Data Management

12月 072017
 

The primary obstacle to becoming a data-driven business is that data is not readily available, leaving valuable insights unused in data silos. To overcome this hurdle, today’s companies are creating a new role: Chief Data Officers (CDO). Responsible for unlocking insights hidden in data silos, the CDO is tasked with [...]

Are you ready for the Chief Data Officer challenge? was published on SAS Voices by Helmut Plinke

12月 052017
 

With SAS Data Management, you can setup SAS Data Remediation to manage and correct data issues. SAS Data Remediation allows user- or role-based access to data exceptions.

Last time I talked about how to register and use a Data Remediation service. This time we will look at how to use SAS Workflow together with SAS Data Remediation. SAS Workflow comes as part of SAS Data Remediation and you can use it to control the flow of a remediation issue to ensure that the right people are involved at the appropriate various steps.

SAS Workflow is very powerful and offers a lot of features to process data. You can, for example, design workflows to:

  • Process tasks (workflow steps) in parallel.
  • Follow different routes through a workflow depending on certain conditions.
  • Set a timer to automatically route to a different user if an assigned user isn’t available for a certain time.
  • Call Web Services (REST/SOAP) to perform some work automatically.
  • Send email notifications if a certain workflow step is reached.
    …and more.

Invoke an external URL via a workflow

In this article, we are going to look at a workflow feature which is specific to SAS Data Remediation.

With the latest version of Data Remediation there is a new feature that allows you to put a URL behind a workflow button and invoke it by clicking on the button in the Remediation UI. In the following we are going to take a closer look at this new feature.

When you have registered a Data Remediation service you can design a workflow, using SAS Workflow Studio, and link it to a Remediation Service. Workflow Studio comes with some sample workflows. It’s a good idea to take one of these sample workflows as a starting point and adding additional steps to it as desired.

Workflow Task

Data Remediation issues using SAS WorkflowIn a workflow, you have tasks. A task is a single step of a workflow and is associated with data objects, statuses, policies and participants.

  • Data Objects are fields to store values.
  • A Status transitions from one task to the next.
  • Policies are rules that will be executes at a certain task event. For example, calling a Web Service at the beginning of a task or send an email at the end of a task.
  • Participants are users or groups who can execute a task; i.e. if you are not a user assigned to a task, you can’t open an issue in Data Remediation to work on it.

When you add a new task to a workflow you must connect it to another task using a status. In the Data Remediation UI, a status will show up as a Workflow Button to transition from one task to another.

Assigning a URL to Status

You can also use a status on a task to call a URL instead of transitioning to the next task. Therefore, you add a status to a task but don’t use it to connect to another task.

At task Open you have four statuses assigned but only Associate, Cancel and Reject connect to other tasks. Status Review is not connected and it can be used to call a URL.

Right mouse click on status Review/Edit to open a dialogue box with a button Attributes… Here, you need to add the Key Attribute with name: URL. The value of URL points to the fully qualified name of the URL to be called:

http://MyServer/showAddtionalInformation/?recordid={Field1}

 

 

 

 

 

The URL can take parameters, in curly brackets (i.e. {Field1}), pointing to the task’s data objects. When calling the URL, the parameters will be substituted with the appropriate data object value. This way the URL call can be dynamic.

Dynamic URL calls via parameters

When you create a Data Remediation issue you can pass up to three values from the issue record to Remediation. For example, the issue record key. When the workflow is called by Data Remediation these values are copied from Data Remediation to the workflow’s top-level fields: Field1, Field2, Field3.

To use these values in the workflow, to pass them to a URL, you need to create an appropriate data object (Field1) at the task where you want to call a URL. You also need to add a policy to the task to copy the data object value from the top-level data object to the task data object.

This will make the value available at the task and you can use it as a parameter in the URL call as shown above.

Link workflow to Data Remediation Service

When you have finished the workflow, you can save it and upload it to the workflow server.

Once it is uploaded you can link the workflow to the Remediation Service. At Data Remediation Administration, open the remediation service and go to Issue Types. Workflows are linked to Issue types, they are not compulsory, but you can link one or more workflows to an issue type, depending on your business requirements.

At Issue Types, under Templates, select your workflow and link it to an issue type. You can make workflows mandatory for certain issue types by ticking the check box: “Always require a task template…” In this case Data Remediation expects one of the nominated workflows to be used when creating a remediation issue for the appropriate issue type.

