Pete Stiglich, Author at Perficient Blogs

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Pete Stiglich

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Database inferencing to get to trusted healthcare data

A health insurance client of mine recently embarked on an initiative to truly have “trusted data” in its Enterprise Data Warehouse so that business leaders could make decisions based on accurate data. However, how can one truly know if your data is trustable?? In addition to having solid controls in place (e.g., unique indexes on […]

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A Wish List for Data Modeling Technology

I was recently a panelist for a Dataversity webinar/discussion focused on the future of data modeling tools, functionality, and practice. Given the holiday season, the panelists discussed their wish list for modeling tools – from currently practical (but maybe not economically viable) to futuristic (e.g., using a 3D printer to print models for model reviews, […]

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Disruption caused by Data Governance?

Instituting Data Governance is a major initiative providing a significant opportunity to leverage enterprise data assets more effectively. As such, there is sometimes concern about being seen as a roadblock or concerns about formulating new enterprise level organizations, such as a Data Governance board and Data Stewardship committees. To be sure, a proper balance between […]

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A Low Cost Big Data Integration Option?

With all of the interest in big data in healthcare, it’s easy to get drawn in by the excitement and not realize that it’s not a silver bullet that’s going to address all your data and infrastructure problems. Unless you are able to understand and integrate your data, throwing all the data onto a platform […]

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The Conceptual Data Model – Key to Integration

The importance of data integration, whether for analytics, mergers/acquisitions, outsourcing arrangements, third party interfaces, etc., is easy to understand but extremely difficult to realize. The technical aspect of data integration is the (relatively) easy part. The hardest part is bridging the semantic divide and agreeing on business rules – i.e., communication. The Conceptual Data Model […]

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Key insights on source data for healthcare analytics & exchanges

Providers and payers need to exchange or source a lot of data, and the rate of this activity will only increase with implementation of Obamacare and other directives. Given the poor state of metadata management (which makes data sharing much more difficult), the decision to incorporate a new data set into an Enterprise Data Warehouse […]

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Big Data as an Archival Platform

Operational systems frequently have to have data archived (i.e., backed up and purged from the database) in order to maintain performance SLA’s. Historical data is frequently maintained for a much longer period in data warehouses, but as a data warehouse is intended for storing integrated, standardized, and historical (and perhaps current) data in order to […]

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Healthcare Data Modeling Governance

I participated in a webinar/panel discussion last week hosted by Dataversity on Data Modeling Governance, which was well attended and lively. The focus was on governance of Data Models and the Data Modeling environment (e.g., tools, model repositories, standards). Data Modeling Governance is supported by Data Governance – and Data Governance benefits significantly from Data […]

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Big Data Security

In the Big Data Stack below, security is a vertical activity touching upon all aspects of a Big Data architecture which must receive careful attention, especially in a healthcare environment when PHI may be involved. NoSQL technologies, which many Big Data platforms are built upon, are still maturing technologies where security is not as robust […]

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Business Glossary – Sooner Rather than Later

If you are undertaking any significant data initiatives, such as MDM, Enterprise Data Warehousing, CRM, integration/migrations due to mergers or acquisitions, etc., where you have to work across business units and interface with numerous systems, one of the best investments you can make is to implement an Enterprise Business Glossary early on in your initiative. […]

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Perficient’s Big Data Stack – Infrastructure Tier

In a previous blog posting, I introduced Perficient’s Big Data Stack in order to provide a framework for a Big Data architecture and environment. This article is the second in a series of articles (click here for the first article on the Application tier) focusing on a component of the Big Data Stack. The stack […]

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Perficient’s Big Data Stack – The Application Tier

In my last blog posting, I introduced Perficient’s Big Data Stack in order to provide a framework for a Big Data environment. The stack diagram below has two key vertical tiers – the application tier and the infrastructure tier. I will discuss the application tier in this article. Figure 1 – Perficient’s Big Data Stack […]

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Perficient’s Big Data Stack

Big Data has generated a lot of interest in the media and in industry, leading to the possible impression that every data problem is a “Big Data” problem. However, the amount of interest is justified given the performance and scalability boost possible and the economic feasibility of Big Data platforms enabled by commodity hardware clusters/grids […]

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Data Governance Organizations – Tying them all together

In my last blog posting, I talked about the Subject Area Data Stewardship Subcommittee and how it is the forum for collaboration and stewardship over a specific data subject area or domain. In this article, I will describe three key roles involved in Data Governance and Stewardship organizations which are critical for setting and maintaining […]

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Data Governance Orgs: The Data Stewardship Coordinating Committee

In my last blog posting, I talked about the role of the Data Governance Board as the key decision making body in Enterprise Data Governance. The decisions which the Data Governance Board should make should be highly strategic in nature – you don’t want executives and senior leaders sitting around arguing over fine distinctions in […]

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Data Governance and Stewardship Organizations

In my last blog posting, I talked about the need for Data Governance and Data Stewardship to work collaboratively across business units and that organizations should be formed over time to help enable this collaboration. While no two Data Governance programs are identical, below is an example structure which might be adopted, in a phased […]

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Data Governance vs. Data Management

When you hear some talk about Data Governance, it is hard to decipher whether they’re really talking about Data Governance or if they’re really talking about Data Management or some ambiguous conglomeration of the two. The DAMA Dictionary of Data Management defines Data Governance as “The exercise of authority, control and shared decision making (planning, […]

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“Schema-less” Big Data

Much has been touted about Big Data being “schema-less,” thus minimizing data modeling, especially Physical Data Modeling. At a low level (e.g., Map Reduce level), there is some truth to this as Map Reduce works with key value pairs. In the Map phase of a Map Reduce job, the input data is read in and […]

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