You may have heard of the concepts of “Analytics Center of Excellence (CoE)” or “Competency Center (CC)” (1).   This is nicely explained in the reference (Wright-Jones, 2015).  This refers to a “cross-organizational group responsible for a specific function …, with an ultimate goal to reduce time to value”.  The main purpose of a CoE is to “establish, identify, develop, and harness cross-functional processes, knowledge, and expertise that have tangible benefits for the business”.  Making this concept become reality is fundamental to the success of healthcare analytics given the critical and central role that analytics plays in the success of Healthcare Organizations (HCOs).   There must be a cross-organizational approach to analytics (the people,  processes and technologies) in order for HCOs to be successful in obtaining insights and then applying the insights to advance the strategic goals of the organizations.

In my previous blog post Collect, Connect, Analyze and Apply – Four Data and Analytics Competencies that All Digital Healthcare Organizations Must Master , I talk about four competencies that an organization must master to be successful.    In this post, I will talk about the competencies that the Leaders of Data and Analytics programs must master.  Just as a Center of Excellence is focused on the organization rather than just the analytics department, the Leaders of the analytics programs must focus on providing leadership to their department, and just as importantly, to the entire organization if they want to truly be “Leaders of Excellence”.  There are three main realms that a leader needs to master – data excellence, analytics excellence, and leadership excellence.

I am indebted to the IT research and advisory firm Gartner, Inc. for the unbelievable amount of research that they have conducted on data and analytics, and I will reference some of their research here.  I don’t usually make plugs for specific organizations, but I have personally obtained an incredible amount of knowledge from their research, and I highly recommend them.  The basic framework for this article was presented in the Gartner research article by Freidman et.al (2018) (2)  and it really challenged me to think about what else was needed for analytics leaders to be successful.  The Freidman article talks about the three “vectors of change and opportunity” that must be “mastered in order to be successful” – data management excellence, analytics excellence, and excellence in change management and leadership.   I have expanded upon this basic framework, adding specific information in the data and analytics sections, as well as talking about additional leadership skills that are needed.

I will use the people, process and technology framework to discuss the elements that are needed under the three categories of data excellence, analytics excellence and leadership excellence.  These three framework elements are important in the data excellence and analytics excellence realms, but in the leadership realm it is mainly the people and the processes that are most important.  While the data/analytics leader does not have to be a deep subject matter expert in the data and analytics realms, they must have deep knowledge of what is necessary to be “excellent” in these realms, and be able to provide leadership and guidance to these departments and to the organization on matters pertaining to these.   However, the data/analytics leader does need to be a deep subject matter expert in the leadership realm, because that is where they bring true value to the organization.

 

Data Excellence

The first realm that needs to be mastered is the data.   It all starts with the data.  If you do not have good quality data that is trusted throughout the organization, then it does not really matter how good your analytics capabilities are or how good your organizational transformation capabilities are – they will be hampered by the lack of good data.

People

You must have people who have deep skill sets in acquiring, transforming, storing, and retrieving data.  It is important to have the capabilities of “Data Engineers”.  Data Engineers are responsible for establishing and maintaining the data and data systems architecture, and work with master data management and data quality.  The organization needs to have the capability to manage data wherever it resides.  This includes whether the data resides in the source systems, enterprise data warehouses, data lakes, logical data warehouses, etc. There needs to be deep capabilities by the people in your organization to manage this data.

Processes

The processes revolve around the ability to manage the data in your organization.  This includes an organization-wide philosophy of moving from collecting data to connecting the data.  It also includes the management of the data, master data management, metadata management, and the use of data catalogs.

There needs to be a focus on not only collecting data, but increasingly on connecting the different data sources (3).  In order for valuable insights to be obtained, data from multiple disparate sources must be connected and analyzed.  It is impractical to put all the data into an enterprise data warehouse and then access it only from there.  There is a need to connect the clinical data from the electronic medical record, financial information from the financial system, customer information from the customer relationship management system, social information from social sources, etc.

Data Management Capabilities

As Edjlali and Friedman (2017)(4) point out, every data and analytics use case requires the following data management capabilities:

• • • Describe:  Describe where the data resides and exactly what type of data it is.  This includes the master data elements used across the organization, as well as the metadata that is used to describe the data.
    • Organize:  Organize your data so that it can be retrieved easily and consumed by multiple applications and people.
    • Integrate:  Have the capability to integrate multiple disparate data sources.
    • Share:  Make the data available to multiple applications and people.
    • Govern:  Provide high level governance over the data process.
    • Implement:  Implement the processes that rely on trusted data.  These could be data exploration for insight by a data scientist, or the development of a report by a distributed analyst embedded in a specific department.

