Category Archives: Metadata Mart

Data Governance Navigating the Information Value Chain Demystifying the path forward

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The challenge for businesses is to seek answers to questions, they do this with Metrics (KPI’s) and know the relationships of the data, organized by logical categories(dimensions) that make up the result or answer to the question. This is what constitutes the Information Value Chain

Navigation

Let’s assume that you have a business problem, a business question that needs answers and you need to know the details of the data related to the business question.

Information Value Chain

Information Value Chain

 

  • Business is based on Concepts.

 

  • People thinks in terms of Concepts.

 

  • Concepts come from Knowledge.

 

  • Knowledge comes from Information.

 

  • Information comes from Formulas.

 

  • Formulas determine Information relationships based on quantities.

 

  • Quantities come from Data.

 

  • Data physically exist.

In today’s fast-paced high-tech business world this basic navigation (drill thru) business concept is fundamental and seems to be overlooked, in the zeal to embrace modern technology

 

In our quest to embrace fresh technological capabilities, a business must realize you can only truly discover new insights when you can validate them against your business model or your businesses Information Value Chain, that is currently creating your information or results.

 

Today data needs to be deciphered into information in order to apply formulas to determine relationships and validate concepts, in real time.

 

We are inundated with technical innovations and concepts it’s important to note that business is driving these changes not necessarily technology

 

Business is constantly striving for a better insights, better  information and increased automation as well as the lower cost while doing these things several of these were examined and John Thuma’s‘  latest article

 

Historically though these changes were few and far between however innovation in hardware storage(technology) as well as software and compute innovations have led to a rapid unveiling of newer concepts as well as new technologies     

 

 

Demystifying the path forward.

In this article we’re going to review the basic principles of information governance required for a business measure their performance. As well as explore some of the connections to some of these new technological concepts for lowering cost       

To a large degree I think we’re going to find that why we do things has not changed significantly it’s just how, we know have different ways to do them.

 

It’s important while embracing new technology to keep in mind that some of the basic concepts, ideas, goals on how to properly structure and run a business have not changed even though many more insights and much more information and data is now available.

 

My point is in the implementing these technological advances could be worthless to the business and maybe even destructive, unless they are associated with a actual set of Business Information Goals(Measurements KPI’s) and they are linked directly with understandable Business deliverables.

 

And moreover prior to even considering or engaging a data science or attempt data mining you should organize your datasets capturing the relationships and apply a “scoring” or “ranking” process and be able to relate them to your business information model or Information Value Chain, with the concept of quality applied real time.  

 

The foundation for a business to navigate their Information Value Chain is an underlying Information Architecture. An Information Architecture typically, involves a model or concept of information that is used and applied to activities which require explicit details of complex information systems.

 

Subsequently a data management and databases are required, they form the foundation of your Information Value Chain, to bring this back to the Business Goal. Let’s take a quick look at the difference between relational database technology and graph technology as a part of emerging big data capabilities.

 

However, considering the timeframe for database technology evolution, has is introduced a cultural aspect of implementing new technology changes, basically resistance to change. Business that are running there current operations with technology and people form the 80s and 90s have a different perception of a solution then folks from the 2000s. 

 

Therefore, in this case regarding a technical solution “perception is not reality”, awarement is. Business need to find ways to bridge the knowledge gap and increase awarement that simply embracing new technology will not fundamentally change the why a business is operates , however it will affect how.  

 

Relational databases were introduced in 1970, and graph database technology was introduced in the mid to 2000

 

There are many topics included in the current Big Data concept to analyze, however the foundation is the Information Architecture, and the databases utilized to implement it.

 

There were some other advancements in database technology in between also however let’s focus on these two

History

 

1970

In a 1970s relational database, Based on mathematical Set theory, you could pre-define the relationship of tabular (tables) , implement them in a hardened structure, then query them by manually joining the tables thru physically naming attributes and gain much better insight than previous database technology however if you needed a new relationship it would require manual effort and then migration of old to new , In addition your answer it was only good as the hard coding query created

2020

In  mid-2000’s the graph database was introduced , based on graph theory, that defines the relationships as tuples  containing nodes  and edges.  Graphs represent things and relationships events describes connections between things, which makes it an ideal fit for a navigating relationship. Unlike conventional table-oriented databases, graph databases (for example Neo4J, Neptune) represent entities and relationships between them. New relationships can be discovered and added easily and without migration, basically much less manual effort. 

 

Nodes and Edges

Graphs are made up of ‘nodes’ and ‘edges’. A node represents a ‘thing’ and an edge represents a connection between two ‘things’. The ‘thing’ in question might be a tangible object, such as an instance of an article, or a concept such as a subject area. A node can have properties (e.g. title, publication date). An edge can have a type, for example to indicate what kind of relationship the edge represents.

Takeaway.

 

The takeaway there are many spokes on the cultural wheel, in a business today, encompassing business acumen, technology acumen and information relationships and raw data knowledge and while they are all equally critical to success, the absolute critical step is that the logical business model defined as the Information Value Chain is maintained and enhanced.