Creating a remediation issue

You now can create a remediation issue for the appropriate issue type and assign a workflow to it by submitting the workflow name via the field "workflowName" in JSON structure when creating the remediation issue. See “Improving data quality through SAS Data Remediation” for more about creating Data Remediation issues.

Call a URL via workflow button

When the Remediation issue is created you can open it in SAS Data Remediation where you will see the Review Button. When you click on the Review button the URL will be called that you have assigned to the URL Key attribute value for status Review.

By using workflows with SAS Data Remediation, you can better control the process addressing data issues. Being able to put a URL behind a workflow button and invoke it will enhance your capabilities around solving the issues and improving data quality.

Process and control Data Remediation issues using SAS Workflow was published on SAS Users.

11月 302017
 

There are so many reasons why SAS programmers love SAS -- as a matter of fact, I wrote a blog on it back in 2012. I now realize that I could've written a whole series, not just a single post. And with the recent publishing of my first book, Big Data [...]

Why SAS programmers love SAS, revisited was published on SAS Voices by David Pope

11月 202017
 

Many SAS users have inquired about SAS Cloud Analytic Services’ (CAS) Distributed Network File System (Learn more about CAS.)

The “NFS” in “DNFS”

Let’s start at the beginning. The “NFS” in DNFS stands for “Network File System” and refers to the ability to share files across a network. As the picture below illustrates, a network file system lets numerous remote hosts access another host’s files.

Understanding DNFS

NFS

There are numerous network file system protocols that can be used for file sharing – e.g. CIFS, Ceph, Lustre – but the most common on Linux is NFS. While NFS is the dominant file-sharing protocol, the “NFS” part of the DNFS does not correspond to the NFS protocol. Currently all the DNFS supported file systems are based on NFS, but DNFS may support file systems based on other protocols in the future. So, it’s best to think of the “NFS” part of “DNFS” as a generic “network file system” (clustered file system) and not the specific NFS protocol.

The “D” in “DNFS”

The “D” in DNFS stands for “Distributed” but it does not refer to the network file system. By definition, that is already distributed since the file system is external to the machines accessing it. The “Distributed” in DNFS refers to CAS’ ability to use a network file system in a massively parallel way. With a supported file system mounted identically on each CAS node, CAS can access (both write) the file system’s CSV and SASHDAT files from every worker node in parallel.

This parallel file access is not an attribute of the file system, it is a capability of CAS. By definition, network file systems facilitate access at the file level, not the block level. With DNFS, CAS actively manages parallel block level I/O to the network file system, making sure file seek locations and block I/O operations do not overlap, etc.

DNFS

 

DNFS as CAS Backing Store

Not only can CAS perform multi-machine parallel I/O from network file systems, it can also memory-map NFS SASHDAT files directly into CAS. Thus, SASHDAT files on DNFS act as both the CASlib data source as well as the virtual memory “backing store,” often negating the need for CAS to utilize memory mapping (mmap()).

Note 1: Data transformations on load, such as row filtering and field selection, as well as encryption can trigger CAS_DISK_CACHE usage. Since the data must be transformed (subset and/or decrypted), CAS copies the transformed data into CAS_DISK_CACHE to support CAS processing.

Note 2: It is possible to use DNFS atop an encrypting file system or hardware storage device. Here, the HDAT blocks are stored encrypted but transmitted to the CAS I/O layer decrypted. Assuming no other transformations, CAS_DISK_CACHE will not be used in this scenario.

DNFS Memory Mapping

Performance Considerations

DNFS-based CAS loading will only be as fast as the slowest component involved. The chosen NFS architecture (hardware and CAS connectivity) should support I/O throughput commensurate with the CAS installation and in-line with the implementation’s service level agreements. So, while NetApp ONTAP clustering architecture. A different file system technology might look a little different but the same basic ideas will apply.

DNFS w/ Multi Machine File System

As described earlier, CAS manages the parallel I/O operations. Requests from CAS are sent to the appliance and handled by the NFS metadata server. The storage device implementing the NFS protocol points CAS DNFS to the proper file and block locations on the NFS data servers which pass the data to the CAS worker nodes directly.

Understanding DNFS was published on SAS Users.

10月 122017
 

With SAS Data Management, you can setup SAS Data Remediation to manage and correct data issues. SAS Data Remediation allows user- or role-based access to data exceptions.

