Enterprise Information Architecture using Data Mining

Reshmi Chakraborty

Digital information proliferation

Our proposal & future research areas

Healthcare Sector

Utility Sector

An integrated solution framework

Digital Information Growth and need for a scalable data mining solution

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In 1986, 14% of earth’s data was stored on

vinyl records. In 2000, 25% of all information was in

digital media form. By 2007, 94% of all information storage

capacity was digital, totaling 276 exabytes.  Computing storage capacity is growing at

around 58% per year.

This is increasing infrastructure requirements,

complexity and straining IT resources. Achieving analytics with traditional data

mining strategies is becoming slow and/or

beyond the financial means of many

organizations.

Healthcare – state of the union

Spending is projected to be around $4.0 trillion by

2015 while OOP expense will grow by 9% only

Consumers “shopping” for their healthcare needs.

Resulting information proliferation and need for

data mining

Managed-Care Organizations (MCO) has

become a merger and acquisition industry

Each MCO has large amounts of digital

prescription claims (often redundant).

MCOs working to use these information to

improve and retain customer loyalty.

They need a Clinical Master Patient Index and

an efficient data mining strategy to remain

profitable. 4

Demand response in Utility sector

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The fundamental problem is

rising utility expense.

Lack of capability to monitor

and control consumptions.

Need to study past data and

need algorithms to extrapolate

past data into possible future

conjectures.

Need a smarter

infrastructure, an integration

of electrical infrastructure and

information infrastructure

A tentative solution approach

The integrated data mining solution (apart from industry specific algorithms) need to cater the

following:

Retain customer loyalty through Portals and Mash-ups

Master Data Management System

A Clinical Master Data Management System

A Meter data management system based on power-line-communication (PLC) architecture.

Actionable analytics based on data mining algorithms.

Unified identity and access management system

Secured content management system.

Canonical data model for all electronic information exchange.

Service Oriented Architecture as part of the enterprise wide information strategy.

To scale this architecture up, implement this solution in SaaS/BPO/ Cloud (EC2/Azure/Google)

model

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Basic Data Mining Process

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AnalyticsOperational Data Store

ETL*

Data Mart

Pre process

Star schema based dimension cubes

Data mining algorithms

Select mining logic

Presentation logic

* Extract, Transform and Load

Operational Data

(ODS)

ETL*

Data Mart

Pre process

Star schema based dimension cubes

Data mining algorithms

Select mining logic

* Extract, Transform and Load

Healthcare version

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Utility sector version

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The solution consists of installation of

smart meters in individual apartments and

controlling them from one single location. The solution is intended to operate in a

service cloud model where different

customer energy profiles can be managed

from the same information cloud.

Customer information security will be maintained at two different levels. First, the information to and from smart meters will be communicated using web

service-security framework and each customer will have their own security algorithm.

Second, at the database level, the data will be stored in partitioned tables. Smart meters, RF links (zigbee protocol will be used to communicate data out of

smart meters into redundant data collection units) and data collection units will be off-the-shelf products.

Is this a viable approach?

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A smart grid enabled demand response system

iPhone based consumer centric healthcare App – adoption trend

Lets look at a SWOT analysis

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Strengths

• Scalable – can be sold in subscription model.

• Standardized – follows industry standard communication protocols

Opportunities

• Strategic alliances, partnerships with cloud providers like Amazon/Google.

• Can be built entirely using open source technologies.

Threats

• User hesitation.• Could be conidered as “Old

wine in new bottle”.

Weaknesses

• Additional industry specific components needed which may increase cost.

• Security and data privacy is still a major concern.

S W

TO

Our approach to implement an integrated information framework

12Learn- Investigate- Stimulate- Tabulate - Enumerate - Net

Three step business-technology strategy development process

Prometheus is our proposed utility sector solution

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Future areas of research

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Following are the future areas of research that we are going to focus in the process of developing our solutions and attempting to commercialize them:

• The techniques will be useful pending change in information storage techniques – i.e. it becomes a hybrid of relational and semantic storage model.

• Developing the solution in open source mode.

• Making the structure flexible so that the end consumer has the capability of choosing from a set of data mining algorithms and compare results.

• Identifying the target set of customers and testing the prototype model in viral networking environment.

In summary:

1. Information proliferation is costly from people, process

and technology perspective.

2. Business decision is a function of information and

intelligence.

3. Combining one and two above, we find that an integrated

hosted data mining framework can improve the top line of

an organization.

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Thank you