Evaluation of Data Auditability, Traceability and Agility leveraging Data Vault Modeling in frequently changing Data ecosystem

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 805
Evaluation of Data Auditability, Traceability and Agility leveraging
Data Vault Modeling in frequently changing Data ecosystem
Sayan Guha1
Sr. Data Architect, AI & Analytics Practice, Cognizant Technology Solutions, West Bengal, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract –Maintaining the data audibility andtraceability
in the frequently changing agile business driven data
ecosystem has been the pain area for every organization.
Designing the storage architecture of a business which
frequently needs to change its business strategy and rules,
requires the agility and flexibility of Data Vault design & data
modeling techniques which can accommodate frequent
changes to the design without having to do much
programming changesontheunderlyingprocessinglogic. This
paper discusses, the critical role of Data Vault modeling to
address scenarios where frequent changes a business rule has
minimal impact on the spend & An evaluation of the data
auditability and traceability have also been studied with the
help of business scenario comparing with other modeling
techniques where Data Vault modeling it was not in practice.
Key Words: Data Vault modelling, Auditability, Data
traceability, third normal form data modelling, dimensional
data modelling, hash key, hash key difference, data
warehouse, Raw Data Vault, Business Data Vault, SQL,
1.INTRODUCTION
In the current time, as we have observed the continuous
changes to business strategy for mostorganizations remains
the pain area because implementation of such frequent
changes to the business data sources, business rules and
relationships between the business entities requires a
considerable spend on the IT infrastructure, its design and
storage architecture modelling techniques and to ensure IT
design & execution doesn’tbecomea bottleneck forseamless
execution & adoption of Data Vault modelling in this context
not only provides the required agility to the business but
also empowers the organization of data governancethrough
complete data auditability and traceability. In this paper, we
will discuss a specific case study where agile business
process of a subscription-based ecommerce business
adopted data vault modelling technique to their benefit of
agility We would try to evaluate how the changes in the
business rules have minimal impact on the design
architecture of the data model in the context using Data
Vault modelling & how the data auditability and traceability
.be maintained.
1.1 Motivation
My motivation here to write this paper is to help the
readers understand about the flexibility, robustness and
agility Data Vault modeling technique has to offer. As
opposed by tradition third normal form or dimensional data
modeling techniques where the datadesignpractitionerscan
address the frequent changes & history data requirements
respectively, Data Vault modeling provides the best of both
schools of thoughts and hence if it is chosen it comes up the
flexibility of adapting to the changes of business rules which
is often required to have a competitive edge in today’s
business. All companies particularly oftenneedtochangethe
relationship between the business entities, get rid of them or
add few more and the changes in relationships resulting
many to many relationships established in the process
between these entities. A Data Vault modeldulydesignednot
only addresses the same but also provides a benefit in
complete data auditability and traceability in the process by
leveraging a Data Vault modeling.
1.2 Aim of this paper
In this paper, I have aimed to discuss the specifics of
business model of an Ecommerce Retailer based on a
subscription-based business model asbusinessscenarioand
sequential break-up of the business rules in terms of
relationship between the customer & subscription. In this
process, I will evaluate the Data Vault model suited for the
purpose which will not undergo any change despite the
changes in the business approach. In the end of the paper, I
will share the evaluation metrics on how this approach have
benefited on the data metrics e.g., auditability,traceability in
scenarios when thedata vaultmodel hasundergonechanges.
2. LITERATURE REVIEW
A literature review was undertaken encompassing few
academic and industry papers. This section reviews Data
Vault as undertaken by organizations & addresses the key
aspect of its agility and robustness.
The evolution of data modeling methodologies in
organizations along with the business scenarioislistedinthe
below table.

Table -1: Data Modeling techniques
Data modeling
method
Construct Scenario
3NF (third
normal form)
Normalized & data
stored in detailed level
transactions in tables.
Operational
systems.
Dimensional
model
Denormalized & data
stored in dimension &
fact tables, can be
aggregated
Business
Intelligence,
used for
dashboards used
by strategic
business
decision
support.
Data Vault 3 major constructs are
Hub (collection of
business keys), Link
(relationship between
hubs) or potentially with
other relationships
(links) & SAT (Satellite
stables with description
data) – changes to the
data reflects on SAT
tables, other remain
unchanged, in case of
relationship change,
reflects only on LINKS
Hybrid approach
encompassing
the best of breed
between 3rd
normal form
(3NF) and star
schema. Flexible
to changes and
less
implementation
cost
In [1], the author presented a case study where schema
changes to the data source as an immediate need to the
business scenario can be handled with complete traceability
and auditability using Data Vault modeling technique. The
ability toadapt to a changingbusiness requirement, addition
& removal of data sources without any change to the design
and with ability to handle large scale database with history
data were identified as the advantages to the Data Vault
design in the author’s research.