 

Master Data Management (MDM)

Master data is the core data that is essential to operations (6).     This is typically the important data that is put in an enterprise data warehouse.  This is be data where the definition of that data elements is well defined, agreed upon, and understood across the enterprise.   This data has to be extremely trustworthy and needs to undergo very robust validation procedures.

Master data management (MDM)  is defined as a comprehensive method of enabling an enterprise to link all of its data to a common reference point (7).   There must be a standardized way to describe, format, store, and access data.  In addition this master data must be updated on a regular basis.    The creation of a data dictionary ( a collection of descriptions of the data objects or items in a data model (8)) is essential to allow this standardization.  There must be a vision and strategy for how MDM is used in the organization, since this master data is central to all import business process, and the there must be confidence and trust in this data (5).

Metadata Management

Metadata is “data that serves to provide context or additional information about other data.  It may also describe the conditions under which the data stored in a database was acquired.” (9)  The amount of data that HCOs have access to now is staggering.   Without a rigorous approach to understanding what data a HCO has access to, it will be impossible for them to realize the full potential of that data.   That is why metadata management is so important.  Metadata is the key to cataloging, identifying and evaluating an organization’s information assets and how they are managed (10).   This is important not only for structured data but is even more important for the larger amount of unstructured data that exists. This directly impacts the data management capabilities referenced above, and will add value to the process if it enables workers to ” describe, organize, integrate, share, govern and implement information assets. (4)”

Data Catalogs

Data catalogs maintain inventories of data assets through the discovery, description and organization of data sets.  They  “offer a fast and inexpensive way to inventory and classify the organizations increasingly distributed and disorganized data assets and map their information supply chains to limit data sprawl. (11)”   However the data catalog initiatives must be linked to the broader metadata management programs described above, as they go hand-in-hand.

Technologies

The technologies underlying the data management excellence realm involve the collection, transformation, storage, retrieval, and connection of data.  This includes the data source systems that are creating the original data, and the numerous data storage systems which include the source systems, the enterprise data warehouse, the logical data warehouse, and data lakes (or whatever term you like for this capability).  This also includes the technology for documenting the master data elements, metadata, and data catalogs.  It is clear that HCOs must have a variety of methods to store data, as it is not practical nor economical to put everything into a structured enterprise data warehouse anymore.  Most HCOs are challenged with storing, retrieving, and analyzing the overwhelming majority of their data – their unstructured data.  These challenges must be addressed – from all three perspectives – people, process and technology.

 

Analytics Excellence

The second realm that needs to be mastered is the analytics realm.  Once the HCO has created data storage and management capabilities, they need to be able to analyze the data to obtain actionable insights and apply these insights to business questions and needs.   The types of analytics that can be applied to the data range from descriptive analytics (what happened),  diagnostic analytics ( why did it happen ),  predictive analytics ( what will happen ), to prescriptive analytics ( what should happen ).    The  analytical techniques  range from basic statistics using spreadsheets all the way up to machine learning  using advanced techniques such as neural networks.    All HCOs  will need to develop these capabilities,  including the advanced capabilities, which in some cases may have to be outsourced.

People

The people component of analytics excellence is in some respects the most challenging.    Unless you are associated with a teaching hospital or University or live in a community with access to highly skilled analytical workers, it can be extremely challenging to both recruit and retain skilled workers.    In order to overcome these challenges most organizations will have to both recruit from the outside, and develop programs to train existing employees on the desired skills and capabilities.    This applies not only to the employees directly under the control of the analytics leader in the analytics department, but also applies to the analysts and citizen data scientists who reside in the other departments.    The analytics leader is responsible for the training and development of all data analysts.   These development programs may take the form of internal classes, access to web based learning, and formal educational classes taking through colleges and universities.  The key here is to support the employees in their journey and to provide them the appropriate funding and time off to pursue these skill developments.  The field of analytics is changing so rapidly that this will be a journey with no finish line, and most employees will need continual development no matter what their level of expertise.

There is a need in some organizations for analysts to move from simple report writers, only providing the data and information that they were asked to provide to the end user – to true analysts providing insight to the end user that helps them answer the business questions or needs.  This requires the analyst to have an understanding of the “why” of the report request as well as some understanding of the domain within which the end user operates.  The analyst then needs to apply what ever analytical techniques they feel are necessary to gather insight from the data so that it may directly answer the business users needs.  This requires an understanding of the analytical techniques appropriate to that analyst’s level of expertise.  This may vary from providing the basic descriptive statistics all the way up to running predictive algorithms and generating forecasts.  The analyst should feel free to suggest to the business user appropriate methods of analysis when these are appropriate, and take every opportunity to educate the business users on how to be more data and insight driven.

