This white paper discusses capturing business requirements for scorecards, dashboards, and reports. It defines the scope of information needed, including the report purpose, measures, dimensions, hierarchies, time periods, and other functional requirements. It also covers non-functional requirements like volume and capacity, performance, availability, and security. Further analysis is then needed to check data availability, prioritize requirements, define validation rules, and design supporting processes.
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
07. Analytics & Reporting Requirements TemplateAlan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of analytics & reporting outputs (including standard reports, ad hoc queries, Business Intelligence, analytical models etc).
Gathering And Documenting Your Bi Business RequirementsWynyard Group
Business requirements are critical to any project. Recent studies show that 70% of organisations fail to gather business requirements well. What is worse is that poor requirements can lead a project to over spend its original budget by 95%.
Business Intelligence and Performance Management projects are no different. This session will provide a series of tips, techniques and ideas on how you can discover, analyse, understand and document your business requirements for your BI and PM projects. This session will also touch on specific issues, hurdles and obstacle that occur for a typical BI or PM project
• The importance of business requirements and a well defined business requirements process
• Understanding the difference between a “wish-list” or vision and business requirements
• The need and benefits of having a business traceability matrix
Start your BI projects on the right foot – understand your requirements
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
07. Analytics & Reporting Requirements TemplateAlan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of analytics & reporting outputs (including standard reports, ad hoc queries, Business Intelligence, analytical models etc).
Gathering And Documenting Your Bi Business RequirementsWynyard Group
Business requirements are critical to any project. Recent studies show that 70% of organisations fail to gather business requirements well. What is worse is that poor requirements can lead a project to over spend its original budget by 95%.
Business Intelligence and Performance Management projects are no different. This session will provide a series of tips, techniques and ideas on how you can discover, analyse, understand and document your business requirements for your BI and PM projects. This session will also touch on specific issues, hurdles and obstacle that occur for a typical BI or PM project
• The importance of business requirements and a well defined business requirements process
• Understanding the difference between a “wish-list” or vision and business requirements
• The need and benefits of having a business traceability matrix
Start your BI projects on the right foot – understand your requirements
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
Enterprise Architecture Governance: A Framework for Successful BusinessNathaniel Palmer
Enterprise Architectures play an important role supporting business transformation initiatives. Enterprise Architecture Governance (EAG) provides a structure for defining relevant strategies and compliance processes. This Level 3 Communications case study presents a detailed framework composed of three essential components of EAG:
1) Organizational Accountability must be clearly defi ned for all EAG aspects, and executive sponsorship is essential. Level 3 formed an executive steering committee with broad representation, preventing EAG from becoming an IT-only initiative.
2) Strategy Defi nition provides the roadmap for business transformation initiatives. Architectural guiding principles defi ne values and offer input into strategies, end states define where the company is going, and roadmaps document how to get there from.
3) Compliance Processes ensure that development initiatives are in alignment with the strategic direction. Level 3 has created a framework that gives each development initiative an architecture rating that indicates its compliance level.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
What is the Value of Mature Enterprise Architecture TOGAFxavblai
Judith Jones received the Open Group award for Outstanding Contributions to the development of TOGAF 9 at 19th Open Group Enterprise Architecture Practitioners Conference Chicago - July 21-23, 2008. Former CEO of Architecting the Enterprise which has been a member of The Open Group for 6 years, she is personnally involved since 1997. As an active member of The Open Group and she is a major contributor and an editor of TOGAF 7, 8 and 9 as well as leading TOGAF projects for localisation, case studies, ADML, synergy and collaboration projects.
http://www.opengroup.org/member/member-spotlight-jones.htm
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Watch full webinar here: https://bit.ly/2N1Ndz9
How is a logical data fabric different from a physical data fabric? What are the advantages of one type of fabric over the other? Attend this session to firm up your understanding of a logical data fabric.
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
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Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
Business analyst 101 program Mumbai IndiaDeepak Kadam
Business analyst Training and certification program Mumbai India
At Ziphertech we have designed a Training program
for students and graduates who aspire to become
business Analysts. A Business Analyst requires niche
skills to become successful in IT industry. Our program
has been designed by veteran IT industry experts who
have combined over 100 years of experience in IT
business analysis. This program will be conducted by
professional Business Analysts from IT industry with a
minimum experience level of 15 years.This program
ensures thorough training and grooming of skills for the candidate to become a
professional Business Analyst. And we never forget to mention that we have trained more
than 400 Business Analysts in just last 2 years.
Contact us - +919004939659 for more Info
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
Enterprise Architecture Governance: A Framework for Successful BusinessNathaniel Palmer
Enterprise Architectures play an important role supporting business transformation initiatives. Enterprise Architecture Governance (EAG) provides a structure for defining relevant strategies and compliance processes. This Level 3 Communications case study presents a detailed framework composed of three essential components of EAG:
1) Organizational Accountability must be clearly defi ned for all EAG aspects, and executive sponsorship is essential. Level 3 formed an executive steering committee with broad representation, preventing EAG from becoming an IT-only initiative.
