Introduction

Analytics Middle Tier 2.0 is becoming a full citizen in the Ed-Fi platform in 2020, rather than just a proof-of-concept on the Ed-Fi Exchange. As it grows up, it needs to correct some architectural concerns that came up as feedback from the field. It also needs to be up to par with the latest release of the ODS/API, version 3.3. This document aims to inform about the challenges and elicit feedback on the utility of the proposed solutions.

Naming Convention

Requirement

Hold names to under 63 characters for PostgreSQL compatibility.

Design

Currently, none of the objects have a name that violates this constraint. To help avoid problems with future views, it is proposed either truncate "Dimension" to "Dim" or drop the word altogether.

Status

Multi Data Standard Support

Requirement

Version 1.3.0 added support for Data Standard 3.1, which was used by ODS/API 3.1.1 and 3.2, through the use of the –dataStandard <Ds2 | Ds31>  argument. Analytics Middle Tier 2.0.0 will continue in this vein with flag Ds32 for Data Standard 3.2 (ODS/API 3.3).

Design

Forcing the user to remember which data standard is installed is sub-optimal. We should be able to detect this implicitly and install the correct version without user input.

1. If table AddressType exists, then install Data Standard 2.
2. Else if table VersionLevel  exists, then install Data Standard 3.1.
3. Else if table DeployJournal  exists, then install Data Standard 3.2.
4. Else throw an error: "Unable to determine the ODS database version".

Status

Student, Parent, and Staff Keys

Requirement

The views should expose "Key" fields based on the natural key of the underlying table. In the case of StudentDimension , ContactPersonDimension , and UserDimension , the original release used StudentUSI , ParentUSI , and StaffUSI  respectively. The "USI" columns are primary keys and were used by mistake. The "UniqueId" columns are the correct natural keys.

Design

Change all instances of StudentKey , ContactPersonKey , and UserKey  to use the corresponding "UniqueId" column from the source table.

Descriptor and Type Mapping

Requirement

Decouple the views from hard-coded Descriptor and Type values. 

Context: many of the views need to lookup records by Descriptor value - for instance, looking up the Attendance records where a student has an "Excused Absence" or "Unexcused Absence." Because the original developer had access to only a limited dataset, it was not realized that the Descriptor values will vary widely from one implementation to the next. Thus the hard-coding needs to be decoupled, allowing the implementation to provide a mapping from their Descriptor value to the concept used by the Analytics Middle Tier.

In theory, the various "Types" values in Data Standard 2 should provide a more universal constant than the Descriptors. However, some community members report that these too are mutable. Therefore, (a) using Types is not a solution for Data Standard 2, and (b) even those views with hard-coding to Types instead of Descriptors must be modified for greater independence. Note: Type tables were removed in Data Standard 3 precisely because they were not being used in the originally-designed manner. 

Design

Summary

1. Move hard-coded values to a "Constants" table.
2. Create mapping tables that link Descriptors or Types to Constants.
3. Modify all views as needed to join to the Constants and new mapping tables.

Example

In Version 1.x, the StudentEarlyWarningFact view reports on absences, looking for StudentSchoolAttendanceEvent  records with attendance descriptor values of either "Excused Absence" or "Unexcused Absence".

In version 2, the view would now search for all StudentSchoolAttendanceEvent records whose descriptor maps to constant "Absent". Thus there would be two relevant DescriptorMap  values, one each for "Excused Absence" and "Unexcused Absence." Any school who uses a different term than these two would create a DescriptorMap  record mapping that term to the DescriptorConstant  value of "Absent".

Implications

Those who install the Analytics Middle Tier will need to carefully assess their Descriptors and Types, and then manage the DescriptorMap table (and TypeMap , for Data Standard 2) accordingly. The release notes for 2.0 will provide a script that maps the default Ed-Fi Descriptors and Types as a starting point.

Status

Student Dimension Uniqueness

Requirement

The Early Warning System fact views both assumed that the StudentDimension  would only have a single record for a student, as shown in these diagrams:

However, a student could be enrolled in multiple schools at the same time, resulting in two records in the StudentDimension for the same StudentKey. This is problematic for the PowerBI Starter Kit, which has a hard requirement for unique StudentKeys.

