Presentation on theme: "Organizational Intelligence For IT Services Shanna Smith & Jordan McCoy Research Consulting ITS User Services The University of Texas at Austin."— Presentation transcript:
Organizational Intelligence For IT Services Shanna Smith & Jordan McCoy Research Consulting ITS User Services The University of Texas at Austin
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Overview Introduce the basics of organizational intelligence Illustrate typical data problems using examples from our organization Demonstrate how to address these problems and move toward a true organizational intelligence system
What is Organizational Intelligence? An intelligent organization: Collects data on its products and services Creates storage systems to allow immediate and transparent access to the data Creates reporting systems to analyze and interpret the data, and Uses those reports to enhance decision- making.
Decision Making Without Data IT organizations face complex decisions which require timely, appropriate data not often available: –The data may not have been collected, or even recognized… –The data may have been collected in part, and information lost… –The data may not be in a conducive format…
ITS User Services User Services is composed of four groups which together present the customer service side of ITS: –Help Desk, Lab, and Training Operations –On-Site Support (desktop management) –Technical Support (server management) –Research Consulting (statistics/mathematics)
As an organization grows… Decisions become more complex and interconnected, and the need for good data is increasingly urgent. Yet… The amount and complexity of the data one could collect increases significantly. Methods of collection and storage are apt to be inconsistent between different areas of the organization. The difficulty and variety of data analysis and publication increases. Data may need to be analyzed to yield higher-level information than initially thought
Example One: SYSMODS E-mail notifications of pending, completed, or emergency changes to a server Mostly descriptive in nature, but do contain data which can be analyzed. Traditionally used for immediate notification purposes; not designed to serve as a data source for later analysis.
Example Two: Assets User Services keeps track of a wide range of technical assets; to date, these assets have been managed using a collectively edited Excel spreadsheet. This approach has worked fairly well as long as policy supported it, but like the sysmods, is not conducive to data analysis.
Requirements of a Solution 1.Centralized Data Management –collect, store, and manage data centrally 2.Clear Policy Framework –security, privacy, integrity and retention 3.Data Exploration and Reporting –real-time, ad-hoc reporting
1. Centralized Data Management Is important to analysis and reporting as data becomes more complex and integrated Ensures we are identifying and collecting all relevant data while providing for security, integrity and privacy Two principal issues: Data Integrity and Source Variability
1. Centralized Data Management Data Integrity Issues Missing records –lost records, field-level omissions, time periods Inconsistent/Changing Data Format –format changes over time, differences in format interpretation Errors –factual or appropriateness errors, measuring/process issues, damaged records
1. Centralized Data Management Data Integrity: Assets The use of a spreadsheet without strong policy behind it invited: –omissions of both fields and rows –divergent use of the document to express data –different levels of data inclusion Use and analysis likewise suffered: –couldn’t ensure the inventory was up to date –hard to conduct analysis to support initiatives like life- cycle planning
1. Centralized Data Management Data Integrity: Solutions Option 1: attempt to clean the data and factor out inconsistencies –Can work well and is often the only option –Often can’t factor out inherent inconsistencies Option 2: change the source of data –Integrity problems often indicate a data format issue –Our eventual approach with Remedy
1. Centralized Data Management Source Variability Issues Access: different, often divergent Availability: both technical and political User: different provisions and interpretations Appropriateness: difference in data responses
1. Centralized Data Management Source Variability: SYSMODS Sysmods are descriptive in nature, so inconsistencies are to be expected: –differences in server identification –overall formatting problems –differences in change description Analysis therefore suffered: –can’t account for server role in analysis –margin of error on server association was high
1. Centralized Data Management Source Variability: Solutions Option 1: establish strong policy to reduce variability –Effective with smaller data sets –Can get unwieldy without support in time Option 2: establish better infrastructure for data collection –Input validation and documentation help enforce policy –We are moving to tracking changes in Remedy
2. Clear Policy Framework Ensures all data are identified, protected, and leveraged Only true means of ensuring consistent data management, especially relative to statutory requirements like HIPAA and FERPA Helps to avoid loss of data, inconsistent use and ad-hoc solutions
2. Clear Policy Framework Security and Privacy A clear policy needs to address: –Physical security of the data –Access rights, approved use, and logging –Privacy requirements, both statutory and institutional Often this policy applies to multiple data sources, but can vary widely: student records vs. desktop logs
2. Clear Policy Framework Security & Privacy: SYSMODS Sysmods do not require high security but do carry some sensitive information: IP addresses, potential vulnerabilities, etc. The lack of a clear security policy, while not creating any disasters, engendered careful omissions from emails and ad-hoc storage solutions
2. Clear Policy Framework Backup and Retention A clear policy needs to address: –Means, interval and protection of backups –Data retention length and purging procedure –Statutory requirements like Open Records Not all data needs to be backed up or retained, though we’ve found we aren’t retaining data as well as we can
2. Clear Policy Framework Backup and Retention: Assets The spreadsheet approach to asset management captures the current state of the assets reasonably well, but keeps no historical information In this case, we are losing data due to both the implementation of the data and the lack of a codified retention policy
3. Data Exploration and Reporting Allows for real-time, on-request use of the data being collected Offers new ways of looking at the data, and identifying other data sources we’ve not considered Enables the data collection and management process to contribute back to the organization
3. Data Exploration and Reporting Data Exploration Organizational intelligence data is often extensive, and we won’t know exactly what we want out of it up front Tools which allow exploration and ‘playing with’ the data not only increase understanding of the data, but help identify new uses for it
3. Data Exploration and Reporting Data Exploration: SYSMODS In its current form, the sysmod data is hard to explore: –Manual parsing and classification of records –Required application of statistical procedures Furthermore, we were having to estimate values in parts of the analysis because we don’t collect all of the data
3. Data Exploration and Reporting Data Reporting We want to be able to pull ad-hoc reports from the data, integrating different data sources and targeting multiple formats Reporting is often the end-product of organizational intelligence, allowing the data to benefit the organization that collected it
3. Data Exploration and Reporting Data Reporting: Assets Assets management in a spreadsheet functions well as a reference, but makes true reporting difficult –Report generation is necessarily manual –Reporting is limited to information present Moving to an asset management database enables complex, integrated reporting
Overview of the Issues 1.Centralized Data Management Solutions Appropriate to the Problem Consolidation and Remedy 2.Clear Policy Framework Balancing Data Responsibility Examples: SYSMODs and Assets 3.Data Exploration and Reporting Advantage of Infrastructure Why Flexibility is Important
Procedure Recommendations Identify and codify the need to collect data Perform a comprehensive data inventory Decide what data is important Establish clear policies for security, privacy, backup and retention Develop infrastructure for the collection, processing and reporting of data Ensure the data contributes to the organization
Contact Information Please feel free to contact us with questions! Shanna Smith, email@example.com Jordan Mc Coy, firstname.lastname@example.org