 

It is a given that all business desire to lower cost and gain insight into information, it is imperative that a business maintain and improve their ability to provide accurate information that can be audited and traceable and navigate the Information Value Chain Data Science can only be achieved after a business fully understand their existing Information Architecture and strive to maintain it.

 

Note as I stated above an Information Architecture is not your Enterprise Architecture. Information architecture is the structural design of shared information environments; the art and science of organizing and labelling websites, intranets, online communities and software to support usability and findability; and an emerging community of practice focused on bringing principles of design, architecture and information science to the digital landscape. Typically, it involves a model or concept of information that is used and applied to activities which require explicit details of complex information systems.

In essence, a business needs a Rosetta stone in order translate past, current and future results.

 

In future articles we’re going to explore and dive into how these new technologies can be utilized and more importantly how they relate to all the technologies.

Merry Christmas Data Classification, Feature Engineering , Data Governance. ‘How to’ do it and some code take a look

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I was heavily involved in business intelligence, data warehousing and data governance as of several years ago and recently have had many chaotic personal challenges, upon returning to professional practice I have discovered things have not changed that much in 10 yearsagovernance The methodologies and approaches are still relatively consistent however the tools and techniques have changed and In my opinion not for the better, without focusing on specific tools I’ve observed that the core to data or MDM is enabling and providing a capability for classifying data into business categories or nomenclature.. and it has really not improved.

  • This basic traditional approach has not changed, in essence man AI model predicst a Metric and is wholly based on the integrity of its features or Dimensions.



Therefore I decided, to update some of the techniques and code patterns, I’ve used in the past regarding the information value chain and or record linkage , and we are going to make the results available with associated business and code examples initially with SQL Server and data bricks plus python

My good friend, Jordan Martz of DataMartz fame has greatly contrinuted to this old mans BigData enlightenment as well as Craig Campbell in updating some of the basic classification capabilities required and critical for data governance. If you would like a more detailed version of the source as well as the test data, please send me an email at iwhiteside@msn.com. Stay tuned for more update and soon we will add Neural Network capability for additional automation of “Governance Type” automated classification and confidence monitoring.

Before we focus on functionality let’s focus on methodology


Initially understand key metrics to be measured/KPI‘s their formulas and of course teh businesse’s expectation of their calculations

Immediately gather file sources and complete profiling as specified in my original article found here

Implementing the processes in my meta-data mart article would provide numerous statistics regarding integers or float field however there are some special considerations for text fields or smart codes

Before beginning classification you would employ similarity matching or fuzzy matching as described here



As I said I posted the code for this process on SQL Server Central 10 years ago here is s Python Version.

databricks-logo Roll You Own – Python Jaro_Winkler(Python)

databricks-logoroll You Own – Python Jaro_Winkler(Python)
Import Notebook
Step 1a – import pandas
import pandas

Step 2 – Import Libraries

libraries from pyspark.sql.functions import input_file_name

from pyspark.sql.types import *
import datetime, time, re, os, pandas

ML Libraires

from pyspark.ml.feature import RegexTokenizer, StopWordsRemover, NGram, HashingTF, IDF, Word2Vec, Normalizer, Imputer, VectorAssembler
from pyspark.ml import Pipeline

import mlflow

from mlflow.tracking import MLFlowClient

from sklearn.cluster import KMeans
import numpy as np
Step 3 – Test JaroWinkler

JaroWinkler(‘TRAC’,’TRACE’)
Out[5]: 0.933333
Step 4a =Implement JaroWinkler(Fuzzy Matching)

%python

def JaroWinkler(str1_in, str2_in):

if(str1_in is None or str2_in is None):
return 0

tr=0
common=0
jaro_value=0
len_str1=len(str1_in)
len_str2=len(str2_in)
column_names=[‘FID’,’FStatus’]

df_temp_table1 = pandas.DataFrame(columns=column_names)
df_temp_table2 = pandas.DataFrame(columns=column_names)

#clean_string(str1_in)
#clean_string(str2_in)

if len_str1 > len_str2:
swap_len=len_str2
len_str2=len_str1
len_str1=swap_len
swap_str=str1_in
str1_in=str2_in
str2_in=swap_str

max_len=len_str2

iCounter=1

while(iCounter <= len_str1):
df=pandas.DataFrame([[iCounter,0]], columns=column_names)
df_temp_table1=pandas.concat([df_temp_table1,df], ignore_index=True)
iCounter=iCounter+1

iCounter=1

while (iCounter <= len_str2):
df=pandas.DataFrame([[iCounter,0]], columns=column_names)
df_temp_table2=pandas.concat([df_temp_table2,df], ignore_index=True)
iCounter=iCounter+1

iCounter=1
m=round((max_len/2)-1,0)
i=1

while(i <= len_str1):
a1=str1_in[i-1]

