Do you think data warehousing has failed in the market?? If yes, what are the possible causes? What is the current status of data warehousing tools? What is required in future upcoming tools? What is the difference between creation of data warehouse and implementation of MDDB?

1. DW Assignment
Submitted By:
Saurav Singhania (15030122031)
Questions & Answers:
Q1. Do you think data warehousing has failed in the market?? If yes, what are the possible causes?
Answer:
Taking an example of Why Healthcare Data Warehouses Fail:
Enterprise data warehouses (EDWs) are notoriously difficult and expensive. The failure rate of data
warehouses across all industries is high – Gartner once estimated as many as 50 percent of data
warehouse projects would have only limited acceptance or fail entirely.
Six of the Most Common Ways a Healthcare Data Warehouse Project Fails:
1. A solid business imperative is missing.
2. Executive sponsorship and engagement is weak or non-existent.
3. Frontline healthcare information users are not involved from start to finish.
4. Boil-the-ocean syndrome takes over.
5. Being too idealistic (and not realistic) about what can be accomplished.
6. Worrying about getting data governance “perfect,” which immobilizes the project.
Building a data warehouse is a big project. To ensure success a health system must begin with a business
and strategic imperative, rather than having the IT department just build an EDW and hope the rest of
the organization will buy in. It’s critical that executive leadership is involved from conception through
deployment and maintenance. It’s also important that those who are actually going to mine the data for
useable insights have a voice in what data is made available. Finally, data governance doesn’t need to be
perfect before starting to build a data warehouse. [1]
Steps of why the data warehouse projects fails:
 Misunderstood Expectations
 Lack of understanding the data
 Treating requirements as an absolute truth
 Over – complicated architectures
 A belief that it’s the data fault [2]

2. Q2. What is the current status of data warehousing tools? What is required in future upcoming tools?
Answer:
Currently data warehousing tools is being used in many companies, which benefit them in better
decision-making, quick and easy access to data, & data quality and consistency.
Today, data warehousing tools typically need to perform the following major operations:
 Batch and near real-time loads to integrate data from multiple resources (internal and external)
 Basic reporting with no drill-down/ drill-across
 Online analytical processing (OLAP)
 Predictive analytics
 Operational business intelligence
Popular data warehouses tools:
1. Ab Initio Software
2. Amazon Redshift
3. AnalytiX DS
4. CodeFutures
5. DATAllegro
New trends i.e. required in future upcoming tools:
1. Cloud computing
It will be more useful for bulk data, and better than application and software-based data
warehousing tools. It will take some time to mature because of security concerns but it will gain
more market attention and many organizations in the services are aggressively pushing it.
2. Transformation technologies
The technologies like in-database analytics and transaction processing can transform the role of
enterprise data warehousing tools.
3. Social media and unstructured data
Social media analytic dashboards to monitor customer awareness, sentiment, and propensities
in real-time.
4. No SQL
Non-relational distributed NoSQL databases
5. In-memory technology
Data warehousing tools are marching towards it’s paradigm with the advent of solid state drives
in order to achieve higher performance.
6. Open Source
The features and functions of open source data warehousing tools are wide and useful which
make it shine brighter. [3]

3. Q3. What is the difference b/w creation of data warehouse and implementation of MDDB?
Answer:
Creation of Data Warehouse:
A data warehouse creation (implementation) represents a complex activity including two major stages.
In the first stage, of system configuration, the data warehouse conceptual model is established, in
accordance with the users’ demands (data warehouse design). Then data sources are established, as
well as the way of extracting and loading data (data acquisition). Finally, the storage technology is
chosen and it’s decided on the way the data will be accessed.
The stages of creating a data warehouse are:
1. Development of a feasibility study
2. Business lines analysis
3. Data warehouse architecture design.
4. Selection of the technological solution
5. Planning the project iterations
6. Detail designing
7. Data warehouse testing and implementation
8. Deployment and roll-out [4]
Implementation of MDDB:
A multidimensional database, or MDDB, is a specialized storage facility that allows data to be pulled
from a data warehouse or other data sources for storage in a matrix-like format. The process of building
an MDDB summarizes the raw data; the data stored in the MDDB is thus said to be pre-summarized. The
MDDB enables users to quickly retrieve multiple levels of pre-summarized data through a
multidimensional view. [5]
References
[1] S. Barlow, "6 Reasons Why Healthcare Data Warehouses Fail," Health Catalyst.
[2] C. Jennings, "Five Key Causes on Why Data Warehouse Projects Fail," Collaborative.com, 2014.
[3] S. D'Souza, "An eye on Indian enterprise data warehousing tools market," Computerweekly.com.
[4] M. VELICANU, "Building a Data Warehouse step by step," Informatica Economică, 2007.
[5] SAS, "Introduction to MDDBs," SAS/MDDB ® Server Administrator’s Guide, vol. 8, 1999.

4. Refernces:
PDF:
Q1.
https://www.healthcatalyst.com/wp-content/uploads/2014/10/6-Reasons-Why-Healthcare-Data-
Warehouses-Fail.pdf [1]
Q1.
http://cdn2.hubspot.net/hubfs/2038852/why-data-warehouses-projects-fail-CC.IM.100-
1.pdf?t=1482258980141 [2]
Q3.
http://revistaie.ase.ro/content/42/velicanu.pdf [4]
Q3.
http://www.okstate.edu/sas/v8/saspdf/mddb/c02.pdf [5]
Links:
Q2.
http://www.computerweekly.com/feature/An-eye-on-Indian-enterprise-data-warehousing-tools-market
[3]