Processes

One of the processes that needs to be developed is the end-to-end analytics report process.  This starts off with the report request.  Ideally this should be a standardized web-based report request that is used both centrally in the analytics department as well as by the distributed analysts.  Once the initial report request is filed, the analyst performing the analysis should meet with the end user to discuss their request, to make sure that they understand the need, and to suggest the various analytical methods that could be used to gain the insight.    Once the analysis has been completed,  the analyst needs to meet with the end user to review the analysis for the insights that were obtained.  Part of the process will be to decide how to deliver the data and insights.  Some end users will be happy with an excel spreadsheet and some simple graphics, while others will need dashboards developed and integration of the insights into work flows and processes.

Another process that needs to be developed is deciding who should analyze the data.  Given the increasing need to analyze the growing amount of data that is being collected and connected, it is impractical to rely on a centralized analytics department to deliver all of the insights that the organization needs to become more data and insight driven.  Therefore, some end users who may not even be analysts should be able to perform some data analyses themselves, ie self-service BI (SSBI – Self Service Business Intelligence).  In other cases it will require the expertise of highly trained data scientists and statisticians to perform these analyses.  Obtaining a modern BI (Business Intelligence) and analytics platform is key to acquiring this spectrum of analytics capabilities to perform these diverse analyses.

Technologies

There are multiple technologies that will be needed to perform these analytics.  At the present time there is no single technology platform that can perform all of the analytical tasks required by a digital health care organization.  Therefore a suite of applications will be required.  As described in the article by Tapadihnas (2016) (12) Gartner uses the term modern BI and analytics platform which is a 3-tiered platform of complementary but interrelated analytic capabilities.  This allows employees with different levels of analytics expertise to use the platform to obtain the insights they need for their level of expertise.  The very first level is the information portal.  This level is designed to be used by end users and report writers to generate reports and dashboards.  The second level is the analytics work bench.  This is used by information analysts to do more sophisticated data discovery.  The third level is the data science laboratory.  This is designed to be used by data scientists and statisticians to perform more advanced analytics including machine learning and other artificial intelligence techniques.  This modern BI and analytical platform should be able to take in data from disparate sources and should be able to create sophisticated dashboards with the data and insights.

Almost all HCOs have an Electronic Medical Record (EMR) now.  Most of the large mega-vendors have robust data storage capabilities within their EMR, and have the capability to put structured data into an enterprise data warehouse (EDW).  Some are developing the capability to store data in a “data Lake”, both internal data from their EMR, and external data from other data sources as well.  However they do not all have advanced analytics capabilities built into their platforms.  So there is always a question on how much of the underlying EMR vendors analytical capabilities does an organization use and when does an organization look to an outside vendor to fill in those gaps in capabilities.  The ability to innovate clearly lies with non-EMR vendors who only have to deal the analytical capabilities that they are trying to develop.  At some point the mega-vendors usually catch up to the innovators, however it may take an unacceptable time frame for this to happen.  Therefore in most organizations it will require a mix of EMR vendor and non-EMR vendor analytical technologies to accomplish all that a healthcare organization needs to accomplish.

Data visualization is another area where both the people skills must be developed and the most effective technology acquired.  It is no longer acceptable for reports to be delivered in an excel spreadsheet without any graphics.  Some end users may be comfortable with a spread sheet but visualizing data in a graphical format is clearly a superior method of communicating insights.  Therefore organizations need to develop both the people skills to effectively communicate data graphically, and acquire the technology platforms that make visualizing the data insights easy.

 

Leadership Excellence

This is THE realm where the data and analytics leader needs to have deep subject matter expertise as this is the area where they deliver real value to the organization.

This leadership applies to both the data and analytics department as well as to the organization as a whole.  At the department level, the data and analytics leader is responsible for the development of the culture of the analytics department.  They are also responsible for the development of the analysts skill sets.  They are responsible for resource allocation – both with existing personnel and financial asset allocation – and with creating a road map for future personnel and technological resource investments.

The data and analytics leader should be responsible for developing the skill sets of the deployed analysts who reside in the business units.   Talent development should apply across the enterprise, and not rely on all of the individual silos to train their analysts to the standardized level that is needed.

