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Active Intelligence for Smart Business

By Mike Ferguson

Intelligent Business Strategies

November 2011

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INTELLIGENTBUSINESSSTRATEGIES

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Copyright Intelligent Business Strategies Limited, 2011, All Rights Reserved 2

Table of Contents

Introduction ........................................................................................................... 3

What is Active Intelligence? .................................................................................. 4

On-demand and Event-driven Analytics - Why every business needs them ......... 6On-demand Active Intelligence .................................................................. 6Event-driven active intelligence .................................................................. 7Near Real-time Data .................................................................................. 8Automated Analysis ................................................................................... 8Automated Actions ..................................................................................... 9

Why Intelligence Must Go Enterprise-Wide to Maximise Business Value ........... 10Key Questions When Implementing Active Intelligence ........................... 11

Whats Possible With Active Intelligence?........................................................... 12Using Active Intelligence To Optimize A Supply Chain ............................ 12Using Active Intelligence To Improve Procurement .................................. 13Using Active Intelligence To Improve Risk Management ......................... 14Summary of Whats Possible ................................................................... 14

Architectural Change: A DW Journey to the Centre of the Enterprise ................. 15

Requirements: What to Look For in an Active Intelligence Solution .................... 17

Product Example: Teradata Active Enterprise Intelligence ............................... 20End-to-End Technologies In A Teradata Active Enterprise IntelligenceEnvironment ............................................................................................. 21Teradata Database In An Active Intelligence Environment ...................... 22

Conclusion .......................................................................................................... 25


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INTRODUCTION

In many organisations today, business intelligence (BI) systems are now well

established, supporting decision making in many business areas. Most of these BIsystems are typically used by business analysts, l ine managers and executives tosupport decision making at tactical and strategic levels with finance, sales andmarketing often dominating BI usage. Yet, despite the maturity in the BI market, thedemand for intelligence has never been so strong. This is still a vibrant market withtechnologies such as data warehouse appliances, big data visualization, Hadoopand new analytical algorithms now making it possible to undertake more complexanalyses on much larger volumes of detailed data to answer questions that couldnever be answered before.

However, while this all continues to offer business value, it still keeps BI in thehands of the few when many executives today would much prefer it to be in thehands of the many. More specifically, they would like to use BI to empower the

people in their business operations and not just in back office analysis andmanagerial roles. For example, what if all customer- facing staff had on-demandaccess to intelligence about each specific customer as they dealt with thatcustomer? What if they were guided on what actions to take to boost customerprofitability, deliver better customer service, be more personal and avoid risk? Butthat is just the front-office. What about other operational areas? Why cant theyalso run smart? For example, what if people in retail distribution centres couldsee real-time intelligence on inbound deliveries as well as actual sales andinventory in each store on a continuous basis? What if they could see trendsemerging and got alerts on predicted stock-outs based on actual sales so theycould match supply with demand every time. There is also a need to automateanalysis to automatically see opportunities and problems and to guide the businessas it operates. For example, continuously monitoring spend activity across a

department could predict problems ahead of time so that spending budgets are notexceeded.

The point here is that there are literally thousands of small decisions that are madeevery day in operations, and an organisation should not be entirely reliant onbusiness analysts to see everything. What many chief operating officers are nowasking is: Why cant people and applications involved in those decisions leverageintelligence to help them act in a more timely and effective way so that all the smalldecisions taken add up to making a major contribution to overall businessperformance?

These requirements mean moving BI systems beyond just having a passiverole insupporting tactical and strategic decision making to having an always on active

role in operational decisions as well. The ultimate objective is to get to the pointwhere BI systems arecontinuouslymonitoring, managing and drivingall businessoperations on a 24x365 basis. Achieving smart operations requires organisationsto integrate BI into their core operational business processes so that front lineemployees are constantly alerted and guided to act in a more timely and effectiveway than they do today.

This paper looks at this transition and asks What is Active Intelligence? It alsolooks at how BI systems have to change to help people and applications becomemore effective in the tasks they perform and more responsiveto business eventsas and when they happen. It then looks at some business examples of what ispossible, the requirements that need to be met by active intelligence solutions andhow one vendor, Teradata, steps up to meeting those requirements.

BI systems are used

mainly at tactical andstrategic levels today

BI is still in the handsof the few when manyexecutives would

prefer it to be pervasive

Thousands of smalloperational decisionsare still made withoutany kind of guidance

BI systems need tobecome active inbusiness operations tohelp people andsystems become moreeffective


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WHAT ISACTIVE INTELLIGENCE?

Active intelligence is about empowering people and systemsacross the enterprise

to be able to act on intelligence at exactly the right time to optimise and improve onbusiness performance. Although applicable to all levels, active intelligence isparticularly important to business operations. It can be defined as:

Using business intelligence and analytics to guide people and applications so thatthey continuously know the best action to take and when to take it in everybusiness process activity. It is about dynamically using BI to keep a businessrunning optimally while remaining compliant, minimising risk and maximizingprofitability

Starting down the road to implementing active intelligence signals a fundamentalchange in the way you intend to use BI systems. The intention of ActiveIntelligence (AI) is to make it possible for everyone to work smarter with far more

people in the organisation being guided by insights to help them contribute tobottom line performance. In addition, the insights provided also need to guidepeople to take actions that all contribute towards achieving targets and objectivesset out in a common business strategy. This can be achieved by improvingprecision of BI delivery so that people get role-based, relevant intelligence in thecontext of every task they perform, as and when each task is performed. It mustalso cater for people who are mobile whether they are employees, suppliers,partners or customers. This kind of capability opens up BI to a much larger numberof concurrent users.

But its not just BI for humans. Many self-service systems like web sites,interactive voice response units at contact centres, bank ATMs, travel kiosks, andeven self-checkouts at stores can be made smarter with active intelligence. In this

case it is applications that need to be guided by insights.

Figure 1 shows the some of the key differences between passive and activeintelligence. It shows that active intelligence encompasses passive back-office usebut adds additional capability to empower people and systems in businessoperations.

Passive Intelligence Active Intelligence

Used by business analysts, managersand executives

Used by business analysts, managersand executives AND front-line

operations staff, partners and suppliers

Historical data Near real-time data and historical data

Human analysis and reporting Human, guidedand automatedanalysisand reporting

Human action taking Human and automatedaction taking

(auto alerting, auto recommendations,auto campaign and transaction

invocation)

Role-based Classic Dashboards

(Historical trends, reports, and KPIvisualizations, drill downs)

Role-basedActiveDashboards

(Alerts, early warnings, real-time ANDhistorical trend visualizations, drilldowns AND guided intelligence,predictions, recommendations)

BI tools for human analysis andreporting

BI servicessupporting on-demandrequests for intelligence from

operational applications, processes,

Active intelligenceallows people andsystems at all levels ofthe enterprise tocontinuously know thebest action to take andwhen to take it

AI extends the reachof BI into operationsso that everyoneworks smarter

Active intelligenceintroduces near real-time data, on-demandBI automated analysis,recommendationsalerts and automatedactions


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portals and mobile devices

Recommendation servicessupportingon-demand requests for automated

analysis from within operationalapplications and processes

Event-driven automated analysis andaction taking

BI integrated into operational businessprocesses

Human escalation of alerts if not actedupon

Automated escalation of alerts if notacted upon

Corporate Performance Management(CPM) Scorecard with business strategy

and KPIs at executive level

Role-based scorecards with personalobjectives, targets and KPIs linked to

higher levels so that all roll-up tocontribute to common objectives in a

multi-level strategy management

implementationMulti-level action management to

cause co-ordinated execution of acommon business strategy across all

levels of the enterprise

Figure 1

Some refer to active intelligence as operational BI however that description wouldbe falling short of what it tries to achieve. It is more than that. An organisation thathas implemented active intelligence can align and drive accelerated actions at alllevels of the business including strategic, tactical and operational actions. It is notjust an operational thing. In that sense the intention is to integrate intelligence into

operational and managerial business processes by making BI more accessibleand by automating analysis and action taking so that management by exception isalso possible. Doing this is a seismic shift because it transitions an organisationfrom just deploying BI as a support tool to becoming a smart businesswith BIunderpinning most decision-making.

