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.

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Don’t Get Caught in the Statistical Cobwebs of Data Quality

November 14, 2012

A couple of days ago, I heard about a data conversion project where the team was taking a statistical sample of source master data and cleaning it.  The discussion I heard was on how big a sample needed to be taken in order to have a 5% margin of error and continued through a series of issues about statistics.  This discussion has brought me up short because sometimes we get so caught up in the mathematics and techniques of our processes that we lose the basic understanding of when different techniques are appropriate.

I applaud the fact that the data conversion project in question had enough foresight to include a data quality stream, certainly not always the case.  Besides the fact that we don’t always know the level of quality of our production data in systems that have been running for many years, it is frequently true that data may have to be additionally cleaned in order for it to be in a state sufficient for the running of a new system to which the data is to be migrated.  The standard method to assess the quality level of the data in the source systems is to take a statistical sample of the source data and assess whether the quality level is sufficient for the target system.  Once we’ve determined how much cleaning of the sample data is necessary to get it into proper shape, we can extrapolate the estimate across the entire set of master data in order to determine how much in time and resources would be necessary to clean the master data.

How does a method for statistically determining an estimate turn into the idea that we only need to clean the statistical sample of data? And even if one person accidentally skipped a few steps in specifying the process, why hasn’t anyone else realized that cleaning a statistical sample of data doesn’t make the entire dataset clean?  Somehow, an entire project team has been dazzled by the implementation of statistics, or no one really thought about it that hard because it wasn’t their job.  Anyway, if you clean a statistical sample of data then only that sample will be sufficiently clean for your target system, the rest of the data will be at the same quality level as the start.

How do we prevent a problem like this?  I believe that a great deal of the problem is that most people like to be as far away from theoretical mathematical discussions as possible because, as Barbie used to say: “Math is hard”.  I think it is important, however, that people with common sense ask questions about project planning and process, even if they don’t understand the complexity of a technical design or approach.  Even very complex issues like encryption and business continuity need to make sense in their implementation and can easily be applied in slightly wrong ways that lose the intent. The economist Kenneth Galbraith proposed in the 1960s that technicians would take over the running of companies because business people wouldn’t understand what the technicians were talking about.  That did not happen because business managers with common sense insisted that the technicians explain the concepts sufficiently to their understanding, regardless of how long it took.  We need to continue that practice with even the implementation of statistics and mathematical concepts in project planning and data management.