CoolData blog

5 December 2016

Amazing things with matching strings

Filed under: Coolness, Data integrity, SQL — Tags: , , , — kevinmacdonell @ 7:44 am


I had an occasion recently when it would have been really helpful to know that a new address added to the database was a duplicate of an older, inactivated address. The addition wasn’t identified as a duplicate because it wasn’t a perfect match — a difference similar to that between 13 Anywhere Road and 13 Anywhere Drive. 


After the fact, I did a Google search and discovered some easy-to-use functionality in Oracle SQL that might have saved us some trouble. Today I want to talk about how to use UTL_MATCH and  suggest some cool applications for it in Advancement data work.


“Fuzzy matching” is the term used for identifying pairs of character strings that may not be exactly the same, but are so close that they could be. For example, “Washignton” is one small typo away from “Washington,” but the equivalence is very difficult to detect by any means other than an alert pair of human eyes scanning a sorted list. When the variation occurs at the beginning of a string — “Unit 3, 13 Elm St.” instead of “Apmt 3, 13 Elm St.” — then even a sorted list is of no use.


According to this page, the UTL_MATCH package was introduced in Oracle 10g Release 2, but first documented and supported in Oracle 11g Release 2. The package includes two functions for testing the level of similarity or difference between strings.


The first function is called EDIT_DISTANCE, which is a count of the number of “edits” to get from one string to a second string. For example, the edit distance from “Kevin” to “Kelvin” is 1, for “New York” to “new york” is 2, and from “Hello” to “Hello” is 0. (A related function, EDIT_DISTANCE_SIMILARITY, expresses the distance as a normalized value between 0 and 100 — 100 being a perfect match.)


The second method, the one I’ve been experimenting with, is called JARO_WINKLER, named for an algorithm that measures the degree of similarity between two strings. The result ranges between 0 (no similarity) to 1 (perfect similarity). It was designed specifically for detecting duplicate records, and its formula seems aimed at the kind of character transpositions you’d expect to encounter in data entry errors. (More info here: Jaro-Winkler distance.)


Like EDIT_DISTANCE, it has a related function called JARO_WINKLER_SIMILARITY. Again, this ranges from 0 (no match) to 100 (perfect match). This is the function I will refer to for the rest of this post.


Here is a simple example of UTL_MATCH in action. The following SQL scores constituents in your database according to how similar their first name is to their last name, with the results sorted in descending order by degree of similarity. (Obviously, you’ll need to replace “schema”, “persons,” and field names with the proper references from your own database.)






UTL_MATCH.jaro_winkler_similarity(t1.first_name, t1.last_name) AS jw

FROM schema.persons t1



Someone named “Donald MacDonald” would get a fairly high value for JW, while “Kevin MacDonell” would score much lower. “Thomas Thomas” would score a perfect 100.


Let’s turn to a more useful case: Finding potential duplicate persons in your database. This entails comparing a person’s full name with the full name of everyone else in the database. To do that, you’ll need a self-join.


In the example below, I join the “persons” table to itself. I concatenate first_name and last_name to make a single string for the purpose of matching. In the join conditions, I exclude records that have the same ID, and select records that are a close or perfect match (according to Jaro-Winkler). To do this, I set the match level at some arbitrary high level, in this case greater than or equal to 98.









UTL_MATCH.jaro_winkler_similarity ( t1.first_name || ' ' || t1.last_name, t2.first_name || ' ' || t2.last_name ) AS jw

FROM schema.persons t1

INNER JOIN schema.persons t2 ON t1.ID != t2.ID AND UTL_MATCH.jaro_winkler_similarity ( t1.first_name || ' ' || t1.last_name, t2.first_name || ' ' || t2.last_name ) >= 98



I would suggest reading this entire post before trying to implement the example above! UTL_MATCH presents some practical issues which limit what you can do. But before I share the bad news, here are some exciting possible Advancement-related applications:


  • Detecting duplicate records via address matching.
  • Matching external name lists against your database. (Which would require the external data be loaded into a temporary table in your data warehouse, perhaps.)
  • Screening current and incoming students against prospect, donor, and alumni records for likely matches (on address primarily, then perhaps also last name).
  • Data integrity audits. An example: If the postal code or ZIP is the same, but the city name is similar (but not perfectly similar), then there may be an error in the spelling or capitalization of the city name.
  • Searches on a particular name. If the user isn’t sure about spelling, this might be one way to get suggestions back that are similar to the guessed spelling.