Active intelligence candrive actions at alllevels of the businessto co-ordinateexecution of a

common businessstrategy


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ON-DEMAND AND EVENT-DRIVEN ANALYTICS -WHY EVERY BUSINESS NEEDS THEM

Looking at Figure 1 it is clear that if BI systems are to become active they need totake on new characteristics over and above what you would typically find in atraditional passive BI set-up. Two key active characteristics that stand out are thesupport for on-demandand event-drivenuse of BI. In addition, Active intelligencesystems also introduce the use of near real-time data, automated analysisandautomated actions rather than relying entirely on the need for human analysisbefore making decisions. Lets explore each of these active characteristics in moredetail to understand why every business needs them to provide the majority of theiremployees who work in operational areas with the insights they need.

ON-DEMANDACTIVE INTELLIGENCE

One of the road-blocks to the use of BI in business operations in the past has beenthe inability to make use of BI because many people in front line businessoperations are in job functions where there is no linkage to BI. A good examplehere would be a contact centre operator, a bank teller or a point of sale operator ina retail store. In many cases these front-line workers are constantly tied to specificoperational applications that they use to do their jobs.

This problem goes beyond people. It also extends to front-line applicationsinbusiness operations where there are no employees involved. Examples hereinclude e-commerce applications, airline kiosks, and other self-service applicationsthat could be customer- or supplier-facing. Many of these applications are alsoaccessible via mobile devices. In this case these applications allow customers,partners or suppliers to interact and transact business as part of self-service

operations.

Any organisation looking to introduce smart operations should not take the noaccess to BI tools problem to mean that they cant leverage intelligence oranalytics to guide people and applications in front-line operations. It simply meansthat there needs to be another way to do this.

On-Demand Intelligence

That way is to design and deploy BI services so that operational applications in useby front-line workers or by customers (as self-service applications) can request theappropriate BI on-demand. So for example, a contact centre agent entering acustomer name into a customer service application gets back not just account

information, but also other valuable BI insights and context like lifetime value ofthe customer, recent purchases, and any recent service interruptions. Equally aself-service on-line insurance quote application could request customer and riskintelligence on-demand so that the pricing engine can leverage specific intelligenceabout a customer (or similar customers) or claims to more accurately calculate aprice before displaying an on-line premium quote. Fortunately today, modern BIplatforms make on-demand access possible by supporting BI services out-of-the-box. This allows reports, queries, and analyses to be published as web services forsubsequent on-demand invocation (in an industry standard way) from anyoperational application, processes or portal.

On-Demand Recommendations

Another form of active intelligence is the on-demand recommendation. This is an

online request for an automated decision to guide someone in operations. It isdifferent from on-demand intelligence because it requires automated analysis ofspecific data and an automated decision based on the intelligence produced by

BI systems need to takeon new characteristics tosupport activeintelligence

Just because people infront-line operational jobshave no time to use BItools, it does not meanthat cant make use of BI

Front-line workers andself-service applicationsneed BI on-demand

Introducing BI servicesopens up the way tomake BI available toapplications on an on-

demand basis

Modern BI platformssupport BI services out-of-the-box


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that analysis. Therefore, services need to exist that will analyse specific data anduse rules to decide what to recommend. A good example here is an on-line retail e-commerce application where a customers data is analysed to produce a cross-sellor up-sell recommendation while the customer is online. Another example is anaccept/decline recommendation (based on a customer risk score) to a customeradvisor dealing with a loan application in a branch of a retail bank. On-demand

recommendations guide people and keep decisions within tolerance limits.

EVENT-DRIVEN ACTIVE INTELLIGENCE

On-demand requests for intelligence are based on applications pulling intelligencewhen needed in the context of a business process activity. For example frontlineapplications request customer intelligence when the user or the application isinteracting with the customer. In addition, in this example, the context is small, i.e.,only the intelligence about one customer is retrieved for each interaction. Bycontrast, another key characteristic of smart business operations is the ability todetect, analyse and act as soon as specific events or event patterns occur inbusiness operations. This is known as event-driven intelligence which involvesdetermining the business impact an event may have. Event-driven intelligence

occurs when an event or event pattern occurs that may have a ripple effect,perhaps big, perhaps small, perhaps noteworthy, perhaps not. The use of BI in thiscase is to figure out what small local events have ripple effects and do earlywarning and analysis of side-effects. This is important because the businessimpact signalled by certain events may warrant the need to take action(s) to keepthe business optimised and on track to achieving its goals.

All kinds of events can occur in business operations throughout a working day.Examples include a sale of shares on the financial markets, a price change, anorder change, an order cancellation, a customers birthday, a large withdrawal on asavings account, the closure of an account, a mouse click on a web site, a missedloan payment, a product or pallet movement in a distribution chain (detected via aradio frequency identification [RFID] tag), a change in flow rate in an oil pipeline, a

tweet, a competitor announcement etc. Whatever the events, there are literallythousands of these that can occur in business operations on a daily basis - millionsin some cases. And while not all events are of business interest, many requiresome kind of responsive action to seize an opportunity or prevent a problemoccurring or escalating. That response may need to be immediate and automatic insome cases or subject to human approval in others.

What event-driven provides is the ability to monitor the pulse of business as ithappens. It means we can set up always on look-out posts to continuouslymonitor for specific conditions in different parts of the business and act when theyoccur. It helps us manage and respond in a more timely way. We can use thiscapability to assist people in operations, middle management and at executivelevel. The purpose of event-driven active intelligence is to detect events that

impact (or are predicted to impact)operational costs, revenue, budget, deadlinesand customer satisfaction and to take the appropriate action when they occur.

The issue with event processing is that in many cases it cannot be done manually.This is especially true if action is required immediately, if the volume of events isvery large (e.g., financial markets) or if event correlations are very complex toidentify. Also, certain conditions may need to be true for a specific combination ofevents to be deemed important. For example if six different events all occur withina certain timeframe (e.g., the last 20 minutes) then action is needed but otherwiseit is not.

