Featured post

Data Governance – Navigating the Information Value Chain

Leave a reply

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

 

  • 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

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 or even Data Architecture Information Relationships it is the hierarchical design of shared information environments; the art and science of organizing and labelling gGossary terms, transactions to support usability and findability; in 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.

Rosetta Stone

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.

   

 

Featured post

Data Governance Navigating the Information Value Chain Demystifying the path forward

Leave a reply

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.

Featured post

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

Leave a reply


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

Featured post

Trial and Tribulations of MDM (Coming Soon)

Leave a reply

Ira Whiteside / Jordan Martz / Victoria Stasiewicz

Advanced Matching for Business Categories(Terms) to Technical Artifacts(Data) in Relation to Master Data Management Governance and or Feature Engineering in Artificial Intelligence Modeling preparation.

We will be covering a top down(Business) and bottoms up Business driven methodology and tactical capabilities to the critical issue of associating Business categories/ dimensions to each other and subsequently transactions. We will include templates and code (Python/SQL/Databricks,etc…)….

Featured post

Eat to LIVE , do not live to EAT

Leave a reply

My journey to better health and the rest of my life, MINUS ==== 85 pounds, eyeball shots(needles), blood pressure meds, diabetes , they are all gone.

Pre Victoria
POSTVictoria

The ending and the beginning

It’s August 27, 2020 I’m 66 years old, and in very bad health..  My wonderful wife Tessie, passed away last night surrounded by her daughters and her grandchild.  I’m contemplating where to go from here I weigh 280 pounds, I get  Avastin shots in my eyes every six weeks,  I take blood pressure medications,  I take cholesterol medication walking is very difficult , I walk with a cane(BubbaStik).  I’ve have Diabetes Type II had two strokes, I get severe IBS(Irratible Bowel Syndrome) attacks.  I am still working , barely, Brandon and Victoria graciously take me in their home.

The beginning

In the beginning I started by just eating what Victoria ate meat, fish, salad, low carb.  Obviously I was very depressed.

Holy shit

I realize I lost 20 pounds in three months. I get laid off from work.

Really

With Brandon and Vics encouragement I start a new consulting business.

I realize I’ve lost 75 pounds in six months

PERCEPTION is not reality, AWAREMENT is reality. More on our understanding on food and the dangers of following the food pyramid recommendations

I did not go on  a diet my daughter has an approach to nutrition, Victoria’s Self Governance for health. I began to study nutrition with Vic deeper and started eating more whole foods, UNPROCESSED meat, fat, and still some vegetables what I would consider normal human food than 10,000 years ago, I didn’t count or measure anything.

Perception vs. reality

I have researched nutrition extensively and have realized that the current food pyramid guidelines are ass backwards and inverted. I will lay out the details in a later post.

Acceptance

What now. I have started  a new consulting business, Brandon and Vic , my grandaughter Julia and best friend Jordan. Vic and Brandon got me a racing Terratrike , and I am moving in an apartment. We’ll see where we go from here.

Note: Yes I still drink Red Wine – Coppolla Claret

With ICE

Featured post

Navigating the Information Value Chain

2 Replies

Data Governance

Navigating the Information Value Chain

Demystifying the path forward

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

 

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 now 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 scienkce 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

Early Relationship Conepts

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.

Current Relationship Concepts

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.

Ancient Relationship Concepts

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 Business outcomes

Featured post

Cancer cannot be cured, “remission” is b#llsh#t, but you can LIVE and LOVE. “Perception is not reality, awarement is reality.”

Leave a reply

My soulmate, my wife, my “partner“ the absolute love of my life passed away last year., NEEDLESSLY. Tessie and I put our faith in our doctors and Tessie as it should be put her faith in me, and I failed her. I didn’t cheat on Tess, I didn’t gamble, I didn’t lie, although I did drink too much, however every second of every day I feel I feel I failed.

Tessie and I survived the first occurrence of cancer in 2009. Once Tess was diagnosed she was able to take advantage of Avastin at the time a new type of immunotherapy., primarily because her idiot husband did not have insurance. Which the doctors credited with putting Tessie‘s cancer into remission, that was BULLSHIT. Eight years later I’m doctors in our regular visit told Tessie she was fine doing well and there were no signs of cancer, that was bullshit. 9 years later Tessie experienced sever back pain and was told she has sciatica, 10 years later Tessie and I were in the emergency room and it was discovered the test had multiple tumors kidney liver back ….

