Centrality-and-Architecture

How does centrality affect your Architecture?

Some time ago, I was responsible for a data architecture I had mostly inherited. There were a number of tweaks I worked to on to refine the monolithic nature of the main database. It was a time of upheaval in this organization. They had outgrown their legacy Computer Telephony Interface application. It was time to create something new. 

A large new application development team was brought in to develop some new software.

There was a large division of labor and processing where some things were handled by the new application, and another thing was developed to handle the data. Reporting, cleansing, analysis, ingress feeds, egress feeds, all of these went through the “less important” system. 

This was the system I was responsible for. 

In thinking about how best to explain a Data Structure Graph, I spent some time revisiting this architecture and brought it into a format that could be analyzed with the tools of Network Analysis. 

After anonymizing the data a bit, and limiting the data flows to only the principle data flows, I constructed a csv file to load into Gephi for analysis.

Source	Target	Edge_Label
Spider	ODS	Application
ODS	Spider	Prospect
Vendor1	ODS	Prospect
Vendor2	ODS	Prospect
Vendor3	ODS	Prospect
ODS	Servicing	Application
Legacy	ODS	Application
ODS	Legacy	Prospect
ODS	Dialer1	Prospect
ODS	Dialer2	Prospect
Gov	ODS	DNC
ODS	Spider	LegacyData1
ODS	Spider	LegacyData2
ODS	Spider	LegacyData3
Spider	ODS	LegacyData1
Spider	ODS	LegacyData2
Spider	ODS	LegacyData3
ODS	ThirdParty	Prospect
ThirdParty	ODS	Application
Legacy	ODS	Application
Legacy	ODS	DialerStats
Dialer1	ODS	DialerStats
Dialer2	ODS	DialerStats

I ran a few simple statistics on the graph, then did some partitioning to color the graph to make it apparent the degree of a node this is the first output of Gephi:

The actual statistics Gephi calculated are in this table:

Id	Label	PageRank	Eigenvector Centrality	In-Degree	Out-Degree	Degree
Vendor1	Vendor1	0.01991719	0.00000000	0	1	1
Vendor2	Vendor2	0.01991719	0.00000000	0	1	1
Vendor3	Vendor3	0.01991719	0.00000000	0	1	1
Gov	Gov	0.01991719	0.00000000	0	1	1
Spider	Spider	0.08121259	0.44698155	1	1	2
Servicing	Servicing	0.08121259	0.44698155	1	0	1
Legacy	Legacy	0.08121259	0.44698155	1	1	2
Dialer1	Dialer1	0.08121259	0.44698155	1	1	2
Dialer2	Dialer2	0.08121259	0.44698155	1	1	2
ThirdParty	ThirdParty	0.08121259	0.44698155	1	1	2
ODS	ODS	0.43305573	1.00000000	9	6	15

From the Data Architecture perspective, which “application” has the greatest impact to the organization if there were a failure?

Which “application” should have the greatest degree of protection, redundancy, and expertise 

associated with it? 

Let's cover in detail the two metrics in the middle of the last table PageRank, and Eigenvector Centrality. 

I will have to create individual blog entries for both PageRank and Eigenvector Centrality to discuss the actual mechanism for how these are calculated. The math for these can be a bit cumbersome, and each algorithm should be given due attention on its own.

The point of this analysis is to determine which component of the architecture should have additional resources devoted to it. For any customer facing application, it should be given due attention, and infrastructure. However, one question I have seen many of my clients struggle with is what is the priority of the back-end infrastructure? Should once component of the architecture be given more attention than another? I have 90 databases throughout the organization, which one is the most important?

These centrality calculations show unequivocally which component of the architecture has the most impact in the event of an outage, or where the most value can be provided for an upgrade.
 
This type of analysis can begin to shed light on the answers to these questions. A methodical approach to an architecture based on data, rather than the division that screams the loudest can give insight into how an architecture is truly implemented.

I call these artifacts a Data Structure Graph

What is a Data Structure?

Since we will be using this as a foundation to introduce a new way of looking at the way data architecture is done, we should start with a formal definition of the components that make up this term.

According to the Wiktionary some of the definitions of structure are:

                A cohesive whole built up of distinct parts.

                The overall form or organization of something. 

Again, according to Wiktionary some of the definitions of data are:

                Pieces of information.

                A representation of facts or ideas in a formalized manner capable of being communicated or manipulated by some process. 

Now that we have these component definitions, we can define a data structure. 

A Data Structure is: The organization of data in support of an application, organization, or workflow.
 

English: Traditional Aggregate Data Structure Diagram (Legend/Map Unit) (11/13/07) (Photo credit: Wikipedia)

A Data Structure is what is created by a Data Architect, Data Modeler, Application Designer, Business Intelligence Architect, Data Warehouse Architect, Enterprise Architect, Data Scientist, or anyone else that designs platforms that capture, store and provide access to data. These artifacts can be in the form of an ERD (Entity Relationship Diagram), JSON schema definition, XML schema definition, a workflow, an Architecture Diagram, and ETL (Extract Transform and Load) source to target mapping document. 

All of these are Data Structures. 

Eric Raymond once said: “Show me your code and conceal your data structures, and I shall continue to be mystified. Show me your data structures, and I won't usually need your code; it'll be obvious.”

 

I have seen this be the case over and over again with multiple customers.

Reading code is a fun past time to some people. Having a diagram that shows how data flows through an application, or enterprise is most useful for a wider audience. The audience would determine the level of detail required for the diagram. The diagrams should be built through the process of progressive elaboration.

In a later post we will talk about how to interpret a Data Structure as a Graph, and how that can help in the design phase of an application, architecture, or workflow to highlight where any bottlenecks, obstructions, or optimizations may be found.