Events and Big Data

In some industries, the volumes of events can be significant. For example e-business web logs on very heavily used web sites can hold millions of mouse clicksas they record behaviour of every user on every page on the site. That can amountto terabytes of data per day. Sensor data is another example of high volume event

On-demandrecommendations arealso needed to guidepeople

The ability to detect,analyse and act onevents is also needed

Thousands of events canoccur in businessoperations thoughout aworking day

People cannot beexpected to spot everyproblem

Event-driven activeintelligence is aboutautomatically detecting,analysing and ifnecessary acting onevents to keep thebusiness optimised

Sensor data is nowgenerating terabytes ofdata a day in someindustries


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data. Even though the use of this technology is still in its infancy, sensor networksare increasingly being used to instrument business operations so thatorganisations can see what is happening in specific parts of their business wherethey had no insight before, e.g., in a supply chain. This allows them to improvethese operational areas and respond if problems are detected in the process.Today there are sensors in mobile phones, on manufacturing production lines, in oil

pipelines, in buildings, on utility grids, in cars, on white goods, and on products totrack their movement. As instrumentation is deployed in more areas of operation,the volume of event data being emitted by sensors (e.g., RFIDs) continues to growinto hundreds of terabytes or even petabytes in some cases. Events involvingunstructured or semi-structured data are also starting to be monitored, such astweets on Twitter. Of course not all sensor data needs to necessarily be stored.Only if a pattern deviates from the norm might the data need to be persisted.Nevertheless, the volumes of data can be considerable.

These new big data sources are opening up new challenges especially aroundsemi-structured and unstructured data types where more complex analyticalconstructs have emerged to walk web logs, analyse social graphs, etc. Given thevolumes of multi-structured data, there is a need to run analytics to pick out

patterns in parallel. This problem is now being addressed by technologies such asHadoop. Hadoop can leverage thousands of servers to store big data volumeswhich can then be analysed in batch using Hadoop Map/Reduce programs. Insome products, it is also possible to store multi-structured data types and invokeMap/Reduce analytical functions as user-defined functions via SQL. This allows BItools and SQL developers to exploit the power of thousands of servers to analyseand report on big data. Massively parallel RDBMSs and Hadoop can both be partof an active intelligence system.

NEAR REAL-TIME DATA

A key difference for active intelligence systems is the ability to capture and react tooperational data in near real-time. Near real time data is needed so that

organisations can act much more quickly when problems and opportunities occur.Near real-time data can be pushed to an active BI system or pulled. Information ispushed when an application puts the required data in a message on an enterpriseservice bus as soon as a transaction occurs. The ESB then routes the data to theactive BI system as opposed to using traditional batch ETL. This is particularlyimportant for event-driven analysis. Listeners can pick up these messages andload the data into a DBMS for analysis. It is common to see event listeners incomplex event processing technology where automated analysis and automatedactions on that event data can occur. Alternatively an event message on an ESBcan trigger event-driven data integration to pull data from one or more sourcesevery time an event occurs. Pulling data in near real-time can also be achieved viamicro-batch extract which could be scheduled to happen at frequent intervals.

AUTOMATEDANALYSISWith the speed of business increasing and the number of data sources feeding BIsystems also increasing, the number of business events that need to be detectedand acted upon is also on the rise. It is therefore not practical in most cases toexpect business analysts using traditional BI tools to manually analyse all data toidentify every problem and every opportunity. In many cases today, it would bepreferable to be able to analyse data automatically,. Complementing human-ledanalysis with automated analysis makes sense in a lot of operational andmanagerial areas. It allows people to start managing by exception while delegatingsome analyses to software. The use of predictive and statistical models toautomatically analyse data is one way in which to make this possible. Power userswho build these models can deploy them to constantly and automatically analyse

data either on an event-driven basis or on a timer-driven basis. Automated analysisusing statistical and predictive models is particularly effective in businessoperations but it is not limited to just operational areas. It is also needed to

Active intelligencesystems can leveragetechnologies such asHadoop when dealing

with multi-structured bigdata sources

Near real-time data isparticularly important for

event-driven analysis

Automated analysis isneeded in eventprocessing and inrecommendationservices

Using predictive andstatistical models toanalyse data is one wayto implement automatedanalysis


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implement on-demand recommendations (discussed earlier) and can be used todetect specific conditions so that automated or manual actions can be taken.

Active intelligence systems should support both manual and automated analysis.

AUTOMATEDACTIONSAutomated actions are automatic decisions. An active intelligence system uses thiscapability to trigger alerts, to automatically invoke transactions in operationalapplications or even to invoke whole business processes. Generally speaking, thisis most effective if used to drive automated actions (e.g., invoke transactions) whenthe most common problems occur and to alert people when exceptions occur thatneed to be dealt with manually. Rules are needed to make automated decisionsand to trigger automated actions. Therefore a rules engine is an importantcomponent of an active intelligence system.

The combination of automated analysis and automated actions is needed tosupport another unique characteristic of an active intelligence system. Thatcharacteristic is on-demand recommendations, i.e., to automatically analyse data

and then make a recommendation decision based on the outcome of the analysis.The same combination is needed for event-driven analytics to automaticallyanalyse the significance of an event correlation and to automatically take action assoon as possible after the business condition is determined. For example, a surgein orders may have a major impact on a manufacturing schedule and materialsinventory requiring action to accommodate the change, (for example, morematerials may need to be ordered, other orders put on hold, shipping may need tochange, etc.) Similarly, scheduled automated analysis of customer and accountdata in a retail bank may detect that a customer has a lot of money just sitting in achecking account that could earn better interest in a savings account. This isautomatic pro-active analysis and decision making.

Automated actions canbe implemented usingrules


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WHY INTELLIGENCE MUST GO ENTERPRISE-WIDETO MAXIMISE BUSINESS VALUE

Having understood some of the key characteristics of an active intelligence systemover and above traditional BI systems, another key point to raise is that on-demandand event-driven analysis and recommendations are not just required in one part ofthe business. They are needed enterprise-wide. Figure 2 shows why this is thecase.

Figure 2

Whether you work in a retailer, a bank, an insurance company or a manufacturer, itis highly likely that your core business processes will span the enterprise or evenbeyond. Figure 2 shows an Order-to-Cash process in a manufacturer. It could haveequally been a Procure-to-pay process or a Trade-to-Settlement process in aninvestment bank. The point here is that the process starts to execute in one part of

the business and then flows across business units and systems as it executes. Toprocess an order in this case, people in different roles and different parts of theenterprise need to perform specific tasks. This is often done using differentfunctional applications. As a process executes, each user may need on-demandaccess to BI and/or recommendations to help them perform their tasks(s) moreeffectively. Integrating BI into processes therefore means that BI andrecommendation services need to be tailored to the role of the user, the task theyare performing and the device that they are using. This is a mission critical successfactor.

Meanwhile process events and other external events are being monitored usingevent-driven automated analysis to keep watch on business operations to makesure they are running smoothly. For example, an external event like a failure of a

supplier to provide needed parts for an order in Figure 2 may have ripple effects inthe chain. Alternatively, if an order change occurs, people further downstream inthe process may need to be notified and changes made. Those notified may haveto make a decision and may need access to on-demand BI and recommendations

Process execution canspan the enterprise

People in different rolesin different parts of theenterprise may performprocess tasks usingdifferent applications

As a process executes,different users in differentparts of the enterpriseparticipating in theprocess may need

access to on-demand BIto help them perform

tasks more effectively

Events occuring in onepart of the business canaffect people in other

parts of the enterprise


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to help guide them. Therefore, normal process execution may involve on-demandBI and recommendations while event-detection, automatic analysis and automatedactions such as alerts may trigger even more use of these services in differentparts of the enterprise. Active intelligence is not restricted to one part of thebusiness. Its uptake becomes enterprise-wide. This is especially important whentrying to co-ordinate different parts of the business so all contribute to common

objectives.

