Dv decision makers presentation 310518[1]

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Data Vault 2.0 is a unique system of Data Warehousing created from the ground up to deal with real-world data challenges. Data Vault 2.0 delivers improved total cost of ownership, greatly enhanced operational agility and traceable data governance.

Business

What is the current landscape for
Information Managers?

Security
Open Data
» Lockdown vs. Democratisation
» Redaction
» De-personalisation
Regulatory compliance
» GDPR
» SOX
» FMA/APRA
» Cloud / ISO27001
External factors

GDPR
› This is likely to be replicated in
New Zealand
› Security Data Breach Notification Law in
effect as at 25 May 2018
› Lack of compliance will result in
heavy penalties

› More data more often
› New data sets all the time
› Data Quality challenges
› Corporate memory
› Reliance on individuals
› Reconciliation and audit
Internal factors

What’s wrong with traditional Data Warehousing methods?
‘Shadow IT’
Big Data
E
T
L
M
A
R
T
S
3rd party
data
Source
System
Source
System
JSON/XML
Semi-
structured
Unstruct-
ured data
BI
Analytics
Data Science

Automation Framework
Data Acquisition
Real-Time
CDC
Messaging
Batch
ETL/ELT
Files
PDF, Docs,
Video, etc.
Staging
Raw
Data
Vault
Data Provisioning
Information Governance - Metadata Management, Lineage, Data Quality
Big Data/No SQL
Information
Marts
(Virtual/Physical)
Operational Data
Store
Business
Data
Vault
Enterprise Data
Vault
Data Vault: Scalable, Extensible, Agnostic
3rd party
data
Source
System
JSON/XML
Semi-
structured
Unstruct-
ured data
BI
Analytics
Data Science
"It is not the strongest of the species that survives, nor the most intelligent
that survives. It is the one that is the most adaptable to change." Charles
Darwin

Data Lake
› Data flows in ‘naturally’
› Some boundaries
› Content flows out with little constraint
Data Swamp
› Uncontrolled flow
› No borders and potentially bottomless
› Filled with flotsam and jetsam
Data Harbour
› Controlled flow
› Trust in the delivery and content of data
› Built to use and extract value from the data

What is Data Vault?
› Invented by Dan Linstedt in the late 1990s, Data Vault is
a System of Information Delivery containing the
necessary components needed to accomplish
enterprise vision in Data Warehousing and Business
Intelligence
› Data Vault includes a data modelling technique for data
repositories that has significant advantages over
traditional methodologies: auditable, extensible,
automated
› With the release of Data Vault 2.0 in 2013, it extended
from just the data model to a full methodology
› Is effectively vendor agnostic: works with multiple data
processing tools, relational databases, and file stores

DV2.0 System: Foundational Pillars
Methodology
Architecture
Model
• Consistent & Repeatable
• Pattern Based
• Automation
• Agile
• Multi-Tier
• Scalable
• Supports NoSQL
• Insert only architecture
• Flexible
• Scalable (Big Data)
• Hub & Spoke

Demystifying Data Vault
Automati
on
Integrati
on
Governa
nce

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There’s no question that organizations across the globe are ramping up their efforts to prepare for the EU’s expansive General Data Protection Regulation. In a recent Veritas survey, over 92% of organizations admitted some degree of preparation, yet only 53% are confident they will be GDPR-ready when the go-live date hits in May 2018. Most organizations are launching their readiness efforts focused on structured data—the data they are most familiar with and have the most control over. The problem is that structured data only makes up one-fifth of all the data in a typical enterprise environment…what are these organizations going to do about the other 80% of unstructured data? GDPR (and the stiff penalties that come with it) is the forcing mechanism that will finally drive organizations to take a proactive governance posture when it comes to unstructured data. This session will explore the key steps required for accelerating GDPR readiness by locating, searching, minimizing, protecting, and monitoring unstructured data. By attending this session, you will learn best-practices for staying off the regulator’s hit list and establishing the most effective workflows for ensuring ongoing unstructured data compliance.

