Visio-QlikView BI Patternsqlikview.oxs.ru/install/Goods/QVDocumentation/Enterprise Framework... · QlikView BI Patterns Introduction Each page in this document describes a BI Pattern

QlikView BI Patterns

Introduction

Each page in this document describes a BI Pattern that can be met with QlikView to solve business and technical needs. Each page is broken into a diagram, a description of the pattern and a description of the use of this pattern.

Most, but not all, of these patterns have publicly available QlikView demo applications that can serve as a template or conceptual example. New patterns will be added to this document over time.

I

Pattern Description:

This pattern brings great benefit to organizations that have many disparate data sources with semantically repetitive data elements.With the help of QlikView we can analyze where these data sources overlap and where our consolidation opportunities are. More importantly, we can do this without creating any new databases or running a large project. We simply hook up QlikView across these three data sources and start analyzing.

BI Pattern – Data Consolidation

Uses:

Identify Overlaps – show me where I have duplicate data

Reduce the overhead of legacy data sources and satellite databases

Analyze and scope data consolidation issues that upcoming projects might face, before they face them

Identify and fix data issues with disparate data sources

Find correlations, outliers, trends and pattern

Understand data sources before attempting data migrations and extractions across data sources

Employee

Sample Data Consolidation Model

Before QlikView

After QlikView

One QlikView

Skills

Roles

Contractor

Project

Skills

ResourceProject Role

Resource Role

Assignments

Planning DB MS Access

Project DB SQL Server

Resource

Skills

Role History

HR DBOracle

Roles Table maintained in 3 placesSkills Table maintained in 3 placesPlanning DB Requires SupportReports are confusing due to overlapping dataData Reconciliation costs time and $

= Managed Table

Project

Skills

Assignments

Resource

Resource Role

Project Role

Project DB SQL Server Resource

SkillsRole History

Data Feed

Data Feed

Roles Table maintained in 1 place (HR)Skills Table maintained in 1 place (HR)Planning DB EliminatedReports StreamlinedIT Recovers Costs from no longer needed reconciliation work

HR DBOracle

= Managed Table = Data Feed

Pattern Description:

This pattern gathers data from disparate data sources and analyzes the intersections, overlaps and inconsistencies among them. The goal is to find out where data sources are aligned and not aligned, and also to find the correlations between data sets that may not have previously been known or well understood. For example, are the returns associated with certain sales items directly related to marketing campaigns that may have misrepresented the items online?

BI Pattern – Data Discovery

Uses:

Data exploration – show me how my data looks

Data usage patterns – show me how my users use my applications

Data integration points – where do my data sources overlap?

Prototyping of data set development

Visual mining in search of data correlation

Data Mining – root cause analysis for data issues

OtherOracle

SQL Server

MS Access MS Excel

Cross-DB Data Correlations

Data Discovery Model

on demand data load

Inputs

QlikView

Outputs

Cross-DB Data

Inconsistencies

Cross-DB Data

Validation

Possible new data mart, EDW or

Reporting DB Fields

Validation of online data sources

Data quality and process

improvements

Pattern Description:

This pattern gathers data from disparate data sources and through ETL processes outputs integrated and analysis-ready prototype repositories for future Data Marts. These ETL processes allow for data cleansing, data mapping, data integration, data de-duplication, data de-normalization or normalization as needed, etc. For example, data from transactional systems can be merged with Xcel based forecasts to produce a Sales Analysis Data Mart.

BI Pattern – Data Mart Prototyping

Uses:

Data exploration – show me how my data looks

Data storage patterns – show me how data is stored. Is it normalized or de-normalized enough?

Data integrity verification – where do my data sources overlap? Do data elements from different sources match?

Prototyping of data sets

Prototyping of future dashboards and reports

OtherOracle

SQL Server

MS Access MS Excel

Data Mart Prototyping Model

on demand data load

Inputs

QlikView

Outputs

Possible new data mart, EDW or

Reporting DB Fields

WorkbookETL App

Prototype Application

Prototype Reports

Data QVDs

Pattern Description:

This pattern gathers data from disparate data sources and presents very specific KPI data aggregated at different levels in a concise and brief manner, with drilling capability for further research and output reporting.

