This blog is the 2nd in a series focused on reviewing the individual Components of a Data Strategy. This edition discusses the concept of data provisioning and the various details of making data sharable.
The definition of Provision is:
“Supplying data in a sharable form while respecting all rules and access guidelines”
One of the biggest frustrations that I have in the world of data is that few organizations have established data sharing as a responsibility. Even fewer have setup the data to be ready to share and use by others. It’s not uncommon for a database programmer or report developer to have to retrieve data from a dozen different systems to obtain the data they need. And, the data arrives in different formats and files that change regularly. This lack of consistency generates large ongoing maintenance costs and requires an inordinate amount of developer time to re-transform, prepare, fix data to be used (numerous studies have found that ongoing source data maintenance can take as much of 50% of the database developers time after the initial programming effort is completed).
Should a user have to know the details (or idiosyncrasies) of the application system that created the data to use the data? (That’s like expecting someone to understand the farming of tomatoes and manufacturing process of ketchup in order to be able to put ketchup on their hamburger). The idea of Provision is to establish the necessary rigor to simplify the sharing of data.
I’ve identified 5 of the most common facets of data sharing in the illustration above – there are others. As a reminder (from last week’s blog), each facet should be considered individually. And because your Data Strategy goals will focus on future aspirational goals as well as current needs, you’ll likely to want to review the different options for each facet. Each facet can target a small organization’s issues or expand to address a diverse enterprise’s needs.
This is the most obvious aspect of provisioning: structuring and formatting the data in a clear and understandable manner to the data consumer. All too often data is packaged at the convenience of the developer instead of the convenience of the user. So, instead of sharing data as a backup file generated by an application utility in a proprietary (or binary) format, the data should be formatted so every field is labeled and formatted (text, XML) for a non-technical user to access using easily available tools. The data should also be accompanied with metadata to simplify access.
This facet works with Packaging and addresses the details associated with the data container. Data can be shared via a file, a database table, an API, or one of several other methods. While sharing data in a programmer generated file is better than nothing, a more effective approach would be to deliver data in a well-known file format (such as Excel) or within a table contained in an easily accessible database (e.g. data lake or data warehouse).
Source data stewardship is critical in the sharing of data. In this context, a Source Data Steward is someone that is responsible for supporting and maintaining the shared data content (there several different types of data stewards). In some companies, there’s a data steward responsible for the data originating from an individual source system. Some companies (focused on sharing enterprise-level content) have positioned data stewards to support individual subject areas. Regardless of the model used, the data steward tracks and communicates source data changes, monitors and maintains the shared content, and addresses support needs. This particular role is vital if your organization is undertaking any sort of data self-service initiative.
This item addresses the issues that are common in the world of electronic data sharing: inconsistency, change, and error. Acceptance checking is a quality control process that reviews the data prior to distribution to confirm that it matches a set of criteria to ensure that all downstream users receive content as they expect. This item is likely the easiest of all details to implement given the power of existing data quality and data profiling tools. Unfortunately, it rarely receives attention because of most organization’s limited experience with data quality technology.
In order to succeed in any sort of data sharing initiative, whether in supporting other developers or an enterprise data self-service initiative, it’s important to identify the audience that will be supported. This is often the facet to consider first, and it’s valuable to align the audience with the timeframe of data sharing support. It’s fairly common to focus on delivering data sharing for developers support first followed by technical users and then the large audience of business users.
In the era of “data is a business asset” , data sharing isn’t a courtesy, it’s an obligation. Data sharing shouldn’t occur at the convenience of the data producer, it should be packaged and made available for the ease of the user.
I read an interesting tidbit about data the other day: the United States Postal Service processed more than 47 million changes of addresses in the last year. That’s nearly 1 in 6 people. In the world of data, that factoid is a simple example of the challenge of addressing stale data and data quality. The idea of stale data is that as data ages, its accuracy and associated business rules can change.
There’s lots of examples of how data in your data warehouse can age and degrade in accuracy and quality: people move, area codes change, postal/zip codes change, product descriptions change, and even product SKUs can change. Data isn’t clean and accurate forever; it requires constant review and maintenance. This shouldn’t be much of a surprise for folks that view data as a corporate asset; any asset requires ongoing maintenance in order to retain and ensure its value. The challenge with maintaining any asset is establishing a reasonable maintenance plan.
Unfortunately, while IT teams are exceptionally strong in planning and carrying out application maintenance, it’s quite rare that data maintenance gets any attention. In the data warehousing world, data maintenance is typically handled in a reactive, project-centric manner. Nearly every data warehouse (or reporting) team has to deal with data maintenance issues whenever a company changes major business processes or modifies customer or product groupings (e.g. new sales territories, new product categories, etc.) This happens so often, most data warehouse folks have even given it a name: Recasting History. Regardless of what you call it, it’s a common occurrence and there are steps that can be taken to simplify the ongoing effort of data maintenance.
