Why don't you have a data management plan final
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The document discusses the importance of data management plans for nonprofits, outlining reasons why many lack them and the consequences of not having one. It details a step-by-step approach to creating a data management plan, emphasizing data quality and governance as essential for effective fundraising strategies. Additionally, it highlights the need for organizations to proactively manage their data to improve outcomes and reduce wasted resources.
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Data degradation
What does that mean?
Data degradation – [DAY-tuh deg-ruh-DAY-shun], noun
Refers to the worsening of data quality over time. With assets like a donor database, degradation is inevitable. Why? Because
of the many, sometimes unavoidable, negative influences acting on your data quality. These include: consumer data naturally
changes as people change jobs, relocate, have families, and go through the normal cycles of life; data inputs are often flawed
and/or manual, and the manual labor can be poorly trained; related data is changed, purged or updated; data migration to new
systems such as CRM software.](https://image.slidesharecdn.com/whydontyouhaveadatamanagementplanfinal-140417133019-phpapp01/85/Why-don-t-you-have-a-data-management-plan-final-14-320.jpg)
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Step 7: Data governance
What is that?
Data governance– [DAY-tuh GUHV-er-nuhns], noun
A set of rules or policies that encompass the people, processes and technologies required to create and maintain higher quality
data assets for an organization. Data governance goals resulting from higher data quality include: better compliance with third
party standards, decreased risk of regulatory violations, improved decision making, improved data and organizational security,
and greater profitability.](https://image.slidesharecdn.com/whydontyouhaveadatamanagementplanfinal-140417133019-phpapp01/85/Why-don-t-you-have-a-data-management-plan-final-44-320.jpg)
Why don't you have a data management plan final
- 1. 1 Present & Why Don’t You Have a Data Management Plan? (it’s not that hard)
- 2. 2 Welcome and Agenda Please participate in our online poll while we get organized Today’s agenda 1. Why we don’t have data management plans 2. Why we need them 3. How to create one 4. What to do with it
- 3. 3 Our Experts Gary Carr President & CEO Third Sector Labs is a data services company helping nonprofits to re-think their data management practices and solve data problems. ThirdSectorLabs.com gcarr@thirdsectorlabs.com www.linkedin.com/in/gpfcarr Leading digital marketing firm serving the nonprofit community through strategic planning, implementation and support for multi-channel fundraising solutions. adcieo.com debbie.snyder@adcieo.com linkedin.com/pub/debbie- snyder/0/448/89b Debbie Snyder VP, Sales & Marketing
- 4. 4 LEVEL 1: ASSESSMENTS AND CLEANING LEVEL 2: DATA MANAGEMENT, ENRICHMENT, MI GRATION LEVEL 3: WAREHOUSING, MINING, V ISUALIZATION Also Exclusively Serving Nonprofits ---- Data Services ---- Founded in 2013 by professionals with 20+ years of technology and data experience with Fortune 500 companies, the federal government, and nonprofits Offices in Washington, DC and Seattle, WA metro areas www.thirdsectorlabs.com
- 5. 5 FUNDRAISING STRATEGIES • Strategy consulting • Multi-channel campaign development • Change management • End user support DIGITAL MARKETING • Website design • Custom development • Implementation • Mobile apps DATA MANAGEMENT • Database migration • Data integration • Data hygiene Exclusively Serving Nonprofits Digital marketing consultants helping nonprofits create, launch and manage multi-channel fundraising strategies. www.adcieo.com
- 6. 6 Let’s get started If you are a typical nonprofit … • You have a strategic plan • You have a fundraising plan • You do NOT have a data management plan • But your fundraising success depends on data Hmmmm ….
- 7. 7 Why don’t we have data management plans?
- 8. 8 Why don’t we have data management plans? 1. We have lots of other plans! 2. Data is intimidating 3. We have a dba, what else do we need? 4. We cleaned our data last year – is that what you are talking about? 5. Plans are time consuming to create 6. We plan events … we react to data
- 9. 9 When we think of data management plans … Examples of data management plans
- 10. 10 It may feel like this … But it’s not
- 11. 11 Why do we need data management plans?
- 12. 12 6 reasons why we need them 1. Data degrades 2. More data than ever to deal with 3. More (newer) technology 4. Fundraising plans change 5. Competition for donors‟ attention 6. Not having a plan is wasting time and money Let’s talk about these a bit more …
- 13. 13 1. Data degrades “If your data isn‟t getting better, it‟s getting worse.” -- TSL data scientist “Why?” -- audience
- 14. 14 Data degradation What does that mean? Data degradation – [DAY-tuh deg-ruh-DAY-shun], noun Refers to the worsening of data quality over time. With assets like a donor database, degradation is inevitable. Why? Because of the many, sometimes unavoidable, negative influences acting on your data quality. These include: consumer data naturally changes as people change jobs, relocate, have families, and go through the normal cycles of life; data inputs are often flawed and/or manual, and the manual labor can be poorly trained; related data is changed, purged or updated; data migration to new systems such as CRM software.