When a data issue is discovered it can be sent automatically or manually to a remediation queue where it can be corrected by designated users.

Let’s look how to setup a remediation service and how to send issue records to Data Remediation.

Register the remediation service.

To register a remediation service in SAS Data Remediation we go to Data Remediation Administrator “Add New Client Application.

Under Properties we supply an ID, which can be the name of the remediation service as long as it is unique, and a Display name, which is the name showing in the Remediation UI.

Under the tab Subject Area, we can register different subject categories for this remediation service.  When calling the remediation service we can categorize different remediation issues by setting different subject areas. We can, for example, use the Subject Area to point to different Data Quality Dimensions like Completeness, Uniqueness, Validity, Accuracy, Consistency.

Under the tab Issues Types, we can register issue categories. This enables us to categorize the different remediation issues. For example, we can point to the affected part of record like Name, Address, Phone Number.

At Task Templates/Select Templates we can set a workflow to be used for each issue type. You can design your own workflow using SAS Workflow Studio or you can use a prepared workflow that comes with Data Remediation. You need to make sure that the desired workflow is loaded on to Workflow Server to link it to the Data Remediation Service. Workflows are not mandatory in SAS Data Remediation but will improve efficiency of the remediation process.

Saving the remediation service will make it available to be called.

Sending issues to Data Remediation.

When you process data, and have identified issues that you want to send to Data Remediation, you can either call Data Remediation from the job immediately where you process the data or you store the issue records in a table first and then, in a second step, create remediation records via a Data Management job.

To send records to Data Remediation you can call remediation REST API form the HTTP Request node in a Data Management job.

Remediation REST API

The REST API expects a JSON structure supplying all required information:

{
	"application": "mandatory",
	"subjectArea": "mandatory",
	"name": "mandatory",
	"description": "",
	"userDefinedFieldLabels": {
		"1": "",
		"2": "",
		"3": ""
	},
	"topics": [{
		"url": "",
		"name": "",
		"userDefinedFields": {
			"1": "",
			"2": "",
			"3": ""
		},
		"key": "",
		"issues": [{
			"name": "mandatory",
			"importance": "",
			"note": "",
			"assignee": {
				"name": ""
			},
			"workflowName": "",
			"dueDate": "",
			"status": ""
		}]
	}]
}

 

JSON structure description:

In a Data Management job, you can create the JSON structure in an Expression node and use field substitution to pass in the necessary values from the issue records. The expression code could look like this:

REM_APPLICATION= "Customer Record"
REM_SUBJECT_AREA= "Completeness"
REM_PACKAGE_NAME= "Data Correction"
REM_PACKAGE_DESCRIPTION= "Mon-Result: " &formatdate(today(),"DD MM YY") 
REM_URL= "http://myserver/Sourcesys/#ID=" &record_id
REM_ITEM_NAME= "Mobile phone number missing"
REM_FIELDLABEL_1= "Source System"
REM_FIELD_1= "CRM"
REM_FIELDLABEL_2= "Redord ID"
REM_FIELD_2= record_id
REM_FIELDLABEL_3= "-"
REM_FIELD_3= ""
REM_KEY= record_id
REM_ISSUE_NAME= "Phone Number"
REM_IMPORTANCE= "high"
REM_ISSUE_NOTE= "Violated data quality rule phone: 4711"
REM_ASSIGNEE= "Ben"
REM_WORKFLOW= "Customer Tag"
REM_DUE-DATE= "2018-11-01"
REM_STATUS= "open"
 
JSON_REQUEST= '
{
  "application":"' &REM_APPLICATION &'",
  "subjectArea":"' &REM_SUBJECT_AREA &'",
  "name":"' &REM_PACKAGE_NAME &'",
  "description":"' &REM_PACKAGE_DESCRIPTION &'",
  "userDefinedFieldLabels": {
    "1":"' &REM_FIELDLABEL_1 &'",
    "2":"' &REM_FIELDLABEL_2 &'",
    "3":"' &REM_FIELDLABEL_3 &'"
  },
  "topics": [{
    "url":"' &REM_URL &'",
    "name":"' &REM_ITEM_NAME &'",
    "userDefinedFields": {
      "1":"' &REM_FIELD_1 &'",
      "2":"' &REM_FIELD_2 &'",
      "3":"' &REM_FIELD_3 &'"
    },
    "key":"' &REM_KEY &'",
    "issues": [{
      "name":"' &REM_ISSUE_NAME &'",
      "importance":"' &REM_IMPORTANCE &'",
      "note":"' &REM_ISSUE_NOTE &'",
      "assignee": {
        "name":"' &REM_ASSIGNEE &'"
      },
      "workflowName":"' &REM_WORKFLOW &'",
      "dueDate":"' &REM_DUE_DATE &'",
      "status":"' &REM_STATUS &'"
    }]
  }]
}'

 

Tip: You could also write a global function to generate the JSON structure.

