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Why Data Quality is the Lifeblood of Identity Management

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Why Data Quality is the Lifeblood of Identity Management

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It has been said that IT systems are a lot like the Old Testament, lots of rules and no mercy!

Identity Management solutions are no different because they process data according to defined rules that grant and revoke user access to the network and key applications. If there is bad data in the IAM system, users cannot get the access they need to be productive.

Let’s walk through a brief example: We hire a new user and enter their name and other personal information into our HR system, but the assigned manager is incorrect. The HR system sends the employee data over to the IAM solution to create the new employee user. However, because the manager field is wrong, all the user’s requests – access requests, expense reports, time sheets – are routed to the wrong person. This delays access, creates additional work for the IT operations team, and reduces productivity across the organization.

The key takeaway is that accurate data enables your Identity & Access Management system to function properly, making IT operations efficient and users productive. Bad data quality simply produces bad results faster. Poor data quality offers no mercy!

Data quality problems can be mitigated with the right effort. Is your organization willing to invest the time and resources necessary to get the data right up front?

Visit our website at www.idenhaus.com.

It has been said that IT systems are a lot like the Old Testament, lots of rules and no mercy!

Identity Management solutions are no different because they process data according to defined rules that grant and revoke user access to the network and key applications. If there is bad data in the IAM system, users cannot get the access they need to be productive.

Let’s walk through a brief example: We hire a new user and enter their name and other personal information into our HR system, but the assigned manager is incorrect. The HR system sends the employee data over to the IAM solution to create the new employee user. However, because the manager field is wrong, all the user’s requests – access requests, expense reports, time sheets – are routed to the wrong person. This delays access, creates additional work for the IT operations team, and reduces productivity across the organization.

The key takeaway is that accurate data enables your Identity & Access Management system to function properly, making IT operations efficient and users productive. Bad data quality simply produces bad results faster. Poor data quality offers no mercy!

Data quality problems can be mitigated with the right effort. Is your organization willing to invest the time and resources necessary to get the data right up front?

Visit our website at www.idenhaus.com.

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Why Data Quality is the Lifeblood of Identity Management

  1. 1. @idenhaus idenhaus.com Iden%ty & Access Management Data Quality
  2. 2. Components 1. Consolidate user data from all Systems of Record to create a single master record for each individual 2. Manage how data is inserted, modified, and deleted from the ID Vault based on business processes 3. And provisioning rules NOTE: Need a unique ID to maintain data relaKonships across all systems Iden%ty Management begins with accurate user data @Idenhaus idenhaus.com 2 Iden%ty Management Provisioning Process User Data
  3. 3. •  Who are you? …What uniquely idenKfies you? •  What is your relaKonship to the organizaKon? •  What is your role? •  Who do you manage? •  What assets do you have? •  How do we link: –  Bob Jones in system A, with –  Robert Jones in system B, with –  R Jones in system C? Our goal is to establish an accurate user iden%ty within organiza%on’s systems @Idenhaus idenhaus.com 3
  4. 4. •  Basis for establishing who you are, and •  What you have access to Data Quality is fundamental… @Idenhaus idenhaus.com 4 Finance HRIS Assets Apps Security eMail
  5. 5. Let’s start by looking at the factors affec%ng data quality… @Idenhaus idenhaus.com 5
  6. 6. Business stakeholders will assure you that: “Our databases are clean, so there won’t be any data cleansing issues.” “We’ve already modeled our processes, so you won’t have to do that.” “It works fine in the lab, so it should work fine in produc%on.” “The plaZorm owner said we just have to select a few configura%on op%ons and we’re done.” Responding to common misconcep%ons about data @Idenhaus idenhaus.com 6
  7. 7. Seldom true •  Data cleansing is o]en the most Kme-consuming task in an idenKty management implementaKon Easily checked •  Assess data quality during the Requirements Assessment phase Hard to fix •  Inconsistent data reveals inconsistent processes •  Client must fix the processes or they will conKnue to create incorrect data “Our databases are clean” @Idenhaus idenhaus.com 7
  8. 8. Check the data •  Inconsistent data reveals inconsistent processes Audit compliance with policies •  Do they have documented IAM policies? •  How are they enforced? •  How are they audited? There are always excep%ons •  The architecture won’t handle the excepKons you don’t know about •  That’s why careful process analysis is essenKal “We have already modeled our processes” @Idenhaus idenhaus.com 8
  9. 9. But what if it doesn’t? •  You have to test in produc/on to be sure the producKon soluKon works •  One project accidentally destroyed 20,000 idenKKes when they went into producKon How do you know the solu%on will scale? •  The technology may have plenty of room to grow •  But how fast can the client organizaKon adopt new processes If something breaks, will we be able to fix it? •  Is the design well-documented? •  Can our repair tools handle the size and complexity of the implementaKon? “It should work fine in produc%on” @Idenhaus idenhaus.com 9
  10. 10. Has anyone ever implemented a successful IAM solu%on out of the box? •  Never! IAM products provide a framework to develop and deploy IAM soluKons •  Products alone are of ligle value without business analysis, soluKon architecture, and deployment methodologies •  Remember manual business processes are being automated and these processes are unique to you “A few configura%on op%ons and we’re done” @Idenhaus idenhaus.com 10
  11. 11. Design Requirements 11 @Idenhaus idenhaus.com Definition of Business Process Identification and Extraction of Business Rules Develop Data Models Examination of Databases, Procedures, and Interfaces Development of Use Cases with data flows Our methodology begins with the business model and drives down to the data…
  12. 12. Data correc%on is difficult at best… @Idenhaus idenhaus.com 12
  13. 13. •  Manual data entry errors •  Lack of data quality checks early in the process •  AdministraKon of user data in local applicaKons and systems •  GeneraKon of duplicate idenKKes across mulKple systems •  Poor coordinaKon between HR and IT funcKons •  GeneraKon of ‘orphans’ When data goes bad… …It’s usually because: @Idenhaus idenhaus.com 13
  14. 14. Let’s look at one aYribute as an example… @Idenhaus idenhaus.com 14 Missing SSNs Invalid SSNs Employee SSN Easy Easy Challenging Valid looking SSNs Shared SSNs Duplicate SSNs
  15. 15. The success of your IAM projects depends on facing data quality issues early @Idenhaus idenhaus.com 15
  16. 16. 1.  Iden%fy systems with a possible problem 2.  Determine the extent of the problem 3.  Inves%gate based on type of error 4.  Validate the inves%ga%on with the core team and key stakeholders 5.  Implement changes to processes 6.  Implement data checks and valida%ons where possible 7.  Focus on con%nuous improvement Data remedia%on is a process… @Idenhaus idenhaus.com 16
  17. 17. •  Execu%ve Sponsorship – Have leadership pave the way for any difficult organizaKonal process changes to reduce barriers to change •  Put a stake in the ground and define an enterprise vision for a quality-centric culture •  Define a systemic approach to improving data collecKon processes and validaKon techniques •  Make a long-term commitment to the process; data quality and management principles will evolve over Kme •  Validate Data when it is Collected – During data collecKon, if data is not protected with validaKon funcKons, then bad data will be captured, created, and propagated to all connected systems in your organizaKon Key Goals of a Data Quality Program @Idenhaus idenhaus.com 17
  18. 18. info@idenhaus.com 404.919.6167 @idenhaus idenhaus.com Thank you

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