- Establish a regularly scheduled data maintenance window. Just like the application maintenance world, identify a window of time when data maintenance can be applied without impacting application processing or end user access
- Collect and publish data quality details. Profile and track the content of the major subject area tables within your data warehouse environment. Any significant shift in domain values, relationship details, or data demographics can be discovered prior to a user calling to report an undetected data problem
- Keep the original data. Most data quality processing overwrites original content with new details. Instead, keep the cleansed data and place the original values at the end of your table records. While this may require a bit more storage, it will dramatically simplify maintenance when rule changes occur in the future
- Add source system identification and creation date/time details to every record. While this may seem tedious and unnecessary, these two fields can dramatically simplify maintenance and trouble shooting in the future
- Schedule a regular data change control meeting. This too is similar in concept to the change control meeting associated with IT operations teams. This is a forum for discussing data content issues and changes
Unfortunately, I often find that data maintenance is completely ignored. The problem is that fixing broken or inaccurate data isn’t sexy; developing a data maintenance plan isn’t always fun. Most data warehouse development teams are buried with building new reports, loading new data, or supporting the ongoing ETL jobs; they haven’t given any attention to the quality or accuracy of the actual content they’re moving and reporting. They simply don’t have the resources or time to address data maintenance as a proactive activity.
Business users clamor for new data and new reports; new funding is always tied to new business capabilities. Support costs are budgeted, but they’re focused on software and hardware maintenance activities. No one ever considers data maintenance; it’s simply ignored and forgotten.
Interesting that we view data as a corporate asset – a strategic corporate asset – and there’s universal agreement that hardware and software are simply tools to support enablement. And where are we investing in maintenance? The commodity tools, not the strategic corporate asset.
Photo courtesy of DesignzillasFlickr via Flickr (Creative Commons license).
In one of my previous blogs, I wrote about Data Virtualization technology — one of the more interesting pieces of middleware technology that can simplify data management. While most of the commercial products in this space share a common set of features and functions, I thought I’d devote this blog to discussing the more advanced features. There are quite a few competing products; the real challenge in differentiating the products is to understand their more advanced features.
The attraction of data virtualization is that it simplifies data access. Most IT shops have one of everything – and this includes several different brands of commercial DBMSs, a few open source databases, a slew of BI/reporting tools, and the inevitable list of emerging and specialized tools and technologies (Hadoop, Dremel, Casandra, etc.) Supporting all of the client-to-server-to-repository interfaces (and the associated configurations) is both complex and time consuming. This is why the advanced capabilities of Data Virtualization have become so valuable to the IT world.
The following details aren’t arranged in any particular order. I’ve identified the ones that I’ve found to be the most valuable (and interesting). Let me also acknowledge not every DV product supports all of these features.
Intelligent data caching. Repository-to-DV Server data movement is the biggest obstacle in query response time. Most DV products are able to support static caching to reduce repetitive data movement (data is copied and persisted in the DV Server). Unfortunately, this approach has limited success when there are ad hoc users accessing dozens of sources and thousands of tables. The more effective solution is for the DV Server to monitor all queries and dynamically cache data based on user access, query load, and table (and data) access frequency.
Query optimization (w/multi-platform execution). While all DV products claim some amount of query optimization, it’s important to know the details. There are lots of tricks and techniques; however, look for optimization that understands source data volumes, data distribution, data movement latency, and is able to process data on any source platform.
Support for multiple client Interfaces. Since most companies have multiple database products, it can be cumbersome to support and maintain multiple client access configurations. The DV server can act as a single access point for multiple vendor products (a single ODBC interface can replace drivers for each DBMS brand). Additionally, most DV Server drivers support multiple different access methods (ODBC, JDBC, XML, and web services).
Attribute level or value specific data security. This feature supports data security at a much lower granularity than is typically available with most DBMS products. Data can be protected (or restricted) at individual column values for entire table or selective rows.
Metadata tracking and management. Since Data Virtualization is a query-centric middleware environment, it only makes sense to position this server to retrieve, reconcile, and store metadata content from multiple, disparate data repositories.
Data lineage. This item works in tandem with the metadata capability and augments the information by retaining the source details for all data that is retrieved. This not only includes source id information for individual records but also the origin, creation date, and native attribute details.
Query tracking for usage audit. Because the DV Server can act as a centralized access point for user tool access, there are several DV products that support the capture and tracking of all submitted queries. This can be used to track, measure, and analyze end user (or repository) access.
Workflow linkage and processing. This is the ability to execute predefined logic against specific data that is retrieved. While this concept is similar to a macro or stored procedure, it’s much more sophisticated. It could include the ability to direct job control or specialized processing against an answer set prior to delivery (e.g. data hygiene, external access control, stewardship approval, etc.)