After creating the JSON structure, you can invoke the web service to create remediation records. In the HTTP Request node, you call the web service as follows:

Address:  http://[server]:[port]/SASDataRemediation/rest/groups
Method: post
Input Filed: The variable containing the JSON structure. I.e. JSON_REQUEST
Output Filed: A field to take the output from the web service. You can use the New button create a filed and set the size to 1000
Under Security… you can set a defined user and password to access Data Remediation.
In the HTTP Request node’s advanced settings set the WSCP_HTTP_CONTENT_TYPE options to application/json

 

 

 

You can now execute the Data Management job to create the remediation records in SAS Data Remediation.

Improving data quality through SAS Data Remediation was published on SAS Users.

8月 182017
 

SAS Viya deployments use credentials for accessing databases and other third-party products that require authentication. In this blog post, I will look at how this sharing of credentials is implemented in SAS Environment Manager.

In SAS Viya, domains are used to store the:

  • Credentials required to access external data sources.
  • Identities that are allowed to use those credentials.

There are three types of domains:

  • Authentication stores credentials that are used to access an external source that can then be associated with a caslib.
  • Connection used when the external database has been set up to require a User ID but no password.
  • Encryption stores an encryption key required to read data at rest in a path assigned to a caslib.

In this blog post we will focus on authentication domains which are typically used to provide access to data in a database management system. It is a pretty simple concept; an authentication domain makes a set of credentials available to a set of users. This allows SAS Viya to seamlessly access a resource. The diagram below shows a logical view of a domain. In this example, the domain PGAuth stores the credentials for a Postgres database, and makes those credentials available to two groups (and their members) and three users.

How does this work when a user accesses data in a database caslib? The following steps are performed:

1.     Log on to SAS Viya using personal credentials: the user’s identity is established including group memberships.

2.     Access a CASLIB for a database: using the user’s identity and the authentication domain of the CASLIB, Viya will look up the credentials associated with that identity in the domain.

3.     Two results are possible. A credential match is:

  • 1.     Found: the credentials are passed to the database authentication provider to determine access to the data.
  • 2.     Not found: no access to the data is provided.

To manage domains in SAS Environment Manager you must be an administrator. In SAS Environment Manager select Security > Domains. There are two views available:  Domains and Credentials. The Domains view lists all defined domains. You can access the credentials for a domain by right-clicking on the domain and selecting Credentials.

The Credentials view lists all credentials defined and the domains for which they are associated.

Whatever way you get to a credential, you can edit it by right-clicking and selecting Edit. In the edit dialog, you can specify the Identities (users and groups) that can use the credential, and the User ID and Password of the credential.  Note that only users who are already listed in the Identities field will be able to edit this field, so make sure you are in this field (directly or through group membership) prior to saving.

To use an authentication domain, you reference it in the CASLIB definition. When defining a non-path based CASLIB you must select a domain to provide user credentials to connect to the database server. This can be done when creating a new CASLIB in SAS Environment Manager in the Data > Libraries area.

If you use code to create or access your caslib, use the authenticationdomain option. In this example, we specify authenticationdomain in the table.addcaslib action.

If a user is not attached to the authentication domain directly, or through a group membership, they will not be able to access the credentials. An error will occur when they attempt to access the data.

This has been a brief look at storing and using credentials to access databases from SAS Viya. You can find  more detail in the SAS Viya Administration Guide in the section titled SAS Viya sharing credentials for database access was published on SAS Users.

8月 172017
 

In this blog post I am going to cover the example of importing data into SAS Viya using Cloud Analytic Services (CAS) actions via REST API. For example, you may want to import data into a CASLib via REST API.  This means you can perform an import of data outside of the SAS Self-Service Import user interface environment using REST API.  Once this data is loaded into CAS it is available for use in applications such as SAS Visual Analytics and SAS Visual Data Builder.