2) Strategy Defi nition provides the roadmap for business transformation initiatives. Architectural guiding principles defi ne values and offer input into strategies, end states define where the company is going, and roadmaps document how to get there from.
3) Compliance Processes ensure that development initiatives are in alignment with the strategic direction. Level 3 has created a framework that gives each development initiative an architecture rating that indicates its compliance level.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
What is the Value of Mature Enterprise Architecture TOGAFxavblai
Judith Jones received the Open Group award for Outstanding Contributions to the development of TOGAF 9 at 19th Open Group Enterprise Architecture Practitioners Conference Chicago - July 21-23, 2008. Former CEO of Architecting the Enterprise which has been a member of The Open Group for 6 years, she is personnally involved since 1997. As an active member of The Open Group and she is a major contributor and an editor of TOGAF 7, 8 and 9 as well as leading TOGAF projects for localisation, case studies, ADML, synergy and collaboration projects.
http://www.opengroup.org/member/member-spotlight-jones.htm
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Watch full webinar here: https://bit.ly/2N1Ndz9
How is a logical data fabric different from a physical data fabric? What are the advantages of one type of fabric over the other? Attend this session to firm up your understanding of a logical data fabric.
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
Business analyst 101 program Mumbai IndiaDeepak Kadam
Business analyst Training and certification program Mumbai India
At Ziphertech we have designed a Training program
for students and graduates who aspire to become
business Analysts. A Business Analyst requires niche
skills to become successful in IT industry. Our program
has been designed by veteran IT industry experts who
have combined over 100 years of experience in IT
business analysis. This program will be conducted by
professional Business Analysts from IT industry with a
minimum experience level of 15 years.This program
ensures thorough training and grooming of skills for the candidate to become a
professional Business Analyst. And we never forget to mention that we have trained more
than 400 Business Analysts in just last 2 years.
Contact us - +919004939659 for more Info
How competitive is your workforce compared with that of other companies? This is a critical question that, up until now, could only be answered by third-party, survey-based benchmarks that are often out-of-date, based on small sample sizes, and lacking the right detail for you to take action. Benchmarking from ADP offers the unmatched ability to compare your workforce data to market averages, based on real, up-to-date anonymized and aggregated HR and pay data from ADP’s U.S. client base of 24 million employees.
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.
David Stack: Reporting for Duty: The Case for a Strong Reporting Structure David Stack
From David Stack: "A critical aspect of a CFO’s role at a startup is facilitating the creation and implementation of a strong, comprehensive reporting structure. This reporting algorithm should evaluate the current health of the business by identifying key sensitivities in the business model, tracking progress in scaling the company, providing a useful dashboard for each separate department, and serving as a tool both the CEO and management team can use to grow the business. Additionally, a great reporting package will allow you to analyze your current data to identify upcoming trends."
Business Intelligence and Its Use for Human Resource Managementf.docxhumphrieskalyn
Business Intelligence and Its Use for Human Resource Management
falseKapoor, Bhushan. The Journal of Human Resource and Adult Learning6.2 (Dec 2010): 21-30.
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Business intelligence plays a crucial role to achieve competitive edge over competitors in the challenging economy we are in. Businesses using a business intelligence methodology are able to develop intelligence based information systems to gain useful business insight and make faster and more reliable business decisions. While many organizations are starting to use business intelligence in many areas of their businesses and make substantial gains, they have not taken advantage of this in Human Resource Management area. In this paper we examine leading BI vendors to look into the business intelligence and data analytics features incorporated in their Human Resource Management modules. [PUBLICATION ABSTRACT]
Business intelligence plays a crucial role to achieve competitive edge over competitors in the challenging economy we are in. Businesses using a business intelligence methodology are able to develop intelligence based information systems to gain useful business insight and make faster and more reliable business decisions. While many organizations are starting to use business intelligence in many areas of their businesses and make substantial gains, they have not taken advantage of this in Human Resource Management area. In this paper we examine leading BI vendors to look into the business intelligence and data analytics features incorporated in their Human Resource Management modules. [PUBLICATION ABSTRACT]
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Financial analysis refers to business assessment in terms of stability, viability, profitability, and other important financial and non-financial factors. It is done through several different techniques, ratios, and charts, with the purpose of transforming static numbers from or in financial statements, to an added value for decision-makers. Usually, the analyzed information and the analysis results are presented frequently as a report or as a dashboard.
A dashboard (or data visualization) is used to present all indicators at once to help owners, investors, or managers make efficient decisions by identifying specific actions that should be taken to reach future targets or goals.
