Drivers for Managing Data Integration – from Data Conversion to Big Data

April 25, 2013

Data management in an organization is focused on getting data to its data consumers (whether human or application). Whereas the goal of data quality and data governance is trusted data, the goal of data integration is available data – getting data to the data consumers in the format that is right for them.
My new book on Data Integration has been published and is now available: “Managing Data in Motion: Data Integration Best Practice Techniques and Technologies”. Of course the first part of a book on data management techniques has to answer the question of why an organization should invest time and effort and money. The drivers for data integration solutions are very compelling.
Supporting Data Conversion
One very common need for data integration techniques is when copying or moving data from one application or data store to another, either when replacing an application in a portfolio or seeding the data needed for an additional application implementation. It is necessary to format the data as appropriate for the new application data store, both the technical format and the semantic business meaning of the data.
Managing the Complexity of Data Interfaces by Creating Data Hubs – MDM, Data Warehouses & Marts, Hub & Spoke
This, I think, is the most compelling reason for an organization to have an enterprise data integration strategy and architecture: hubs of data significantly simplify the problem of managing the data flowing between the applications in an organization. The number of potential interfaces between applications in an organization is an exponential function of the number of applications. Thus, an organization with one thousand applications could have as many as half a million interfaces, if all applications had to talk to all others. By using hubs of data, an organization brings the potential number of interfaces down to be just a linear function of the number of applications.
Master Data Management hubs are created to provide a central place for all applications in an organization to get its Master Data. Similarly, Data Warehouses and Data Marts enable an organization to have one place to obtain all the data they need for reporting and analysis.
Data hubs that are not visible to the human data consumers of the organization can be used to significantly simplify the natural complexity of data interfaces. If data being passed around in the organization is formatted, on leaving the application where it has been updated, into a common data format for that type of data, then applications updating data only need to reformat data into one format, instead of a different format for every application that needs it. Applications that need to receive the data that has been updated only need to reformat the data from the one common format into their own needs. This approach to data integration architecture is called using a “hub and spoke” approach. The structure of the common data format that all applications pass their data to and from is called the “canonical model.” Applications that want a certain kind of data need to “subscribe” to that data and applications that provide a certain kind of data are said to “publish” the data.
Integrating Vendor Packages with an Organization’s Application Portfolio
Current best practice is to buy vendor packages rather than developing custom applications, whenever possible. This exacerbates the data integration problem because each of these vendor packages will have their own master data that have to be integrated with the organization’s master data and they will either have to send or receive transactional data for consolidated reporting and analytics.
Sharing Data Among Applications and Organizations
Some data just naturally needs to flow between applications to support the operational processes of the organization. These days, that flow of data usually needs to be in a real time or near real time mode, and it makes sense to solve the requirements across the enterprise or across the applications that support the supply chain of data rather than developing independent solutions for each application.
Archiving Data
The life cycle for data may not match the life cycle for the application in which it resides. Some data may get in the way if retained in the active operational application and some data may need to be retained after an application is retired, even if the data is not being migrated to another application. All enterprises should have an enterprise archiving solution available where data can be housed and from which it can still be retrieved, even if the application from which it was taken no longer exists.
Moving data out of an application data store and restructuring it for an enterprise archiving solution is an important data integration function.
Leveraging External Available Data
There is so much data now available from government and other sites external to a company’s own, for free as well as data available for a fee. In order to leverage the value of what is available the external data needs to be made available to the data consumers who can use it, in an appropriate format. The amount of data now available is so vast and so fast that it may not be warranted to store or persist the external data, rather using techniques with data virtualization and streaming data, or not to store the data within the organization, choosing instead to leverage cloud solutions that are also external.
Integrating Structured and Unstructured Data
New tools and techniques allow analysis of unstructured data such as documents, web sites, social media feeds, audio, and video data. Greatest meaning can be applied to the analysis when it is possible to integrate together structured data (found in databases) and unstructured data types listed above. Data integration techniques and new technologies such as data virtualization servers enable the integration of structured and unstructured data.
Support Operational Intelligence and Management Decision Support
Using data integration to leverage big data includes not just mashing different types of data together for analysis, but being able to use data streams with that big data analysis to trigger alerts and even automated actions. Example use cases exist in every industry but some of the ones we’re all aware of include monitoring for credit card fraud as well as recommending products.

