1. business and industrial

Hexaware Business Intelligence & Analytics Blogs Guide to
“A Practitioner’s View”
Latest Trends and Information on BI/ DW Technologies for 2012
Actionable Intelligence Enabled
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TOC
·
Trim Tabs in Business Intelligence
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·
2012 Wish list for Data Integration
Platforms
5
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·
Managing Counterparty Risk with
Actionable BI Solutions
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·
Data Management, Information
Management and then Decision
management
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·
Business Intelligence and Analytics- The
Different Dimensions
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What are Trim Tabs? – Trim tabs are small surfaces connected to the
trailing edge of a larger control surface on a boat or aircraft, used to
control the trim of the controls, i.e. to counteract hydro- or
aero-dynamic forces and stabilize the boat or aircraft in a particular
desired altitude without the need for the operator to constantly apply a
control force. This is done by adjusting the angle of the tab relative to
the larger surface.
Trim Tabs in
Business
Intelligence
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As a metaphor, Trim Tabs are used to denote tiny components, which
nevertheless have a great impact on things that they are attached to.
This blog post is about the relevance of this metaphor to BI in
enterprises.
Business Intelligence practitioners acknowledge the fact that BI &
Analytics in any organization is a journey, an evolution over a period of
time. The canvas for BI is extensive and spans the business
technology continuum.
Given that BI can add value in many areas of an organization and there
are many solutions possible in each of those areas, it is important to
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identify the BI ‘Trim Tabs’, i.e. those small areas that can provide the
maximum value for invested money. In one of my earlier blogs, I had
talked about the concept of “analytics anchor points” – Business
processes that should be considered first for optimization through
analytics.
Identifying the analytics anchor points in an organization is a non-trivial
exercise. In my humble opinion, such a process should start with a
complete understanding of the organization’s ‘Business Model’.
Business Model is a set of assumptions about how an organization will
perform by creating value for all the players, on whom it depends,
including its customers. From BI perspective, once the practitioner
understands the business model of the company, many questions
gets answered:
1) What drives the company’s success and how BI can help?
2) Who are the stakeholders and what information are they looking
for?
3) What needs to be optimized and how it can be done?
4) What is the architectural blueprint and how will it evolve?
5) How fast should information get delivered?
6) How much data needs to be collated and how far into the past
should one go?
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7) What are the regulatory requirements for the company?
And many more. In essence, clarity on one aspect of the problem
(business model) will go a long way in selecting the analytics anchor
points (BI Trim Tabs) for that particular organization.
-by Karthikeyan Sankaran
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Data Integration (DI) platforms like Informatica, DataStage, BODS,
ODI, SSIS and others are the backbone for a well designed Business
Intelligence application. DI platforms ensure that all the required data
are provisioned at the right time, with quality.
In the process of configuring and running DI platforms, we come
across few scenarios where DI platform needs to be extended with
tools/utilities. Some of these scenarios are like analysis of the
metadata captured by DI platform, integration with other applications
like helpdesk and others. These scenarios can be added as a product
feature to the DI platform.
2012 Wish list
for Data Integration
Platforms
5
Following are the features that Data Integration platform vendors
should consider introducing in the DI product.
• A feature which can provide a Network-like view to understand the
dependency between tables present in DI process components.
DI process components are ‘Jobs in DataStage’ or ‘mappings in
Informatica’. The Network view should enable us to drag a table
and focus on its dependency on other DI processes and tables
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• Provide an interface to configure the default object names that
would be assigned to an object, like widget or transformation (as in
Informatica), or an object like stage (as in DataStage). The
configured name should be assigned when the icon is dragged
into the design interface by developer.
• Design templates for scenarios like data archival or data cleanup
from large tables, for comparison of two data sets (data set can be
a table or file), auto data type conversion will be useful for migration
of data from one database to another database
• Prebuilt integration with Helpdesk applications like Remedy to log
data errors and job failures as a ticket into the helpdesk system
Thanks for reading, let me know your thoughts on what are the other
features that you feel should be considered in a DI platform.
• ‘Data Element Aware’: This feature should understand a column’s
data values like of ‘people name’ and then suggest checks and
validations that needs to be performed for such columns
-by Muneeswara C Pandian
• ‘Domain Data Aware’: This feature should understand the business
areas like Investment Bank, and have pre built data validation rules
to handle external data like Bloomberg
• Enable calculations like aggregation and metric derivation to be
interoperable with the semantic layer metadata of reporting tools.
For example if a Business Objects Universe is given as input to a
DI product, then the DI product should be able to suggest what
calculations in the Universe can be moved to the DI layer and the
suggested data structure to store the calculations
• Data Correction interface for analysts with the workflow feature to
look at the error data, correct, approve and re-process them back
into the target tables
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What is Counterparty risk?
Managing Counterparty
Risk with Actionable
BI Solutions
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Counterparty credit risk is traditionally defined as the risk that a
counterparty to a transaction fails to perform under its contractual
commitment for the variety of products that the Bank trades with the
counterparty such as forex instruments, letters of credits,
repossessions, derivatives, etc. Counterparty credit risk is an
inevitable component of capital markets trading businesses and
banking activities. The ability to measure, aggregate and monitor
counterparty credit risk ensures that bank’s external customers are
willing and able to honor their contractual commitments and credit
losses are minimized. Any changes to the credit quality and the
exposures should be regularly evaluated and communicated to the
senior management to make prudent credit decisions in their
investment portfolios and to reduce exposure to the deteriorating
counterparties.
Why is it important?
