Using Smart Technologies to Modernize and Transform the Customer Experience in Banking.
•
0 gostou•162 visualizações
This document discusses the challenges of legacy data systems and provides recommendations for addressing them. It notes that customer expectations around data access and use are increasing due to digital transformation trends. Legacy data systems often have fragmented, disconnected data stored in multiple formats with limited documentation. The document recommends three basic requirements to unlock the value of legacy data: understand the data by finding and documenting it, manage the data by defining governance and access rules, and exploit the data by building new capabilities. It emphasizes that data needs clear ownership, documentation, understanding of usage, and well-defined architecture. The best approach is often to co-exist with legacy systems by implementing a common access layer instead of a risky, costly migration.
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a Using Smart Technologies to Modernize and Transform the Customer Experience in Banking.
Semelhante a Using Smart Technologies to Modernize and Transform the Customer Experience in Banking. (20)
Mais de Nuxeo
Mais de Nuxeo (20)
Último
Último (20)
Using Smart Technologies to Modernize and Transform the Customer Experience in Banking.
- 1. The Ticking Time Bomb of Data David Jones & Norman Wren
- 2. Introductions Digital Transformation – A Reality Check The Ticking Time Bomb of Data Q&A 1 2 3 4 Agenda
- 3. Introductions 3 1 David Jones VP Product Marketing Nuxeo 2 Norman Wren Former Technical and Operations Director Santander
- 4. Introductions 4 David Jones VP of Product Marketing Nuxeo @InstinctiveDave 2 Norman Wren Former Technical and Operations Director Santander Digital Transformation A Reality Check
- 5. Digital Transformation in Financial Services Massive Spend Average $42M in 2018 Rising to $45M in 2019 Purpose 66% Customer facing innovations Success? 88% - project delayed, reduced scope, or cancelled 26% - Digital Transformation = Insurmountable Task Statistics courtesy of Couchbase
- 6. “Everyone who hears these words of mine, and doesn't do them will be like a foolish man, who built his house on the sand. The rain came down, the floods came, and the winds blew, and beat on that house; and it fell—and great was its fall.” — Matthew 7:24–27
- 7. Building on Sand? Legacy Systems Disconnected Systems Search not find Customer Experience Mobile & Web Apps Performance & Scalability
- 8. The Ticking time bomb of Data Making Sense of Legacy Data Norman Wren
- 9. Why is this important? Customer Expectation: Customer in control Unlimited data access Always on 24 x 7 Real time Added value services Regulation : Customer rights Data portability Open access to third parties Remediation of historic practices Internal Driver: Exploit data assets
- 10. Legacy Data Challenges • Fragmented data ; not real time; internal view; unstructured data; access limited • Multiple formats; limited documentation; Knowledge gap • Integrity within applications - not across • Degradation over time • Security and data leakage • Obsolescence • Compliance with Regulation • Cost of Change; Time to market • Consolidation of data stores. • Architecture and technology compatibility • BAU Running costs • No scalability • Access limitations • Poor schema design Access Data Quality and Integrity Risk Cost Performance
- 11. Value: 3 Basic Requirements Find Data Document attributes and meaning Understand usage and context Define Architecture Set usage, access and security rules Build governance and ownership Organise around common Business Purposes Make accessible through common access layer Use Meta data to organise and add value Build new Capabilities – Data Driven Understand the Data Manage the Data Exploit the Data 3 1 2
- 12. Considerations Archaeology: Find and document data and how used Architecture: Define data architecture and principles Data ECO system: Distributed data Define data usage Update; query; analytics Common Layer: To bridge technologies Scaleability Cloud processing; distributed data Ownership Governance and accountability
- 13. Key Points Data as an Asset needs: • Clear Ownership and Accountability • Knowledge and documentation Meta data • Clear Understanding of usage and value • Data architects and engineers • Architectural readiness • Capability • Data Strategy
- 14. Stick or Twist? Obsolescence Security Maintenance Cost Risk Cost Risk Data Quality Integrity Business case Less Risk Unlock assets Bridge old and new Create data eco system for the future Do Nothing Big Bang Migration Co-existence/ Common Layer
- 15. Summary Data knowledge and documentation fundamental • Hard work and time consuming Clear target architecture addressing data • technology stack and data usage Common layer to separate out data from business processing Avoid migration if possible • Avoid pitfall of access in situ Define common Business purposes Build road map • Balance value, cost and risk Invest in capability
- 16. Questions?
