<---- Click on image to view GRAPH ANIMATION
In the first part of this two part blog series, I discussed the competitive importance of cross-functional analytics . I also proposed that by treating Data and Analytics as a network of interconnected nodes in Gephi , we can examine a statistical metric for analytics called Degree Centrality . In this second part of the series I will now examine parts of the sample Healthcare industry graph animation in detail and draw some high level conclusions from the Degree Centrality measurement for analytics.
In this sample graph , link analysis was performed on a network of 3428 nodes and 8313 directed edges. Majority of the nodes represent either Analytics or Source Data Elements. Many Analytics in this graph tend to require data from multiple source systems resulting in cross functional Degree Centrality (connectedness). Some of the Analytics in this study display more Degree Centrality than others.
The zoomed-in visualization starts with a single source system (green) with its data elements (cyan). Basic function specific analytics (red) can be performed with this single Clinical source system data. Even advanced analytics (Text Analysis) can be applied to this single source of data to yield function specific insights.
But data and business never exist separately in isolation. Usually cross-functional analytics emerge with users looking to gain additional value from combining data from various source systems. Notice how these new analytics are using data from source systems in multiple functional areas such as Claims and Membership. Such cross functional data combination or data coupling can now be supported at various levels of sophistication. For instance, data can be loosely coupled for analysis with data virtualization, or if requirements dictate, it can be tightly coupled within a relational Integrated Data Warehouse.
As shown in the graph, even advanced analytics such as Time Series and Naïve Bayes can utilize data from multiple source systems. A data platform that can loosely couple or combine data for such cross-functional advanced analytics can be critical for efficient discovering insights from new sources of data (see discovery platform). More importantly as specific advanced analytics are eventually selected for operationalization, a data platform needs to easily integrate results and support easy access regardless of where the advanced analytics are being performed.
Degree Ranking for sample Analytics from the Healthcare Industry Graph
|3||How can we reduce manual effort required to evaluate physician notes and medical records in conjunction with billing procedure codes?|
|10||How can number of complaints to Medicare be reduced in an effort to improve the overall STAR rating?|
|22||What is the ratio of surgical errors to hospital patients? And total medical or surgical errors? (Provider, Payer)|
|47||What providers are active in what networks and products? What is the utilization? In total, by network, by product|
|83||What are the trends over time for utilization for patients who use certain channels?|
|104||What is the cost of care PMPM? For medical, For Pharmacy, Combined. How have clinical interventions impacted this cost over time?|
The sample analytics listed above demonstrate varying degree of cross-functional Degree Centrality and should be supported with varying level of data coupling. This can range from non-coupled data to loosely coupled data to tightly coupled data. As the number of Analytics with cross-functional Degree Centrality cluster together it may indicate a need to employ tighter data coupling or data integration to drive consistency in the results being obtained. The clustering of Analytics may also be an indication of an emerging need for a data mart or extension of Integrated Data Warehouse that can be utilized by a broader audience.
In-Degree Ranking for sample Data Elements from the Healthcare Industry Graph
|46||Accounts Receivable*PROVIDER BILL-Bill Payer Party Id|
|31||Clinical*APPLICATION PRODUCT-Product Id|
|25||Medical Claims*CLAIM-Claim Num|
Similarly if Data start to show high Degree Centrality it may be an indication for re-assessing whether there is a need for tighter coupling to drive consistency and enable broader data reuse. When the In-Degree metric is applied, Data being used by more Analytics appears larger on the graph and is a likely candidate for tighter coupling. To support data design for tighter coupling from a cross functional and even a cross industry perspective Teradata offers reference data model blueprints by industry. (See Teradata Data Models)
This calls for a data management ecosystem with data analytics platforms that can easily harvest this cross-functional Degree Centrality of Analytics and Data. Such a data management ecosystem would support varying degrees of data coupling, varying types of analytics, and varying types of data access based on data users. (Learn more about Teradata’s Unified Data Architecture.)
The analysis described above is exploratory and by no means a replacement for a thorough architectural assessment. Eventually the decision to employ the right degree of data coupling should rest on the full architecture requirements including but not limited to data integrity, security, or business value.
In conclusion, what our experiences have taught us in the past will still hold true for the future:
• Data sources are exponentially more valuable when combined or integrated with other data sets
• To maintain sustained competitive advantage business has to continue to search for insights building on the cross-functional centrality of data
• Unified data management ecosystems can now harvest this cross-functional centrality of data at a lower cost with efficient support for varying levels of data integration, analytic types, and users
Contact Teradata to learn more about how Teradata technology, architecture, and industry expertise can efficiently and effectively harvest this centrality of Data and Analytics.
 Gephi is a tool to explore and understand graphs. It is a complementary tool to traditional statistics.
 Degree centrality is defined as the number of links incident upon a node (i.e., the number of ties that a node has).
 This specific industry example is illustrative and subject to the limitations of assumptions and quality of the sample data mappings used for this study.
Ojustwin Naik (MBA, JD) is a Director with 15 years of experience in planning, development, and delivery of Analytics. He has experience across multiple industries and is passionate at nurturing a culture of innovation based on clarity, context, and collaboration.