Now back to reality … When you run the two code examples above, you will probably find that the first executes relatively quickly, while the second takes a very long time or fails to execute at all. That is due to the fact that you’re evaluating each record in the database against every other record. This is what’s known as a cross-join or Cartesian product — a very costly join which is rarely used. If you try to search for matches across 100,000 records, that’s 10 billion evaluations! The length of the strings themselves contributes to the complexity, and therefore the runtime, of each evaluation — but the real issue is the 10,000,000,000 operations.


As intriguing as UTL_MATCH is, then, its usage will cause performance issues. I am still in the early days of playing with this, but here are a few things I’ve learned about avoiding problems while using UTL_MATCH.


Limit matching records. Trying to compare the entire database with itself is going to get you in trouble. Limit the number of records retrieved for comparison. A query searching for duplicates might focus solely on the records that have been added or modified in the past day or two, for example. Even so, those few records have to be checked against all existing records, so it’s still a big job — consider not checking against records that are marked deceased, that are non-person entities, and so on. Anything to cut down on the number of evaluations the database has to perform.


Keep strings short. Matching works best when working with short strings. Give some thought to what you really want to match on. When comparing address records, it might make sense to limit the comparison to Street Line 1 only, not an entire address string which could be quite lengthy.


Pre-screen for perfect matches: A Jaro-Winkler similarity of 100 means that two strings are exactly equal. I haven’t tested this, but I’m guessing that checking for A = B is a lot faster than calculating the JW similarity between A and B. It might make sense to have one query to audit for perfect matches (without the use of UTL_MATCH) and exclude those records from a second query that audits for JW similarities that are high but less than a perfect 100.


Pre-screen for impossible matches. If a given ID_1 has a street address than is 60 characters long and a given ID_2 has a street address that is only 20 characters long, there is no possibility of a high Jaro-Winkler score and therefore no need to calculate it. Find a way to limit the data set to match before invoking UTL_MATCH, possibly through the use of a WITH clause that limits potential matching pairs by excluding any that differ in length by more than, say, five characters. (Another “pre-match” to use would check if the initial letter in a name is the same; if it isn’t, good chance it isn’t going to be a match.)


Keep match queries simple. Don’t ask for fields other than ID and the fields you’re trying to match on. Yes, it does make sense to bring down birthdate and additional address information so that the user can decide if a probable match is a true duplicate or not, but keep that part of the query separate from the match itself. You can do this by putting the match in a WITH clause, and then left-joining additional data to the results of that clause.


Truth be told, I have not yet written a query that does something useful while still executing in a reasonable amount of time, simply due to the sheer number of comparisons being made. I haven’t given up on SQL, but it could be that duplicate detection is better accomplished via a purpose-built script running on a standalone computer that is not making demands on an overburdened database or warehouse (aside from the initial pull of raw data for analysis).


The best I’ve done so far is a query that selects address records that were recently modified and matches them against other records in the database. Before it applies Jaro-Winkler, the query severely limits the data by pairing up IDs that have name strings and address strings that are nearly the same number of characters long. The query has generated a few records to investigate and, if necessary, de-dupe — but it takes more than an hour and half to run.


Have any additional tips for making use of UTL_MATCH? I’d love to hear and share. Email me at


13 June 2016

Nifty SQL regression to calculate donors’ giving trends

Filed under: Coolness, Predictor variables, regression, SQL — Tags: , , , — kevinmacdonell @ 8:28 pm


Here’s a nifty bit of SQL that calculates a best-fit line through a donor’s years of cash-in giving by fiscal year (ignoring years with no giving), and classifies that donor in terms of how steeply they are “rising” or “falling”.


I’ll show you the sample code, which you will obviously have to modify for your own database, and then talk a little bit about how I tested it. (I know this works in Oracle version 11g. Not sure about earlier versions, or other database systems.)


with sums AS (
 select, t1.fiscal_year, log(10, sum(t1.amount)) AS yr_sum
 from gifts t1
 group by, t1.fiscal_year),

slopes AS (
 select distinct,
 regr_slope(sums.yr_sum,sums.fiscal_year) OVER (partition by AS slope

from sums

 when slopes.slope is null then 'Null'
 when slopes.slope >=0.1 then 'Steeply Rising'
 when slopes.slope >=0.05 then 'Moderately Rising'
 when slopes.slope >=0.01 then 'Slightly Rising'
 when slopes.slope >-0.01 then 'Flat'
 when slopes.slope >-0.05 then 'Slightly Falling'
 when slopes.slope >-0.1 then 'Moderately Falling'
 else 'Steeply Falling' end AS description

from slopes
That’s it. Not a lot of SQL, and it runs very quickly (for me). But does it actually tell us anything?