Finally (but now shown), active dashboards provide role-based views into the real-time activities enabling managers in different parts of the business to get earlywarnings, see trends in each functional area and get KPI-rollups for entire end-endprocesses. And of course, all of these new process-oriented active intelligenceoperations occur while the traditional use of BI and analytics also continues in itsnormal way, with business analysts and managers accessing analytical databasesusing BI platform tools such as ad hoc reporting and on-line analytical processing(OLAP).

KEY QUESTIONS WHEN IMPLEMENTINGACTIVE INTELLIGENCE

To implement active intelligence and move beyond traditional BI systems, newdetailed questions need to be asked that help to formulate requirements tointegrate BI in business operations. These questions include:

Users

During what tasks is BI needed?

What BI do they need to help them make operational decisions moreeffectively?

In what form do they need BI to help them contribute to an objective (e.g.,reports, guided analytics, instant live recommendations integrated intoanother application, alerts..)?

Do they have time to use a BI tool or not?

Do they need to use a mobile device?

What actions does a person in this role need to take?

Is the action expected to be automatic (i.e., no people involved)?

Data/Events

What events need to be monitored?

What data are needed to monitor these events?

Insights

What are the rules that dictate if action is needed?

Actions

What people and/or applications get notified if a problem/opportunity isdetected?

How do they get notified / alerted?

What are the possible actions that can be taken and what governs whichaction is the best to take?

When should they act and what happens if action has not been taken by acertain time?

All of these questions need answered to help make active intelligence successful.The more collaboration there is between business process professionals and BIprofessionals, the greater the chances of success.

Active intelligencetherefore needs to be

deployed enterprise-wideto maximize businessbenefit

Roles, processes, eventsand actions need to beunderstood tosuccessfully implementactive intelligence


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WHATS POSSIBLE WITHACTIVE INTELLIGENCE?

Given the characteristics of an active intelligence solution and how it empowers

people in business operations as well as traditional back office roles, the obviousquestions are how does active intelligence make a difference to business? Whatsthe value? These questions can be answered with a few examples.

USINGACTIVE INTELLIGENCE TO OPTIMIZEASUPPLY CHAIN

The first example is in the area of supply chain management. Keeping a supplychain optimized is a very challenging task, especially when things can changerapidly. The faster moving the supply chain, the more challenging the task to keepit running smoothly while minimizing cost.

One of the fastest supply chains involves newspapers.. Eight hours from point ofproduct manufacture to point of sale. So many things can happen in a fast moving

supply chain that can impact: Human resources

Packing allocations for outbound distribution

Goods-in processing

Distribution centre inventory management

Packaging requirements for distribution

Correct, complete and on-time deliveries

Correct invoicing

Correct delivery documentation

The need to do delivery re-runs if they are wrong

Customer satisfaction

Operational costs

Profitability

Any kind of event in a fast-moving supply chain like this has to be monitored mainlybecause there is often very little time to react while remaining within service levelagreements. To guarantee smooth-running operations requires continuousobservation of the supply chain and related events. Therefore the logisticsoperation in each distribution centre needs access to on-demand BI on near real-time data and also needs insight about events that could impact operations.

To simplify consumption of information in such a time-constrained business meansthat data needs to be integrated in near real-time into a data warehouse so that itcan be interpreted quickly and acted upon if necessary. Lets drill into the printingand distribution part of the overall supply chain. This data comes from core

operational data sources including: Publisher data feeds

Order entry system (to see demand changes and spikes)

Distribution allocation systems

Distribution centre inventory management

Goods-in

Claims management

Customer service

Returns

A combination of active intelligence, near real-time data integration and event-detection is needed to keep distribution centres aware of all changes as theyhappen.

Active intelligence isparticularly effective inhelping to optimise and

manage supply chains

Managing a fastmoving supply chainrequires access to

near real-timeinformation so thatpeople can act quicklyto resolve problems


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Several events are monitored on operational dashboards including order changes,bundle sizes, last-minute price changes, in-stock inventory, goods-in, damagedinventory counts, in-bound shortages, out-bound packing allocations, packingadjustments, driver and route information and returns inventory. The objectivehere is to always have up-to-the-minute data on anything and everything thatwould result in additional operational cost, late deliveries and customer

dissatisfaction.

For example a late-breaking news story may cause additional pages to be added toa newspaper resulting in an increase in its weight. The publisher may then make alast minute change in newspaper bundle size to stay within weight regulations. Thiswould invalidate all packing sheets and all delivery notes in all distribution centres.In fact if this one event is not detected early enough, the business could notrespond in time and it would stall distribution altogether. As it is, it could result ininventory having to be taken off partially loaded distribution trucks only to be re-loaded back on again once delivery amounts on packing sheets have been re-calculated for each customer to reflect the new bundle size. Equally a shortage onnewspaper titles in goods-in can cause out-bound delivery shortages and deliveryre-runs potentially causing customer dissatisfaction.

With so many things to keep an eye on across different internal and external datasources, event-driven active intelligence makes it possible to stay in control whileminimising un-planned operational cost. Event-detection of a bundle-size changeresults in invocation of transaction services to re-calculate and re-print packingsheets. It can also send alerts to distribution centre supervisors to delay loading,destroy existing packing sheets and pick-up new ones on the printer. The benefitfrom implementing event-driven active intelligence is to keep the supply chainoptimised, keep deliveries on-time and avoid millions of Euros in un-plannedoperational costs through delays, incorrect deliveries and missed salesopportunities due to product arriving too late to sell.

USINGACTIVE INTELLIGENCE TO IMPROVE PROCUREMENT

Another example is in procurement. An organisation that wanted to improveoperational effectiveness with respect to cost control used active intelligence alongwith other infrastructure to achieve their goal. First, business process management(BPM) software was used to introduce a common integrated procure-to-payprocess to help improve efficiency and reduce cost. In addition, they alsointroduced a BI system for spend analytics. However while these were functioningreasonably well in a stand-alone capacity, the introduction of active intelligencebrought them together so that spend intelligence services became available for usein procurement tasks as they were actually being performed. That meant allemployees with purchasing authority started to be guided by BI in every task. Ithelped them make the right decisions while staying within budget boundaries setby the executive.

With active intelligence, they went further. Adding event processing allowedautomatic monitoring of expenditure against budget and cash flow providing theability to monitor spending on a continuous, real-time basis. Using eventmonitoring, they monitor purchase requests across the business looking foropportunities to save money. The company can, for example, monitor to see ifseveral purchase requests have been detected within a set period (e.g., the last20-minutes). Correlation of multiple events in this case indicates that severalrequests are for materials from the same supplier. Automated analysis spots adiscount opportunity if these purchases are batched together and automated actioncauses alerts to a procurement manager to take action.

