InetSoft Webinar: Data Management Tools Versus Data Modeling

Below is the continuattion of the transcript of a Webinar hosted by InetSoft in April 2018 on the topic of "Managing Data Complexity." The presenter is Mark Flaherty, Vice President of Marketing at InetSoft.

Mark Flaherty (MF): So that being said, let's take a little bit of a deeper dive into these data management tools versus data modeling and the data model. It's a mechanism to help you visualize the power in your data. It lets you document the existing data assets and associated models. It lets you brainstorm conceptual models that can be used for consolidation or merging or reduction and complexity.

It lets you consider alternatives for entity hierarchies and class inheritance. It lets you look at relationships between different data concepts, and it lets you look at the relationships between data concepts and their use within different business processes. Note that I am not talking about their use within an application, but rather within the business process that the application is actually the manifestation of.

It is the method by which you try to automate some of that business process. But in fact, we are less concerned with how the data is being used as part of an application and more about how the information that’s within your data asset is being used to help you run your business or make your business better. So we want to rationalize representations across the different data sources and the applications to simplify the environment.

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Another one of our tools is data profiling. It’s a way that we analyze the variants of values within columns or across columns or across tables so that we can find embedded mappings associated with the data in its different formats and enable us to link knowledge and concepts together. So in this slide, for example, we are looking three different data sets, each of which has one column. That one column has two values in the blue version. The values have zeros and ones in the green version, there are Ms and Fs and in the pink version. There are Fs and Ms in the other. But it turns out that if we look at this, and we are able to map these tables together and see that there is a correlation between the occurrence of a zero in the blue and M in the green and F in the pink that we actually see that all three of those data domains are value domains that map to a single conceptual domain. If you haven’t figured it out, the conceptual domain is gender or sex in the blue version, zero stands for male and one stands for female. And the green version M stands for male and F stands for female.

And in the third version, in the pink version, believe it or not F stands for male and M stands for female. How is that? Well it came from a database at a school where each one of those individuals is the parent of one of the students, F stands for father and M stands for mother in that situation. F, actually even though you might think it means female, it refers to the male parent, while M refers to the female parent, the mother. And profiling the sources is going to reveal that even though you have got different value domains, they are actually referring to the same conceptual domain.

And then our third set of tools are metadata management tools. In fact, our metadata register or metadata repository becomes the control panel for all the different aspects of information sharing across your organization. It helps us with building master data repositories, with developing and managing applications for databases, looking at the development of services, sharing data through data exchanges, complying with standards that have been defined and then essentially allowing you to integrate upwards across the infrastructure so that you have got similarities in sharing in and around the environment consistent in concepts.

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So what have we looked at? We have looked at the growing complexity in our ability to manage and take advantage of information and a need for visualizing the power within your data through the use of some specific types of data management tools and technologies coupled with good data management practices. What are some considerations to take away? We have got massive data volumes, but fewer resources to manage and control those volumes. We have got multiple dimensions of systematic and organizational complexity that are associated with the creation and use and modification and retirement and archiving of data.

And there are challenges that exist for interoperability, especially when you have this need to repurpose data for multiple reasons, whether it's for supply chain management or for data warehousing or business intelligence or for reporting or analytics or customer relationship management or any of another variety of three-letter acronym application types.

But with the right processes coupled with the right tools, you can get a visual representation of what could be referred to as a data asset. And then having data subject matter experts reviewing those visual representations in tandem with the business consumers of that visual representation, you can help standardize models and lead to greater consistency of information use across your organization.

That visual presentation can simplify the communication of the value of information and the power of that data across management spectrum, the skill spectrum and in fact, even level of attention as well. So again some straightforward and simple recommendations are to look at the use of the right types of modeling and metadata management tools and technology that can supplement your business processes.

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