Similar to Capturing Business Requirements For Scorecards, Dashboards And Reports (20)
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
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Capturing Business Requirements For Scorecards, Dashboards And Reports
1. JULIAN RAINS White Paper
Capturing business requirements for
scorecards, dashboards and reports
September 2010
2. JULIAN RAINS White Paper
September 2010
Synopsis.
This paper helps Management Information and Business Intelligence related projects
build a solid foundation for their reporting business requirements gathering. It defines the
scope of the information needed to design and build dashboards, scorecards and other
types of report. The paper considers the requirements of multiple stakeholders and
describes the business analysis activities following the initial requirements draft.
Although geared towards project-level requirements, the same types of information
should be captured at the programme-level (albeit at a broader and less-detailed level)
when assessing the entire reporting landscape to agree scope and priorities with
sponsors. All content and opinions are solely drawn from the author’s personal
experience and no liability will be accepted.
Requirements are important.
High quality requirements are fundamental to the success of IT-related initiatives. They
are the foundation upon which projects build or adjust systems, processes and teams.
They are one of the first tasks and are widely used in every stage of a project’s lifecycle.
They must be done quickly and blend the right levels of breadth, depth and accuracy.
Capturing what the report is supposed to do.
With a structured approach, establishing the functional (what the solution must “do”)
requirements for reporting need not be difficult. Some examples are given in this section
using a fictitious company called Alpha Group Ltd.
Report Purpose. Will the report deliver operational, tactical or strategic information?
Operational reports deliver frequent data to alert controllers to operational exceptions.
Tactical reports help to manage particular processes or departments. Strategic
information shows the enterprise-level progress against its strategic goals. Further, it’s
tempting to assess only the reports needed by a Manager or Executive but don’t forget
the data needs of Testers or Analysts tasked with understanding or verify the information
presented in the management’s report.
Measures (aka Metrics and Key Performance Indicators). What information is
presented in the report and how is each measure calculated (from its source data, see
Data Definitions below)? For example “Actual Average Number of Staff” might be
calculated, for any given year, by counting the actual staff numbers at the end of each
month and dividing by the number of months counted.
Dimensions. How will each measure be qualified? For example users might want to
view the actual, planned and forecasted “Actual Average Number of Staff” by month for
the current and prior years, by company, by department and by staff type. The
“members” or “attributes” of each dimension must also be identified e.g. staff type may
have 3 members … Permanent, Temporary and Contractor.
Page 2 of 6 Capturing business requirements for scorecards, dashboards and reports
3. JULIAN RAINS White Paper
September 2010
Hierarchies. For each dimension, what is the granular (lowest) level of data required for
the report and how should it be aggregated? For example:
• Department – each staff member is assigned to one department only i.e. Sales,
Marketing, Production, Finance, HR or Distribution. Aggregated, these make the total
staff numbers for the Group.
• Company – each staff member is assigned to only one company subsidiary
Aggregated, these make the total staff numbers for the Group.
• Staff Type – each staff member is assigned to one staff category i.e. Permanent,
Temporary or Contractor. Aggregated, these make the total staff numbers for the
Group.
Time. What relative time periods are needed for comparison? For example, should the
report show the month’s performance for both the prior-year and current-year?
Consider using the simple matrix like the one shown in Figure 1 to record the
relationship between measures, dimensions and granularity.
Figure 1
Dimension Time
Staff Current Prior
Name Unit Department Company
Type Year Year
Weekly Monthly
Actual Average
Count
No of Staff
Measure
Planned Avg No
Count
of Staff
Forecast Avg No
Count
of Staff
Frequency (aka Latency). How often are the reports needed? For example information
might be needed each month and to be available by the 2nd day of the following month.
Changing Dimensions. The “members” or “attributes” of dimensions are not always
fixed. For example a “Month” dimension will always contain the same 12 members
(January, February etc). However, the Staff Type dimension’s members might reduce
from “Permanent, Temporary or Contract” to “Permanent or Non-Permanent”. For those
that will change, it’s important to know how users want to report the measures up to the
point that a dimension’s member changed. Users might want to view all historic “Actual
Average Number of Staff” information using the new members i.e. as either “Permanent
or Non-Permanent”; alternatively they may want to preserve the historic data which
shows whether a staff member was “Permanent, Temporary or Contract”; finally they
might ask for a hybrid solution giving the option to report the historic data using either
the old or new attributes.
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September 2010
Data Sources. What is the location of the source (fact, reference) data needed to
produce the reports? In a perfect world all data will be available from the organisation’s
data warehouse or from an appropriate data mart, although it is not unusual to have to
bring data together from multiple locations, including manual sources and those outside
the organisation.
Data Definitions. What is the definition of the source (fact, reference) data needed to
produce the reports? Clear definitions ensure all parties understand what’s required,
reduces the likelihood of misunderstanding and contributes significantly to the meta data
dictionary.
