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Pervasive Business Intelligence

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PCQ Bureau
New Update

Today's enterprise data warehouses (EDW) — high-end analytical computing

platforms — continue to provide powerful strategic analysis. However, the newest

wave in database-driven insight is operational data warehousing. This involves

new capabilities that enable operational decision making in real time — as well

as strategic data mining. The use of these real-time centralized data

repositories has escalated and expanded. The term most commonly used for this

new approach is Pervasive Business Intelligence (PBI).

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This is not a future vision but a present reality. By implementing PBI, the

best businesses are taking knowledge management to new levels. The day of

passive knowledge management is quickly ending. Pervasive BI rapidly evolves

knowledge management into competency optimization for employees — who can truly

interact with customers and the business with a panoramic view, compressed into

actionable tactical intelligence for tangible economic value.

Pervasive knowledge management



In simple terms, pervasive BI delivers data warehouse insights to everyone

in the enterprise, not just an elite group of technologists, analysts or

knowledge workers. This is truly a revolution in the use of knowledge. You might

even call it pervasive knowledge management. The goal is to make thousands of

daily tactical decisions better by using facts instead of intuition alone.

Imagine every employee making 40-50 small decisions a day. Next, imagine adding

the use of Pervasive BI, so that 20 of those decisions are supported by facts

instead of guesswork. Generally, the line-of-business managers understand

pervasive BI instantly and wonder why they haven't had it all along. Some

knowledge management (KM) practitioners don't think of technology as a best

practice arena, however, we would propose that this is indeed an emerging best

practice. Look at it this way: you distill facts, insights, and information then

systematically deliver it to the thousands who need to make smarter decisions.

Maybe think of this 'knowledge distribution' — as the purpose of the larger

server platform. At times, summary data is all that is required.

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However, the use of more detailed, actionable information in real time can

certainly provide much more valuable knowledge. Agreed?

Unfortunately, computer technologies were once limited, forming a barrier

between the front line user and the EDW data until around 2001. It existed

because front line users need 'fresh data' in addition to the historical

information normally found in the EDW. While historical data is loaded into the

EDW nightly or weekly, front line employees need today's data as well. For

example, a call center representative (CCR) needs to know a lot about the

consumer calling in: residence, prior calls, profitability score, and the next

best offer to suggest.

All this can be calculated nightly, ready to go each morning. But the CCR

also needs to know what calls the consumer made this morning and what the

consumer did on the company website today. Fresh and historical data need to be

displayed while the consumer is still on the phone. Getting that data from the

production system, into the data warehouse, and back to the front lines is

sometimes called 'real time BI;. Until 2001-2002, the software and computers

could not do this.

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As it often occurs, some enterprises weren't daunted by the seeming

limitations of technology. The business-IT visionaries wanted the benefits of

pervasive BI immediately, and they became creative. So in 2001-2002, numerous

enterprises found ways to speed up getting data into and out of the EDW. These

visionaries were able to connect their point of sale machines, airline gate

agents, web site, call center, and dozens of other front line users straight

into the EDW. As IT organizations charged ahead, the software vendors saw

opportunities to improve their products in the same direction. In a symbiotic

relationship, IT experts and IT vendors collaborated to make the technologies

work in real time.

Elements in pervasive BI



How did they do it? What were the barriers that were overcome? First, three

technology subsystems had to evolve. Second, IT organizations needed new designs

and processes. The three technology subsystems — Data Integration Services,

Decision Repositories, and Decision Services — are part of an information supply

chain. Data originates in raw form, gets transformed and cleansed. Next, it's

repackaged, stored and repackaged, then finally analyzed and distributed.

Data Integration Services is a point of convergence, a hub, taking all kinds

of data from many sources and production applications, and preparing it for the

EDW. To meet the 'service level” goal of 'under one hour' or 'under five

minutes,' delivery of the raw data needs to be “guaranteed delivery” to the data

integration hub so nothing is lost and everything is on time. Data integration

services also must clean up the data quickly or else the inaccurate data will

lead to bad decisions. It must remove duplicates like name and address, and swap

out 'codes' for more understandable values. All this must occur as multiple

streams of data flow into the data integration server continuously or in spurts.

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The Decision Repositories (data warehouses and marts) need to load the data

and serve it up as fast as possible. This is difficult because data loading can

consume the entire EDW server's capacity, leaving no access for the front-line

and back office employees. Performance for the front line and back office users

is terrible during loading. But around 2002, Teradata began offering two

components to solve this: a real time data loading utility and mixed workload

management tools. Mixed workload management means prioritizing work inside the

data warehouse according to business rules. This enables an enterprise to give

the call center workers top priority, reports medium priority, and data loading

low priority. The result is data loading tasks perform well while reports run

fast, and the call center runs fastest.

Decision Services grabs the EDW data, reformats it, analyzes it, and delivers

it to the front line user. Since the front line user doesn't have time to read

reports, the requested facts have to be short and specific to the business task.

This means delivering analytic information to the entire knowledge network: BI

dashboards, employee portals, mobile devices, and modern 'composite

applications. 'Since nearly 50% of all IT development projects are now composite

applications, the EDW and Decision Services roles must be fit into those

developer tools and applications.

The result is the ability to deliver analytic insights in any business

process: Pervasive BI.

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The IT organization, led by the CIO, also has some critical success

requirements. First, there must be a 'service level agreement' between the IT

group and the front line users for performance and availability. Negotiating the

agreement wrings out all the cost issues, true business needs, and project

objectives — resulting in information access priorities. The second success

factor is mission critical availability. The entire information supply chain

must be 'always on, always available.' Since many business processes run any

time of the day, the pervasive BI subsystems must as well. But most companies

have not made the end-to-end information supply chain mission critical. This is

the area of the biggest risk. The good news is most IT teams know how to do it

with their mainframes and UNIX servers. They simply need to apply those

principles to the pervasive BI infrastructure.

With the emergence of Pervasive BI, organizations can at last activate the

terabytes of data sitting passively in massive repositories. They can convert

knowledge into economic value at cyber-speed — in seconds, not hours. They can

create limitless adaptability in their businesses and create competitive

advantage that is truly as sustainable as the capabilities of their centralized

database. Interested in best practice examples? There are examples at many

leading companies across industries. If the readers and editors want further

information on Pervasive BI — and its contributions to the field of knowledge

management, we can report on those instances in the hope that the field of KM

can at last welcome data warehousing technology into its sacred provinces with

open arms. Again, this is about data being distilled into knowledge and

systematically distributed to thousands of knowledge workers.

Pervasive BI is trending upwards, having moved from the early adopters into

the mainstream. The visionaries have blazed a path but there is still time to be

a leader in your market using pervasive BI. You need a vision, a clear strategy,

and a capabilities roadmap, in order to more effectively and economically

differentiate your business. Just don't wait until this trend is, well,

pervasive.

Ashok Ekbote,Country Manager, Teradata India

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