In [2], the authors have discussed the major challenges that
are encountered by enterprises using traditional
datawarehouse architectures encompassing complex
updates, difficulties in source data reconciliation and
integration, slow loading, brittle transformations (due to
integration close to volatile sources and even moresoamong
reporting viewpoints), and lack of an integrated system of
record and how Data Vault conceptual data model is the
potential answer to all the above pitfalls.
While understanding the work that hasalreadybeingdonein
this field covering the areas of speed of loading, quick
updates, addressing the scenarios of quick change of system
of record, I have taken one step ahead to analyze the impact
of the Data Vault design on a real-life business scenario with
an Online Retailer who operates in a subscription-based
business model in sections to follow.
Before considering Data Vault modeling, I have also studied
the other streams of agile data modeling to check their
suitability for purpose and business scenario. For example,
Anchor modeling by which domain driven needs are
prioritized over data driven needs as in Data Vault. While
both forms belong to Ensemble modeling, Data Vault being
data driven, audibility needs take precedence. In [3], the
authors have narrowed down the specific guidelines for
Anchor Modeling suited for the needs of domain driven
needs. Inclusion of concepts covering business defined
schemas, models suited to adapt to changes are similar
between the 2 types of modeling techniques , however
abilities of traceability & auditability are found to made for
purpose in DV modeling .Every object in theDataVault(Hub,
Sat or Link entities refer Table-1) contains 2 additional
attributes called Record Source and Load_Datetime which
provides a detailed auditability and traceability back to
record source along with history of changes. This is a
recommendation from DV methodology [4] and I have
followed the same in resolving the business scenario in my
model & design.
In the context, where Data auditability definition,asIwanted
to define can be phrased as the ability to provide a audit trail
associated with a data transfer and important information,
such as who sent the data, when they were sent, when they
were received, what data structure(e.g.,xls,csv,txt,xml)was
used, how the data were sent (i.e.,viawhatmedium)andwho
received the data. I have referred to [5] as a definition
particularly suited at the scenario. Henceforth whiledefining
auditability in our evaluation section, I would try to break up
the model into the above defined factors.
In connection to Data Traceability leveraging Data Vault, I
will define and share a matrixwhichwillcontainthedifferent
hop points for the data flow and will have checkpointing
enabled at every level. In real life data systems, this
traceability was enabled by data lineage using standard Data
governance tool and using Data Vault based architecture it
ensured 100 percent traceability from the target database
through to the source systems. In [6], the authors have
defined and designed asimilar data governancemodelbased
on data traceability and can get data feedback and revision
through this model. The proposed method considers the
different ownership of data and may form a closed-loop data
service chain including effective data validation.
Therefore ,in the background of all of the above related work
and having studied their research literature , I hereby
promise that in subsequent section the reader will have
detailed view of thebusinessscenarioofonlineretailerwhich
we discussed initially in this section as our requirement for
agile frequently changing data coming from 2 different
sources (both sourcesprovidingdataforonlinesubscription)
& the requirement will undergo a change which will result in
the minimal change in DV model ( solution will contain data
vault layers in the data architecture ). The data model design

diagrams also follow along with the framework whichshows
how data auditability and traceability is maintained in this
ecosystem.
3. BACKGROUND AND BUSINESS SCENARIO
We have the business scenario of an online retailer that
pertain to the customer to subscription relationships in the
subscription-based model the company operates with. The
below Business logic flow diagram(s) shows the business
flow before and after a business rule changes.
.
Fig. 1: Business logic flow diagram (before change)
Subsequently, the sales and marketing team decided to
change one subscription model viz. One subscriptiontosame
customer applicableforCustomersegmentsnamelyImpulse
buyers and High Sales groups to many subscription & also
same subscriptions can also be utilized by other customers
belonging to the same family as base customer which
essentially means, same customer id can be used by a family
member of the customer to login to the Ecommerce website
and order items andat the sametimemanysubscriptionscan
be linked with same customer. As this process engages more
with the portal , I have observed in the processofthisproject,
based on the flexibility of using the same/different customer
id(s) belonging to the base customer and his/her one /many
family members the total time spent on the Ecommerce
portal have increased, hence customer/customergroupscan
be eventually targeted by campaigns and promotionsrelated
to their buying habits as a family /group resultinginnetsales
increase in the scenario.
