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Data Governance Navigating the Information Value Chain Demystifying the path forward

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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

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.

1 Reply

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

Ira Warren Whiteside's Blog – Information Sherpa

Perception is Perception “Awareness is Reality”