if m >= i:
  f=1
  z=i+m 
else:
  f=i-m
  z=i+m 
if z > max_len:
  z=max_len
while (f <= z):
  a2=str2_in[int(f-1)]
  if(a2==a1 and df_temp_table2.loc[f-1].at['FStatus']==0):
    common = common + 1
    df_temp_table1.at[i-1,'FStatus']=1
    df_temp_table2.at[f-1,'FStatus']=1 
    break
  f=f+1
i=i+1

i=1
z=1
while(i <= len_str1): v1Status=df_temp_table1.loc[i-1].at[‘FStatus’] if(v1Status==1): while(z <= len_str2): v2Status=df_temp_table2.loc[z-1].at[‘FStatus’] if(v2Status==1): a1=str1_in[i-1] a2=str2_in[z-1] z=z+1 if(a1 != a2 ): tr = tr+0.5 break break i=i+1 wcd = 1.0/3.0 wrd = 1.0/3.0 wtr = 1.0/3.0 if (common != 0): jaro_value = (wcd * common)/ len_str1 + (wrd * common) / len_str2 + (wtr * (common – tr)) / common return round(jaro_value,6) Step 4b – Register JaroWinkler spark.udf.register(“JaroWinkler”, JaroWinkler) Out[6]:
Step8a – Bridge vs Master vs AssociativeALL

%sql
DROP TABLE IF EXISTS NameAssociative;
CREATE TABLE NameAssociative;

SELECT
Name
,NameInput

,sha2(replace ( NameLookput,’%[^a-Z0-9, ]%’,’ ‘) , 256) as NameLookupCeaned ,a.NameLookupKey
,sha2(replace( NameInput,’%[^a-Z0-9, ]%’,’ ‘) , 256) as NameInput,b.NameInputKey
,JaroWinkler(a.NameLookup, b.NameInput) MatchScor
,RANK() OVER (Partition by a.DetailedBUMaster ORDER BY JaroWinkler(a.NameLookupCleande, b.NameInputCleaned) DESC) NameLookup,b.NameLookupKey
FROM NameInput as a
CROSS JOIN NameLookup as b

DNA and the concept of MDM(Master Data Management) have many similarities. 

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DNA and the concept of MDM( Master Data Management) or Modern ML/AI Data Preparation have many similarities.

 

 “There is a subtle difference between data and information.”
We in IT have complicated and diluted the concept and process of analyzing data and business metrics incredibly in the last few decades. We seem to be focus in on the word data.

And if you consider the primary business objectives of MDM is provide consistent answers with standard business definitions and an understanding of  the relationship or mappings of business outcomes to data elements.

 DNA vs MDM or IT’s version of DNA.

 The graphic I’ve chosen for this post symbolizes the linkage or lineage of human beings to DNA. What I’d like to do is relate the importance of lineage of data to examples of human how DNA communicate lineage and then discuss it in our IT as it relates to business functionality

Living Organisms are very complex as is a company and its data or information.

The genetic information of every living organism is stored inside these nucleic the basic data.

There are two types of nucleic acids(NA) namely:

 DNA– Defines Traits, Characteristics

 RNA – Communication, transfers information and synthesis.

 Lets examine them.

 DNA- Defines Traits, Characteristics


  DNA-Deoxyribonucleic acid – In most living organisms (except for viruses), genetic information is stored in the form of DNA.   


RNA – Communication, transfers information and synthesis

.


RNA – can move around in the cells of living organisms and serves as a  genetic messenger, passing the information stored in the cell’s DNA from the nucleus to other parts of  the cell for protein synthesis.  

 So here goes this is a bit of a stretch but if you consider DNA is the “Data” and a person the “Information” is created from the communication through the RNA.

 To continue the analogy the DNA or chromosomes in and by themselves are out of context. It’s only once they been passed from one person to the next driven by RNA and result in a human being in that they become contextually realized as a human.

Again if we break down human DNA and inspected it, we can tell many things origin, or ancestory , traits of the person possible, diseases of the person, but it needs to be processed for us to understand the actual person. 

So my point in relation to various approaches to traditional  MDM  or Master Data Management yis that this(DNA) is how life is created, it’s science, it’s not a methodology or product in approach a vendor guess work it’s real and the main point is “lineage” is the key

“There is a subtle difference between data and information. Data are the facts or details from which is derived. Individual pieces of data are rarely useful alone. For data to become information, data needs to be put into context.”

 

Examples of Data and Information as it relates to MDM

The history of temperature readings all over the world for the past 100 years is data.

If this data is organized and analyzed to find that global temperature is rising, then that is information.

  •  The number of visitors to a website by country is an example of data.
  •  Finding out that traffic from the U.S. is increasing while that from Australia is decreasing is meaningful information.
  •  Often data is required to back up a claim or conclusion (information) derived or deduced from it.
  •  For example, before a drug is approved by the FDA, the manufacturer must conduct clinical trials and present a lot of data to demonstrate that the drug is safe.