Is High Availability Sexy?

April 10, 2013

The subject of business continuity has grown in appeal for me as my years in IT have grown, especially as I have personally experienced disasters big and small and the need to recover systems and facilities. I became particularly interested during my training as an IT auditor.
The area of business continuity isn’t necessarily a “sexy” part of data management, but it is a franchise requirement for most organizations and corporations and certainly critical for financial services organizations. Interestingly, the responsibility for business continuity is a business responsibility and yet the knowledge and training for how to implement it is a specialty within information technology (IT). I call this “the tail wagging the dog” because the responsibility cannot be delegated to IT and yet IT needs to lead the process of how to implement it.
The way we implement business continuity is using techniques in disaster recovery and high availability. Disaster recovery is how to bring back up systems and access after the loss of power, services, or access to a facility. High availability is a similar concept except maintaining system continuity by switching to alternative resources automatically at the loss of any resource, system, connection, facility, etc.
The rule of thumb with business continuity is that the lower the amount of time of any disruption at the loss of a resource, the higher the cost. Thus, a high availability solution that has no disruption has the highest cost. Organizations that require high availability solutions are therefore frequently spending millions of dollars on their disaster recovery solutions and millions more on their high availability solutions.
EMC has recently released a new high availability services product and is now asking the question “why invest in both disaster recovery and high availability?”
Maybe organizations that require high availability should put their business continuity budgets into that rather than dividing between both high availability and disaster recovery. Well, it may not be such a simplistic answer. Should every single application in the organization be set up with high availability? And yet, dividing systems between continuity solutions makes testing and effecting business continuity much more difficult. As long as the organization can prove they have a high availability solution for everything that would serve any necessary disaster recovery requirements.
OK. High availability isn’t sexy. But to me, it is slightly sexier than disaster recovery. Certainly it is time to consider whether it is more cost effective to put the entire business continuity budget into high availability.

Big Data and NoSQL – the problem with relational databases

September 25, 2012

The NoSQL movement, where “NoSQL” stands for “Not Only SQL” is based on the concept that relational databases are not the right database solution for all problems.  Relational databases are so ubiquitous in most organization these days that many people may not even be aware that there are other types of databases, let alone when using another database might be preferable. Relational databases perform transaction update functions very well, particularly handling the difficult issues of consistency during update. Production strength relational databases can handle the complexity of two phase commit capability, where one business transaction affects multiple databases and tables, and all updates have to be effected at the same moment.

However, relational databases apply much of the same overhead required for complex update operations to every activity, and that can handicap them for other functions. Relational databases struggle with the efficiency of certain operations key to big data management.  Firstly, they don’t scale well to very large sizes, and although grid solutions can help with this problem, the creation of new clusters on the grid is not dynamic and large data solutions become very expensive using relational databases. Secondly, they don’t do unstructured data search very well (i.e. google type searching) nor do they handle data in unexpected formats well. Thirdly, but not lastly, it is difficult to implement certain kinds of basic queries using SQL and relational databases, such as the shortest path between two points.

Social networking and big data organizations such as Facebook, Yahoo, Google, and Amazon were among the first to decide that relational databases were not good solutions for the volumes and types of data that they were dealing with, hence the development of the Hadoop file system, the MapReduce programming language, and associated databases such as Cassandra and HBase.  One of the key capabilities of a Hadoop type environment is the ability to dynamically, or at least easily, expand the number of servers being used for data storage. The cost of storing large amounts of data in a relational database gets very expensive, where cost grows geometrically with the amount of data to be stored, reaching a limit in the petabyte range.  The cost of storing data in a Hadoop solution grows linearly with the volume of data and there is no ultimate limit.

