Analytics & Data

Data is changing. Not only is it becoming “bigger” and more complex, it’s often unstructured and coming from an increasing variety of sources. There are few initiatives in today’s financial services sector that do not have analytics and data at their heart.

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How you can use the RetailTech Model

The first stage is Research & Development, when an innovation is not fully-fledged and has not yet been adopted beyond prototypes, trials or POCs.

New technologies typically go through 5+ years of R&D, though the timeframe will vary substantially depending on the degree of innovation entailed.

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Model Stages

The Leading Edge stage indicates when an innovation has moved out of R&D and into operation. Approximately 5% of the market adopts the innovation at this stage, usually start-ups and a few industry players known for being forward-looking.

Sometimes, an innovation is picked up from another sector. As indicated in the timeline below, it typically takes 1 to 3 years to move from the Leading Edge to Early Adopters stage.

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Model Timeline

At this stage organisations are more risk averse than those at the Leading Edge, but are still keen to be in the industry’s upper quartile and adopt a new technology.

The broad timeline for technologies to remain at this stage is 2 to 5 years at which point they will have reached around 25% market adoption.

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Model Origins

By this point a technology or business innovation can be considered as Mainstream since it will have been implemented by around 50% of the market.

2-5 years is the typical timeframe for this stage.

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Model Lenses

Technologies in the Late Adopters stage have been widely adopted across the industry with 80% - 100% of the market using them after a further 5+ years.

Not all technologies end up being adopted by everyone, with some 20% of technologies never reaching full adoption.

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R&D

The first stage is Research & Development, when an innovation is not fully-fledged and has not yet been adopted beyond prototypes, trials or POCs.

New technologies typically go through 5+ years of R&D, though the timeframe will vary substantially depending on the degree of innovation entailed.

. .
5+
Leading Edge

The Leading Edge stage indicates when an innovation has moved out of R&D and into operation. Approximately 5% of the market adopts the innovation at this stage, usually start-ups and a few industry players known for being forward-looking.

Sometimes, an innovation is picked up from another sector. As indicated in the timeline below, it typically takes 1 to 3 years to move from the Leading Edge to Early Adopters stage.

. .
5%
1-3
Early Adopters

At this stage organisations are more risk averse than those at the Leading Edge, but are still keen to be in the industry’s upper quartile and adopt a new technology.

The broad timeline for technologies to remain at this stage is 2 to 5 years at which point they will have reached around 25% market adoption.

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25%
2-5
Mainstream

By this point a technology or business innovation can be considered as Mainstream since it will have been implemented by around 50% of the market.

2-5 years is the typical timeframe for this stage.

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50%
2-5
Late Adopters

Technologies in the Late Adopters stage have been widely adopted across the industry with 80% - 100% of the market using them after a further 5+ years.

Not all technologies end up being adopted by everyone, with some 20% of technologies never reaching full adoption.

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80%-100%
5+

The first stage is Research & Development, when an innovation is not fully-fledged and has not yet been adopted beyond prototypes, trials or POCs.

New technologies typically go through 5+ years of R&D, though the timeframe will vary substantially depending on the degree of innovation entailed.

The Leading Edge stage indicates when an innovation has moved out of R&D and into operation. Approximately 5% of the market adopts the innovation at this stage, usually start-ups and a few industry players known for being forward-looking.

Sometimes, an innovation is picked up from another sector. As indicated in the timeline below, it typically takes 1 to 3 years to move from the Leading Edge to Early Adopters stage.

At this stage organisations are more risk averse than those at the Leading Edge, but are still keen to be in the industry’s upper quartile and adopt a new technology.

The broad timeline for technologies to remain at this stage is 2 to 5 years at which point they will have reached around 25% market adoption.

By this point a technology or business innovation can be considered as Mainstream since it will have been implemented by around 50% of the market.

2-5 years is the typical timeframe for this stage.

Technologies in the Late Adopters stage have been widely adopted across the industry with 80% - 100% of the market using them after a further 5+ years.

Not all technologies end up being adopted by everyone, with some 20% of technologies never reaching full adoption.

Using data as a driver of disruption

The saying, “Personal data is the new oil” has gained wide currency, but what does it mean for a bank? In the first place, personal data now has to go well beyond transactional data. This information, once regarded as a bank’s crown jewels, will be open to third parties under PSD2 and Open Banking measures. Consequently, banks need to enrich transactional data by creating a single customer view that combines structured and unstructured data, and by building data lakes that store large amounts of data.

Banks need to be more innovative in how they harness and use external data. With hybrid analytics infrastructures, FSIs can employ a combination of analytical environments best-suited to specific analytical tasks, instead of a one-size-fits-all platform. In addition, hybrid analytics environments can combine data lakes, Hadoop, traditional data warehousing and cloud-based capabilities such as data factories.

Leading-edge banks are looking to third parties to provide useful data. Some organizations are now using telco data to assess credit-worthiness, particularly when potential customers have limited financial history. Social media data is being used for sentiment analysis, to assess customer feedback and to identify influential individuals who can be targeted to boost word-of-mouth marketing. FinTechs such as Kabbage are even using social media data for credit decisions.

Real-time predictive analytics has become an essential element in personalizing customer experiences. Data can be used to generate dynamic content and “next-best” offers. In operations, data from online and ATM activity can be used to predict back-office workload. Likewise, branch staffing can be optimized with predictive analytics, drawing on transaction history, network proximity, customer demographics and market competition. A few banks have invested so much in their analytics capability that they are able to monetize their data and offer analytics services to customers, especially when they can extrapolate from their own data to obtain a whole-of-market view.

Another development in algorithms is the growing maturity of learning systems, such as cognitive computing, machine learning and artificial intelligence. These tools can be used across the finance value chain, from customer service, to default predictions, security, fraud and compliance. Error rates for speech recognition and natural language processing have fallen closer to human rates; data volumes have increased exponentially, making it easier to train systems; and cloud computing has made possible the processing of ever larger data volumes.

For, banks a real challenge is that there are not enough data scientists to go around. A partial solution is to free up data scientists to do work only they can do. For example, non-experts can use advanced data visualization to view large, complex, datasets by using multi-dimensional visualization tools, such as clustering. Business users can use search-driven analytics to create ad hoc reports and dashboards in seconds through the translation of searches into SQL. Answers can be generated on the fly using a custom-built, in-memory relational data cache.

Explore these and other trends in the Analytics & Data lens of our Innovation Model.

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Analytics & Data Expert

Walid Jelassi
DXC Analytics & Data Practice

Arrange a meeting with Walid Jelassi

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