New Zealand
New Zealand New Zealand
Consumers make most of their payments by internet banking
  • 74%
    BFSI
  • 70.5%
    TELCO
  • 54.5%
    RETAIL
  • 46.5%
    BFSI
  • 39.6%
    TELCO
  • 40.7%
    RETAIL
  • A higher percentage make payments via internet banking to banks and insurance companies, telcos, and retailers, respectively, compared to the regional average
  • Impact: Anti-fraud capabilities critical to the increased digital transaction frequency and customers’ trust in banks
Australia
Australia Australia
Consumers are most satisfied with the post-fraud service of banks and insurances companies
  • More than 70% satisfaction rate compared to 59.7% on average
  • Impact: Increased trust in BFSIs
Indonesia
Indonesia Indonesia
Consumers that encountered most fraud incidents in the past 12 months
49%
34.7%

AP Average

  • 49.8% have experienced fraud at least once compared to 34.7% on average
  • Impact: Overall anti-fraud capabilities need improvement
Singapore
Singapore Singapore
Consumers have the highest trust towards government
AP Average
  • 75.5% choose government agencies, compared with 51.7% on average
  • Impact: Trust of personal data protection is centered around government agencies
Vietnam
Vietnam Vietnam
Consumers encountered most fraud incidents in retail and telco during the past 12 months
  • 55%
    TELCO
  • 54.5%
    RETAIL
  • 32.8%
    TELCO
  • 35.2%
    RETAIL
  • 55% and 54.5% have experienced fraud at least once in retail and telco, respectively, compared to 32.8% and 35.2% on average
  • Impact: Overall anti-fraud capabilities need improvement
Thailand
Thailand Thailand
Most Thai consumers believe speed and resolution are severely lacking (response/ detection speed toward fraud incidents)
AP Average
  • 60.5% think it is most important, compared to 47.7% on average
  • Impact: Response time as one of key factors to fraud management to retain customers and gain their trust
India
India India as standalone
Consumers have the largest number of shopping app accounts in the region
India
  • Average of three accounts per person
  • Impact: Highest exposure to online fraud
Hong Kong
Hong Kong Hong Kong
The least percentage of consumers with high satisfaction level toward banks and insurance companies’ fraud management
AP Average
  • Only 9.7% are most satisfied compared to 21.1% on average
  • Impact: effective response towards fraud incidents to be improved
China
China China
Consumers are the most tolerant toward submitting and sharing of personal data
AP Average
  • 46.6% compared to the AP average of 27.5% are accepting of sharing personal data of existing accounts with other business entities
  • Impact: higher exposure of data privacy and risk of fraud
alert
Japan Japan as standalone
Consumers most cautious on digital accounts and transactions
50.7% Actively maintain digital accounts’ validity
27% AP Average
45.5% Do not do online bank transfers
13.5% AP Average
  • More than 70% did not encounter fraud incidents in past 12 months, compared to 50% on average
  • Impact: Relatively low risk of fraud

BI and Big Data systems are only as useful as the data you provide them

BI and Big Data systems are only as useful as the data you provide them

Some estimates suggest that 90% of the world’s data was only created in the past two years. With a massive influx of new data, we’ve also seen huge growth in the demand for big data systems and business intelligence (BI) tools for businesses to make effective decisions. 

 

While it’s great that we have systems that are capable of storing these vast volumes of data, being able to take the data sets stored within, and use them for a new tool is still one of the biggest challenge.

 

Many business leaders get caught up in the excitement of a digital transformation program, and the opportunity to replace their legacy systems with the latest CRM, BI tool, or credit reporting system.BHowever, consolidating data from numerous platforms can be a major challenge when these systems have widely varying source and target structures. Poor data quality also makes the migration of data into a new system incredibly difficult, and drastically limits the effectiveness of the new system you’re trying to implement. 

 

Poor data quality has a variety of different causes including:

  • duplicates 
  • incorrect data
  • differing formats between source and target 

In the haste to implement a new system, we see many businesses underestimating the complexity of a data migration and the level of data preparation involved to ensure it is successful. Failing to prepare your data adequately may potentially cost you hundreds (or thousands) of lost man-hours, and you might never see the return on your investment in a new technology solution.

 

You can’t control what you can’t see

What many people fail to realise is that data is rarely ever at the level of quality required for use in analytics when it’s extracted from a source repository. Before anyone in your business can actually begin building dashboards and generating reports, the data needs to be:

  • cleansed
  • standardised
  • deduplicated
  • transformed specifically for analysis

This is the process of data preparation, and it includes all the activities required for bringing source data up to the level of quality required for actual analysis and decision making. Simply put, the better the quality of data, the smoother your transformation process will be.

 

36 percent of organisations surveyed are planning data preparation projects in the next 12 months (up from 27% the prior year), according to our 2018 global data management benchmark report. Key drivers of these data preparation activities are most likely to be:

  • analytics (48%)
  • operations (45%)
  • financial reporting (44%)
  • business intelligence (40%) 

For any migration into a new system, it’s essential to gain full visibility into your data so that you can exercise real control over the preparation process. With the right data quality management solution, you will be empowered to:

  • analyse all of your data upfront
  • expose all of the data quality issues 
  • generate mapping specifications
  • de-risk and streamline your migration project
  • move your data to the new environment, on time and on budget

Alan Thornton
General Manager, Data Quality Southeast Asia

 

Read full article

Alan Thornton

By Alan Thornton 08/21/2018

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