“Mislesading” Data”

Data needs to be interpreted and analyzed, it is quite possible — indeed, very probable — that it will be interpreted incorrectly. 

When this leads to erroneous conclusions, it is said that the data are misleading. Often this is the result of incomplete data or a lack of context.

For example, your investment in a mutual fund may be up by 5% and you may conclude that the fund managers are doing a great job. However, this could be misleading if the major stock market indices are up by 12%. In this case, the fund has underperformed the market significantly.

Comparison charts

 

 

 

 

 

 

“Synthesis: the combining of the constituent elements of separate material or abstract entities into a single or unified entity ( opposed to analysis, ) the separating of any material or abstract entity into its constituent elements.”

 

Synthesis in MDM 

Communication, transfers information and synthesis, like RNA several companies are prevelant in terms of data movement and replication, in essence data logistics

Defines Traits, Characteristics like DNA many companies have developed and refined products and the techniques required for during these. They are data profiling , domain pattern profiling and record linkage, the basis of transforming data into information .

However lineage is the key, and without this to serve as a connection between data and information, in essence there is no information. And in this case the “information” is the business term from the business glossary.

 The integration of data movement capability and the linking of data profiling capabilities can result in providing a business the capabilities answering business question with certitude through the transparency of lineage.

 To translate this into “business terms” it’s very similar to providing an audit trail in that a business can ask this questions like what customers are the most profitable look at those customers and then drill into wood products what areas with those profits coming

 What lineage accomplishes is to lay a trail of cookie crumbs for data movement but for your business questions, it’s simply makes sure that you can connect the dots as data gets moved and/or translated and/or standardize and or cleansed throughout your enterprise.

And with the simple action of linking data file metadata of files, columns , profiling results to a businesses glossary or business terms, will result in deeply insightful and informative business insight and analysis.

“Analysis the separating of any material or abstract entity into its constituent elements”

  In order for a business manager for analyze you need to be able to start the analysis at a understandable business terminology level. And then provide the manager with the ability to decompose or understand  lineage from a logical perspective.

 There are three essential capabilities required for analysis and utilizing lineage to answer business questions via a meta-data mart and these are very similar to the pattern that exist in DNA.

 1. Data profiling and domain(column) analysis as well as fuzzy matching processes that are available in many forms:

a.  – Scan all the values within each column and provide statistics(counts) such as minimum value, maximin value, mode(most occuring) value, number of missing value etc…

 b. Frequency or Column value Patterns – determine the counts of distinct values within a column and also identify the distinct pattern occurring for all values within a single column and associated counts ie(SSN = 123-45-6789 , SSN PATTERN = ‘999-99-9999’

c. Fuzzy Matching( similarity algorithms) – This capability enables the find the counts of duplicate or similar text values.

 

2. The results need to be stored is a “Metadata-mart” in order to see the patterns, results and associations providing lineage and retatingraw data to business terms and hierarchies.

3. Visualization and analysis capability to allow for analysis, drill down into data mart aggregated and statistical contents and associated business hierarchies and businessterminology

Underlying each of these analytical capabilities is a set of refined processes, developed and proven code for accomplishing these basic fundamental task.

 In future post I will describe how to implement these capabilities with or without vendor products, from a logical perspective.

 References:

http://www.diffen.com/difference/Data_vs_Information
 

 

 

 

 

 

 

 

     

Organic Data Quality vs Machine Data Quality

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In reading a few recent blog post on centralized data quality vs. distributed data quality have provoked me to offer another point of view.

Organic distributed data quality provides todays awareness  for all current source analysis and problem resolution and they are accomplished manually by individuals in most cases.

In many cases when  management or leadership(Folks worried about their jobs) are presented with any type of organized machine based data quality results that can easily be viewed, understood and is permanently available, the usual result is to kill or discredit the messenger.

Automated “Data Quality Scoring” (Profiling + Metadata Mart) brings them from a general “awareness”  to a realized state “awarement”.

Awarement  is the established form of awareness. Once one has accomplished their sense of awareness they have come to terms with awarement.

It’s one thing to know that alcohol can get you drunk, it quite another to be aware that you are drunk when you are drunk.

metadata scales

Perception = Perception

Awarement = Reality

Departmentral  DQ = Awareness

vs

Centralized DQ = Awarement

Share the love… of Data Quality–  A distributed approach to Data Quality may result in better data outcomes

Metadata Mart the Road to Data Governance

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 or Guerilla Data Governance

Implementing a Metadata Mart the Road to Data Governance best viewed in PRESENTATION Mode, there is animation.

iradg

This presentation has narrative, play in presentation mode with sound on.

https://onedrive.live.com/redir?resid=E7059817CA5789D0!10028&authkey=!AE6eWl-gzh2mq_M&ithint=file%2cpptx

Self Service Semantic BI (Business Intelligence) Concept

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metadatamarttripleWe want to build an Enterprise Analytical capability by integrating the concepts for building a Metadata Mart with the facilities for the Semantic Web