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Cloud computing is an online storage media where we access, store and manage the data. It stores the data on remote servers rather than a local server and that data can be accessed through the internet. For example Google Drive is personal cloud storage from Google. When there are number of request in cloud computing, then load balancer is used to distribute request between the remote servers and efficiently handle those request. Load balancer distributes client request or network load efficiently across multiple servers. By using cloud infrastructure, we don't have to spend huge amount of money on purchasing and maintaining equipment. Cloud data management is a way to manage data across cloud platforms, either with or instead of on premises storage. Deepali Rai | Dinesh Kumar "Load Balancing and Data Management in Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31035.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31035/load-balancing-and-data-management-in-cloud-computing/deepali-rai

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IT needs to regain control over shared data, but not at the expense of the user’s ability to collaborate both internally and externally. Cloud-based file-sync and share solutions can now be replaced with internal solutions that are more secure, provide greater insight, and are less expensive. Join Storage Switzerland, Cloudian and Storage Made Easy (SME) for our roundtable discussion to learn: - The risks associated with cloud-based file sync and share - Why GDPR and data compliance has forever changed file collaboration - The advantages of on-premises file collaboration - The challenges of on-premises file collaboration - How to get it all: Cost-effective, compliant, easy to use file collaboration

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Dv decision makers presentation 310518[1]

1. DEMYSTIFYING DATA VAULT 2.0

2. What is the current landscape for Information Managers?

3. Security Open Data » Lockdown vs. Democratisation » Redaction » De-personalisation Regulatory compliance » GDPR » SOX » FMA/APRA » Cloud / ISO27001 External factors

4. GDPR › This is likely to be replicated in New Zealand › Security Data Breach Notification Law in effect as at 25 May 2018 › Lack of compliance will result in heavy penalties

5. › More data more often › New data sets all the time › Data Quality challenges › Corporate memory › Reliance on individuals › Reconciliation and audit Internal factors

6. What else?

7. Data Vault vs. Kimball/Inmon/L ake

8. What’s wrong with traditional Data Warehousing methods? ‘Shadow IT’ Big Data E T L M A R T S 3rd party data Source System Source System JSON/XML Semi- structured Unstruct- ured data BI Analytics Data Science

9. Automation Framework Data Acquisition Real-Time CDC Messaging Batch ETL/ELT Files PDF, Docs, Video, etc. Staging Raw Data Vault Data Provisioning Information Governance - Metadata Management, Lineage, Data Quality Big Data/No SQL Information Marts (Virtual/Physical) Operational Data Store Business Data Vault Enterprise Data Vault Data Vault: Scalable, Extensible, Agnostic 3rd party data Source System JSON/XML Semi- structured Unstruct- ured data BI Analytics Data Science "It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change." Charles Darwin

10. Data Lake › Data flows in ‘naturally’ › Some boundaries › Content flows out with little constraint Data Swamp › Uncontrolled flow › No borders and potentially bottomless › Filled with flotsam and jetsam Data Harbour › Controlled flow › Trust in the delivery and content of data › Built to use and extract value from the data

11. What is Data Vault? › Invented by Dan Linstedt in the late 1990s, Data Vault is a System of Information Delivery containing the necessary components needed to accomplish enterprise vision in Data Warehousing and Business Intelligence › Data Vault includes a data modelling technique for data repositories that has significant advantages over traditional methodologies: auditable, extensible, automated › With the release of Data Vault 2.0 in 2013, it extended from just the data model to a full methodology › Is effectively vendor agnostic: works with multiple data processing tools, relational databases, and file stores

12. DV2.0 System: Foundational Pillars Methodology Architecture Model • Consistent & Repeatable • Pattern Based • Automation • Agile • Multi-Tier • Scalable • Supports NoSQL • Insert only architecture • Flexible • Scalable (Big Data) • Hub & Spoke

13. Demystifying Data Vault Automati on Integrati on Governa nce

Editor's Notes

Even Gartner is advising clients to beware of the data lake fallacy (http://www.gartner.com/newsroom/id/2809117). Excerpt …. "In broad terms, data lakes are marketed as enterprise-wide data management platforms for analyzing disparate sources of data in its native format," said Nick Heudecker, research director at Gartner. "The idea is simple: instead of placing data in a purpose-built data store, you move it into a data lake in its original format. This eliminates the upfront costs of data ingestion, like transformation. Once data is placed into the lake, it's available for analysis by everyone in the organization." However, while the marketing hype suggests audiences throughout an enterprise will leverage data lakes, this positioning assumes that all those audiences are highly skilled at data manipulation and analysis, as data lakes lack semantic consistency and governed metadata. This is why the data harbour is taking shape. It allows the skilled people to use the data under lesser control; but still a level of trust

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Editor's Notes