Presentation on theme: "Data Integrity – Your questions answered Sarah Denner Brown SDB Talking Direct."— Presentation transcript:
Data Integrity – Your questions answered Sarah Denner Brown SDB Talking Direct
Don’t ask her any questions about data integrity and perhaps she’ll go away.
A statement of the blindingly obvious Data is very, very, very, important to effective operational processes across the whole organisation.
A few examples Billing and collections Legal compliance Managing customers on and offline Preventing fraud and money-laundering Marketing
Marketing -Who do you mail? Miss Florence Jones Holly Cottage 3 Church Hill Lane Monmouth NP13 2QT Mr. Kevin Jones Flat 21 Nelson Mandela House 135 London Road CHELMSFORD CM3 4AA
Shaded bar = statistically significant difference Difference In % Under & Over Unknown ACTIVE SUBS.INACTIVE SUBS NUM.% % Age 343 7,714 10,401 13,126 12,860 15,711 3,577 22, ,687 2,172 2,177 1,800 1, ,012 0% 9% 12% 15% 18% 4% 26% 1% 12% 16% 13% 11% 2% 29%
And e-CRM THE BIG PICTURE
…Excellent customer service Quality data, intelligently used
Managing customers via the web? If anything…they manage us more often than not –Managing customers frequently translates to £0 –Engaging customers CAN sometimes translate to lots of £ –This all assumes that there isn’t someone in the organisation just giving that engagement away!
A typical corporate CRM vision “Become the provider of choice having the closest relationships with our customers… delivering the best treatment to each customer at the right time through the most suitable channel at the appropriate cost: a customer centric approach with each customer contact maximised in terms of value to the business and value to the customer” What does that mean in data terms?
Warehouse in CRM context Serves three key objectives; 1.One consistent view of the customer and a level of data integrity that is fit for purpose 2.Permits direct access to data by business functions and manages its transformation into critical information and intelligence 3.Ensures that this intelligence is exploited across customer facing operations
Retail stores Contact centres SalesFinanceWeb etc. Raw data in Data factory: clean, match, dedupe. QA Data Refinery; analysis, business rules Other Customer intelligence out
“ The b****y customers just do not understand the problems we have” Major utilities supplier discussing the problems of migration from legacy systems and multiple systems
What is involved in CRM? Identification and collection of relevant data Moving the culture of the business data-wards Maintaining the integrity of your data Converting raw data to marketing information
Applying statistical techniques to analyse behaviour, isolate segments, score and rank individuals Evaluate the economics of data collection and analysis Capitalise on the economics of data-driven marketing programmes Creatively act on the opportunities that emerge to develop individual relationships and the business as a whole
Money Laundering and Fraud (anti )
How to Commit Identity Fraud
How to Commit Identity Fraud
Regulation Requirements Identity checking Know Your Customer (KYC) Monitoring transactions Checking against Sanctions data Checking against Politically Exposed Persons data At the start of a relationship Throughout the life of the relationship
Know Your Customer KYC should be viewed as a corporate asset Type of job - need to pay in cash? Likely demographics? Salary – affordability? Supports credit? Essential tool in the fight against money laundering
Validity is satisfied where there is sufficient supporting evidence to confirm that a person of that name exists Verification is the process of establishing that the applicant is the ‘data subject’ or ‘owner’ of the valid identity references Assessing the identity risk
Assessing the credit risk Look at the person’s: - stability –time at address, time in employment credit track record –adverse data –degree of commitments –‘experience’ of handling credit demographics –age, marital status, number of dependants
Assessing the fraud risk Soft fraud - misrepresentation –yesterday £10K salary in job 6 months –today £25K salary in job 3 years Hard fraud - fabrication –inferred alias –‘shared’ attributes –hits against known frauds name address
Customer Data Sources of customer data Physical documents Source data relating to documents Electronic data sources –customer records –transaction records
Cabinet Office ID Fraud Study “The extent of the problem” “Most current processes for issuing government documentation used for identity verification ….. do not meet the highest private sector standards of security”
Cabinet Office ID Fraud Study “The way forward” “ For most people, checks against databases run by credit reference agencies will give much more satisfactory validation and verification of identity “
Some costs of poor data Company image Annoyed customers Failure to maximise opportunity Out of control costs Operational inadequacy Fraud Competitive disadvantage Demoralised staff.
But data costs… Must invest to succeed Data is for life not just for Christmas! And there is a lot conspiring against you!
Strategic difficulties Multiple systems No overall responsibility No data strategy Multiple channels of collection Lack of senior management buy-in Lack of personnel / technology / budget Training and cultural issues
What can we do to minimise the problems? Be strategic about it!
Five things that successful data-centric companies do 1.Make data a corporate priority 2.Specify and implement a coherent data strategy 3.Enforce consistent house rules on collection 4.Integrate data from multiple channels 5.Have a strong data hygiene policy including getting it right from the start!
What does a data strategy involve? Understanding the day to day needs of the data Determining the development of data usage and technical maintenance in the medium and long term Relating data to every nook and cranny of the business Determining the data to be collected Implementing appropriate processes for collection
What does a strategy involve? (2) Quality assurance Training and education from top to bottom Establishment of strict business rules Determining standards of data hygiene Identifying and clearing problems (audits) Implementing data maintenance procedures Integrating customer data across multiple systems, channels and touch-points
Purposes of a data strategy Operational Marketing Legal compliance Diversity and Equality Strategic direction and butt covering!
Self Assessment Data Checklist Does your organisation have a customer information strategy in place? What customer details are captured? How do you capture customer data? What information is captured online? What market research activities are done?
Self assessment 2 How do you work with your intermediaries? How do you work with third parties and the information they provide? How do you control and ensure the quality of your customer data? How do you comply with the data protection act?
Self Assessment 3 How secure is your customer information? How well are you achieving CDI across your various systems? How well can you consolidate and deduplicate customer information? How are your data systems designed and are they compatible with each other?
Self assessment 4 How well do your customer systems support the way in which you operate? What are the analytical skills in your organisation and how are they organised? What analytical tools does your organisation employ? What processes are in place for customer data analysis?
Why the strategic approach is best – Data collection example
Getting it right from the start Audit existing data Batch clean Exemplary data collection methods Data capture tools to minimise error Security levels of access Checks and verifications on input Give customers the chance to tell you of changes Regular maintenance Feed back
So much for the five things But how do you do it? That is for next time.