I was a working programmer before relational databases were in common use.  Yes, we did have electricity back then.  And the databases I used were of the type called “hierarchical”.  In fact, they were more efficient, in general, for high volume individual transaction processing than relational databases, although like relational databases they were not good for data that was structured inconsistently.  But what we considered “high volume” then could be handled reasonably by my laptop now and those databases couldn’t handle dynamically allocating unlimited additional space, either.

In my next blog post I’ll describe some of the new classes of NoSQL databases and what problems they solve well.

Big Data Modeling – part 1 – Defining “Big Data” and “Data Modeling”

July 15, 2012

Last month I participated in a DataVersity webinar on Big Data Modeling .  There are a lot of definitions necessary in that discussion. What is meant by Big Data? What is meant by modeling? Does modeling mean entity-relationship modeling only or something broader?

The term “Big Data” implies an emphasis on high volumes of data. What constitutes big volumes for an organization seems to be dependent on the organization and its history.  The Wikipedia definition of “Big Data” says that an organization’s data is “big” when it can’t be comfortably handled by on hand technology solutions.  Since the current set of relational database software can comfortably handle terabytes of data and even desktop productivity software can comfortably handle gigabytes of data, “big” implies many terabytes at least.

However, the consensus on the definition of “Big Data” seems to be with the Gartner Group definition that says that “Big Data” implies large volume, variety, and velocity of data.  Therefore, “Big Data” means not just data located in relational databases but files, documents, email, web traffic, audio, video, and social media, as well.  The various types of data provides the “variety”, and not just data in an organization’s own data center but in the cloud and data from external sources as well as data on mobile devices.

The third aspect of “Big Data” is the velocity of data.  The ubiquity of sensor and global position monitoring information means a vast amount of information available at an ever increasing rate from both internal and external sources.  How quickly can this barrage of information be processed?  How much of it needs to be retained and for how long?

What is “data modeling”? Most people seem to picture this activity as synonymous with “entity relationship modeling”.  Is entity relationship modeling useful for purposes outside of relational database design?  If modeling is the process of creating a simpler representation of something that does or might exist, we can use modeling for communicating information about something in a simpler way than presenting the thing itself. So modeling is used for communicating.  Entity relationship modeling is useful to communicate information about the attributes of the data and the types of relationships allowed between the pieces of data.  This seems like it might be useful to communicate ideas outside of just relational databases.

Data modeling is also used to design data structures at various levels of abstraction from conceptual to physical. When we differentiate between modeling and design, we are mostly just differentiating between logical design and design closer to the physical implementation of a database. So data modeling is also useful for design.

In the next part of this blog I’ll get back to the question of “Big Data Modeling.”