Fig. 2: Business logic flow diagram (after change)
In the context of this paper, however we are restricting our
discussion on the agility of Data Vault modeling to address
this frequently changing data and its impact on the data
auditability and traceability. In Fig-2, the highlighted boxes,
are the customer segments which have undergone the
aforesaid changes in the business rule(s).
4. SOLUTION DESIGN AND DATA VAULT MODEL
The key design considerationsfortheDataVaultmodelareas
follows:
 Resolve the conflict of relationships for frequent
changing business rule(s) e.g., subscription model
changed from one to one to one to many for 2
customer segments (Impulse buyers & High Sales
Group)
 Change of any relationships because of frequent
changing business rule(s) will have zero/minimal
impact to the data model compared to conventional
data modeling techniques.
 Change in any record source will have zero impact
on the data model and data processing logic for new
development.
 Notable improved price performance of storage,
data traceability and auditability.
4.1 Architecture Solution
The standard Data Vault architecture solution [7] has been
referred for the business use case in place, below is depicted
which has been derived from standard Enterprise Data
Warehouse which consists of the Data Vault model.
Fig. 3: Solution Architecture (DV approach)
The above architecture contains the below building blocks:
 Data Sources: The point of origin from where all
data is brought to EDW (Enterprise Data
Warehouse).
 DV layer of EDW: The DV model is composed of 2
data models. first is more source aligned called the
Operational Vault & the second layer depicts the

system of record and oriented aligned towards the
reporting side called the Business Vault.
 Reporting DW: Information Marts are constructed
in this layer.
 Information Delivery:Usersperformdifferentdata
analysis tasks; this layer is anabstractionfromITfor
end users.
For evaluation of data traceabilityandauditabilityoftheData
Vault model, the Raw Data Vault model (RDV) model is
considered as below in Fig.4, the reason being the RDV, or
Raw Data Vault contains Source system information &
context without going much transformation changes.
For evaluation of agility of the data model subject tofrequent
changes in the business rule (in this context the subscription
rule change for the online retailer), the Business Data Vault
model (BDV) is considered inFig.5, thereasonbeingtheBDV,
or Business Data Vault is designed from RDV and added
Master data and business rules in the context.
4.2 Raw Data Vault
In Fig.4, the Raw Data Vault model for the business scenario
of the online retailer is shared. The business scenario of
multiple subscription associated with the Customer
Segments in the data model. The navigation will happen
from Hub Customer to Hub Subscription using the Link
Customer Subscription, designed in such a way to address
many-to-many relationship, thus any changebetweenanyof
the major entities will not have any change impact on
underlying processing logic even with rapidly changing
frequent business logic changes.
Fig. 4: Raw Data Vault model (RDV)
DV Entity Color
Code
Entity Name  Logical Name
Hub Blue Hub Customer -Customer
Hub Product- Product
Hub Subscription-Online
Subscription
Hub Online Influencer- Online
Influencer associated with
Customer.
Hub Online Platform- Online
Platform /Social Media handle
Hub Business Partner- Partners
associated with marketing and
advertisements.
Satellite (Sat) Brown Sat Customer Address & Sat
Customer Segment- Associated
with the Customer Segments
Sat Subscription Detail-
Associated with Hub Subscription
Sat Product- Associated with Hub
Product.
Sat Online Platform- Associated
with Hub Online Platform
Sat Influencer – Associated with
Hub Influencer
Sat Business Partner- Associated
with Hub Business Partner.
Link Green Link Customer Subscription –
Resolves the many-to-many rule
change between Customer and
Subscription
Link Customer Platform. Link
Customer Influencer and Link
Customer Partner – kept as
futureproof design for any rule
change association with Customer.
Link Product Detail – Captures
Product line-item level
Table -2: Raw Data Vault Entities
4.3 Business Data Vault
In Fig.4, the snippet of the Business Data Vault model for the
business scenario of the online retailer is shared. The
business scenario of changes that are associated Customer
Segments in the data model. The address of Customer is
through a slowly changing attribute but as per the business
scenario the customer is segment changes rapidly. Business
Data Vault will ensure the agility to have minimum impact
on the change and thereby attributing agility to the data
vault model by separating the attributes out by rate of
change, we help reduce the overall disk storage required for
the Hub.

