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What is Smart Data?
What is Smart Data?
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What is Smart Data?

17/07/2018

Data refers to any raw and unstructured information (text, number, media). Big data refers to the inflation of data quantity, sometimes impairing quality as well. The aim of smart data is therefore to create cost-effective and innovative ways to process large amounts of data for better insights, decision-making and process automation

Data refers to any raw and unstructured information (text, number, media). Big data refers to the inflation of data quantity, sometimes impairing quality as well. The aim of smart data is therefore to create cost-effective and innovative ways to process large amounts of data for better insights, decision-making and process automation.

KEY FIGURES

 
Only 41% of banks use advanced big data tools.
 
Only 41%
of banks use advanced big data tools.
Source: Economist Intelligence Unit
 
90% of the world's data has been generated over the last 2 years.
 
90%
of the world's data has been generated over the last 2 years
Source: SINTEF
 
$6.4bn the investment of financial institutions in data in 2016. $
 
$6.4bn
the investment of financial institutions in data in 2016.
Source: Bain Insights
 

DATA LIFE CYCLE

 
 
Collect
Explore & catalogue
Transform & improve
Access & share
Protect & display

FROM BIG DATA...

...TO SMART DATA

  • Volume: quantity of generated and stored data determines the value and potential insight
  • Variety: type and nature of the data, combining internal and external sources data
  • Veracity: quality of captured data. NB: inconsistency can also impair the output
  • Velocity: speed at which the data is generated and processed, if possible in real-time
  • Value: answers new questions and delivers new solutions
  • Strategy: to define the benefits through a clear definition of the use cases
  • Sourcing: identify the sources inside or outside existing IT systems (partners, internet 3rd parties)
  • Selection: store extra large volume of data, but display it wisely
  • Signification: refine the raw data to create indicators that will be easier to use
  • Symbolise: represent volumes, relations or abstract links thanks to data visualisation

WHAT ARE WE DOING?

Our smart data team constructed a 3 year plan around 10 domains of activity. As of May 2018, 6 domains were launched and 4 minimum viable products are planned for development by the end of 2018.
  • Client buying power: Developing new services using predictive analysis based on complex algorithms of past and future probability
  • Risk Management: provide time to market analytics to improve risk management efficiency
  • Finance: provide custom reporting on costs and economic figures for faster decisions
  • Investment office: Enabling client to have better forecasting tools, make faster decisions, and have a wider understanding of their business landscape
  • Transaction Advanced Analytics: Provide insightful analytics based on transactions that inform our clients of market trends, risk analysis, etc
  • Operational Process Efficiency: Improve our processes by leveraging historical data in advanced data models

INDUSTRY IMPLICATIONS

  • Enhanced customer experience: thanks to a better understanding of clients’ needs and pain points (analysis, segmentation) provide better offers, tailored to a client’s needs
  • Better security and fraud management: data analytics can help identify and predict compliance issues as well as the accurate response for each risk

KEY DATES

 
1881
1980s 1990s
2000s
2005 2010
2010s
2015 TODAY
Hollerith tabulating machine created to deal with census data overload in the US – one of IBM’s founding technologies
Data storage migration as digital becomes more cost effective than paper (1996)
Rise of software as a service (SaaS) + democratisation of data with better internet speed and personal computers
Rise of open data. SQL is the new HTML
Data migration to the cloud
Rise of data as a service (DaaS) and wider usage for smart city, internet of things (IoT) from public service to arts & luxury fields

OUR VISION

We believe the huge amount of data traditionally processed in securities services take a whole new potential when combined with the staggering processing power of machine learning. Key for this potential to materialise is the capability to source, clean, structure and store data in a fundamentally new way, combining factory like approaches and open banking principles.

Beyond the wide ranging positive operational and client impacts mentioned above, ultimately we believe in the model of smart data factory where everyone has access to mining data for multiple uses while data itself flows in a secure yet seamless fashion between systems, teams and stakeholders.

IN A CHANGING WORLD

Digital technologies are changing the way we work. Anticipating how the world will change is much more than just a legitimate concern for the BNP Paribas Group, it is a major strategic priority.

The adoption of new technologies can represent a huge cost, but missing an opportunity that could shape the future of our industry is perhaps an even bigger cost.

Additionally, one of our main business principles is safety of assets and as such, cybersecurity is a cornerstone of our digital transformation. The technical, legal and regulatory environment for these matters is increasingly complex, hence our continuous investment in data and information security.

The pace of change in technology calls for agility and a need to adapt our business model. We hope our insights will help you address these issues.

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