A Practitioner s View

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Hexaware Business Intelligence & Analytics Blogs Guide to

Actionable Intelligence Enabled

“A Practitioner’s View”

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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.

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

Trim Tabs in

Business

Intelligence

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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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Click Here> 5

2012 Wish list

for Data Integration

Platforms

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.

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.

• Prebuilt integration with Helpdesk applications like Remedy to log data errors and job failures as a ticket into the helpdesk system

• ‘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

• ‘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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• 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

Thanks for reading, let me know your thoughts on what are the other features that you feel should be considered in a DI platform.

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Managing Counterparty

Risk with Actionable

BI Solutions

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What is Counterparty risk?

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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Click Here> 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

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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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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Click Here> 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

• 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.

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

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Click Here> 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 Analytics-

The Different

Dimensions

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BUSINESS INTELLIGENCE STACK Information Delivery Performance Management Reporting & Dash boarding Analytics (Forecasting, Predictive, Optimization) Data and Information Management

Business Process Industry Domain

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For more queries on our BI&A offerings please get in touch with

corporatemarketing@hexaware.com and

BIInnovations@hexaware.com

“Want to keep up with the industry's latest trends? Visit our blog on BI http://blogs.hexaware.com/index/business-intelligence to hear from our experts

Click Here> 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.

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

A Practitioner s View