A counterparty credit rating reflects the best expectation of its
Probability of Default (PD) based on an analysis of its economic and
financial strength. In our experience it is typically assessed at the level
of senior unsecured creditor. Some of the banks and financial
institutions also consider country risk ratings and adjust the
counterparty rating based on this. This rating reflects a long term view
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of the creditworthiness of the counterparty (i.e. through a credit cycle)
though suitable adjustments (normally downgrades) are made based
on the market data.
All trading counterparties are categorized into broad, high level
industry classes. These broad classifications are designed to identify
pools of counterparties with similar business environment, regulatory
requirements and common macro-economic risk drivers. This
segmentation helps in tracking credit quality across different portfolios
and setting portfolio limits.
What should you look for in a framework that captures all analytics
dimensions?
Counterparty risk analytics framework should provide automated
aggregation of data from various source systems that house the credit
and limit information for domestic and international counterparties.
Additionally this should enable automation of the agency ratings and
CDS swap spread data based on the information available.
The framework should help create a central risk database that stores
master data around counterparties, products, exposures and risk
factors. It should also provide an application to extract and manipulate
exposure & ratings data from underlying source system. MS Excel files
can be linked to load data into the historical risk database. This
database can be used to produce a set of reports and dashboards
that enables senior management to take critical decisions around
counterparties and their exposures.
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The framework should also create a work flow to make manual
additions and changes to the counterparty and exposure information
easily. Also provide critical alerts for risk rating and exposure
downgrades and upgrades in the desired frequency.
Some of the common business benefits are
• Reduce your turn around time for consolidation of counterparty
reporting.
• Create a Master repository of counterparties, grouping, products,
exposures and risk factors.
• Get alerts for ratings, total exposure fluctuations beyond
thresholds.
• Monitor trends for dimensions mentioned above and faster
decision making.
Technical benefits are
• Pre-Built connectors across source systems using excel interfaces
• Prebuilt data models , Business metrics and scenarios
• Prebuilt data loaders
• Standard KPIs covered
• Prebuilt Reports and Dashboards
• Data and Report Centralization analytics.
-by Sheba Fernando
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Data Manageent,
Information Management
and then Decision Management
In the architectural set up of a Data Warehouse (or Business
Intelligence/BI) system, we can define two layers, they are
• Data Management and
• Information Management
Data Management performs the function of data collection from
Business Transaction systems, validation of data, integration of data
and provisioning the data for analysis and interpretation.
Information Management performs the function of deriving information
from the data stored in the Data Management layer and presenting
them as analytical reports and dashboards.
Decision Management is a new layer that gets added into a Data
Warehouse landscape acting as a connector between the Information
Management layer and the Business Transaction Systems.
To understand further on Decision Management, let us define what is
decision and types of decisions.
What is Decision?
Decision is defined as ‘a solution or direction provided to resolve a
problem’. A decision leads to an action and in a Business context
action invokes a business process. Some examples of decisions in a
business are
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1. A manager initiating a training program for resources on observing
quality issues in deliverable
2. A store manager providing the discounted price for preferred
customers on observing lesser stock and faster movement of
products
3. An accountant sending an e-mail note to supplier on seeing a
repeated delay in delivery
What is Decision Management?
Managing the ‘data of decisions’. Being able to capture and
understand the decisions that have been taken is called Decision
Management.
What ‘Decisions’ can be Managed?
There are three types of Decisions, they are
• Strategic
• Tactical
Tactical Decisions are made by Managers who work on predefined
target values set against a metric and have continuous monitoring
process over the metrics. Metrics are measures which define
performance of a business process like revenue per sales person.
The decisions here are based on how a metric is performing
against a target value. These decisions are covered by the
Decision Management Systems.
• With the increase in the regulatory requirements, enforcement on
data trail and auditing is becoming very vital for every decisions
being made in the business. In the future organizations will have
Decision Management as one of their key initiatives to address
regulatory requirements.
Current products in the market serving ‘Decision Management’ area
look at automation of the decision process and do not to define
process or tools to ‘capture and store the decisions’. To build an
effective Decision Management System, a repository of ‘Decisions’ is
very essential.
Thanks for reading, let me know your observations.
-by Muneeswara C Pandian
• Operational
Strategic Decisions are made by Senior Management and CXO’s;
these are long term in nature and are dependent on the market
conditions. These are not covered by the Decision Management
Systems.
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One of the most interesting aspects of Business Intelligence is the
fact that it has a wide canvas for the practitioners to paint their strokes.
We at Hexaware have made an attempt to draw a boundary around BI
to make sense of its different dimensions. The artifacts that have been
used in this exercise are mentioned below.
In BI & Analytics context, the technology stack has the following
components, viz.
•
•
•
•
Industry Domain
Business Process
Data & Information Management
Reporting & Dashboards, Performance Management, High-end
Analytics
• Information Delivery
Business Intelligence
and AnalyticsThe Different
Dimensions
BUSINESS INTELLIGENCE STACK
Information Delivery
Reporting &
Dash boarding
Performance
Management
Analytics
(Forecasting, Predictive,
Optimization)
Data and Information Management
Business Process
Industry Domain
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Business Intelligence Stack
A prezi showing the components in each of the layers is accessible at
this link –http://bit.ly/pbzNiS
A more detailed view of the specific components is provided as a
Mindmap at this link – http://bit.ly/gEYGIR.
Product Vendors
Ecosystem
Mode of Delivery
BI – The Dimensions
Layers
Competencies
With this framework in place, Hexaware’sBusiness Intelligence &
Analytics practice is involved in developing many tools and
frameworks that can add significant value to enterprises. Please do
reach out to us for any queries. And thanks for reading.
Tagged as: Business Analytics, Business Intelligence – A Practitioner’s
View, Data & Information Management
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