 

    • Metadata Mart Source
      • (Metadata Mart as is) Source Profiling(Column, Domain & Relationship)
    • +
      • (Metadata Mart Plus Vocabulary(Metadata Vocabulary)) Stored as Triples(subject-predicate-object) (SSIS Text Mining)
    • +
      • (Metadata Mart Plus)Create Metadata Vocabulary following RDFa applied to Metadata Mart Triple(SSIS Text Mining+ Fuzzy (SPARGL maybe))
    • +
    • Bridge to RDFa – JSON-LD via Schema.org
      • Master data Vocabulary with lineage (Metadata Vocabulary + Master Vocabulary) mapped to  MetaContent Statements)) based on person.schema.org
      • Creates link to legacy data in data warehouse
      • +RDFa applied to web pages
      • +JSON-LD applied to
      • + any Triples from any source
  • Semantic Self Service BI
    • Metadata Mart Source + Bridge to RDFa

 

 

 

 

I have spent some time in this for quite a while now and I believe there is a quite a bit of merit in approaching the collection of domain data and column profile data, in regards to the meta-data mart, and organize them in a triple’s fashion

 

The basis for JSON-LD and  RDFa is the collection of data as a triple. Delving into said deeper

 

I believe with the proper mapping for the object reference and deriving of the appropriate predicates in the collection of the value we could gain some of the same benefits as well as bringing the web data being “collected, there by linking to source data.

Consider the following excerpt regarding Vocabularies derived from MetaContent via Metadata Structure

“Metadata structures[edit]

Metadata (metacontent), or more correctly, the vocabularies used to assemble metadata (metacontent) statements, are typically structured according to a standardized concept using a well-defined metadata scheme, including: metadata standards and metadata models. Tools such as controlled vocabularies, taxonomies, thesauri, data dictionaries, and metadata registries can be used to apply further standardization to the metadata. Structural metadata commonality is also of paramount importance in data model development and in database design.

Metadata syntax[edit]

Metadata (metacontent) syntax refers to the rules created to structure the fields or elements of metadata (metacontent).[11] A single metadata scheme may be expressed in a number of different markup or programming languages, each of which requires a different syntax. For example, Dublin Core may be expressed in plain text, HTML, XML, and RDF.[12]

A common example of (guide) metacontent is the bibliographic classification, the subject, the Dewey Decimal class number. There is always an implied statement in any “classification” of some object. To classify an object as, for example, Dewey class number 514 (Topology) (i.e. books having the number 514 on their spine) the implied statement is: “<book><subject heading><514>. This is a subject-predicate-object triple, or more importantly, a class-attribute-value triple. The first two elements of the triple (class, attribute) are pieces of some structural metadata having a defined semantic. The third element is a value, preferably from some controlled vocabulary, some reference (master) data. The combination of the metadata and master data elements results in a statement which is a metacontent statement i.e. “metacontent = metadata + master data”. All these elements can be thought of as “vocabulary”. Both metadata and master data are vocabularies which can be assembled into metacontent statements. “

 

The MetadataMart serve as the source for both metadata vocabulary and MDM for the Master Data Vocabulary.

For the Master Data Vocabulary consider schema.org which defines most of the schemas we need. Consider the following schema.org Persons Properties of Objects and Predicates:

 

Thing > Person

A person (alive, dead, undead, or fictional).

Property Expected Type Description
Properties from Person
additionalName Text An additional name for a Person, can be used for a middle name.
address PostalAddress Physical address of the item.

The key is to link source data in the Enterprise via a Business Vocabulary from MDM to the Source Data Metadata Vocabulary from a Metadata Mart to conform the triples collected internally and externally.

 

In essence information from the web applications can be integrated with the dimensional metadata mart, MDM Model and existing Data Warehouses providing lineage for selected raw data from web to Enterprise conformed Dimensions that have gone thru Data Quality processes.

 

Please let me know your thoughts.

Mastering Microsoft SQL Server tools required for EIM (MDS, DQS, Profiling and SSIS) – Complete Kit

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Overview

 

Understanding and mastering the various Microsoft SQL Server Tools available for Data Quality , Master Data Management , Fuzzy Matching and Enterprise Information Management can be daunting and exasperating. In the hopes of reducing the anxiety and frustration I am providing  a practical roadmap using openly available resources and requiring only a few days to complete.

I have reviewed and organized several articles and blog post that would allow you within a few days to master the skills required to utilize several SQL Server 2012 capabilities mentioned above.

From a business perspective where to be covering the following:

Basic

Master Data Management (MDS)

Data Quality (DQS)

Integration (SSIS)

Advanced

Fuzzy Matching(data Deduplication)

Data Profiling (Source analysis against business rules)

Basic EIM

 

Master Data Services

Nick Barclay: BI-Lingual: MDS Architecture Notes

 

This article will provide overview of the MDS architecture in support of MDM (Master Data Management)

 

http://nickbarclay.blogspot.com/2009/12/mds-architecture-notes.html

 

Nick Barclay: BI-Lingual: Beginning Master Data Services (Part 1 thru 7)

 

 

This article will teach you the steps for implementing Microsoft MDS as well as the tasks required to develop and load a Model. Complete all exercises in order. MDS is the tool Microsoft provide to support creating and maintaining reference data set (Lookup and Code tables) in support of Master Data Management.