In addition, they integrated BI services and event-processing with their Corporate

Performance Management (CPM) software to be able to co-ordinate execution of acorporate procurement strategy (via active dashboards), and to dynamicallymanage budgets at multiple levels of the enterprise.

Being able to see a nearreal-time picture ofoperations at a glancehelps minimizeunplanned operationalcost, late deliveries and

customer dissatisfaction

Last minute changescan delay distributionif not seen early

Inventory shortagescan drive upoperational cost if notseen early

On-demandintelligence onexpenditure andsuppliers helped keepprocurement budgetson track

Event processing isalso used to identifycost savingopportunities

Integration with CPMhelps to dynamically

manage budgetsacross all levels of theenterprise


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USINGACTIVE INTELLIGENCE TO IMPROVE RISK MANAGEMENT

In this last example we will look at insurance risk management and at underwriting,in particular. We will look at the manual underwriting process and also the idea ofautomated underwriting. Lets consider a corporate property underwriter. Thesepeople regularly have to make decisions on whether to underwrite the insurance of

corporate and industrial properties in response to broker or direct quote requests.

They also have to decide on whether or not to renew policies for existingcustomers on a daily basis. All kinds of factors influence these decisions. Theyinclude fire risks, flood risks, property features, previous claims history, industrialhazards, similar property claims, and more. . Also, the BI needed to decide onwhether or not to underwrite a property in a new quote will be different from thatneeded for a renewal decision.

With active intelligence, both are accommodated by having access to on-demandBI and recommendation services from within an underwriting application.Alternatively an underwriters portal may have a portal page for new business anda portal page for a renewal. Each task therefore has its own corresponding page

with transaction portlets, BI portlets and other information portlets. On performingthe task (for example, policy renewal), the appropriate portal page is launched.This page requests BI and recommendations on-demand to guide the underwriterin making a decision.

Now consider the same insurance company trying to expand into the mid-market.The impact of this is that it will be inviting a much greater volume of inboundproperty quote requests from a larger broker network and/or prospects. To do this,it cannot afford to hire large numbers of underwriters. Therefore the underwritingdecision process needs to be automated to handle much larger volumes ofinbound quote requests coming in via online applications or via electronicmessages from more brokers.

In this case, the insurance company makes use of active intelligence automatedanalysis and rules-based automated actions to create rating (pricing) decisionservices with underwriting expertise represented in the decision rules used by eachservice. The rating decision service repeatedly makes use of automated analysisand rules to improve the pricing accuracy and automate underwriting decisionsevery time a quote request occurs.

In addition, the same rating decision services are used to guide underwriters as tothe correct premium price to minimise risk or to recommend re-insurance if aproperty risk borders on the uncomfortable side.

These are examples of manual and automated insurance underwriting decisionsbeing guided by intelligence every time. Also the ability to automate underwriting

decisions laid the foundation to go more into commercial insurance lines ofbusiness where automated quote management was needed.

SUMMARY OF WHATS POSSIBLE

These examples of Active Intelligence for Supply Chain Optimization, ProcurementImprovement and Risk Management can be augmented with examples for ActiveIntelligence applied to areas like Customer Service, Sales, and Marketing, drivingdramatic improvements in frontline decision making by both humans andautomated systems like self-service websites or interactive voice responsesystems. For example, synchronizing all front-office channels with the samecustomer intelligence and customer recommendations ensures all front officeemployees and systems treat each customer uniquely, consistently, and well.

Access to on-demand intelligence as well as event monitoring means that activeintelligence is not specific to a single business function but can be deployedenterprise-wide.

On-demand BI andrecommendations areused to guideunderwriters in

making decisions

Automated analysisand automated actionsmakes automatedquote managementpossible


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ARCHITECTURAL CHANGE:ADWJOURNEY TOTHE CENTRE OF THE ENTERPRISE

The decision to implement active intelligence has a significant impact on datawarehouse (DW) architecture. With active intelligence, we move away from theclassic split of operational systems over here and passive BI systems over thereto one where BI systems move to the centre of the enterprise and are wired toeverything operational (for example,, operational applications and processes) andmanagerial (such as scorecards and dashboards).

This is achieved by the bringing together of BI infrastructure with businessintegration infrastructure software. In particular, enterprise service bus (ESB)technology and business process management (BPM) software have a major roleto play in active intelligence. An ESB is the spinal cord in a modern serviceoriented architecture (SOA). The ESB makes it possible for BI and

recommendation services to be integrated into business processes alongsidetransactions and other services so that relevant business insightbecomesavailable in the context of each process task as it is being performed. In this way,organisations can deliver relevant intelligence to the right people (and systems) atthe right-time to guide them in continually keeping the business optimised. Figure 3shows how the use of SOA and the ESB makes smart business possible.

Figure 3

On-demand requests for BI and recommendations can be made by operationalapplications, executing business processes, portals, CPM scorecards, dashboards,office applications and search engines - all accessible from a browser or mobiledevice. Monitoring of real-time event streams is also possible. This can beintegrated into role based dashboards alongside historical data to allow managersto see what is happening over time as well as what is happening now.

This change in architecture to position BI at the centre of the enterprisecloses the

loop with operational systems, making business insights accessible to everyoneleveraging common BI services. In addition, it provides the capability to monitor liveevents, undertake traditional data warehouse analysis and reporting, deploymultiple DW appliances for specific projects and analyse large amounts of data in

BI systems need tomove to the centre ofthe enterprise and beintegrated intooperations to make

intelligence actionableacross all areas

Continuous businessoptimisation is alsopossible

Accessing businessanalytics in a SOAhelps organisations runsmarter and improveeffectiveness


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Hadoop MapReduce technologies. Choice of analytical data store will depend onresponse time, the complexity of analysis and the ability to scale easily.

In addition, being able to do real-time analytics close to the data and in-memorycaching in the BI tools platform across multiple analytical data stores are importantto an active intelligence set-up. Note however that irrespective of whether there are

one or multiple analytical data stores, data governance is controlled by a commondata transformation and management platform feeding all analytical data stores.

Common data definitions and governance should also be implemented acrossanalytical data stores to guarantee common understanding and consistency ofdimension and metrics data.

Finally, given that there can be multiple different types of analytical data store in anactive analytical environment, it should be possible to move analytical workloadsbetween these so as to match the workload to the appropriate technology. Thefocus should be on the analysis that needs to be done and not the underlying datastore. Therefore workload management needs to seamlessly manage analyticalworkloads across analytical appliances, data warehouses and ultimately Hadoop

MapReduce platforms irrespective of whether they are event-driven workloads, on-demand operational BI workloads or traditional analysis and reporting or complexanalysis on large volumes of data.

There are many more requirements that are part of an active intelligence system. Acomplete set of requirements is discussed below.

Common data definitionsare important toconsistency

Multiple analytical datastores may be deployed inan active analyticalenvironment


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REQUIREMENTS:WHAT TO LOOK FOR IN ANACTIVE INTELLIGENCE SOLUTION

Still referring to Figure 3 and working from the top of the diagram down, thefollowing requirements need to be met by an active intelligence solution.

