Data Collection & Challenges with Cost Transparency Executive - - PowerPoint PPT Presentation

Data Collection & Challenges with Cost Transparency

Executive Summary

Key Issues

• Level of effort– “There’s a lot of work to do.”
• Lack of ownership – “If no one else will, I’ll have to own it.”
• Quality data– “We have incomplete and inaccurate data. Credibility and adoption are at risk.”

After this session, you’ll be able to:

• Identify common types of data
• Formulate your ask in 4 easy steps
• Leverage the data you have and gain value with 5 keys to success

Elevate IT. Ignite Possibility.

Acknowledging Data Challenges

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Practitioner Challenges with Data

• Common terminology speak, fear of “data”
• Data quality issues
• Weak partnership with data source owners
• You’re forced to be a data jockey

Elevate IT. Ignite Possibility.

IT Finance Data Requirements: Categories

• Financial Systems
• Infrastructure (Configuration and Usage)
• Metrics Data
• Project Management (PMO, PPM, Time Tracking)
• Security & Identification

Elevate IT. Ignite Possibility. 5

IT Finance Data Requirements: Examples

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IT Finance Dataset

(Forecast, Budget, Actual) GL (Consolidated) GL AP Payroll FA D & A Contract Data Capital Tracking Project View

Resource View

Service (Technical) Application Service Usage Config Business Service

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Types of Data

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Type of Data Examples Complexity Collection Method Financial

• GL
• AP
• Fixed Assets
• Procurement

Low Transactional Catalogs or Listings

• Service catalog
• Application directory
• Listing of users

Low Manual Usage: Configuration

• Server Listing (CPU count, Memory

Allocation, Physical/Virtual)

• Storage Allocations (Size, % Used)

Medium Discovery or manual Usage: Consumption

• Server CPU Usage (Avg % Used or GHz

Used)

• Mainframe CPU Usage (MIPS or Hours)
• Cloud Usage (AWS, Google, Azure, IBM)
• Time Tracking (Hours by Project, Phase,

Resource) High Interval measurements, high volume, accumulation, aggregation

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Service Consumption Data

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Consumption Data Basics

• What’s the right unit of measure?
• Data Availability
• Data Quality
• Assigning Service and Consumer
• Server Counts or CPUs
• Storage GB
• App Development & Maintenance Hours
• Device Counts

Examples of Typical Consumption Data

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Server Services

Common Units of Measure

• CPU
• Physical Server Count
• Tiered Physical Server Count
• Operating System Count

Common Data Mappings & Translations

• Application Listing
• X used to Tier physical server counts
• Physical and Virtual indicators
• Applications by Server
• Application to Consumer

Common Sources

• CMDB (e.g. ServiceNow)
• Spreadsheets
• Native to ITFM Tool

Common Pitfalls & Complexities

• Incomplete data
• Large data sets
• Lack of Business Processes
• IT delivering services with their own infra
• Precision – (e.g. Split CPUs across Applications evenly)

10 Elevate IT. Ignite Possibility.

Storage Services

Common Units of Measure

• GB

Common Data Mappings & Translations

• Application Listing
• X used to identify types of storage
• Physical and Virtual indicators
• Applications by Server
• Application to Consumer

Common Sources

• CMDB (e.g. ServiceNow)
• Spreadsheets
• Native to ITFM Tool

Common Pitfalls & Complexities

• Data quality
• Incomplete data
• Large data sets
• Allocated vs. utilized
• Precision – (e.g. Split CPUs across Apps evenly)

11 Elevate IT. Ignite Possibility.

Labor Services

Common Units of Measure

• Hours
• FTE percentage

Common Data Mappings & Translations

• Time tracking work IDs to apps & projects
• Resources to ADM roles

Common Sources

• PPM tools
• Spreadsheets

Common Pitfalls & Complexities

• Timing of PPM tool timesheets
• Shift to Agile methods and tools
• Capitalization of internal labor

12 Elevate IT. Ignite Possibility.

Device Counts

Common Units of Measure

• Desktops
• Laptops
• Mobile devices

Common Data Mappings & Translations

• Time tracking work IDs to apps & projects
• Resources to ADM roles

Common Sources

• ITAM tools
• HR tools
• Spreadsheets
• Native to ITFM tool

Common Pitfalls & Complexities

• Timing
• Unallocated equipment
• Equipment in shared spaces

13 Elevate IT. Ignite Possibility.

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How to Get the Data: Interface Methods

• Files – Most applications can export data into files. Common

formats include delimited (CSV), fixed-width, Excel, and Access.

• Direct Connections – Use database to database connections

to extract your data. This is often from source systems or a data warehouse.

• Web Portals – Some applications provide a web portal to

report and extract data.

• API – Generally requires some level of development.

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Formulate the Ask

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Know the Data Source Owners’ Obstacles

• Data quality concerns
• Concerns about level of effort
• Existing solution and toolset limitations
• In-flight projects to improve data and process
• Competing priorities

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Formulate the Ask

• Ownership – Quality, completeness, delivery
• Content - Be precise & detailed
• Delivery – Format, timing, and refresh rate
• Automation

17 Elevate IT. Ignite Possibility.

Common Challenges

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Recognize the Impact of Service Offering Changes

• Constant change in technology & service offerings
• Every service must be costed and measured
• Engage in development of service offerings
• Examples:
• Server Charges: Per CPU or Usage (GHz Used)
• Volume Discount Methods

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5 Keys to Success

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5 Keys to Success

• Understand your requirements
• Iterate - proxy or perfection?
• Limit scope
• Clear communication
• Delay automation

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Thank You

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