 

http://nickbarclay.blogspot.com/2009/11/beginning-master-data-services-part-1.html

 Data Quality Services

Enterprise Information Management using SSIS, MDS, and DQS Together [Tutorial]

 

 

This next article will teach you how to implement and utilize the Data Quality cleansing and matching capabilities.

 

http://technet.microsoft.com/en-us/library/jj819782.aspx

 

 Fuzzy Matching and Deduplication

Advanced SSIS Fuzzy Matching via Record Linkage Methodology – SQLServerCentral

This article will teach you the concepts and methodology recommended for Fuzzy Matching or Deduplication, in the context of a well known Record Linkage Methodology.

 

http://www.sqlservercentral.com/articles/Integration+Services+(SSIS)/71486/

 

 

Advanced Matching and Data Profiling

 

I have also included the next two articles to further explore the code and capabilities to solve complex matching and deduplication efforts

 

Roll Your Own Fuzzy Match / Grouping (Jaro Winkler) – T-SQL – SQLServerCentral

http://www.sqlservercentral.com/articles/Fuzzy+Match/65702/

 

 

 Roll Your Own SSIS Fuzzy Matching / Grouping SSIS  (Jaro – Winkler) – SQLServerCentral

http://www.sqlservercentral.com/articles/Integration+Services+(SSIS)/65616/

 

 

 

 

Creating a Metadata Mart via TSQL – Complete Data Profiling Kit – Download

 

https://irawarrenwhiteside.com/2014/04/13/creating-a-metadata-mart-via-tsql/

 

 

 

 

Guerilla MDM via Microsoft MDS: Force Model Validation or TSQL Script to force set Validation Status ID ‘s

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Using Metadata and code generation to programmatically set Validation Status ID’s  for all leaf members in an MDS Entity.

Primarily this script relies on the MDS mdm.udpMemberValidationStatusUpdate procedure for a single member. Unfortunately I could not get the stored proc for multiple members(mdm.udpMembersValidationStatusUpdate) to work, so I created a script to rely on the single member version.( mdm.udpMemberValidationStatusUpdate). I could not resolve the “operand type clash” error.

The script requires the model name and entity name, to generate a TSQL statement to update each member id to the designated Validation status.

IF OBJECT_ID('tempdb..#MemberIdList') IS NOT NULL
DROP TABLE #MemberIdList

DECLARE @ModelName nVarchar(50) = ‘Supplier’
DECLARE @Model_id int
DECLARE @Version_ID int
DECLARE @Entity_ID int
DECLARE @Entity_Name nVarchar(50) = ‘Supplier’
DECLARE @Entity_Table nVarchar(50)
DECLARE @sql nVarchar(500)
DECLARE @sqlExec nVarchar(500) = ‘EXEC mdm.udpMemberValidationStatusUpdate ‘
DECLARE @ValidationStatus_ID int = 4
— Found the following information intable [mdm].[tblList]
–ListCode ListName Seq ListOption
–lstValidationStatus ValidationStatus 0 New, Awaiting Validation
–lstValidationStatus ValidationStatus 1 Validating
–lstValidationStatus ValidationStatus 4 Validation Failed
–lstValidationStatus ValidationStatus 3 Validation Succeeded
–lstValidationStatus ValidationStatus 2 Awaiting Revalidation
–lstValidationStatus ValidationStatus 5 Awaiting Dependent Member Revalidation
–MemberType_ID = 1 (Leaf Member)
DECLARE @MemberType_ID int = 1
–Get Version id for Model
SET @Version_ID = (SELECT MAX(ID)
FROM mdm.viw_SYSTEM_SCHEMA_VERSION
WHERE Model_Name = @ModelName)
print @Version_ID
–Get Model IDfor Model
SET @Model_ID = (SELECT Model_ID
FROM mdm.viw_SYSTEM_SCHEMA_VERSION
WHERE Model_Name = @ModelName)
print @Model_ID
–Get Entity ID for specific Entity
SET @Entity_ID =
(SELECT [ID]
FROM [mdm].[tblEntity]
where [Model_ID] = @Model_ID
and [Name]= @Entity_Name)

–Get Entity Table Namefor specific Entity

SET @Entity_Table =
(SELECT[EntityTable]
FROM [mdm].[tblEntity]
where [Model_ID] = @Model_ID
and [Name]= @Entity_Name)
print ‘Processing Following Model ‘ + convert(varchar,@ModelName)
print ‘Model ID = ‘ + convert(varchar,@Model_id)
print ‘Version ID = ‘ + convert(varchar,@Version_ID)
print ‘Entity = ID ‘ + convert(varchar,@Entity_ID)
print ‘Entity Name = ‘ + convert(varchar,@Entity_Name)
print ‘Entity Table Name ‘ + convert(varchar,@Entity_Table)
print ‘Validation Status ID being set to ‘ + convert(varchar,@ValidationStatus_ID) + ‘ for all members in ‘+ convert(varchar,@Entity_Name)