An active intelligence solution should be architected with BI and analyticsin the centre of the enterprise

In addition to accessing data via BI tools, it should be possible for a BIplatform in an active intelligence solution to publish BI artifacts such asqueries, reports, dashboard components, predictive models, etc., as webservices that can be invoked on-demand by applications, processes andportals

It should be possible for an active intelligence solution to scale to support

large numbers of concurrent users invoking BI services on-demand fromoperational applications and mobile devices as well as traditional back-office BI tools

If operational applications requesting on-demand BI services are available24x365 then an active intelligence solution must meet the same availabilityrequirement

An active intelligence solution should engage business users at all levels inthe enterprise within the context of the business strategy. Therefore activeintelligence should include active CPM to provide multi-level strategymanagement with active dynamic scorecards and active dynamicbudgeting and planning

To become pro-active, it should be possible for an active intelligencesolution to support automaticanalysis via use of predictive and statisticalmodels as well as traditional human led analysis via BI tools

An active intelligence solution should be capable of supporting largenumbers of concurrent requests for business insights and triggering ofautomatic analysis

It should be possible to embed predictive and statistical models in oralongside the DBMS (see Figure 4) for better performance

It must be possible to create and actively update predictive and statisticalmodels on-demand or at selective intervals. These models must becapable of being invoked on-demand from front line business processes or

on an event-driven basis

It should be possible for an active intelligence solution to support rule-driven automatic actions to automate decision making. This can besupported via a rules engine accessing the outcomes of predictive /statistical models and using historical data to put the outcomes into context(as shown in Figure 4).

An active intelligence solution should be capable of offering highperformance access to specific intelligence. Support for hardwarecomponents that speed up access to data such as solid state disk [SSDs]are therefore a key requirement

BI services need to be

supported

An active intelligence

system must be capableof managing largenumbers of concurrentusers and offer highavailablility

Active multi-level strategymanagement is a keycomponent of an activeintelligence system

Hardware componentsthat speed up access todata will help manage theincrease in concurrentuser requests foroperational BI


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Figure 4

It should be possible for an active intelligence solution to support eventprocessing to monitor operational activity by:

o Using complex event processing (CEP) on streaming event datathat is in motion

or by

o Supporting event processing in the DBMS via event-driven loading

of near real-time data into an analytical database and triggering in-database analytics for automatic analysis.

Both satisfy the need to automatically analyse events as soon as possibleafter they occur

It should be possible for an active intelligence solution to combineautomated analysis and rule-driven automated actions to create decisionservices that can be invoked on-demand, on an event driven basis andalso on a scheduled timer-driven basis. These decision services wouldautomatically analyse specific data and use rules to make a decisionbased on the outcome of this analysis. An example of a decision service isa recommendation. Recommendation services are a key part of an activeintelligence solution

It should be possible to automatically invoke alerting services (such asemail or SMS), transaction services and/or whole business processworkflow services as part of an automated action

It should be possible to integrate real-time event data and alerts into a BIplatform to provide early warning alerts and more, on role-basedmanagerial dashboards alongside historical data

If possible, an active intelligence solution should understand organizationstructure so as to escalate alerts if a user has not taken an action within auser-defined timeframe

Automated analysis via in-database analytics isneeded for on-demand

recommendations andevent processing

An active intelligencesolution includes event

processing

Automated analysis andrule-driven automateddecisions are bothneeded to create decisionservices


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In addition to on-demand and event-driven decision services, it should bepossible to schedule automated analysis and action taking at user-definedintervals to automatically identify opportunities on a timer-driven basis

It should be possible for an active intelligence solution to take into accountrole-based KPI targets set in CPM products during automatic analysis. Thepurpose here is take actions that keep particular users or parts of thebusiness on track to achieve their business strategy targets and objectives.This can be achieved by including CPM data as input to automatedanalyses so that any automated actions taken and recommendations madeguide users towards achieving their targets

It should be possible for an active intelligence solution to manage a mixedworkload of

o Concurrent requests for on-demand BI services from operationalsystems

o Event-driven automatic analysis

o Traditional BI complex analytical queries on large data volumes

o Continuous near real-time and batch data loading

Workload management needs to be able to prioritise specific workloads

It should be possible in an active intelligence solution to seamlesslymanage and optimize workloads across multiple analytical data stores inthe active intelligence environment so as to exploit the best technology forspecific analytical workloads

It should be possible for relational data stores to integrate with Hadoopbased systems to exploit the power of massively parallel batch analysis onlarge volumes of multi-structured data to extract additional insight forloading into a data warehouse. Doing this broadens the level of insightavailable to people and systems in front-line operations

It should be possible to support trickle feed and fast incremental loading ofdata using the data management platform

It should be possible to integrate an active intelligence solution with otherbusiness integration infrastructure software already running in theenterprise to make BI pervasive. This includes Enterprise Service Bus(ESB), business process management (BPM) and portal technology

The foundation of any active intelligence solution offering right-timeintelligence is enterprise data governance. Without trusted data all else willfail. An active intelligence solution should therefore enforce use of commondata definitions for the same dimension, transaction data and metrics dataacross all analytical data stores used. Common definitions (metadata)

should be accessible to other technologies such as multiple BI tools andOffice applications (such as Microsoft Excel) throughout the enterprise soas to drive consistency everywhere.

To that end, an active intelligence solution should make use of a commondata management platform (suite of data management tools) to define,model, discover, profile, clean, transform and integrate data and toconsistently supply it to one or more analytical data stores in the activeintelligence analytical ecosystem

Automated analysisshould also be capable ofbeing scheduled as thisallows conditions inhistorical data to also be

automatically detected

Workload management isalso a key requirement tomanage operational BI

and traditional analyticalworkloads

Integration with ESB andBPM infrastructure opensup BI access across the

enterprise

A common datamanagement platformsupplying clean,integrated trusted data is

fundamental to success


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PRODUCT EXAMPLE:TERADATAACTIVEENTERPRISE INTELLIGENCE

Having looked at the implications on traditional DW/BI architectures and therequirements for an active intelligence solution, this section of the paper looks atone vendors technology to see how it rises to meet these requirements and deliverthe benefits of pervasive BI to all parts of the enterprise. That vendor is Teradata.

Teradata was founded in 1979 and manufactures the Teradata massively parallelrelational DBMS which runs on an optimized hardware solution assembled fromindustry standard technology from Intel and NetApp. The Teradata Purpose-BuiltPlatform Family includes several products that span customer database size,concurrency and performance needs. These are:

The Teradata Data Mart Appliance-An entry-level Teradata database

appliance for production data warehousing and data marts with up to5.8TB or 12TB disk storage

The Teradata Extreme Data Appliance A Teradata database applianceaimed at complex analytical workloads on large amounts of data. It scalesfrom 45TB up to 196PB of storage with 4096 nodes

The Teradata Extreme Performance Appliance A Teradata databaseappliance with Solid State Disk (SSD) only storage components aimed athot operational BI workloads with high volume concurrent on-demandrequests from operational applications in an Active Enterprise Intelligenceenvironment

The Teradata Data Warehouse Appliance A Teradata databaseappliance scaling from 5.8TB to 343TB disk storage aimed at companiesthat are just starting out or for those with other analytical platformrequirements in their enterprises