17 months later after much suffering and pain and at this point multiple tumors, my incredible wife passed away, and I feel as her husband.

As of today in 2021, cancer occurs in our bodies at the DNA levels. We’re told and led to believe that the issue of cancer is tumors, however it’s more basic than the issue was cancer is that are mRNA, copies are DNA to create new cells and when those cells are created they’re not coded with an expiration date. It isn’t widely known but our bodies create approximately 259 billion cells every day and in general they live only for three days, and then they die with the preschool describe date, If they don’t die, they form tumors and that is cancer.

The reason I say, that I feel I failed Tessie., is that if we would’ve not a relied on doctors and not relied on PURLEY faith and her husband would’ve done more research and realize that the only way to live longer with cancer, I emphasize the only way is to monitor and be aware of certain very low levels of substances that are accumulating in her body that can be tracked with things called cancer marker test, which generally are blood Tess’s case there is a very simple test called ACA 27/29 which basically measures the result of a certain enzyme in your blood and if that enzyme is add a measurement of 40 you’re OK, and Tessie’s case it was 4000.

In 2009 this it (CA27/29) was not revealed or taken by our doctors, and once I realized that after doing research we should’ve done years earlier Tess was told that they don’t want to do cancer marker test and they were not 100% reliable and that they wait for symptoms to occur, which in Tess’s case were massive tumors.

The point here is while they may not be 100% reliable like many things in life there can be an early indicator, even if it’s news you don’t want to hear trust me you want to know. My Tessie was fiercely independent, seriously protective of her children and her grandchildren and her husband , her family and relied on her doctors exclusively. She had “hope“ and “faith” in her doctors and God, Tess kept her faith in God however she was let down by her doctors and her husband.

The point of all this is for women and men who have been “lucky“ enough who have discovered you have cancer, you must and I repeat must get a test of your DNA and your mRNA understand mutations that exist because the various cancers she may experience and monitor them whether or not your insurance company covers his test they are critical, if you wait until a tumor

Our a wonderful daughter Victoria . who is of course supported by the worlds greatest son-in-law , has recently given a presentation to 6000 people extolling on the fact that you need to take your life your health your family and your priorities into your hands to monitor them to control them and to do everything you can to achieve the outcome you desire, obviously health and life, and love.

What frustrates me the most and why I feel I let Tessie down, is lack of knowledge which resulted from the lack of analysis. While Victoria was working on her presentation for the conference and emphasizing the need for analysis and information, in regards to meeting your health your life your goals, I realized this

Tessie is in heaven now, and I am here and thank God for Tessie‘s insistence on caring and protecting for her children and her grandchildren and her son-in-law and being protective and analytical throughout her life. However at this point I cannot stand by and not try to help informant increase awareness for other men and women that “remission“ is bullshit, there is no cure for cancer however if you monitor it and you catch it early when it’s at the cellular level and not at the tumor level you can continue to live your life experience the love of your family and your partner. there is no such thing as remission, you must get the INFORMATION you need to slow it done. You can’t prevent it, you can control it.

If anyone has any question , please let me know, I am not any kind of expert but as I tell my children “Perception is not reality, awarement is reality.”

Evolving Data Into Information through Lineage

Leave a reply


Information lineage through data DNA

 

Ira Warren Whiteside         

 

ORIGINS
COMMON SENSE

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 focusing on the word data.         

“There is a subtle difference between data and information.”

Information vs data

There is a subtle difference between data and information. Data are the facts or details from which information 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

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.

“Misleading” Data

Because 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 chart

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         

 Data into Information is dominant in terms of data movement and replication, in essence data logistics.

 

Lineage is the key.

 

 And with the simple action of linking data file metadata names to a businesses glossary  or 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 analysis you need to be able to start the analysis at a understandable business terminology.

 And then provide the manager with the ability to decompose or break apart the result.

They are three essential set of capabilities and associated techniquestechniques for analysis and lineage.

 

  1. Data profiling and domain analysis as well as fuzzy matching components available on my blog https://irawarrenwhiteside.com/2014/04/13/creating-a-metadata-mart-via-tsql/
  2. Meta-data driven creation of a meta-data mart through code generation techniques, implemented.