–Create local temp table to hold member ids to update
CREATE TABLE #MemberIdList
( id int,
Processed int)
–Generate SQL to populaet temp table
set @sql = N’INSERT INTO #MemberIdList select id, 0 from mdm.’ +convert(varchar,@Entity_Table)

–Generate SQL to display stored proc update
EXECUTE sp_executesql @sql

–Create Tabe Variable to hold SQL Commands and prepare for execute
Declare @Id int
DECLARE @MemberSQL TABLE
(
ID int,
mdssql nvarchar(500),
Processed int
)
While (Select Count(*) From #MemberIdList Where Processed = 0) > 0
Begin
Select Top 1 @Id = ID From #MemberIdList Where Processed = 0

set @sql = N’ mdm.udpMemberValidationStatusUpdate ‘ +convert(varchar,@Version_ID) +’,’ +convert(varchar, @Entity_ID) +’,’ +convert(varchar,@ID) +’,’ +convert(varchar, @MemberType_ID) +’,’ + convert(varchar,@ValidationStatus_ID)

print @sql

INSERT INTO @MemberSQL
Select @id , @sql, 0

Update #MemberIdList Set Processed = 1 Where ID = @Id

End

Declare @sqlsyntax nVarchar(500)

While (Select Count(*) From @MemberSQL Where Processed = 0) > 0
Begin
Select Top 1 @id = id From @MemberSQL Where Processed = 0
Select Top 1 @sqlsyntax = mdssql From @MemberSQL Where Processed = 0

print @sqlsyntax
— comment this line to not execute update Validation Status ID
EXECUTE sp_executesql @sqlsyntax

Update @MemberSQL Set Processed = 1 Where ID = @Id
End

Declare @Entity_TableSQL nVarchar(500)
set @Entity_TableSQL = ‘SELECT * FROM [mdm].’ + @Entity_Table
print @Entity_TableSQL
exec sp_executesql @Entity_TableSQL
Here are several links I referenced:

The code for forcing the Validation of the Model is here. Jeremey Kashel’s Blog

Microsoft SQL Server 2012 Master Data Services 2/E Tyler Graham

Table Variables in TSQL

I am sure this can be improved, please contact me with questions or suggestions.

-Ira Warren Whiteside

Agile MDM Data Modeling and Requirements incorporating a Metadata Mart.(Guerilla MDM)

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Recently an acquaintance of mine asked about my thoughts on the approach for creating a MDM data model and the requirements artifacts for loading MDM.

I believe with this client and with most clients that the first thing that we have to help them understand is that they have to take an organic and evolving a.k.a. an “agile” approach utilizing Extreme Scoping in developing and fleshing out their MDM data model as well as the requirements.

High-level logical data models for customer, location, product etc, such as Open Data Model are readily available and generally not extremely customized or different.

If we look at the MDM aspect of data modeling and requirements, I look at it as three layers:

1.the logical data model.

2.the sub models(reference models) which detail the localization in the  sources for each domain, which in essence is our source-based reference models.

3.an associative model that allows for the nexus/mapping between the sources their domain values in the master domain model.

It doesn’t really get interesting until you’re able to dive into the submodel and associative models and  relationships creation and understand the issues that you’re dealing with and anomalies in the source data, which inherently involves quite a bit of data profiling and metadata analysis.

In my experience the creation of the MDM model and sub models involves three simultaneous and parallel tracks.

1. Meet with users of and preform metric decomposition(defined in my slides) and define the (information value chain) and create logical definitions of metrics , groupings of dimensions and hire attributes  and subsequently hierarchies that are required from an analysis perspective. A data dictionary.

1a. The business deliverable here is a business matrix which will show the relationship between the metrics and the dimensions in relation to the business processes and/or KPI’s. I’ll send an example.

1b. The relationship of the above deliverable to the logical model, is that the business deliverables here specifically the business matrix and the associated hierarchies will drive the core required data elements from the KPI and therefore the MDM perspective. In addition it will drive the necessary hierarchies( relationships) and thereby the associations that will  be required at the next level down which will involve the source to target mapping required for loading the reference tables.

1c. Lastly and once we’ve truly understand the clients requirements with enough detail in terms of metric definition and hierarchies we can then select and incorporate reference models or industry experts and validating and vetting our model, this should be relatively straightforward.

1d. From an MDM perspective, where multiple teams are simultaneously gathering information/requirements we would want to use and have access to the artifacts especially for new business process modeling or any BI requirements modeling.