The Teradata Active Enterprise Data Warehouse - A Teradata databasesolution that introduces a hybrid storage environment with both SSD andtraditional hard disk drive (HDD) technologies enabled by the TeradataVirtual Storage feature. This optimizes the use of storage by automaticallyplacing often used hot data on high speed SSD storage and less usedcold data on traditional speed HDD. Teradata offers two Active EnterpriseData Warehouse models, the 6680 and the 6650. The 6680 scales from

4TB to 36PB while the 6650 is based on traditional HDD storage only andscales from 4TB to 96PB but is field upgradable to SSD storage

In addition Teradata also offers analytic applications and accelerators for specifichorizontal and vertical needs

In March 2011, Teradata acquiredAster Data, which offers the Aster Data nClusteranalytic platform. This is a massively parallel relational database solution that iscapable of embedding Hadoop MapReduce analytic application logic within theAster Data nCluster for big data analytics on multi-structured data sources. It runsSQL-MapReduce analytic application logic inside the Aster Data MPP system, foranalysis of massive data sets. To speed up development, Aster Data also providesa pre-built suite of optimized SQL-MapReduce analytic modules known as the

Aster Data Analytic Foundation and a visual development environment known asAster Data Developer Express to exploit the Analytic Foundation and generateMapReduce analytic modules. SQL-MapReduce analytic application logic can be

Teradata has over 30years of experience inbuilding BI systems

The Teradata Platformfamily includes a numberof appliances

Teradata also acquiredAster Data to helpintegrate big data into anactive intelligenceenvironment
http://www.teradata.com/data-mart/http://www.teradata.com/extreme-data-appliance/http://www.teradata.com/extreme-performance-appliance/http://www.teradata.com/data-appliance/http://en.wikipedia.org/w/index.php?title=Aster_Data&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Aster_Data&action=edit&redlink=1http://www.teradata.com/data-appliance/http://www.teradata.com/extreme-performance-appliance/http://www.teradata.com/extreme-data-appliance/http://www.teradata.com/data-mart/


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written in a variety of programming languages including Java, C, C++, C#, Python,and R.

Figure 5 show how the Aster Data nCluster integrates with Teradata.

Figure 5

Insights discovered in semi-structured data on Aster Data can be fed into theTeradata Active EDW for integration with traditional data to increase theeffectiveness of decision making. For example, in a Telco, customers churning

because of bad network experiences may influence others to churn. To minimisechurn, Aster Data can be used to identify clusters of callers where one individualleads the way on churning behaviour and influences others. With this insightloaded into the Teradata EDW, marketing campaigns can be launched to quicklyturn around potential defectors and their followers.

END-TO-END TECHNOLOGIES IN A TERADATA ACTIVE ENTERPRISEINTELLIGENCEENVIRONMENT

With reference to Figure 3 and the requirements defined for an end-to-end solution,an Active Intelligence environment built around Teradata Database would include:

A data management platform from a Teradata partner and/or near real-

time data capture technology such as Teradata Active Load

Teradata DBMS technology that could include:

o The Teradata Extreme Performance Appliance

o The Teradata Active Enterprise Data Warehouse

o Aster Data nCluster

SAS predictive and statistical analytical models embedded in the TeradataDBMS

Data virtualization software from a Teradata partner to see across multipleDW appliances in a Teradata Active Enterprise Intelligence analyticalecosystem if more than one is used

A service-oriented BI platform from a Teradata partner like MicroStrategy,IBM Cognos, SAP Business Objects or Microsoft accessing the Teradataand Aster Data RDBMSs

Aster Data uses SQLMapReduce to find cluesin big data sources thatcan be fed into a TeradataActive Enterprise DataWarehouse to drive

actions

Teradata has multipleofferings that are allcapable of being part ofan active intelligenceecosystem


22/26



CPM software from a Teradata partner accessing Teradata Database thatsupports multi-level strategy management

Business Process Management, Enterprise Service Bus and EnterprisePortal infrastructure to integrate BI into the enterprise

For straight operational BI, the Teradata Extreme Performance Appliance has

been designed to handle hot workloads with SSD technology. Therefore certainoperational BI workloads could be placed on this appliance to handle largenumbers of concurrent users requesting specific BI on-demand.

For a mixed workload of traditional analysis and reporting, on-demand operationalrequests for BI services and event-driven analytics, the Teradata Active EDW isthe most appropriate platform. Organizations could start with the 6650 andupgrade to the 6680 with hybrid storage as more integration with operationalprocesses occurs. Mature organizations may already have the powerful 6680 andbegin building out active intelligence from that starting point.

TERADATA DATABASE INANACTIVE INTELLIGENCE ENVIRONMENT

With respect to the Teradata DBMS, Teradata positions its active intelligenceoffering as having the following capabilities

Active Access

Active Load

Active Events

Active Workload Management

Active Enterprise Integration

Active Availability

Active AccessThis is the ability to handle concurrent queries coming from on-demand BI andrecommendation services being invoked by operational applications, processes

and portals. These BI services sit in a service-oriented BI platform on top of theTeradata DBMS. To cater for an increase in concurrent users invoking operationalBI services on-demand, join indexes and parameterized queries can be created onthe Teradata DBMS. Join indexes help retrieve frequently used data withoutneeding to join tables in real time. Instead, pre-computed answers can be storedand accessed quickly. Parameterized queries allow the Teradata optimizer tocache SQL it has seen before and reuse the execution plan the next time it seesthe same SQL. This means that popular BI services (such as those invoked bycontact centre representatives) may be turned around quickly.

Active Load

Given that Teradata does not provide a CEP engine for event-processing, it needs

to support another way of handling events. That way is based on the ability to getdata into the Teradata DBMS as close to real-time as possible. Teradata ActiveLoad is the mechanism for doing this. Using Active Load, near real-time data canbe loaded into the Teradata DBMS from a messaging backbone (for example, fromJMS message queuing software), via mini-batch and also via change data capture.Teradata Parallel Transporter caters for streaming messages and mini-batch whileTeradata Replication Services (via Oracle GoldenGate) handles change datacapture. In the case of messaging, changes to operational systems can be postedto message queues on middleware such as IBM WebSphereMQ. Teradata ParallelTransporter then reads the message queue(s) and directly updates the TeradataDBMS. Note that queries can still access Teradata table structures while they arebeing updated by Teradata Parallel Transporter.

Parameterised queriesand join indexes are

particularly well suited tovery specific on-demand

BI requests

Event-driven trickle feed,

micro-batch and changedata capture help get datainto the Teradata DBMS

quickly


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Acti ve Events

In addition to near real-time data coming into the Teradata DBMS via Active Load,event-processing needs to trigger automated analysis to analyse that data. As datais loaded into the Teradata DBMS, database triggers can fire and invoke analyticson data in the DW. Support for automated analysis is taken care of by the Teradatapartnership with SAS Institute. Statistical and predictive models developed in SAS

can be deployed alongside the Teradata DBMS and executed in parallel to analysedetailed data as shown in Figure 4. In-database analytics pushes automatedanalysis as close to the data as possible which is an important performance featurein an event-driven operational environment. Data or events can then be insertedinto a queue table which fires a trigger to drive appropriate actions like sendingalerts to users and invoking transaction services to keep the business optimised.