I devised a simple test. Adapting this query, I calculated the “slope of giving” for all donors over a five-year period in the past: FY 2007 to FY 2011. I wanted to see if this slope could predict whether, and by how much, a donor’s giving would rise or fall in the next five-year period: FY 2012 to FY 2016. (Note that the sum of a donor’s giving in each year is log-transformed, in order to better handle outlier donors with very large giving totals.)


I assembled a data file with each donor’s sum of cash giving for the first five-year period, the slope of their giving in that period, and the sum of their cash giving for the five-year period after that.


The first test was to see how the categories of slope, from Steeply Rising to Steeply Falling, translated into subsequent rises and falls. In Data Desk, I compared the two five-year periods. If the second period’s giving was greater than the first, I called that a “rise.” If it was less, I called it a “fall.” And if it was exactly the same, I called it “Same.”


The table below summarizes the results. Note that these numbers are all percentages, summed horizontally. (I will explain the colour highlighting later on.)




For Steeply Rising, 60.6% of donors actually FELL from the first period to the next. Only 37.8 percent rose, and just 1.6% stayed exactly the same. Not terribly impressive. Look at Steeply Falling, though: More than three-quarters actually did fall. That’s a better result, but then again, “Falling” dominates for every category; in the whole file, close to 70% of all donors reduced their giving in the next period. If a donor has no giving in the second period of five years, that’s zero dollars given, and this is called a “Fall” — more on that aspect in just a sec.


(I’ve left out donors with a FY2007-11 slope of Null — they’re the ones who gave in only one year and therefore don’t have a “slope”.)


Let’s not give up just yet, however. The colour highlighting indicates how high each percentage value is in relation to those above and below it. For example, the highest percentages in the Falling column are found in the Slightly, Moderately, and especially Steeply Falling slope categories. The highest percentages in the Rising column are in the Slightly, Moderately, and Steeply Rising slope categories. And in the Same column, the Flat slope wins hands-down — as we would hope.


So a rising slope “sort of” predicts increased giving, a falling slope “sort of” predicts decreased giving. Unfortunately, many donors are not retained into the second five-year period, so there’s not a lot to be confident about.


But what if a donor IS retained? What if we exclude the lapsed donors entirely? Let’s do that:




Excluding non-donors seems to lead to an improvement … The slope does a better job sorting between the risers and fallers when a donor is actually retained. Again, the colour highlighting is referencing columns, not rows. But notice now that, across the rows, Rising has a slight majority for the Rising slope categories, and Falling has a slight majority for the Falling slope categories. (The bar is set too high for Flat, however, given that a donor’s giving in the first five years has to be exactly equal to her giving in the second five years to be called Same.)


Admittedly, these majorities are not generous. If I calculated a donor’s slope of giving as Steeply Rising and that donor was retained, I have only a 56.4% chance of actually being right. And of course there’s no guarantee that donor won’t lapse.


(Note that these are donors of all types — alumni, non-alumni individuals, and entities such as corporations and foundations. Non-alumni donors tend not to have patterns in their giving that are repeated, not to the extent that alumni do. However, when I limit the data file to alumni donors only, the improvement in this method is only very slight.)


Pressing on … I did a regression analysis using total giving in the second five-year period as the dependent variable, then entered total giving in the prior five-year period as an independent variable. (Naturally, R-squared was very high.) This allowed me to see if Slope provides any explanatory power when it is added as the second independent variable — the effect of giving in the first five-year period already being accounted for.


And the answer is, yes, it does. But only under specific conditions: Both five-year giving totals were log-transformed and, most significantly, donors who did not give in the second period were excluded from the regression.


There are other way to assess the usefulness of “slope” which might lead to an application, and I encourage you to give this a try with your own data. From past experience I know that donors who make big upgrades in giving don’t have any neat universal pattern such as an upward slope in their giving history. (The concept of volatility is explored here and here.) “Slope” is probably too simple a characteristic to employ on its own.


But as I’ve said before, if it were easy, obvious, or intuitive, it wouldn’t be data analysis.


20 July 2014

Eliminate your duplicate data row problems with simple SQL

Filed under: Data, SQL — Tags: , , , , , , , — kevinmacdonell @ 1:38 pm

We’ve all faced this problem: Duplicate rows in our data. It messes up our reports and causes general confusion and consternation for people we supply data to. The good news is you can take care of this problem fairly easily in an SQL query using a handy function called LISTAGG. I discovered this almost by accident just this week, and I’m delighted to share.