2. Introduce the metadata mart concept and utilize local techniques and published capabilities(I have several that can easily be incorporated) to instantiate a metadata mart and incorporate metadata and  data profiling as part of the data modeling process and user awarement process.

2a. The deliverable here is a set of Excel pivot tables and/or reports that allow the users to analyze and understand the sources for domains and how they’re going to relate to the master data model even at logical level

3. Capture relations to defining master domains and documenting in Source to Target Matrixs the required mappings and transformations.

This process will result in a comprehensive set of deliverables that while in their final state will be the logical and physical data models for MDM, in addition the client will have the necessary data dictionary definitions as well as I high-level source to target mappings.

My concern with presenting a high-level logical MDM model to user is that it is intuitively too simplistic and to straightforward. Obviously at the high-level the MDM constructs look very logical for customer, location, products etc..

They don’t expose the  complexity and frankly the magic(hard work) from an MDM perspective of the underlining and supporting source reference models and associative models that are really the heart and soul of the final model. The sooner and more immediate that we can engage the client an organized! facilitated, methodological  process of simultaneously profiling and understanding their existing data and defining the final state a.k.a. the MDM model that they are  looking for the better we’re going to be in managing long-term expectations.

There’s an old saying which I think specifically applies here especially in relation to users and expectations it is “They get what they inspect, not what they expect

I know that this went on a bit long, I apologize I certainly don’t mean to lecture, but I think the crux of the problem in a waterfall or traditional approach to creating an MDM model is overcome by following a more iterative or agile approach.

The reality is that the MDM model is the heart of the integration engine.

Fast, Data Driven Netezza Load – The Prittie Good Approach

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A few years ago I have a client that required migrating and re hosting a data warehouse from the Oracle environment to the Netezza Appliance. The application had hundreds of tables and almost 2900 Source to Target Mappings. This would have taken 1000′s of hours of manual coding. For this client we implemented a reusable data driven architecture that relys on a metadata mart. The end result a greatly reduced TCO for generating the code required vs manual cosing. In addition the process leaves behind a metadata mart to report on for auditing and other Data Governance efforts. We generated over 100,000 lines of code and over 3000 load scripts.After implementation the application can be maintained via the tables and automatically regenerated, providing a pro active or self healing application architecture to respond to changes in incoming source files or target changes. Code Here is a version for Oracle Loader

Prittie Good Approach: The objective is to “tactically” enable the clients analytical and reporting tools to their data via the Netezza platform very fast, with minimal coding effort, but in a “strategic fashion”. The “Prittie” approach will load a client’s data into the Netezza platform as fast and inexpensive as possible while still providing a reusable “production level” set of code for implementation. The approach as defined is to minimize upfront source analyses and data profiling and leverage the “speed” of Netezza , while applying specific coding techniques developed by you to automatically generate a the SQL code necessary to import data into the Netezza environment from industry standard delimited files with headers and a mapping document. The application will build a Metadata Mart for support data driven code generation and as a tactical tool for Data Governance. The primary TCO driver or value add gained with using our approach is to significantly reduced time and cost for an initial load , data profiling and evaluation of data quality, and speed up the process of Business Analysis, as well as leveraging an iterative methodology. In addition business data profiling as well as source analysis can then be accomplished on the Netezza platform, very quickly, while capturing critical load “halting” problems as well as basic profiling metadata immediately and resolving them.

Target: Tables and Columns are derived from parsing machine generated DDL for existing tables, for table columns formats and column order.
Source: Filenames and Column are derived from parsing Header files extracted from industry standard “tab” delimited files with Headers, this provide columns contained in the files and there order.
Mapping: (Source to Target) are derived from parsing the “Bill of Lading” file containing cross reference of multiple filenames to target tables and a grouping column in this case. You original file also had external name this is no longer needed;

Prittie Approach Specific Logic: From these three inputs the application generates Netezza ANSI Standard SQL Code that create an external table with columns ordered by the incoming filename and associated with the DDL Column format, and the associated Netezza logic for external filename allocation and logging. A simple SQL statement is generated that checks for duplicates and data errors , that can be logged. In addition I have includde the ability to generate the Drop and Insert Logic to complete the “Prittie” Import and Load Architecture for Netezza.

Application Stability: The Source and Mapping files design are relatively stable. The Target is based on some of the idiosyncrasies found in the DDL created by the query tool used, with some additional effort this code can be enhanced to handle some “looseness”. The issue is in how the code (DDL) is generated and by what tool. The rest of the application is shielded from this by the creation of a “Source-Target-Mapping Cross Reference” table that is used in the actual code generation.

ETL Potential: As we have discussed the application could be extended easily to handle basic ETL. This could be accomplished by including one more that that contained transformations for specific table columns combinations or data types. An example would be to include a new mapping table that converted Oracle Datetime to ANSI as required. This is a simple example but the underlying design could handle relatively complex transformations. We could also move the Location and Logging Directory to a table to further make the application Metadata Driven.

Doug Prittie and Ira Warren Whiteside