Notas do Editor
- Intro: Is this a problem or an opportunity. Want to show that management of data needs to be a priority and do nothing is not an option. Data in itself is not adding value but combined with technology and customer and business insight it is game changer. Data is the only constant
- Data historically poor relation but world has changed: The way data is used , managed and controlled has changed fundamentally in the digital age: This has significant consequences for traditional businesses. There are three critical events which have changed the way we understand and use data: The first is customer experience and expectation: Financial systems traditionally have been developed for the benefit of the institution not customer: the institution controlled access eg branch hours; presented data to fit internal processes batch oriented today customers control access eg always on always available, real time performance and updates, want open access to data and to get added value from data. Secondly Regulators have changed the game: Customers hve strong rights over their data. PSD2 champions data portability and opens access to third parties. The impact of this is significant and compliance costly. GDPR highlighted the difficulty in being able to find data and obscure it. PPI also demonstrated the cost and difficulty in remediating customers as data is often fragmented and difficult to use Thirdly utilising new technologies such as Big data, analytics, AI, machine learning they can all bring significant advantage but all require access to massive amounts of data and significant computing power . They also need new ways to access and process data which highlight shortfalls in legacy architectures and technologies
- So to make sense of legacy data and to be able to exploit the data for commercial advantage, meet customer expectations and be regulatory compliant there are some significant challenges to be overcome. The first is around access to data: many systems limit access as they are reliant on batch update, the data is designed for internal use, it is fragmented and much is not accessible including vast amounts of images and pdf information Data quality and data integrity issues become much more visible and have an instant impact eg customers report issues in social media and the reputation damage is instant. Presentation of data is hampered by multiple systems and formats, different rules and definitions, limited knowledge and documentation within the organisation and third party suppliers. Data integrity is generally within a system not across applications and finally data standards have changed and some data degrades in quality over time. With the opening up of data and systems data related risks are becoming more prominent and likely. legacy systems and obsolescence contribute to data access breaches and data leakage with severe consequence both financial and reputationally. As technology cycles increase applications and infrastructure become obsolete quicker and the cost of maintaining, updating and upgrading become more costly. This combined with regulatory agenda of compliance, data security and protection risks and the question of obsolescence. The costs of IT are rising; BAU running costs soar because access to leagcy data is inefficient and MIPS are costly. Re building architecture to be scaleable real time and always on requires significant investment. Exploiting the data requires further investment Accessing legacy data in situ is inefficient and expensive. Performance suffers due to lack of scaleability, batch and real time issues, ensuring integrity and security in the new world place heavy overheads in a traditional environment
- So big question- How to avoid being caught in a costly problem!. Unfortunately no easy answer, except doing nothing is not an option. Three basic requirements 1 Building detailed understanding of the data, where it is, what it means and how it is used. This is like archaeology: time consuming, hard work and if you are not careful you may not find anything of value 2 Be focused on managing the data. Have a clear enterprise wide architecture for data. Understand how it will be used, who can access it and how it will be kept secure and up to date Implement clear ownership and governance around data. Treat it as an asset. 3 Be clear on the business purpose around exploitation and usage. Not all data is worth keeping or being available for everyone to access Define the data eco system, and how to access it. make it scaleable, performant and up to date: Add value ie it is not enough to give access need to organise and add value – build meta data Build skills and capability in the organisation. Be more data driven eg data architects, data engineers, data administrators. Build new skills and approaches To get to the third level is challenging: trying to migrate all legacy data to new technology, architecture ,infrastructure, Or dump all the data into a massive data lake is cheap, easy, cheap or without risk. Migrating data, extracting transforming and loading into new environments and /or maintaining co-existence all require major programs and carry high risk as recent high profile examples have shown.
- So if we assume doing nothing is not an option and we wish to either migrate or use co-existence to buy time and reduce risk as well as allowing islands of success there are some considerations that apply regardless The first is that understanding what your data is, where it goes and how it is used is like archaeology as frequently the knowledge is not available and documentation is either limited, missing or out of data. No matter what route you go down you need to have this information. There is some good news in that there are an increasing range of tools to assist discovery, definition and creation of n Meta data – but this is not to be underestimated. Traditionally architectures have focussed on application and infrastructure but today it is critical that there is a defined data architecture and set of data principles around, definition, storage and usage Big data was all the rage but I think increasingly as the scale of the data and the complexity of management and different usage profiles there is a need to define an Eco systemnwhich allows ability to utilise new cloud technologies and make co-existence simpler. Distributed data, cloud native processing and data mangement tools are critical Rules need to be defined around how data is used and therefore how it is stored and utilised as well as how integrity an qaulity are maintained in this environment eg transaction updates are not the highest volume of demand, customer inquiries are this needs to be thought about in the design. Similarly to process massive amounts of data independent of the application use cannot impact on the standard customer or business processes. In looking at the future consideration should be given to create a common layer which allows separation of data from process and makes use of meta data. This is critical to be able to a) access legacy data B) reduce the risk from migration and rapidly changing technology cycles and allowing a a staged migration approach With the explosion of demand both internally, consumer and regulator combined by the visibility of any failure any architecture has to be scaleable and on demand as well as a need to move data to lower unit cost storage. Finally until recently the business ownership has not been well defined and as an asset data needs to be managed and so needs clear ownership and accountability as well as sound governance
- Developing on these points there are some fundamentals that need to be in place: Business ownership for the data needs to be defined and accountability clear for the maintenance, integrity and accuracy of the data with appropriate governance needs to be in place Investment needs to be in place to ensure that the data is understood which requires documentation being clear on definition and having ability to maintain the meta data in a cost effective manner. Not all data needs the same attention and therefore being clear what needs to be kept and is accessible and clear use over usage are important There is a need to upgrade skills around data: investment in Data architects and data engineers is a requirement Architecture definition and rules need to be clear and finally a clear road map needs to be developed to set out how the data will be used, whether it will be left, co-exist or migrate and how and when this will happen
- So big question _ does one stick or twist. Unfortunately no easy answer except doing nothing is not an option. Most organisations look at two options: First option to migrate all data to new technology, infrastructure and applications. There was a desire to put everything into a single data lake but I think most organisations have found that to be too difficult. The challenges are: cost of managing a migration are significant because of the complexity of data compatibility, knowledge and quality as well as a desire to use data differently Migration is a major program in its own right and carries high risk as recent experience of TSB has shown. Post migration their can be issues because of missing data and changes of rules that may not be discovered until post migration. These migrations mean that other changes cannot occur and as a result they demand significant management attention and time which in itself carries a cost Most organisations are looking at a form of co-existence as a stepping stone to minimise the risk of a big bang change. But this means that organisations are still faced with the risks of obsolescence, security, access and bau running costs
- So to summarise: If you are to survive, meet consumer expectations, be compliant and get value from your data you will need to Understand and document your data and make it available in simple understable way. Build nechanisms to maintain the data definitions and usage Be clear what the architecture looks like for data, infrastructure and applications and how the new world will interface with the old Have a road map to eat the elephant in manageable pieces Build a common layer as a critical step on the route map. Thank you