Introduction

To import data into SAS Viya via REST API, you need to make a series of REST API calls:

1.     Start CAS Session
2.     Load Data into a CASLib
3.     End CAS Session

I will walk through these various REST API calls in the sections below using the REST API testing application HTTPRequestor, which is a free add-on to the Mozilla Firefox browser.

Before I perform any of my REST API calls, I need to Base-64 encode my credentials. The input for encoding the credentials is: I used the site https://www.base64encode.org/ to encode my credentials.  Note: You can use other methods (e.g., Python) to encode your credentials. Use the preferred method by your organization to ensure you are meeting their security protocols.

Below is the header Authorization information I will be sending with each of my requests.

Authorization Header

1.     Start CAS Session

First, I need to start a CAS Session. Below is an example request for starting a CAS Session:

POST https://<YourCASServer:Port>/cas/sessions

Authorization: Basic <Base-64EncodedCredentials>
 Content-Type: application/json

{}

This request returns the CASSessionUUID needed in the next step.

I construct my request in HTTPRequestor as follows and submit the request:

Start CAS Session Request/Response

Here is a screenshot of the raw transaction information.

Start CAS Session Raw Transaction

I need to copy the CAS Session UUID information that was returned for use in the subsequent REST API calls since their CAS Actions must be performed within a CAS Session.

2.     Load Data into a CASLib

Now that I have started my CAS session and have its UUID, I can load the table to CAS. Below is an example request for the table.loadTable CAS Action:

POST 
https://<YourCASServer:Port>/cas/sessions/<CASSessionUUID>/actions/table.load
Table

Authorization: Basic <Base-64EncodedCredentials>
 Content-Type: application/json

{"casLib":"<InputCASLib>","importOptions":{"fileType":"<FileType>"},"path":"<InputFilePathAndName>",
 "casout":{"caslib":"<OutputCASLib>","name":"<OutputTableName>","promote":true}}

 

This request returns a log message: “NOTE: Cloud Analytic Services made the file <InputFilePathAndName> available as table <OutputTableName> in caslib <OutputCASLib>.”

For my example, I will load the SAS data set BASEBALL located in the helpdata CASLib to the Public CASLib and call the CAS Table SAS_BASEBALL.  I am copying the data to the Public CASLib to make it more readily available to all CAS users. Let’s first confirm that the SAS_BASEBALL table does not currently exist in the Public CASLib.

Public CASLib Before LoadTable CAS Action Called

I construct my request in HTTPRequestor as follows and submit the request:

Load Table Request/Response

Here is a screenshot of the raw transaction information.

Load Table Raw Transaction

Next, I will confirm that the SAS_BASEBALL data set is now loaded in the Public CASLib.

Public CASLib After LoadTable CAS Action Called

The SAS_BASEBALL data set is now available for use in applications such as SAS Visual Analytics and SAS Visual Data Builder.

3.     End CAS Session

Finally, I need to terminate my CAS Session. Below is an example request for the session.endSession CAS Action:

POST https://&lt;YourCASServer:Port&gt;/cas/sessions/&lt;CASSessionUUID&gt;/actions/session.endSession

Authorization: Basic &lt;Base-64EncodedCredentials&gt;
 Content-Type: application/json

{}

 

This request returns a status of 0 indicating there was no error and the CASSessionUUID specified in the request has ended.

I construct my request in HTTPRequestor as follows and submit the request:

End CAS Session Request/Response

Here is a screenshot of the raw transaction information.

End CAS Session Raw Transaction

Conclusion

These calls can be strung together so you could schedule their execution. For more information on SAS Viya and REST APIs, refer to the following documentation the SAS Cloud Analytics REST API documentation.

Load Data into SAS Viya via REST API was published on SAS Users.

8月 152017
 

CAS data modelingThe CAS physical data model, i.e.what features CAS offers for data storage, and how to use them to maximize performance in CAS (and consequently SAS Visual Analytics 8.1 too).

So, specifically let’s answer the question:

What CAS physical table storage features can we use to get better performance in CAS and SAS Visual Analytics/CAS?