The problem statement requires us to understand how we
get the data out of multiple Satellite tables when the
Satellites are loaded independently and with different rate
of change. The solution to the problem is Point in Time
(PIT) tables in Business Data Vault.
Fig. 5: Business Data Vault model (RDV) ~PIT Tables
The introduction of PIT tables (Point in Time) tables shown
in the Fig. 5 on the portion of the Raw Data Vault Model
where we have initially created morethanoneSATtablesviz
Hub Customer and associated Sat_Customer_Segment and
Sat_Customer_Address.
In [8], the author has researched the existing approaches of
Bill Inmon and Ralph Kimball which lead to difficulties
related to traceability of the of the entire data flow makingit
laborious modeling exercise. The author also states the
complex ETL flow that are involved in preparation and
refinement steps results in lack of agility in changing
requirements-based data ecosystem like data warehouse
Hence the author is of an opinion of the using the power of
design Data Vault 2.0 brings can substantially increase the
speed & agility in such data flow and thereby resulting an
agile data ecosystem more realistic.
For the example we have considered the retrieval SQL to be
written using the PIT table adds agility to the overall
Business Data Vault & the same being user interfacing layer,
I have observed the complexity of SQL queries getting
considerably lower & thereby supporting our initial
approach of having increased agility using Business Data
Vault. I have attached and highlightedthecomplexityof Joins
in Table.3.
Without Business
Data Vault
With Business Data
Vault (PIT)
Data
Retrieval
SQL
SELECT
C.CUSTOMER_ID, CA.
ADDRESS, CS.
CUSTOMER_SEGMENT_N
AME
FROM HUB_CUSTOMER C
JOIN
SAT_CUSTOMER_ADDR
CA
SELECT
C.CUSTOMER_ID, CA.
ADDRESS, CS.
CUSTOMER_SEGMENT_NA
ME
FROM HUB_CUSTOMER C
JOIN
SAT_CUSTOMER_ADDR CA
ON
ON
C. HASH_CUSTOMER
=CA.HASH_CUSTOMER
WHERE CA.
LOAD_DATETIME =
(SELECT (MAX
(CA2.LOAD_DATETIME)
FROM
SAT_CUSTOMER_ADDR
CA2
WHERE
C. HASH_CUSTOMER
=CA2.HASH_CUSTOMER
JOIN
SAT_CUSTOMER_SEGME
NT CS
ON C. HASH_CUSTOMER
=CS.HASH_CUSTOMER
WHERE
CS. LOAD_DATETIME =
(SELECT (MAX
(CS2.LOAD_DATETIME)
FROM
SAT_CUSTOMER_SEGME
NT CS2
WHERE
C.HASH_CUSTOMER=CS2.
HASH_CUSTOMER
C. HASH_CUSTOMER
=CA.HASH_CUSTOMER
JOIN
SAT_CUSTOMER_SEGMENT
CS
ON
C. HASH_CUSTOMER
=CS.HASH_CUSTOMER
JOIN PIT_CUSTOMER P
ON
P. HASH_CUSTOMER =C.
HASH_CUSTOMER
AND
P.PIT_LOAD_DATETIME
=’<PASS CURRENT DATE>’
AND P.
CUSTOMER_SEGMENT_DAT
ETIME= CS.
CUSTOMER_SEGMENT_DAT
ETIME
AND P.
CUSTOMER_ADDRESS_DAT
ETIME= CA.P.
CUSTOMER_ADDRESS_DAT
ETIME
Complexit
y of Joins
High Medium
Agility of
design
Low High
Table -3: Agility of SQL Joins in Business DV
Key takeaways from the data model designs and core
analysis of the retrieval SQL I have framed above are
 Raw Data Vault: In the scenarioIhavespecified,the
simple usage of Raw Data Vault modeling standards
addressed the entities by Hub-Link design
considering many to many with a complete
traceability & auditability of source data.
