Global IDs landscape discovery and governance

While individual parts of the Global IDs portfolio can be used to address any individual data quality or data governance issue, the company’s primary focus is on understanding large data landscapes in the first instance and, in the second, taking those management and governance issues on board. For example, major mergers and acquisitions often involve very large numbers of data sources, in both companies, and trying to understand the relationships – both consistencies and inconsistencies – that cross corporate boundaries, and then managing and migrating those relationships, is the sort of complex management issue that Global IDs targets. Similar problems also arise in very large enterprises, even leaving aside mergers and acquisitions, where thousands or even tens of thousands of database instances may be in place and any sort of rationalisation must start with an understanding of that data landscape prior to implementing data quality, master data management or governance processes.

Complex landscapes such as these contain vast amounts of redundant data that describe real world things a business cares about (or once cared about but no longer does). The core problem Global IDs seeks to solve is to help firms make sense of what data exists, what it is about, and how accurate it is, so that they are able to begin systematically and efficiently weeding out the parts of their landscapes which are causing them the most pain. While there are many individual tools and products, from a variety of companies that can be used to start to address these issues, the cost of using these techniques tends to escalate to the point where it is no longer economical (or prudent, because of the risks involved) to tamper with the status quo. As a result, these landscapes continue to expand over time in increasingly complex ways. What Global IDs aims to do is to cut this Gordian knot by making landscape discovery and governance a practical proposition.

As noted, the company targets the world’s largest organisations across all verticals. While Global IDs has its own sales force it also works with systems integrators. Partners in this area include Cap Gemini, Cognizant and others. In addition, the company has a number of notable partnerships with other technology vendors including EMC, Pitney Bowes, Acxiom, Red Hat, Cray and SAP.

The company has customers in the Financial Services, Healthcare, Pharmaceuticals, Telecom and Retail sectors. None of its clients are publicly named but the names of some can easily be deduced from their descriptions, such as “one of the world’s largest providers of both mobile telephony and fixed telephony. This company is an icon of its industry and can trace its foundational roots back to over 125 years ago” and “one of the world’s leading retail giants is an American public corporation that runs a chain of large, discount department stores. It has the largest number of stores, supercentres and neighbourhood markets in the US.”

The basics of Global IDs’ ‘landscape discovery and governance’ is that you iteratively profile all of your data sources to discover the relationships that exist across those data sources. There are very few other data profiling tools that were designed from the outset with this sort of capability and none at the scale that Global IDs is supporting. In this latter context, Global IDs supports an elastic computing model designed to scale to support very large environments with many data sources.

Another major focus of Global IDs is automation. The company sees this as critical to the success of understanding and managing large data landscapes and its technology is based on semantic principles (for example, recognising that a client is a customer in whatever these things are called in foreign languages, and so on). Of course, the implementation of automation is an on-going process.

In addition, when you try to govern large data landscapes one of the problems that you will encounter is that there is so much information to explore and manage that it is difficult to visualise the environment using traditional techniques. Global IDs’ approach to this problem is to store the semantics it captures in a graph database (the company embeds the Titan distributed graph database) so that you can explore inter-relationships using graph technology which, in our view, gives Global IDs a significant competitive advantage. This doesn’t mean that it is ever going to be simply to visualise large, complex landscapes but, in our opinion, the use of graphs is the best starting point even if this remains a work in progress.

Of course, discovery across the landscape is only stage one. Typically you are doing this because you want to rationalise across multiple systems, implement master data management, consolidate database systems or implement data governance. You might also want to do this if you have appointed a Chief Data Officer and want to know about all relevant sources of data for analytic purposes. Whatever the case, there will certainly be additional data management functions that are required and Global IDs offers relevant capabilities for these tasks also.

While individual parts of the Global IDs portfolio can be used to address any individual data quality or data governance issue, the company’s primary focus is on understanding large data landscapes in the first instance and, in the second, taking those management and governance issues on board. For example, major mergers and acquisitions often involve very large numbers of data sources, in both companies, and trying to understand the relationships – both consistencies and inconsistencies – that cross corporate boundaries, and then managing and migrating those relationships, is the sort of complex management issue that Global IDs targets. Similar problems also arise in very large enterprises, even leaving aside mergers and acquisitions, where thousands or even tens of thousands of database instances may be in place and any sort of rationalisation must start with an understanding of that data landscape prior to implementing data quality, master data management or governance processes.

Complex landscapes such as these contain vast amounts of redundant data that describe real world things a business cares about (or once cared about but no longer does). The core problem Global IDs seeks to solve is to help firms make sense of what data exists, what it is about, and how accurate it is, so that they are able to begin systematically and efficiently weeding out the parts of their landscapes which are causing them the most pain. While there are many individual tools and products, from a variety of companies that can be used to start to address these issues, the cost of using these techniques tends to escalate to the point where it is no longer economical (or prudent, because of the risks involved) to tamper with the status quo. As a result, these landscapes continue to expand over time in increasingly complex ways. What Global IDs aims to do is to cut this Gordian knot by making landscape discovery and governance a practical proposition.