This pattern is generally used for departmental and section / district managers to monitor performance within their business area, giving them both a high level view as well as the ability to drill down into the granular details of the business, e.g. The Sales Department Dashboard

BI Pattern – Departmental Dashboards

Uses:Departmental overview of KPI performance to targets. Immediately identify problem areas impacting Departmental Goals and TargetsQuickly identify trends in KPI's Know which key departmental areas (i.e. channels, sales reps, customers, products) are performing, and which ones are not. Review cost performance (actuals) to budget by analyzing data from unrelated or disparate data sources. Monitor specific targets (i.e. MTD Sales, are they meeting target?) Drill down to individual levels of granularity if desired (i.e. orders to identify exceptions). A single version of the truth to align corporate goals with departmental strategies

External and / or Data

Warehouse / Data Mart

Data (Optional)

Departmental Business

Application Data (i.e. SAP, JDE,

Oracle, Peoplesoft,

Navision, Sales Force, Siebel,

etc.)

Individual Contributor Data (MS Excel)

Time Data (Fiscal or Calendar)

Master Data (i.e.

Customer, Product, etc.)

On-Going KPI Monitoring at a Departmental

Level

Departmental Dashboards Model

on demand data load

Inputs

QlikView

Outputs

Improved Business

Decisions

DepartmentalData Validation

and Security

Improved / More Accurate Reporting

and Analysis

Validation of data sources across the department and restricted visibility

as needed

Aggregated-to-Granular visibility allows for easy research and

problem solving

Pattern Description:

This pattern gathers data from disparate data sources and presents very specific KPI aggregated data in a concise and brief manner, yet allows for drilling capability or delegation for further research. It can be designed to contain guided analysis to minimize technical skill requirements for the busy executive

This pattern is generally used for senior managers to monitor performance within their business, giving them both a high level view as well as the ability to drill down into the granular details of the business, e.g. an organization that manufactures fast moving consumables, using a reseller model to distribute its products across multiple regions.

BI Pattern – Executive Dashboards

Uses:High level overview of KPI performance to targets. Immediately identify problem areas impacting High Level Business Drivers and KPI's. Quickly identify trends in KPI's. Know which key corporate areas (i.e. channels, sales reps, customers, products) are performing, and which ones are not. Review cost performance (actuals) to budget by analyzing data from unrelated or disparate data sources. Monitor specific targets (i.e. stock turns, are they meeting target?) Drill down to individual levels of granularity if desired (i.e. orders to identify exceptions). A single version of the truth to align corporate goals with departmental strategies

External and / or Data

Warehouse / Data Mart

Data (Optional)

Cross-Functional Business

Application Data (i.e. SAP, JDE,

Oracle, Peoplesoft,

Navision, Sales Force, Siebel,

etc.)

Budget / Forecast Data (MS Excel)

Time Data (Fiscal or Calendar)

Master Data (i.e.

Customer, Product, etc.)

On-Going KPI / Score Monitoring

Executive Dashboards Model

on demand data load

Inputs

QlikView

Outputs

Improved Business

Decisions

Cross-Functional

Data Validation

Improved / More Accurate Reporting

and Analysis

Validation of data sources across the

enterprise

Aggregated-to-Granular visibility allows for easy delegation and problem solving

Pattern Description:

This pattern integrates highly granular business or other data that is fed along the path of a business process’ execution. For example: Procure2Pay, Order2Cash. Inventory Cycles, Revenue Cycles. Processes can have any time horizon, so they can be operational processes, or planning processes with long or short term horizons. i.e. Daily Inventory Turns, or Weekly Invoicing, or Monthly Procure2Pay.