 Business Data Vault: In our scenario with the
addition of PIT tables, point in time information is
obtained adding agility benefits to the data
5. EVALUATION EXPERIMENT
5.1 Traceability in Data Vault as function of time
In [9], the authors in their article on requirements
engineering have studied different frameworks on
traceability & discussed the suitabilityofexplicittraceability
strategies for diﬀerent companies and diﬀerent projects. In
their framework called TraciMo, they have defined the
significance increased the correctness of identifying change
sets for a given requirement, from the developer’s point of
view. Hence drawing the grounddefinitionfromthisexisting
literature, I find the motivation to define
Traceability (T)

= f (Connected checkpoints in Development) (Dα) X Et
(Elapsed time in each checkpoint)
T = Dα X Et. (i)
5.2 Auditability in Data Vault as function of time
The guidance that is obtained from the latest from the
international Data Vault & Ensemble modeling Enthusiast
(DVEE) consortium, Data Vault Modeling should include the
classic features of auditability at the granularity of every
record mapped to every record source and record loading
timestamp as the basic modeling standardinRawData Vault
modeling. If we look back in Fig.4 and for any change in the
Sat_Customer_Segment, the Record_Source in the
transactional side is captured along with the
Record_Datetime. This is an example in the current data
ecosystem for most enterprise, where a customer can buy a
product from multiple channels and the based on the same
the segment of customer may changeovertimebasedon this
Segment (business segment the customer belongs to), but
Data Vault ensures that the whole auditabilityforthehistory
trail of the entire transaction history is captured and
auditable.
Auditability (Ad)
= f (Connected Record Attributes) (DR) X Ts (time measured
at the lowest granularity)
Ad = DR X Ts (ii)
5.3 Agility in Data Vault as function of time
The ability to adapt to changes quickly and deliver value
with point in time data to the end users with sustainable
output produced continuously and repetitively makes Data
Vault particularly suitable for Agile environments. In Fig.5
I have shared the PIT (point in time placeholder) which
contains the point in time data in the user interfacing
Business Data Vault. Therefore, adding and augmenting to
the traceability and Auditability created in the Raw Data
Vault layer with Agility.
In [10], the authors have made elaborate research, on
definitions of agility many definitions of agility as there are
agile practitioners and per the observations of the authors
case, the understanding and definition of agility remarkably
vary. In the conclusion section, the authors acknowledged
that the concept of agility is complex and multidimensional
(i.e. not simply about responsivenesstochanges)Itconceals
many facets, the definitions of it vary considerably and as
part of their research, various definitions of agility were
gathered through a literature review.
To define my work in terms of agility I felt motivated and
having studies the literature in [11], the researchers have
devised an Agility Framework and illustrates the multi-
faceted nature of Agility in terms of responsiveness,
productivity, new innovations etc.
Hence, I have defined Agility in our context of Data Vault
Agility (AG)
=f (Response time to connect all checkpoints in
Development) (Dα) X Connected Record Attributes) (DR)
AG = d (Dα) (DR) / dTR (iii)
TR=Response time in lowest granularity.
For the sake of our experiment and combining the
understanding obtained for Traceability, Auditability and
Agility from the above definitions the following approach is
defined:
Fig. 6: Evaluation Approach
 Validation with Dimensional Model: Industry
established Dimensional Model is taken as our
standard to validate the response time for one use
case scenario of Customer Segment change across
the parameters of Traceability, Auditability and
Agility. In the next section I have framed the
response times for each of the above along with the
design changestimebeingconsideredintheprocess.
 Validate the Data Vault Model: For the same
changes, for a CustomerSegmentchangeasmodeled
in Fig 4 as part of Raw Data Vault and Fig 5 as par of
Business Data Vault (PIT tables) we have captured
the response timesfromtheDataVaultModelacross
the parameters of Traceability. Auditability and
Agility as defined in above section(s) where I have
drawn the definitions.
5.4 Evaluation Results
 In our context of making 1 change in Customer
Segment dimensionaltableVsthesameinDataVault
2.0 for the change being discussed as our use case in
Fig.4 -below results are obtained as the time taken

(time to design the change + time for getting
response) as illustrated in Table 4 below
Serial
#
Activity
Description
Time in
Dimensional
Model
Time in
Data
Vault
1
Identify the
businessprocess
1800
seconds
1800
seconds
2 Identify the
Source of the
Change
900
seconds
10
seconds
3 Declare thegrain
of the customer
Segment change
120
seconds 0
4 Identify the
impacted tables
with the change
900
seconds
500
Seconds
5 Add / redesign
the dimensional
model
300
seconds 0
6 Reload thedata/
refresh the data
for the change
600
seconds
300
seconds
7 Time to traverse
back to Source
through all data
layers
900
seconds
10
seconds
8 Provide a Point
in time data
(Dimensional
Model requires
querying at the
time range
granularity) Not Possible
10
seconds
Table -4: Response Time Comparison
Dimensional Model Vs Data Vault
 From theaboveevaluationresultsspecificallyacross
the parameters of Traceability, Auditability and
Agility, as I observe, serial #2 and serial #7 for
Traceability & Auditability and Serial #8 on Agility.
and speed of response time plotted for Data Vault
Model.