Events can also be analysed outside of the Teradata environment by ComplexEvent Processing engines in real-time or by Aster Data off-line. The reason fordoing the latter is to analyse event data to determine if event patterns constantlyreoccur over time. The use of Aster Data nCluster is particularly compelling in thisregard especially in environments with very large numbers of events. Sensorevent data is a good example. This big data source can be loaded into Hadoopand analysed using SQL- MapReduce logic built into the Aster Data nCluster MPPDBMS. Event patterns that are of interest could then be passed into the TeradataActive EDW to be combined with other data to determine what action should betaken to prevent operational disruption or unplanned operational cost fromcontinually reoccurring.

Acti ve Workload Management

To cater for the much larger numbers of concurrent user requests for on-demandBI, on-demand recommendation, near real-time event-driven analysis, as well astraditional BI usage and data loading, it is important to be able to balanceworkloads so that the system continues to satisfy the needs of all users. Thatmeans being able to fence off resources for some workloads and dynamically

change priorities at peak times while continuously monitoring workloads. To caterfor this requirement, Teradata offers a set of Active System Management tools.They include the Teradata Workload Analyzer, which analyses Teradata Databaseuser logs and system tables to profile actual usage behaviour and analyseworkloads over time. Teradata Workload Analyzer recommends workload groupsand parameters which can then be established in Teradata Dynamic WorkloadManager as workgroup categories and control settings. Also workloads can beprioritized by time or by user group. Dynamic Workload Manager thencontinuously monitors resources at run time. So for example, on-demandoperational BI services could be separated from traditional complex analyses andloading to give them a high priority.

Acti ve Enterprise Integrat ionA key part of integrating with business operations is the ability to plug into aservice-oriented architecture (SOA). We have already seen that BI platformsaccessing Teradata and Aster Data can publish BI services for invocation byoperational applications and processes. The Teradata DBMS itself can also exploitESB and BPM software. The Teradata Parallel Transporter can capture processevent messages streaming over an ESB (running on top on a JMS messagingmiddleware) to trigger event-driven automated analysis using SAS modelsdeployed in the database. In addition, triggers in the database that fire to carry outactions can also send requests over an ESB to invoke transaction services andwhole processes as part of an automated action. In addition, Eclipse IDEdevelopers can also create custom BI services using the Teradata Eclipse plug-in.

Teradatas partnershipwith SAS makes in-database automatedanalysis of eventspossible

Large volumes of eventdata can also be analysedin Aster Data using AsterData SQL- MapReduce

Teradatas dynamicworkload managementallows operational BI andtraditional analysis andreporting to co-exist in an

active intelligence system

Integration with ESB andmessaging middlewaresupports event detectionand allows automatedactions to invoke alertingand transaction services

in a standard way


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Acti ve Availabi li ty

A key requirement when integrating BI into operational environments is to becapable of inheriting service level agreements of operational systems that need on-demand access to BI. That may mean having to operate on a 7x24 basis.Therefore it is important that the BI environment is capable of meeting highavailability and reliability requirements. Teradata answers this requirement by

automatically managing component failover in the Teradata software, and byproviding hardware redundancy such as dual uninterruptible power supplies, dualI/O controllers, RAID controllers, and dual interconnections to the TeradataBYNET.

High availability meansTeradata can easilyaccommodate servicelevels imposed byoperational systems need

to access BI on-demand


25/26



CONCLUSION

To conclude, organisations are now looking to work smarter in all corners of their

business-- from executives to front-line workers-- to improve strategic, tactical andnow operational decisions. To make that happen the BI systems are moving to thecentre so insights can be integrated into every business process. The intent is todeliver role-based contextual intelligence in the context of every operational task.The new operational intelligence workloads can run on the same BI infrastructurethat supports traditional BI processing. We are also starting to automate themonitoring of internal and external events to keep a finger on the pulse of thebusiness as processes execute.

To compete, organisations have to become active to make sure that people arealways aware of events going on around them and able to make informeddecisions. To get there it is important to

Understand your processes

Understand the roles of people who participate in those processes such ascustomer facing contact centre operators, bank tellers, store managers,salespersons, etc.

Understand the activities (tasks) they perform Understand the applications they use to perform the activities in a process Determine the relevant BI and/or actions needed in each process activity

such as alerts and on-demand recommendations Identify the correct strategy for integrating BI to fit with the user needs,

e.g., portlets that display information needed by contact centre agents Create required BI web services and integrate them into business

processes to guide operational activity Create an inventory of events and identify which ones are worth monitoring Connect data integration tools or DBMS utilities to your ESB to capture live

events as they happen in operational systems Deploy predictive models in your analytical databases and/or CEP

technology to automatically analyse data in near realtime Integrate BI, BPM and event-processing into role-based dashboards and

scorecards Integrate active dashboards and alerts with collaborative workspaces and

mobile devices

There is no question that Teradata has already recognised the importance of activeintelligence and has added the functionality to the Teradata DBMS and hardwareplatform family to allow it to easily cope with large numbers of concurrent requestsfor operational BI. Hardware advances like SSDs in the Teradata ExtremePerformance Appliance and the Teradata Active Enterprise Data Warehouse with

Teradata Virtual Storage, as well as workload management and high availability allmake it fit for purpose in this much more agile and responsive environment. TheSAS partnership also makes automated analysis possible in servicing requests foron-demand recommendations or for analysing the business impact of events.Finally, Aster Data has added another string to their bow to analyse event datafrom big data sources such as sensor networks, web logs and social networks. Allthis, plus integration with other infrastructure, makes Teradata a strong competitorto sit front and centre in an always on intelligent enterprise.

Integration into business

operations is now astrategic requirement forBI systems

Organisations have toundertake some

business analysis tounderstand how theyoperate to get maximumvalue from an activeintelligenceimplementation

Key infrastructuresoftware also needs tobe integrated

Teradata has alreadyadded the functionalityneeded to support activeintelligence andcompete in this market


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About Intelligent Business Strategies

Intelligent Business Strategies is a research and consulting company whose goal isto help companies understand and exploit new developments in businessintelligence, analytical processing and enterprise business integration. Together,these technologies help an organization become an intelligent business.

Author

Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. Asan analyst and consultant he specializes in business intelligence and enterprisebusiness integration. With over 30 years of IT experience, Mike has consulted fordozens of companies on business intelligence, enterprise architecture, businessintegration and data management. He has spoken at events all over the world andwritten numerous articles. Mike is a resident expert on the Business IntelligenceNetwork, providing articles, blogs and his insights on the industry. Formerly hewas a principal and co-founder of Codd and Date Europe Limited the inventors ofthe Relational Model, a Chief Architect at Teradata on the Teradata DBMS and

European Managing Director of Database Associates. He teaches popular masterclasses in Business Intelligence, Enterprise Data Governance, Master DataManagement, and Enterprise Business Integration.

INTELLIGENT

BUSINESSSTRATEGIES

Water Lane, WilmslowCheshire, SK9 5BG

EnglandTelephone: (+44)1625 520700

Internet URL: www.intelligentbusiness.bizE-mail: [email protected]

Active Intelligence for the Smart Business

Copyright 2011 by Intelligent Business Strategies

All rights reserved

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