CAS Physical Table Storage Features

The following data storage features affect how CAS tables are physically structured:

  • Compression
  • Partitioning
  • Sorting
  • Repeated Tables
  • Extended Data Types (Varchar)
  • User Defined Formats

Compression — the Storage Option that Degrades Performance

data public.MegaCorp (compress=yes);
   set baselib.MegaCorp;
run;

Partitioning and Sorting

Partitioning is a powerful tool for improving Bar Charts, Decision Tree, Linear Regression) provide grouping as well as classification functionality.

When performing analyses/processing, CAS first groups the data into the required BY-groups. Pre-partitioning on commonly-used BY-groups means CAS can skip this step, vastly improving performance.

Within partitions, tables can be sorted by non-partition-key variables. Pre-sorting by natural ordering variables (e.g. time) allows CAS to skip the ordering step in many cases just like partitioning allows CAS to skip the grouping step.

For a full use-case, consider a line graph that groups sales by region and plots by date. This graph object would benefit greatly from a CAS table that is pre-partitioned by region and pre-sorted by date.

Join Optimization

Partitioning can also support join operations since both the CAS FedSQL Merge Join algorithm utilize BY-GROUP operations to support their processing.

Pre-partitioning tables in anticipation of joins will greatly improve join performance. A good use case is partitioning both a large transaction table and an equally large reference table (e.g. an enormous Customer table) by the common field, customerID. When a DATA Step MERGE or a FedSQL join is performed between the two tables on that field, the join/merge will take advantage of partitioning for the BY-GROUP operation resulting in something similar to a partition-wise join.

Like Compression, partitioning and sorting can be implemented via CAS actions as well as data set options. Using the data set options is demonstrated below:

data mycas.bigOrderTable (partition=(region division) orderby=(year quarter month));
   set CASorBase.bigOrderTable;
run;

Repeated Tables

By default, in distributed CAS Server deployments, CAS divides incoming tables into blocks and distributes those blocks among its DUPLICATE data set option or the Repeated Tables have two main use-cases in CAS:

1.     Join Optimization
2.     Small Table Operation Optimization

Join Optimization

For join operations, the default data distribution scheme can result in significant network traffic as matching records from the two tables travel between worker nodes to meet. If one of the two tables was created with the DUPLICATE/REPEAT option, then every possible record from that table is available on every node to the other table. There is no need for any network traffic.

Small Table Operation Optimization

For small tables, even single table operations can perform better with repeated instead of divided distribution. LASR actually implemented the “High Volume Access to Smaller Tables” feature for the same reason. When a table is repeated, CAS runs any required operation on a single worker node against the full copy of the table that resides there, instead of distributing the work.

As stated, repeated tables can be implemented with the DUPLICATE data set option, it can also be implemented with the REPEAT option on the PROC CASUTIL LOAD statement. The CASUTIL method is shown below:

proc casutil ;
   load data=sashelp.prdsale outcaslib=”caspath”
           casout=”prdsale” replace REPEAT ;
quit ;

Extended Data Types (VARCHAR)

With Viya 3.2 comes SAS’ first widespread implementation of variable length character fields. While Base SAS offers variable length character fields through compression, Viya 3.2 is the first major SAS release to include a save storage space, it also improves performance by reducing the size of the record being processed. CAS, like any other processing engine, will process narrower records more quickly than wide records.

User Defined Formats

User defined formats (UDFs) exist in CAS in much the same way they do in Base SAS. Their primary function, of course, is to provide display formatting for raw data values. Think about a format for direction. The raw data might be: “E”, “W”, “N”, “S” while the corresponding format values might be “East”, “West”, “North”, “South.”

So how might user defined formats improve performance in CAS? The same way they do in Base SAS, and the same way that VARCHAR does, by reducing the size of the record that CAS has to process. Imagine replacing multiple 200 byte description fields with 1 byte codes. If you had 10 such fields, the record length would decrease 1990 bytes ((10 X 200) – 10). This is an extreme example but it illustrates the point: User defined formats can reduce the amount of data that CAS has to process and, consequently, will lead to performance gains.

CAS data modeling for performance was published on SAS Users.

8月 082017
 

If I’m having a tough week, I might feel sorry for myself for a minute or two. But to snap myself out of it, all I have to do is think of the data managers. Those folks have to contend with duplicate records, bad information, data proliferation, accountability, permissions, privacy, [...]

Data management headaches? We have the cure was published on SAS Voices by Nick Lisi