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 3

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 IT Finance Dataset (Forecast, Budget, Actual) Contract Capital Project Service Business GL (Consolidated) Application Data Tracking View ( Technical ) Service Service Resource GL Config View Usage AP Payroll FA D & A Elevate IT. Ignite Possibility. 6

Types of Data Type of Data Examples Complexity Collection Method Financial GL Low Transactional • AP • Fixed Assets • Procurement • Catalogs or Listings Service catalog Low Manual • Application directory • Listing of users • Usage: Configuration Server Listing (CPU count, Memory Medium Discovery or manual • Allocation, Physical/Virtual) Storage Allocations (Size, % Used) • Usage: Consumption Server CPU Usage (Avg % Used or GHz High Interval measurements, high • Used) volume, accumulation, aggregation Mainframe CPU Usage (MIPS or Hours) • Cloud Usage (AWS, Google, Azure, IBM) • Time Tracking (Hours by Project, Phase, • Resource) Elevate IT. Ignite Possibility. 7

Service Consumption Data 8

Examples of Typical Consumption Data Basics Consumption Data • What’s the right unit of measure? • Server Counts or CPUs • Data Availability • Storage GB • Data Quality • App Development & Maintenance Hours • Assigning Service and Consumer • Device Counts Elevate IT. Ignite Possibility.

Server Services Common Sources Common Units of Measure • CMDB (e.g. ServiceNow) • CPU • Spreadsheets • Physical Server Count • Native to ITFM Tool • Tiered Physical Server Count • Operating System Count Common Pitfalls & Complexities Common Data Mappings & Translations • Incomplete data • Application Listing • Large data sets • X used to Tier physical server counts • Lack of Business Processes • Physical and Virtual indicators • IT delivering services with their own infra • Applications by Server • Precision – (e.g. Split CPUs across Applications evenly) • Application to Consumer Elevate IT. Ignite Possibility. 10

Storage Services Common Units of Measure Common Sources • GB • CMDB (e.g. ServiceNow) • Spreadsheets • Native to ITFM Tool Common Data Mappings & Translations Common Pitfalls & Complexities • Application Listing • Data quality • X used to identify types of storage • Incomplete data • Physical and Virtual indicators • Large data sets • Applications by Server • Allocated vs. utilized • Application to Consumer • Precision – (e.g. Split CPUs across Apps evenly) Elevate IT. Ignite Possibility. 11

Labor Services Common Units of Measure Common Sources • Hours • PPM tools • FTE percentage • Spreadsheets Common Pitfalls & Complexities Common Data Mappings & Translations • Timing of PPM tool timesheets • Time tracking work IDs to apps & projects • Shift to Agile methods and tools • Resources to ADM roles • Capitalization of internal labor Elevate IT. Ignite Possibility. 12

Device Counts Common Units of Measure Common Sources • Desktops • ITAM tools • Laptops • HR tools • Mobile devices • Spreadsheets • Native to ITFM tool Common Pitfalls & Complexities Common Data Mappings & Translations • Timing • Time tracking work IDs to apps & projects • Unallocated equipment • Resources to ADM roles • Equipment in shared spaces Elevate IT. Ignite Possibility. 13

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. • Elevate IT. Ignite Possibility. 14

Formulate the Ask 15

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 • Elevate IT. Ignite Possibility.

Formulate the Ask Ownership – Quality, completeness, delivery • Content - Be precise & detailed • Delivery – Format, timing, and refresh rate • Automation • Elevate IT. Ignite Possibility. 17

Common Challenges 18

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 • Elevate IT. Ignite Possibility.

5 Keys to Success 20

5 Keys to Success Understand your requirements • Iterate - proxy or perfection? • Limit scope • Clear communication • Delay automation • Elevate IT. Ignite Possibility.

Thank You 22