This pattern is generally used for intra-departmental detailed analysis and reporting of specific and very granular or mid-range aggregated KPIs. e.g. Procure2Pay cycle with Suppliers

BI Pattern – Process Dashboards

Uses:Intra-departmental overview of specific KPI performance to targets.Corporate-wide measure of a broad business process, such as Order2Cash Immediately identify problem areas impacting Process Performance Targets (i.e. On Time Delivery)Quickly identify trends in low-level KPI’s for immediate action Know which key process areas (i.e. Order entry, Order fulfillment, Invoicing steps of Order2Cash), are performing, and which ones are not. Drill down to individual levels of granularity if desired (i.e. orders to identify delivery exceptions). Process failure detectionQuality Control / Process Flow Control Monitoring

External and / or Data

Warehouse / Data Mart

Data (Optional)

Process Execution Data (i.e. transactions,

machine operations, etc) (i.e. ERP, MRP, PRM, CRM, etc.)

Individual Contributor Data (MS Excel)

Time Data (Fiscal or Calendar)

Master Data (i.e.

Customer, Product, etc.)

On-Going KPI Monitoring at a

ProcessLevel

Process Dashboards Model

on demand data load

Inputs

QlikView

Outputs

Improved Process

Performance

ProcessData Validation

and Security

Improved / More Accurate Reporting

and Analysis

Validation of data sources across the

process and restricted visibility

as needed

Granular visibility allows for easy research and

problem solving

Pattern Description:

This pattern shows how you can use QlikView to help remediate software licenses from hosted applications. This can be a difficult thing for I.T. to manage, but potentially a large expense savings if done regularly.

Consider using this pattern and creating applications based on it for your hosted software (SAP, Peoplesoft, Sales Force, CA-Clarity, HP Service Desk, etc.). ?

BI Pattern – License Remediation

Uses:

Quantify license remediation $ opportunity on a regular basis

Use what-if scenarios to adjust the license costs and the anticipated recovery rates for licenses that are going unused (this is important since some licenses are seldom used)

Clearly identify recoverable vs. non-recoverable licenses

Assess License Usage by Resource and monitor compliance with business policies

Uncover root causes for lack of software use and business policy compliance

Licensed Application License

Cost / Value by User / User Type /

Role

Active Directory (Or other User/Role/Type

Repository)

AllocatedLicense-to-User Data

Licensed Application Usage Log

data

MS Excel Timesheets or other time/

attendance tracking record

Licenses Usage by Resource

License Remediation Model

on demand data load

Inputs

QlikView

Outputs

Recoverable and non-recoverable

Licenses

License Remediation

Opportunity

Possible Data Mart to analyze user performance and other compliance

Reuse/Redeploy unused Licenses

and save $

Quantified Ongoing Recovery

Opportunity in $

Pattern Description:

This pattern gathers data from disparate data sources and presents very specific KPI aggregated as well as low-granularity detailed data for reporting needs at any level, usually deployed at a departmental or functional group level.

This pattern is generally used for management as well as individual contributors to monitor information within their business e.g. Sales Reports for the sales department of an organization that manufactures fast moving consumables, using a reseller model to distribute its products across multiple regions.

BI Pattern – Reporting

Uses:High level to low level view of KPI performance Monitor specific targets (i.e. stock turns, are they meeting target?) Drill down to individual levels of granularity (i.e. orders to identify exceptions such as late or short orders). A single version of the truth to align corporate goals with departmental strategiesUnderstand, communicate, execute and follow up based on reported data from various sources integrated in a single repository or modelEliminate bad data and process inefficienciesEliminate ‘bottlenecks’ by extending self-service to users

Data Warehouse / Data Mart

Data (Optional)

Cross-Functional Business

Application Data (i.e. SAP, JDE,

Oracle, Peoplesoft,

Navision, Sales Force, Siebel,

etc.)

Causal Factor External Data (i.e. weather, stocks) (MS Excel)

Time Data (Fiscal or Calendar)

Master Data (i.e.

Customer, Product, etc.)

Accurate Detailed Reports

Reporting Model

on demand data load

Inputs

QlikView

Outputs

Improved Business

Decisions

Cross-Functional

Data Validation

A Single Data Source for

enterprise-wide reporting

Validation of data sources across the

enterprise

Aggregated-to-Granular visibility allows for easy

research, analysis and problem solving