Packaged Application Templates. Most packaged applications (CRM, ERP, etc.) contain thousands of tables and columns that can be very difficult to understand and query. Several DV vendors have developed templates containing predefined DV server views that access the most commonly queried data elements.
Setup and Configuration Wizards. Configuring a DV server to access the multiple data sources can be a very time consuming exercise; the administrator needs to define and configure every source repository, the underlying tables (or files), along with the individual data fields. To simplify setup, a configuration wizard reviews the dictionary of an available data source and generates the necessary DV Server configuration details. It further analyzes the table and column names to simplify naming conventions, joins, and data value conversion and standardization details.
Don’t be misled into thinking that Data Virtualization is a highly mature product space where all of the products are nearly identical. They aren’t. Most product vendors spend more time discussing their unique features instead of offering metrics about their their core features. It’s important to remember that every Data Virtualization product requires a server that retrieves and processes data to fulfill query requests. This technology is not a commodity, which means that details like setup/configuration time, query performance, and advanced features can vary dramatically across products. Benchmark and test drive the technology before buying.
Unless you’ve been hiding in a cave in the past year, you’ve probably heard of CEP (Complex Event Processing) or data stream analysis. Because a lot of real-time analysis focuses on discrete data elements rather than data sets, this technology allows users to query and manipulate discrete pieces of information, like events and messages, in real-time—without being encumbered by a traditional database management system.
The analogy here is that if you can’t bring Mohammed to the mountain, bring the mountain to Mohammed: why bother loading data into a database with a bunch of other records when I only need to manipulate a single record? Furthermore, this lets me analyze the data right after its time of creation! Since one of the biggest obstacles to query performance is disk I/O, why not bypass the I/O problem altogether?
I’m not challenging data warehousing and historical analysis. But the time has come to apply complex analytics and data manipulation against discrete records more efficiently. Some of the more common applications of this technology include fraud/transaction approval, event pattern recognition, and brokerage trading systems.
When it comes to ETL (Extract, Transform, and Load) processing, particularly in a real-time or so-called “trickle-feed” environment, CEP may actually provide a better approach to traditional ETL. CEP provides complex data manipulation directly against the individual record. There is no intermediary database. The architecture is inherently storage-efficient: if a second, third, or fourth application needs access to a particular data element, it doesn’t get its own copy. Instead, each application applies its own process. This prevents the unnecessary or reckless copying of source application content.
There are many industries need a real-time view of customer activities. For instance in the gaming industry when a customer inserts her card into a slot machine, the casino wants to provide a custom offer. Using traditional data warehouse technology, a significant amount of processing is required to capture the data, to transform and standardize it, to load it into a table, only to make it available to a query to identify the best offer. In the world of CEP we’d simply query the initial message and make the best offer.
Many ETL tools already use query language constructs and operators to manipulate data. They typically require the data to be loaded into a database. The major vendors have evolved to an “ELT” architecture: to leverage the underlying database engine to address performance. Why not simply tackle the performance problem directly and bypass the database altogether?
The promise of CEP a new set of business applications and capabilities. I’m also starting to believe that CEP could actually replace traditional ETL tools as a higher performance and easier-to-use alternative. The interesting part will be seeing how long before companies emerge from their caves and adopt it.
photo by Orin Zebest via Flickr (Creative Commons license)
In the motion picture industry, studios separate responsibilities for creating content from responsibilities for distributing content. The people who make the movies option the scripts, hire the talent, and film the scenes. The distributors of the films, on the other hand, figure out how to package and deploy the films. They need to know which theaters require 30 millimeter versus 70 millimeter formats, or even IMAX. They also deal with DVD packaging, including different international DVD formats. The industry understands the importance of having a supply chain that differentiates between the roles of content creation, content packaging, and distribution.
In IT we’re very quick to point to our operational systems as creators and owners of data. But maybe the solution is that IT establishes a functional team that’s responsible for data packaging and distribution, just like the movie industry.
Traditionally data formats and standards have fallen into the realm of the architecture team. Unfortunately this is typically a paper-only activity without teeth. A data distribution team wouldn’t focus on paperwork. They would be focused on data logistics, receiving content from the various source systems and packaging the data for consumption by other systems. This isn’t about implementing a specific platform to store or move data. It’s about active management of corporate data content.
One of the biggest development challenges is the hunting expedition that developers go on to find and acquire the data they need. Most aren’t aware of all their choices, let alone the optimal systems of record.
Currently every application, data mart, data warehouse, reporting system that needs data from another system follows a specific set of procedures to obtain that data. Each system requests different data formats, different delivery schedules, and different content. Everything is custom, there are few if any standards, and there are no economies of scale.
This will also unburden the various application teams from building and maintaining the never ending volume of custom extract requests. The only way to stop the madness is to compartmentalize content creation from data packaging and distribution. This means establishing a data supply chain that separates data creators from data distribution from consumers. Who knew IT infrastructure was just like the movies?