Big data might be the new business buzz word, but smart brokers will take a prudent approach to the latest fad, say experts.
Big data might be the new business buzz word, but smart brokers will take a prudent approach to the latest fad, says SAS high-performance analytics expert James Foster.
The hype surrounding big data has seen a number of businesses jump on the bandwagon, without first stopping to think why, says Foster.
“Just because everyone is talking about big data doesn’t mean you should have a big data project. It’s about thinking about what issues you have in your business and the ways big data could help with those,” says Foster.
“A lot of businesses have all these big data products that are really about capturing data –without thinking about what the actual business benefit of doing it is, what the project is going to achieve and how it’s going to pay for itself.”
Big data can create a huge competitive advantage for mortgage brokers, says Foster, if business owners are smart about it.
“If you look at the core concept: ‘Can using information better drive a better business outcome? For mortgage brokers, even if it’s a one-man-band organisation, the answer is yes.
“In this day and age information still is power and being able to use information better can definitely help support every style and size of organisation.”
The key advantage for small to medium brokerages, says Foster, is the potential for targeted customer analysis.
Today’s customers have expectations regarding fast, individualised service, he says – and by failing to achieve this brokers could be damaging relationships with even their most engaged customers.
In a recent Gallup survey of retail banking customers, 66 per cent of fully engaged customers found offers they received from their banks were too generic, 41 per cent found the offers annoying, and over half already had the product they were being offered.
By harvesting customer data that is currently available internally in mortgage broking businesses, as well as that available online and through social media, brokers can offer targeted and effective marketing and service, says Foster.
Smaller businesses are often hesitant about the money and expertise required to implement big data projects, but Foster says those worries are unfounded.
“Organisations are realising they don’t have to do everything at once, they don’t have to take a big bang approach in terms of having a complete, massive big data strategy.”
There are a wide range of products available to smaller businesses that require minimal cost and expertise, and that can be scaled up when necessary, says Foster.
“Everyone likes talking about data scientists, and everyone gets freaked out that they’ve got to hire a guy in a lab coat with a weird hairstyle and glasses. You might eventually want to hire a data scientist if you get to that stage, but a big focus at SAS has been making our products easy to use so that small businesses can start small and quickly, without the need for those concentrated skills.”
And big data is already making its mark on the financial services industry.
Queensland-based short-term lender Nimble uses big data algorithms to speed up the rate of its lending decisions, which now average less than 11 seconds per decision.
In the US, marketing developer Ampush Media used big data to analyse a mortgage site which allows customers to input data over a few steps to be connected to a mortgage counsellor.
After discovering that a percentage of customers would give up if there was a hesitation of even a few milliseconds between steps, Ampush set up the site to deliver inspirational messages whenever there was a hesitation – improving conversion rates by over 30%.
As big data becomes more accessible, the finance industry is starting to see that it’s not just something for the big banks and large corporates, says Foster.
“What we’re starting to see is a broader awareness in all sorts of organisations, not just the big end of town, that there is value in data and there is value in using business analytics.
“In the past small to medium businesses might not have had the skills and capabilities to leverage that data, but now they do and they’re starting to make use of that.”
Five simple steps for taking a lean approach to big data:
- Develop awareness
- Identify the key business issues or outcomes that data and analytics can support – one or two trial programmes. Start small but think ‘big’
- Identify other areas within the business that may already be tackling social connectivity or business convergence, and draw on their experience
- Identify the key steps in each business process and use the lean principles to identify data that can be used to measure progress towards these outcomes.
- Explore
- Assess business maturity, data quality and other relevant capabilities needed
- Listen to customers and their needs
- Assess competitors’ activities
- Explore data-sharing possibilities with service providers in other industries to exploit the increasing intersection of data – especially location data – around customers.
- Build a strategy and roadmap
- Define your strategy and build a business case
- Define key priorities and requirements
- Choose and design only those data resources and analytics necessary to extract the relevant insights needed to improve the process
- Develop a roadmap for your deployment.
- Deploy
- Deploy analytics in accordance with the strategy and roadmap.
- Learn and expand
- Assess results and feed back into the business process to improve customer engagement
- Use the experience to assess and enhance business capabilities where needed, including strategy, controls and governance, people and technology elements.
* Source: Deloitte Analytics white paper: Big data: Time for a lean approach in financial services
