day example of a machine learning-based tool. It provides you with suggestions to improve your grammar, conciseness and readability, among others, while you type.

Microsoft Outlook uses machine learning to suggest what emails you should read first. It can also read out your message. Outlook uses machine learning and natural language processing to suggest quick replies to the emails you receive. These replies may include a “looking forward to it,” or schedule a meeting.

Microsoft Excel uses natural language processing (NLP)) to enable you to ask questions about your Excel spreadsheet data, and Microsoft PowerPoint uses machine learning as it analyses the structure of your slides and makes suggestions on different slide layouts.

Robotic Process Automation (RPA)

RPA is the automation of physical and digital tasks that human workers usually perform. The task is performed by software and/or hardware which are called “robots”.

The robot can be taught a workflow with multiple steps and applications. For example:

• taking received forms from emails • sending a receipt message to an email recipient

• checking a form for completeness

• filing the form in a folder or document management system, and

• updating your CRM or back office system with the name of the form, the date filed, and so on.

RPA software is designed to reduce the load of repetitive, simple tasks that your staff perform.

RPA in your office

RPA can help your staff manage the volume of information and requests you receive every day. Instead of spending time updating various systems, your staff can be doing more valuable things for your business like talking to clients.

Some more simple processes you can automate using RPA include:

1. Application form automation

2. Proposal form automation

3. Updating client profiles

4. Claim lodgement notifications

5. Certificates of insurance

6. Policy administration/servicing

But there are few “gotchas” you have to know and some key things you need to look out for with robotic process automation.

RPA won’t fix a process you don’t fully understand or that is otherwise fundamentally broken. That’s a basic – and frequent – misstep that commonly leads to an RPA project failing to achieve its goals.

Most processes can be automated but they may not save time, and the cost to automate the process may outweigh the value it brings to your business. A recent ITnews article detailed how RACQ has been automating processes for the past two and a half years in its contact centre.

Although a number of these were successful, they attempted to automate a complex process that required the robots to access multiple systems, analyse information and deliver the results back to the service staff.

RACQ shut the project down before it went to pilot because of the time it took the robot to perform tasks. It just wasn’t feasible for them to expect their customers and service staff to wait that length of time for a result.

Vendors can prototype potential processes very quickly and can show you “quick wins”. As tempting as it may seem, RPA must be treated like any other application from an implementation and ongoing maintenance perspective.

You need to consider the setup and implementation costs as well as your ongoing costs.

• What are the ongoing licence fees?

• What other hardware and software will I need?

• Can my existing servers and desktops handle the additional capacity?

Implementing any new system or application requires a degree of rigor and planning. EY produced a paper recently titled “Get Ready for Robots” Why planning makes the difference between success and disappointment. This explains why RPA projects fail and the top 10 things you need to prepare for to ensure you get the value from your investment.

So how does all this tie back to data and helping to deal with the data overload?

To leverage any of the new tools, you need data.

The insurance industry has a phenomenal volume of data. Both insurers and brokers have access to more data than ever before.

However, we have a data challenge.

A report published by Jeffrey Bohn, the Chief Research Officer at Swiss Re Institute, says the challenge facing the insurance industry “is that much of these data are incomplete, noisy, not well curated, not available at the time most needed, and sometimes lost somewhere in an organisational silo”.

He says that finding and curating the right data “is a vital first step in the creation of an algorithm”.

“A data item only has value if it is collected, curated, transformed, and processed in a way that meets a specific need at the right time and in a form that makes sense for the objective.”

So accessible, clean data will make your AI and RPA journey faster and cheaper.

But before you start, you need to think about:

• How much do you want to know about your customers, staff, partners and products?

• Where will you store the data?

• How will you continually collect, clean and validate the data?

Where should you start?

Sadly there is no silver bullet. But data is like unrefined gold buried deep in a mine. It is a precious resource for your business, but you need to know where it is and unearth it.

In order to find gold, you need a good map. To find data, you need a good plan. To build your plan, you first need to:

1. Understand your Data

Identify and review your data sources.

Examples of data sources include:

• Databases attached to different business systems

• Accounting software

• CRM platforms

• Email sources

• Document management systems

• Web application data

• Insurer platforms or other data exchanges

• Information related to mobile app usage statistics

You should measure the quality of your data, because it is often:

• Inconsistent: It contains both relevant and irrelevant data.

• Imprecise: It contains incorrectly entered or missing information

• Repetitive: It often contains duplicate data.

So you may need to invest in cleaning up your data.

2. Understand your business processes

Once you identify your data sources, you then must determine what business processes update these data sources. Some questions you might want to ask are:

• What kind of information do they contain?

• How does the information in one data source relate to another source?

• What’s the process to connect different data sources together.

Once you’ve completed these steps, you will then be able identify the data and business processes that will deliver the most value to your business. This will help you prioritise your investment in automation. You are now ready. Start digging for gold!

Lisa Woodley has worked in insurance technology for her entire career spanning some 40 years. She was until recently EGM Broker Technology at Steadfast, working on the development of the Steadfast Client Trading Platform and the redesign of the group’s Insight broking platform.

insuranceNEWS October/November 2020