 

 

Underlining each of these capabilities is a set of refined, developed and proven code says for accomplishing these basic fundamental task.

 

One case study

 I have been in this business over 45 years and I’d like to offer one example of the power of the concept of a meta-data mart and lineage as it regards to business insight.

 

A lineage, information and data story for BCBS

I was called on Thursday and told to attend a meeting on Friday between our companies leadership and the new Chief Analytics Officer. He was prototypical of the new IT a “new school” IT Director.

 I had been  introduced via LinkedIn to this director a week earlier as he had followed one of my blogs on metadata marts and lineage.

 

After a brief introduction, our leadership began to speak and the director immediately held up his hand he said “Please don’t say anything right now the profiling you provided me is at the kindergarten level and you are dishonest”

 

 The project was a 20 week $900,000 effort and we were in week 10.

 The company has desired to do a proof of concept and better understand the use of the informatics a tool  DQ as well as direction for a data governance program.

 

 To date what had been accomplished was in a cumulation of hours of effort in billing that has not resulted in any tangible deliverable.

 

The project had focused on the implementation and functionally of the popular vendor tool, canned data profiling results and not providing information to the business.

 

 The director commented on my blog post and asked if we could achieve that at his company, I of course said yes.

 Immediately I proposed we use the methodology that would allow us to focus on a  tops down process of understanding critical business metrics and a bottoms up process of linking data to business terms.

 My basic premise was that unless your deliverable from a data quality project can provide you business insight from the top down it is of little value. In essence you’ll spend $900,000 to tell  a business executive they have dirty data. At which point he will say to you “so what’s new”.

 The next step was to use the business terminology glossary that existed in informatica  metadata manager and map  those terms to source data columns and source systems, not an extremely difficult exercise. However this is the critical step in providing a business manager the understanding and context of data statistics.

 The next step, was the crucial step in which we made a slight modification to the IDQ tool and allowed the storing of the profiling results into a meta-data mart and the association of a business dimension from the business glossary the reporting statistics.

 We were able to populate my predefined metadata mart dimensional model by using the tool the company and already purchased.

 Lastly by using a dimensional model we were able to allow the business to apply their current reporting tool.

 

 Upon realizing the issues they faced in their business metrics, they accelerated the data governance program and canceled the data lake until a future date.

 

 

 

Now for the results. 

 Within six weeks we provided an executive dashboard based on a meta-data mart that allowed the business to reassess their plans involving governance and a data lake.

 Here are some of the results of their ability to analyze their basic data statistics but mapped to their business terminology.

 

  •  6000 in properly form SS cents
  • 35,000 dependence of subscribers over 35 years old
  • Thousands of charges to PPO plans out of the counties they were restricted to.
  •  There were mysterious double counts in patient eligibility counts, managers were now able to drill into those accounts by source system and find that a simple Syncsort utility had been used improperly and duplicated records.

 

                    

                               

                                                 

 

 

 

 

 

 

         

 

                                        

 

         

 

 

 

 

 

 

 

 

J

Getting healthy eating well staying alive

Leave a reply

if you wanna be healthy , eat well , no diet , eliminate disease (Alzheimer’s, Type 2 diabetes, kidney, heart, etc,,) . Period.)just watch this one video I’ve done it. none of these diseases existed in the 1800s they came after the. Civil War introduced processed foods from 1865 to 1910 this was a experiment without INFORMED CONSENT we are being poisoned We are led to believe that medical treatment has improved that is true however if he CONSIDER LESS women dying in childbirth and children’s / infant deaths we are not living longer and the and the increase in the rate of these diseases has increased exponentially in the last 100 years only. keep in mind the planet is 5 billion years old SEED OILS QRE KILLING US Safflower oil, Corn oil, Canola oil, cottonseed oil, grapeseed oil, sunflower oil

Diseases of civilization are cause by excessive Seedoil‘s Consumption
Dr. Chris Knobbe

Truth about us, What we know.

Leave a reply

youtube.com/watch

excellent science based, fact based presentation on where we came from which is realistically a blend of Darwinism and intelligent design we still don’t know but it looks to me like now we’re at least letting signs come out and there are many things we don’t understand excellent presentation #DNA #information governance #artificialintelligence #AI #Misinformation #Darwinism #Data #Informationvaluechain #Data governance