Drill Through. Do users need to view information at different levels? For example they
may wish to view the “Actual Average Number of Staff” up and down the Department,
Company and Staff Type hierarchies.
Navigation. What links between reports are necessary? There maybe a logical
progression from one report to another, for which a useful link would improve usability.
Presentation. How does the user wants to view the information e.g. as a cross-tab
and/or graphically with graphs, pie charts and traffic lights? What should each unique
page of the report look like i.e. its dimensions and measures, titles and headings, units
of measure, decimal places, graph ranges, traffic lights, colours, fonts etc? What are the
rules for any dynamic graphics such as traffic lights? Does the report need to include a
particular brand or style to reinforce its corporate identify or data origin?
Commentary. Do users need to add free text commentary to the reports? Describe
where and how much commentary will be added, by which users and when during the
reporting cycle.
Report Authoring. Do users need to custom-build their own reports, using the
measures and dimensions available in the system?
Output. How and where do users need to access their reports? For example online via
an intranet / internet browser and/or in a document such as PDF or MS PowerPoint. Is
there a corporate information portal into which reports should be embedded?
User Groups. How does access to the information need to be controlled? For example:
• There could be a user group for each of Beta, Gamma and Delta companies to which
users are assigned to one or more. Only users with access to all subsidiaries are
able to also view the consolidated Alpha Group Ltd report.
• There could be user groups relating to the way in which the report is used e.g.
Report Viewer and Report Commentator where users are be assigned to only one or
both, depending on their role.
Access. Will the reports be used internally i.e. only by the organisation’s staff or
externally e.g. by suppliers, customers, shareholders or the general public?
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Reconciliation. Are controls needed to ensure that the data included in the report has
been checked against an independent data source of assured high-quality?
Validation. Is functionality required to assure the quality of data using business rules?
For example checking that postcodes always follow a defined format or that a number is
always in a particular range. What are these business rules?
Capturing how the solution is supposed to do it.
This section aims to establish the non-functional (what the solution must “be”)
requirements by asking questions related to the known constraints e.g. number of users
and to the expected quality-levels e.g. response time.
Volume and Capacity. What is the likely volume of data to be stored? State how many
users will access the reports and when. Estimate the maximum number of concurrent
users.
Growth. Will the data and/or user volumes grow? State all growth assumptions.
Performance. How quickly does the solution’s functionality need to respond to its users’
requests?
Availability and Accessibility. When and where does the report need to be available
and what are the users’ critical days and/or hours for reporting and dealing with any
issues?
Backup / Business Continuity. How quickly, in the event of a failure, does the system
need to be restored?
Compliance. What is required to adhere to internal policies or externally-imposed
regulations e.g. Data Protection, Sarbanes Oxley or Solvency II?
Audit. Is an audit trail of adjustments made to (fact, reference or meta) data required?
Should the system capture information on who’s using it?
Security. What are the requirements related to granting, updating and removing access
to the reports? Note that many organisations will have their own Information Security
policies.
Usability. Consider how easy to use the solution must be considering the users’
expertise and familiarity with reporting technology, their roles and the time they have
available.
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What’s next.
After the Requirements Definition draft, more analysis work is usually needed to add
sufficient detail for those responsible for detailed design, build, test and implementation.
Data Availability. Checks that the data needed actually exist at the required granularity,
completeness and frequency should be made as early as possible. Systematic
identification of each item of source data, expressed in terms of databases, tables and
fields, will achieve this goal.
Functional Availability. Requirements must prioritised, for example, using a recognised
classification such as MoSCoW. It’s important to check that the current or proposed
solution can actually deliver at least the users’ most highly-valued requirements.
Data Definitions. Further detail to define how the solution must calculate each Measure.
This information will contribute significantly to the meta data dictionary.
Validation Rules. Confirm the business rules needed to assure the quality of any
particular items of data. Note that these rules are often defined as part of initial data
cleansing initiatives but can also be built into the “business as usual” data processing in
order to sustain high quality data.
Process and Organisational Design. Confirm the supporting processes needed for the
reporting to operate normally. Processes impacted by new reporting initiatives can be
wide-ranging but examples include:
• Manage report access i.e. granting, updating and removing user access permissions
• Manage report production and distribution i.e. handling data validation and/or control
exceptions, producing the report and distributing to its users.
• Manage queries i.e. how users can raise support issues the response handling.
Prioritise.
Don’t expect to get all the requirements stable in one go – they’ll further evolve during
the detailed design, development and even testing phases of the project. But getting
early agreement on both the scope of the requirements to capture and their relative
priority will provide a solid foundation.
About JULIAN RAINS.
Julian Rains is a freelance consultant and contractor working in the UK. He is an
experienced Project Manager and Business Analyst and has run Management
Information and Business Intelligence projects at major international companies in the
Financial Services and Energy industries.
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