At the same time, the Program fact and event views are looking at a Student in a given LocalEducationAgency, whereas the facts above are looking at a Student at a School. Although the ODS database supports a Student enrolled in a Program associated with a School, our input from the field tells us that Program association is on the LEA not the school. Thus we need to balance these competing tensions in the current model.

Design

Data Standard 3 moved the student demographics into the StudentEducationOrganizationAssociation  table, away from the Student table. The current StudentDimension  view provides student name, primary contact, and demographics. The proposal is to split this into two different views, change the Early Warning System star schema to a snowflake.

The demographics on the view are secondary to the student-school relationship. A data analyst might naturally look for school enrollment data. Therefore we will have a new view called StudentEnrollment to show active enrollment/school association information, with a StudentEnrollmentKey  that combines the StudentUniqueId  and SchoolId (maintaining the principal of needing only one column for a join). The early warning views would then change to use the single StudentEnrollmentKey  in addition to the two separate StudentKey  and SchoolKey  columns. The original keys are retained to facilitate aggregating by student or school.

The StudentDimension  would continue to exist in a reduced form, providing only name and primary contact information. The Program views would continue to have the StudentKey  in them.

Problem

What if someone wants to build queries that analyze program participation by demographic data?

Suppose we have the following data (partial representation):

ProgramTypeDimension

StudentProgramEvent

StudentEnrollmentDimension

Here a student is enrolled at two schools, presumably in the same district. Perhaps the program enrollment is due to a specialized class in the student's home language, at a different school than the one attended daily. Since data entry occurred twice - once for each school - there is an opportunity for inconsistency. In this case, one of the school enrollment entries failed to note that the student has limited English Proficiency.

We can write the following query to analyze this program proficiency by LimitedEnglishProficiency.

select
	ProgramTypeDimension.ProgramType,
    count(1) as EnrolledCount,
    sum(case when StudentEnrollmentDimension.LimitedEnglishProficiency <> 'Not applicable' then 1 else 0 end) as LimitedEnglishProficiencyCount
from
	analytics.ProgramTypeDimension
inner join
	analytics.StudentProgramEvent on ProgramTypeDimension.ProgramTypeKey = StudentProgramEvent.ProgramTypeKey
inner join
	analytics.StudentEnrollmentDimension on StudentProgramEvent.StudentKey = StudentEnrollmentDimension.StudentKey
where
	StudentProgramEvent.ProgramEventType = 'Enter'
group by
	ProgramTypeDimension.ProgramType

The result will be

In reality there is only one student, but this correct - yet naive - query counted two students. And it will have the odd result of showing that 50% of the students have limited English proficiency!

Arguably, there is a data quality problem that the Analytics Middle Tier cannot solve. But there is also a training issue at play: the data analyst needs a deeper understanding of the nuances of Ed-Fi data storage and of the Analytics Middle Tier views. Ignoring the data inconsistency problem, the following query would resolve the double-count problem:

select
	ProgramTypeDimension.ProgramType,
    count(1) as EnrolledCount,
    sum(case when StudentDimensionEnrollment.LimitedEnglishProficiency <> 'Not applicable' then 1 else 0 end) as LimitedEnglishProficiencyCount
from
	analytics.ProgramTypeDimension
inner join
	analytics.StudentProgramEvent on ProgramTypeDimension.ProgramTypeKey = StudentProgramEvent.ProgramTypeKey
outer apply (
	select distinct
		LimitedEnglishProficiency
	from
		analytics.StudentDimensionEnrollment
	where
		StudentKey = StudentProgramEvent.StudentKey
) as StudentDimensionEnrollment
where
	StudentProgramEvent.ProgramEventType = 'Enter'
group by
	ProgramTypeDimension.ProgramType

These problems are rather ugly, and would be difficult to detect unless you're actively looking for them. Furthermore, while writing an updated SQL query was easy, how would one represent this in a business intelligence tool like Tableau or PowerBI?

Status