- 15. 15 Data degrades Cause #1: your organization – Lack of data entry standards – Unskilled data entry workers – Common mistakes – Record fragmentation Cause #2: the technology – Multiple, disparate systems – System upgrades – Integration, processing errors – Sheer volume of data Cause #3: those darned donors … life! – Change in address … every 5 to 7 years – Change in jobs … 9 to 11 jobs in a lifetime – Family / life event … divorce rate, birth of children, death … what else?
- 16. 16 Your CRM view of your data Manage those contact data changes.
- 17. 17 Our view of your data (once we export and analyze) Sal utat ion Last Name First Name M. I. Address 1 City State Zip Phone Email DOB Gender MR Setters m MS SIMMS Laurie 1313 Danger Ln Appleton CA 73111 310.555.5555 Laurie@mail 04/29/81 F Mr. singletary Mike T 310.555.1234 mts@mail.com Singletary Michael 310.555.1234 mike@mail.com M Solvington Allen 5201 Marshall Lane Cupertino CA 91001 323.555.5990 also@mail.com 05/30/75 Mr. soprano Cindy P. 222 Main St. Cupertino CA 91002 cindy@mail.com f Dr. Standish Bradford 1141 Duke Ave Los Angeles CA 91010 Stevens Allison 8726 Elm Ave Appleton CA 90009 310.555.5551 01/01/01 f STEVENS ROBERT 2 2101 Data Ave Los Angeles CA 91010 rs2@mail.com 12/14/60 m Sr Tahoma Juan 20B Eldora Mexico City +52-55-5222- 2222 jtahoma@mail.com 01/14/59 M Incomplete record Salut./gender mismatch Incomplete email CA ≠ 73111 Multiple normalization issues M.I. = 2 ? Moved last year (NCOA) Foreign address ? DOB is bad Potential dupe (ph #)
- 18. 18 2. More data than ever Donation$ Special events volunteering Emails and emails Broker lists Socialmedia Job change Address change Financial transactions newsletters Directmail Alma mater Family updates polls Robo calling spreadsheets Donations to other charities Onlinesurveys We leave footprints everywhere
- 19. 19 3. More technology Takes us from this …
- 20. 20 More technology Aha! Here she is! To this …
- 21. 21 Recent TSL webinar poll question How many different technologies doe you depend upon to manage a fundraising appeal/campaign? • 53% of attendees used 3 – 5 technologies • 47% used 6 or more • 0% used 1 - 2 But …
- 22. 22 4. Fundraising plans change Raise more money 1. From new / more donors 2. Who we reach over more mediums 3. With specific messaging 4. Relying on outcome reporting 5. Targeting donor segments 6. Identified by data analysis
- 23. 23 5. Competition 1. Nonprofits encounter donors / prospects / volunteers / advocates in more „places‟ 2. There is more “noise” 3. Data cuts through the noise to – Anchor outcomes – Communicate results – Establish and maintain relationships
- 24. 24 6. Wasting time and money Some examples are very visible • Direct mail production and delivery costs • Spam, ISP blocking • Staff time
- 25. 25 Wasting time and money Some are hidden • Mail to a deceased donor • Make a large ask to a donor with small giving potential • Make a small ask to a donor with a large giving potential • Hospital references wrong healthcare issue when trying to build up a new relationship • University sends rejection letter to a student and donation request to his parent … on the same day! Lost donors = ??
- 26. 26 Remember Data is your organization’s knowledge and memory What you know What your organization knows Vs.
- 27. 27 How do we create a data management plan?
- 28. 28 How to create a data management plan • Multiple types of data – Donor – Event – Newsletter – Social – Service outcomes – Financial • Plan for each • We will focus on constituent / donor data for the rest of this presentation Constituents
- 29. 29 How to create a data management plan Remember … • We aren‟t trying to get to the moon • Start simple and … • Be practical. • Think of a data management plan as a commitment to proactively manage your data!
