10. Data Accuracy: Legal MPS/TPS et al + internal opt-outs eCommerce Data Protection Gift Aid Security
11. Data Accuracy: Supporter (Customer) Service/Support What are your “touch points”? http://blog.itforcharities.co.uk
12. Data Accuracy: Analysis Data Analysis Subjective data… Data Enhancement http://blog.itforcharities.co.uk
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15. Thank You Ivan Wainewright [email_address] http://blog.itforcharities.co.uk @itforcharities The Importance of Data Accuracy for Marketing
Hinweis der Redaktion
Fundamentally: to contact your supporters/constituents and services Not accounting data But… different things to different people Fully and accurately understanding your Supporters – individually and as a whole
Example Slide: Last Name: no e; not capitalised [straying from data accuracy to data format/best practise – but if other surnames on other records are capitalised, then it is accuracy issue in terms of consistency] First name: more complete if says “Ivan”; what if 4 I’s at same add?! [Isabelle, Irene, Icarus] Can’t say Dear Ivan…, can’t determine gender Title/Suffix: can’t address me properly (Mr Ivan Wainewright OBE…) Gender: blank - analysis DOB: more complete if day too Marital Status: Address: Tonbridge ideally in Town… analysis, mailsort Address: Lilac Cottage – good if I like that; difficult for PAF Post code: Phone nums: no indication of type of phone number. How to know which to use for texting? Photo: not uptodate [DP act… Not really about photos… but important for, say, Major Donor event…] Email: incomplete GAD: fine… although no indication of Oral Confirmation Mem history: fund not the same – analysis Mem history: date different – poss matter if took data out of db Depends on your needs and usage of data E.g. If contact 1:1 need v accurate data; if only anonymously, reactively thru web-site then less so E.g. “Just when does data accuracy become important for me” But think about future too… e.g. even if don’t need full name now, still good to collect
Usually: “ Correct” E.g. if my DOB is actually the wrong date, a post code is held in the First Name field Uptodate E.g. address, membership status, gift aid eligibilty Rem DP Act
Complete (a) on one record, and (b) across the whole db (a) e.g. complete address, all parts of name fields, complete donation history, complete history of events attended (b) DOB on all records, post code on all records Consider: using Mandatory fields, regular checks Consistent One of the most common issues I see on databases is consistency One value per “item” in a drop down table Each field used for the same thing all the time; e.g.a field called Region (where you live vs where the service is delivered), Cost Centre Tate and financial history – multiple DBAs, different Directors of FR, even a Fund/Source code used differently over the years Non-ambiguous e.g. Easyjet satisfaction survey… who here has flown with Easyjet? “ on a score of 1 – 5, please rate your overall satisfaction with using them… who would give 5? 4?... What is the problem with me asking it like that? “ if I say: who would always fly with Easyjet regardless of cost or airport location? Who would strongly consider flying with Easyjet if the price was right for them? Who would never fly with Easyjet even if it was free?! I’m not suggesting you never use numbers… what I’m trying to show is that people see things differently and what may be obvious/clear to one person may have a different meaning to someone else Each value in a drop down table used for the same thing all the time; e.g. in event registration, How Heard About… one org tried to “re-use” codes over the years to mean slightly different things each time… Consistent format Ensure online and off-line data the same
Relevant DP Understood by all users Non-ambiguous Field names, values in fields, reason for collecting & using data Data terminology: E.g. Web sites: hits vs page requests… unique visitors vs number of page visits Why matter? Not necessarily if internal only if consistent, but what if comparing against other similar orgs… Trusted by users If not trusted then not used… then gets worse… self fulfilling prophecy… spirals down…
Financial (fundraising, optimising e.g. Gift Aid) Rem MARKETING and NOT accounting – although for some of you, that may mean the same data source – but still may not have complete accuracy for marketing
Planning: event invites, membership reactivation Selection ‘ receiving’ – first mention of this ‘ using’ e.g. at event, dietary req Analysis… and back to planning
Legal (MPS et al, ecommerce regs, DP, Gift Aid) & security
Though not quite so marketing, but yes in terms of esp. when they contact you Ask yourself: what are your ‘touch points’?
V important – can really affect strategies and income & data enhancement (e.g. enhance data based on someone’s Post code) Subjective Data; e.g. questionnaires: scoring systems using numbers vs words
Data Audits Data Dictionary (user/system manuals) For db managers (e.g. when someone leaves and new starter) and for users Initial collection point(s) Most common area for data errors A better database If your db cant support drop down tables, data validation Data Manager Data Cleaning (inc. de-duplication) Take care over “data movement” E.g. incorrect mailing story Single databases (sharing data) Multiple data repositories one of the worst culprits for inaccurate data: dupes, inconsistent, not uptodate Also consider: Data migration “ Data decay” If you do nothing, data will decay (change of address, change name, die)