DATA QUALITY ASSESSMENTTHROUGH EXPLORATORY DATA ANALYSIS USING DATA QUALITY HUB

Why Data Quality? Why Now?Gartner Research indicated that by 2017, 33% of the largest global companies will experience an information crisis due to their inability to adequately value, govern and trust their enterprise information. In addition, data quality impacts business driven initiatives by only achieving 40% of the anticipated business value from projects and impacts labor productivity by 20%. Creating and measuring business metrics correlating to financial outcomes may improve operational efficiency, business processes, analytics, and overall productivity for decision making. Based on the survey conducted in our Client Value Networks (CVN), here’s why you have to start thinking about data quality in your organization:

• There seems to be disconnect between business’s P&L and data quality improvements.• Information leaders and managers continue to struggle getting data quality onto their business agendas.• Organizations struggle to find business champions to solve data quality issues.• Data governance councils often come across diverse and conflicting definitions of the same data.• IT leaders seemed to focus only on project specific data quality deliverables instead of data quality information culture.• High cost for bad data quality such as risk assessment, member enrollment, and claims adjudication.• As payer industry is becoming increasingly customer centric, payers are building analytics to serve members. However,

analytics is “Garbage In, Garbage Out”.

Why build Payer Data Quality Hub with Highpoint Solutions?With Payer Data Quality Assessment using Data Quality Hub (PDQH) from HighPoint Solutions, you benefit with Proof of Technology (POT), User Acceptance, and tangible outcome on areas of improvement for data quality using your data. PDQH utilizes Informatica Data Quality (IDQ) and Proactive Monitoring for Data Quality (PMDQ) to build prototypes (on premise or cloud based solution) that can be productionized upon user approval. In addition, our CARE methodology is a metadata driven approach which will assist your needs utilizing your current investment of data quality technology stack.

Keep reading to find out more advantages of PDQH

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PDQH addresses the challenge of discipline of data quality with respect to technology and governance and adds payer specific data quality touch into the solution. The following are some PDQH advantages:

• Pro-active data quality identification, notification, and data trending of information assets’ data quality over time can captureconsistent data quality issues to be addressed in the source.

• Major events such as a large, new employer group sign up or data sourcing from a third party vendor can trigger many dataquality issues in the internal IT systems such as data warehouse and operational data stores. Utilizing re-usable rules builtusing IDQ will assist in proactively identifying data quality issues before it runs through internal IT systems.

• Payer specific requirements catalog provides a head start on data subject areas with requirements that are generic in payerindustry, especially the ones that has regulatory compliance such as claim status codes, ICD codes, and physician attributessuch as NPI numbers.

• Our user acceptance methodologies involves users in early stages of implementation creating ownership of data qualitymetrics that can be utilized for tangible return on investment for your data assets

• Available options of choosing an in-house or cloud based solution using Informatica Data Quality will let you choose thetechnology based on budget constraints.

• Significant financial savings can be achieved by avoiding custom report development for data quality metrics and by utilizingout of box IDQ functionalities such as scorecards, profiles, and rules which provides agile prototyping.

• Cost savings on return postal mails for incorrect or non-existing addresses can be achieved using our automated addressclean up templates for individuals and organizations.

• Utilizing IDQ rules for data quality team for root cause analysis can gain faster closure of data quality issues especiallydealing with atomic systems such as data warehouses where original data such as member and claims information is notmodified.

HighPoint Tips on how to improve your Data Quality• Establish a governing body agreeing on foundational data quality metrics and measures.• Create a causal link between the identified metrics and measures and business outcome bringing tangible business benefits.• Assign business accountability to champion data quality. An example would be to enable data stewards to collaborate with

specific business units.• Establish a process for continuous improvement. An example of such activity is data profiling, which can reveal new

opportunities of data quality improvement.• Create specific dashboards that fit the need. Examples include member enrollment, facility, professional claims, and clinical

programs.• Track data quality investment and improvements like any other information asset such as data warehouse.