- 30. 30 Data management plan: 3 questions 1. Where do you want to go? 2. Where are you now? 3. How will you get from here to there? You need a map … and a plan Map first!! Assess problems Revise db Web capture THERE HER E Assess problems Clean data
- 31. 31 The map 1. Determine the data you need to support your fundraising strategy – Markets, segments, messaging 2. Get a data quality assessment 3. Determine your data gaps – What do you need but are not collecting? Assess problems Clean data Web capture THERE HERE
- 32. 32 The plan – phase 1 1. Document your fundraising/events/communications schedule 2. Set that aside!!! We are going to separate data management from events management Fundraising / events schedule Data management schedule
- 33. 33 The plan – phase 2 1. Prioritize the data quality problems from the data assessment / gap analysis that you intend to address 2. Create one or more tasks to close each gap Assess problems Revise db Web capture THERE HERE Assess problems Clean data
- 34. 34 The plan – phase 2 3. Create a new schedule for data management … monthly, quarterly, per data type needed 4. Establish data quality standards for data governance
- 35. 35 Sounds harder than it looks … an example
- 36. 36 Remember: start simple The map 1. What data do you need 2. Data assessment 3. Gaps The plan 4. Prioritize gaps 5. Action item for each 6. Schedule 7. Governance
- 37. 37 Step 1: (The map) What data do you need? Start with your fundraising plan Fundraising strategies Tasks 1 Target lapsed donors 2 communications 2 Increase prospects 10% Outreach thru web, events 3 Convert 20% of Target conversion plan for constituents to donors newsletter subscribers, event attendees, volunteers 4 Develop donor Data analysis to produce segmentations 3 – 4 segments for future communications … helps #3
- 38. 38 Step 1 (cont.) Continue to the data requirements Fundraising strategies Tasks Data requirements 1 Target lapsed donors 2 communications Lapsed report, contact info 2 Increase prospects 10% Web, event outreach Upgrades to website features, Event forms for data capture 3 Convert 20% of constituents to donors Target conversion plan for newsletter subscribers, event attendees, volunteers Run a constituent report, identify donor data fields needed, can you create donor profiles? 4 Develop donor segmentations Data analysis to produce 3 – 4 segments for future communications Same as #3
- 39. 39 Step 2: (Map) Data assessment says 1. 20% of records are duplicates 2. 30% lapsed donors 3. 15% incomplete addresses 4. Not tracking gender, DOB, alma mater, family status and other data needed to segment messaging 5. 35% of all constituents are also donors
- 40. 40 Step 3: (Map) Gaps to close 1. Bad data to be cleaned 2. Duplicate records to be removed 3. Address fields to be completed 4. 10 new data fields to be captured – DOB, gender, alma mater, family, employer, other charitable interests, contacting preference, social media usage, etc. 5. Donor profiles to be completed (from #3 and #4)
- 41. 41 Step 4: (The plan) Priorities 1. Clean bad data and remove duplicate records 2. Enable capture of more data – easy tasks – ID needed fields – Add fields to db – Add website registration option for Facebook login – Create Facebook page 3. Address clean up – NCOA check 4. Enable capture of more data – harder tasks – Marketing service to capture website visitors – Add survey and poll questions to website, newsletter – Develop communications piece to invite more constituent “conversations” and sharing of data 5. Determine new data segmentations to support fundraising in the future
- 42. 42 Step 5: (Plan) Actions Fundraising strategies Tasks to support Data requirements Actions to improve data quality 1 Target lapsed donors 2 communications Lapsed report, contact info A – clean data B – update addresses 2 Increase prospects 10% Web, event outreach Upgrades to website, other forms of data capture A – marketing service to capture web visitors B – event capture tool C – social media / Fb 3 Convert 20% of constituents to donors Target conversion plan for newsletter subscribers, event attendees, volunteers Constituent report, expanded donor data, donor profiles A – add new db fields B – add website data capture features C – data broker D – test donor profile capacity and analysis 4 Develop donor segmentations Data analysis to produce 3 – 4 segments for future communications Same as #3 Similar to #3 … but focus on segments you want to market to for future fundraising
- 43. 43 Step 6: (Plan) Schedule First 3 months Clean bad data Modify dstabase(s) Improve website registration Create Facebook page NCOA clean up Second 3 months Data broker service for one-time augmentation Implement website survey, polls Lapsed donor data cleanup (after lapsed donor campaign has completed) Third 3 months Create new donor segmentations Test against target message marketing program Fourth 3 months Measure results of segmentation Revise data management plan Every quarter Data cleaning Re-run assessment Measure new data collection methods
- 44. 44 Step 7: Data governance What is that? Data governance– [DAY-tuh GUHV-er-nuhns], noun A set of rules or policies that encompass the people, processes and technologies required to create and maintain higher quality data assets for an organization. Data governance goals resulting from higher data quality include: better compliance with third party standards, decreased risk of regulatory violations, improved decision making, improved data and organizational security, and greater profitability.
- 45. 45 In other words, standards 1. Schedule for monthly or quarterly data management … cleaning, enrichment, etc. – Be proactive, not reactive 2. What defines a “complete” record? – Focus on better data, not more 3. How old is too old? – Depends on the type of record? 4. How many versions do you retain? – How many old addresses? – Event attendance records?