Duplicate rows are a result of what we choose to include in our database query. If we are expecting to get only one row for each constituent ID, then we should be safe in asking for Gender, or Name Prefix, or Preferred Address. There should be only one of each of these things for each ID, and therefore there won’t be duplicate rows. These are examples of one-to-one relationships.

We get into trouble when we ask for something that can have multiple values for a single ID. That’s a one-to-many relationship. Examples: Any address (not just Preferred), Degree (some alumni will have more than one), Category Code (a person can be an alum and a parent and a staff member, all at the same time) … and so on. Below, the first constituent is duplicated on Category, and the second constituent is duplicated on Degree.


That’s not the worst of it; when one ID is duplicated on two or more elements, things get really messy. If A0001 above had three Category codes and two degrees, it would be appear six times (3 x 2) in the result set. Notice that we are really talking about duplicate IDs — not duplicate rows. Each row is, in fact, unique. This is perfectly correct behaviour on the part of the database and query. Try explaining that to your boss, however! You’ve got to do something to clean this up.

What isn’t going to work is pivoting. In SQL, the PIVOT function creates a new column for each unique category of some data element, plus an aggregation of the values for that category. I’ve written about this here: Really swell SQL: Why you must know PIVOT and WITH. Using PIVOT is going to give you something like the result below. (I’ve put in 1’s and nulls to indicate the presence or absence of data.)



What we really want is THIS:


The answer is this: String aggregation. You may already be familiar with aggregating quantitative values, usually by counting or summing. When we query on a donor’s giving, we usually don’t want a row for each gift — we want the sum of all giving. Easy. For strings — categorical data such as Degree or Category Code — it’s a different situation. Sure, we can aggregate by using counting. For example, I can ask for a count of Degree, and that will limit the data to one row per ID. But for most reporting scenarios, we want to display the data itself. We want to know what the degrees are, not just how many of them there are.

The following simplified SQL will get you the result you see above for ID and Category — one row per ID, and a series of category codes separated by commas. This code works in Oracle 11g — I can’t speak to other systems’ implementations of SQL. (For other Oracle-based methods, check out this page. To accomplish the same thing in a non-Oracle variant of SQL called T-SQL, scroll down to the Postscript below.)

Obviously you will need to replace the sample text with your own schema name, table names, and field identifiers.




The two arguments given to LISTAGG are the field CATG_CODE and a string consisting of a comma and a space. The string, which can be anything you want, is inserted between each Category Code. On the next line, ORDER BY sorts the category codes in alphabetical order.

LISTAGG accepts only two arguments, but if you want to include two or more fields and/or strings in the aggregation, you can just concatenate them inside LISTAGG using “pipe” characters (||). The following is an example using Degree Code and Degree Code Description, with a space between them. To get even fancier, I have replaced the comma separator with the character code for “new line,” which puts each degree on its own line — handy for readability in a list or report. (This works fine when used in Custom SQL in Tableau. For display in Excel, you may have to use a carriage return — char(13) — instead of a line feed.) You will also notice that I have specified a join to a table containing the Degree data.






Before I discovered this capability, the only other automated way I knew how to handle it was in Python. All the previous methods I’ve used were at least partially manual or just very cumbersome. I don’t know how long string aggregation in Oracle has been around, but I’m grateful that a data problem I have struggled with for years has finally been dispensed with. Hurrah!



After publishing this I had an email response from David Logan, Director of Philanthropic Analytics at Children’s Mercy in Kansas City, Missouri. He uses TSQL, a proprietary procedural language used by Microsoft in SQL Server. Some readers might find this helpful, so I will quote from his email:

I was reading your post this morning about using the LISTAGG function in Oracle SQL to concatenate values from duplicate rows into a list. A couple of months ago I was grappling with this very problem so that I could create a query of Constituents that included a list of all their phone numbers in a single field. LISTAGG is not available for those of us using T-SQL … the solution for T-SQL is to use XML PATH().

Here’s a link to a source that demonstrates how to use it:

One key difference in this method from LISTAGG is that the delimiter character (such as the comma—or characters as in your example where you have comma space) is added before each value returned so the raw string returned will have this character before the first value. You can use the STUFF() function to eliminate this first delimiter.

Here’s my solution using XML PATH() to return a list of phone numbers from our donor database:

   '; ' + PH.NUM + ' [' + re7_ReportingTools.dbo.GetLongDescription(PH.PHONETYPEID) + ']'
     re7_db.dbo.PHONES PH

I concatenated my list using a semi-colon followed by a space ‘; ‘. I used the STUFF() function to remove the leading semi-colon and space.

Here are a few sample rows that are returned (note I’ve “greeked” the prefixes for donor privacy):


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