People who Tweet about Data Management

April 30, 2012

Data Management & Architecture

Karen Lopez @datachick

Neil Raden @NeilRaden

Robin Bloor @robinbloor

M. David Allen @mdavidallen

Sue Geuens @suegeuens

Mehmet Orun @DataMinstrel

Alec Sharp @alecsharp

Loretta Mahon Smith @silverdata

Eva Smith @datadeva

Corine Jasonius @DataGenie

Peter Aiken @paiken

Tony Shaw @tonyshaw

Glenn Thomas @Warduke

Bonnie O’Neil @bonnieoneil

Rob Paller @RobPaller

Pete Rivett @rivettp

Charles T. Betz @CharlesTBetz

Tracie Larsen @RelatedStuff

Wayne Eckerson @weckerson

Julian Keith Loren @jkloren

Christophe @mydatanews

Steve Francia @spf13

Gorm Braavig @gormb

Jim Finwick @jimfinwick

Alexej Freund @alexej_freund

Corinna Martinez @Futureatti

Data Quality

Jim Harris @ocdqblog – blog

David Loshin @davidloshin – blog

Rich Murnane @murnane

Daragh O Brien @daraghobrien

Jacqueline Roberts @JackieMRoberts

Steve Tuck @SteveTuck

Vish Agashe @VishAgashe

Julian Schwarzenbach @jschwa1

Henrik L. Sorensen @hlsdk

MDM and Data Governance

Jill Dyche @jilldyche – blog

Charles Blyth @charlesblyth

Steve Sarsfield @stevesarsfield – blog

Dan Power @dan_power

Philip Tyler @tylep0

Business Intelligence and Analytics

Marcus Borba @marcusborba

Tamara Dull @tamaradull

Claudia Imhoff @Claudia_Imhoff – blog

Scott Wallask @BI_expert

Peter Thomas @PeterJThomas – blog

Barney Finucane @bfinucane

Matt Winkleman @mattwinkleman

Stray_Cat @Stray_Cat

Brett2point0 @Brett2point0

Risk Management

Peter Went @Bank_Risk

Joshua Corman @joshcorman

Michael Rasmussen @GRCPundit

Nenshad Bardoliwalla @nenshad

Gary Byrne @GRCexpert

Helmut Schindlwick @Schindwick

Technology Companies and Data Organizations

Oracle @Oracle

DAMA international @DAMA_I

McKinsey on BT @mck_biztech

SmartData Collective @SmartDataCo

DataFlux InSight @Datafluxinsight

Gartner @Gartner_inc


Scientific  Computing @SciCom

Wearecloud @wearecloud

CloudCamp @cloudcamp

Panorama Software @PanoramaSW

Data Hole @datahole

BI Knowledge Base @biknowledgebase

EnterpriseArchitects @enterprisearchitects @dataqualitypro

RSA Archer eGRC @ArcherGRC

Exobox @Exobox_Security

EA_Consultant @EA_Consultant

Cloudbook @cloudbook

ID Experts @idexperts

IAIDQ @iaidq

EMC Forum @EMCForums

Data Junkies @datajunkies

True Finance Data @truefinancedata

Madam @TheMDMNetwork

IBM Initiate @IBMInitiate

Accelus_GRC @PaisleyGRC

DQ Asia Pacific @DQAsiaPacific

Data Guide @DataGuide

PCI PA-DSS Data @DataAssurant

DataFlux Corporation @DataFlux

Data Virtualization – Part 1 – Business Intelligence

March 26, 2012

The big transformation that we’re all dealing with in technology today is virtualization.  There are many aspects to virtualization: infrastructure, systems, organization, office, applications. When you search on the internet for “data virtualization,” most of the references are regarding business intelligence and data warehousing uses.  In part 2 of this blog I will talk about data virtualization and transaction processing.

In the day, when I used to build data warehouses (1990s+), there was a reference to a concept of “federated data warehouses”, where data in the logical warehouse would be separated physically either with the same schemas in multiple instances or different types of data in different locations.  The thought was that the data would be physically separate but brought together real time for reporting.  We also used to call that “data warehouses that don’t work”.  After all, the reason we created data warehouses in the first place was that we needed to instantiate the data consolidation in order to make the response time reasonable when trying to report on millions of records. No, really, the response time on these “federated data warehouse” systems used to be many minutes or more.

Now, however, the technologies involved have made huge leaps in capabilities.  The vendors have put thousands and thousands of man hours into how to make real time integration and reporting work.  There are many techniques involving specialized software and hardware (data appliances) that enable these capabilities, query optimization, distributed processing, and other optimization techniques, and hybrid solutions between pure virtualization and instantiation.  Specialized tuning is necessary, and the fastest solutions involve instantiating the consolidated data in a central place.

Ultimately, having to do a project to incorporate new data into the data warehouse physically isn’t responsive enough to the business need for information.  Better to have a short term solution that allows for the quick incorporation of new data and then, if there is a continued need for the data in question and you want to speed up the response, possibly integrate the additional data into the physical data warehouse.

The problems being solved now for business intelligence and data virtualizations include real time data integration of multiple regional instances of a data warehouse, integrating data of different types and kinds, and integrating data from a data warehouse with big data and cloud data.  This enables much more responsive business intelligence and analytical solutions to business requests without having to always instantiate all data for analysis into a central, single, enterprise data warehouse.