Fig. 7: Traceability & Auditability Response Time
(Dimensional Model Vs Data Vault)
 Agility as defined and as per my experiment
observation cannot be successfully satisfied by
Dimensional Model design hence, I have not made
any comparative visualizations between
Dimensional Model and Data Vault. Agility as I
derived from the existingliteraturehasbeendefined
in the context of this paper as responsiveness or
response time to provide point in time data at any
instance – the Data Vault model defines PITtablesin
the Business Data Vault (reference to section 4.3 in
this paper) defines agility within a timeframe of 10
seconds when queried to get the response. Such
provisionsarenotpartofDimensionalModeldesign.
The inability to provide agility and the slow
response to traceability and auditability for the
business scenario we have chosen of an ecommerce
customer whose Customer Segment can change
frequently owing to his changed buying habits
established a business scenario of frequently
changing data ecosystems , and I observe through
the research already accomplished in this area of
Data Vault Modeling and through my evaluation,
Data Vault Modeling is found to be suitable and
meeting the needs in this business scenario.
 As I observed, the way I have defined before in
section 5.3 and considering various research study,
the Dimensional Modelisnotcapabletoprovidethat
Agility and hence Agility in terms of providing Point
in Time data is not possible and hence it can be
observed that Data Vault 2.0 is the one of the best
suited modeling techniques to have agility of data in
a changing data ecosystem. In the below graphs I
have plotted Traceability & Auditability response
times compared betweenDimensionalModel&Data
Vault Model specific toourbusinessscenarioinFig.7

6. CONCLUSIONS
I expect that my work to augment to the existing research
work in the field of exploration of features of frequent
changing data ecosystem as achieved by Data Vault and
traceability, auditability and agility in this respect is
noteworthy.
With real business scenario obtained from a retail
ecommerce organization and their business strategy to
frequently changing the customer segment based on buying
behaviors I have observed using Dimensional modeling
school of thought not satisfying the needs of quick results
expectations the organization expects to meet the
downstream business & reporting demand. I acknowledge
Dimensional modeling remains the de-facto standard for
analytics and reporting with less frequent changing data, on
the other hand Data Vault becomes a choice for scenarios
like mine with a complete assessment and evaluation being
done to align to the agile standards.
I have captured such data to predict from where we can
seamlessly understand and acknowledge the usage of Data
Vault requires minimal design changes and minimal
response time owing to its unique design standards.
I, trust this work will motivate upcoming avenues of future
research where data is stochastic & frequently changing,
data driven approach to maximize needs of agility,
traceability and auditability is necessary & an alternate
thought process is expected to be applied from de-facto
standards of dimensional modeling.
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Vault Approach for Evolutionary Integration of BigData
Sets: Case Study Using the NCBI Database for Genetic
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[3] Lars Rönnbäck, Olle Regardt, Maria Bergholtz, Paul
Johannesson,"Anchor modeling — Agile information
modeling in evolving data environments”, pp 8-10
[4] D. Linstedt and M. Olschimke, "Building a scalable data
warehouse with Data Vault 2.0, 2016"
[5] Tommie W. Singleton, “TestingControlsAssociated with
Data Transfers”, ISACA Journals.
[6] Guobao Zhang, data traceabilitymethodtoimprovedata
quality in a big data environment”, 2020 IEEE Fifth
International Conference on Data ScienceinCyberspace
(DSC), pp 2-5
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the Data Warehouse” ,2022, pp-2-3
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BIOGRAPHY
Sayan Guha completedhisBachelorsin
Electronics & Communication
Engineering in 2006. He has been
serving Information Technology
industry supporting Data & Analytics
Space, Data Modelling & Architecture
across business domains of Retail,
Banking, and Insurance & Telecom.He
is currently working with Cognizant
Technology Solutions and his area of
interests are in the fields of Artificial
Intelligence, Cloud Solution
architecture,BigData Integration,Data
Modeling, and Information
Architecture.
REFERENCES

Evaluation of Data Auditability, Traceability and Agility leveraging Data Vault Modeling in frequently changing Data ecosystem

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Evaluation of Data Auditability, Traceability and Agility leveraging Data Vault Modeling in frequently changing Data ecosystem