- 46. 46 Also … 1. Control data inputs – Via people, systems, imports 2. Review data capture tools against strategic data needs for fundraising regularly 3. Do you enable donors/consumers (or a subset) to manage their own information via online accounts? 4. Do you have self-select removal processes from (e)mailing lists?
- 47. 47 When it comes to data … garbage in, garbage out
- 48. 48 What do we do with our data management plan?
- 49. 49 Do this! 1. Assign responsibilities 2. Budget 3. Work it for 6 months 4. Measure results – Quality of data up? – Event or appeal results? – Data governance standards being followed? – No more delays for communications due to reactive data cleaning? – Cost savings 5. Review with leadership 6. Revise and continue
- 50. 50 Let’s wrap it up Takeaway Old thinking: we plan our events, we react to data New thinking: we plan our events AND we plan our data management
- 51. 51 We’d like to hear from you! Please submit your questions… Q & A
- 52. 52 Thank You! Gary Carr President & CEO ThirdSectorLabs.com gcarr@thirdsectorlabs.com linkedin.com/in/gpfcarr adcieo.com debbie.snyder@adcieo.com linkedin.com/pub/debbie-snyder/0/448/89b Debbie Snyder VP, Sales & Marketing
Editor's Notes
- #3: Moderator: Welcome everyone … and thank you for attending.
- #5: Presenter: GaryIntroduce TSL’s areas of expertise:
- #6: Presenter: DebbieIntroduce Adcieo’s areas of expertise
- #7: Debbie starts here …
- #8: Debbie’s section
- #10: These diagrams represent samples of data management plans you can find on the Internet … they are complex and confusing to many people.
- #11: We are NOT trying to put a man on the moon.
- #12: Transition to Gary …
- #13: Gary’s section … quickly click through the animation for this slide.
- #16: The problem is NOT some gremlin in your database …The problems are normal, predictable and SOLVABLE.
- #17: Summary details rolled up and transaction summary data view. Address information changes at 14% per year. Emails and phone numbers change even more frequently.
- #18: We export into a flatten database and analyze. This slide has many examples of the problems we find. These are all examples of data degradation.
- #19: We – consumers – are leaving data footprints everywhere …
- #20: With so much data available, our records … OUR DONORS and constituents … become fragmented.
- #21: Gary: The important point here is that we can de-fragment our donors without having to replace all of our existing databases. The reality is that no software will ever be “one size fits all”. Relationships, communications … life! … are too complicated. In the data science world, the goal is to build databases and create data architectures that make it easier to both store and share data.
- #22: Gary: How did I copy and save in this tool vs that tool? How many tasks are repeated between systems?Multiple systems … each has its own interface … each is used differently … collectively, they are wasting your time
- #24: Ask Debbie to comment here …
- #27: This point is really important … many organizations don’t like hearing it, because it sounds almost like we are de-humanizing the donor … we are not.Organization knowledge REQUIRES … CRM … database store … actual data
- #28: Gary’s sectionSo, we can all agree that we need a data plan to help us manage data / information / knowledge proactively, then how to we get one?
- #32: What does a map DO for us?It defines the journey … we need to get from here to there. Since we “aren’t there yet”, we have gaps to fill in.
- #36: Transition to Debbie …
- #37: Debbie’s section …This slide is a quick review of Gary’s points about … what goes into the map, what goes into the plan. Let’s review each one …
- #38: It is critically important to start with your Fundraising Strategy … you can’t have a reliable data management strategy without knowing what your fundraising strategy requires of the data.In the example, we have 4 strategies to this basic fundraising plan. Each strategy has one or more tasks associated with it.
- #39: In this slide, we now identify the data requirements associated with each task that we need to complete in order to support the fundraising plan.In the case of the data requirements column, YOUR plan will have more details around each point. For example, here we see the need to identify additional data fields that we need to capture … those will vary nonprofit to nonprofit.
- #40: Getting a data assessment is very important. What do you have now? What is the quality of your data? Here we show a sample Data Quality Assessment from Third Sector Labs – they run these for FREE if you provide them the data.
- #41: Now we identify the gaps between where we are now (from the Data Quality Assessment) and where we need to be (from our Fundraising Strategy needs).Great … we have our map … now, on to the plan!
- #42: Prioritization is always a challenge. Here we’ve broken down priorities from the gap analysis, and we’ve focused on what is easier to accomplish vs what is more difficult / time consuming to accomplish.
- #43: Debbie – you can ask Gary to comment on the Action steps in the far right column. Basically, the point is to assign one or more actions to fulfill the data requirements.
- #45: Debbie … do you want to throw this over to Gary?
- #48: Back to Debbie
- #49: Debbie’s section
- #50: Debbie takes this …If no one is responsible, it does not get done. No budget? See previous point.Measuring results can take many forms.
- #51: We PLAN our events … now let’s PLAN our data managementIf your data isn’t getting better, it’s getting worse …