The data and analytics leader should have the responsibility for making the organization more data and insight driven.  This is a HUGE responsibility as it is extremely difficult for an organization to make this transition.  The most logical person to be assigned this responsibility is the data and analytics leader.  However it obviously requires the support of the executive leadership of the organization from the Board of directors to the CEO to the Senior Leadership Team.  This requires a change in the entire culture of the organization.   The culture needs to change so that everyone asks the same questions – what does the data tell us, and how can these insights be used to improve things.

In order to become more data and insight driven, the data and analytics “literacy” of the organization must be improved.  As defined by Gartner (13) ,  data literacy is “the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, the application and resulting value.”  Being data literate is a new “organizational readiness factor”.  Improving this data literacy will require another huge culture change.  The data and analytics leader should be responsible for this initiative.  The “base vocabulary” of using “information as a second language” can be understood using the VIA (Value, Information, Analytics) model.  For any analytical need the Value of that analysis needs to be understood.  This is the question, business problem, or process outcome that needs to be understood and improved.  How is the value going to be realized?  The Information component involves understanding what data is available to be analyzed, and what additional data is needed.  The Analytics component involves understanding what analytical or data science methods are appropriate to be applied to the data.  While it may be difficult for the end user to understand the analytics component of this VIA model, they should understand that it is important to look at all three of these components when considering an analytics project.

The data and analytics leader is responsible for developing the data and analytics vision and strategies, not only for the data/analytics department, but for the entire organization.   The vision and strategies need to be laser focused on understanding and improving specific strategic business priorities.  The vision and strategies of the data/analytics departments should reflect the vision and strategies of the organization.  For HCOs these priorities most often are associated with the factors laid out in the “Quadruple Aim” – improving the patient experience (quality of care delivered and satisfaction with their experience), improving the health of populations, reducing the cost of healthcare, and improving the experience of health care providers and workers.

The last area that data and analytics leaders need to have deep competence in is change management and change leadership.  Having effective change management and change leadership skills are a foundational requirement of any data and analytics leader.  Much of what the leader has to do involves change of some sort on both a micro-scale and a macro-scale.

On a macro-scale transforming the organization to a data and insight driven organization will require a tremendous amount of change leadership.  So will improving the data literacy of the organization.  So will getting the organization to understand the importance of investing in it’s data and analytics people and developing the needed skill sets to move forward.

Organizational Change Management (OCM) “describes any set of best practices for introducing and guiding specific, defined changes in the current environment.” (14)  and involves applying these best practices.  Gartner has identified seven OCM best practices:

• Define the change
• Build executive support
• Communicate to the organization
• Develop the plan
• Execute the plan
• Persist through the challenges
• Reinforce adoption

Gartner has developed a robust change leadership model called the ESCAPE model (14).  This model can be used to develop a resilient organization that is ready and able to respond to needed changes.  The ESCAPE model approaches this from a person-centered perspective.  This person-centered approach helps individuals understand the impact of changes on themselves and others, see their role in the future vision, embrace change opportunities rather than view changes as a threat, and feel a sense of ownership and responsibility for the change success.  Change leadership “requires leaders who invite participation, build trust, foster commitment and strengthen teamwork.” (14)  The goal in a change culture is to “integrate this thinking and these practices into the culture through daily activities, language and interactions”, so that change is seen as something normal and acceptable, rather than something that is negative and “done to them”.

There are two phases to the ESCAPE model.  The first phase is the Inspire phase and has three components.  The components are Envision, Share and Compose.  Individuals need to be Inpired to change by helping them Envision the change and imagine what life will be like after the change.   The new vision needs to be succinctly Composed and then communicated.  The vision needs to be Shared repeatedly and often, so that it is embedded in the organization.

The second phase is the Engage phase, and it has three components.   The components are Attract, Permit and Enable.  Early adopters need to be Attracted (recruited).  A growth mindset needs to be Permitted, and opportunities for experimentation and new behaviors Permitted.  Building, supporting and training for new structures, processes and thinking must be Enabled.

In order to be successful, leaders must get out into the organization and be the go-to person constantly advocating for and then delivering the data and analytics to answer the wicked business questions and issues that face healthcare today.

Conclusion

In order to become “Leaders of Excellence” in their organizations, data and analytics leaders must become masters of data management excellence, analytics excellence and leadership excellence.

 

References

1. Wright-Jones B.  (2015, December 10). Analytics Centre of Excellence – What’s the Value?  Retrieved from https://blogs.msdn.microsoft.com/data_insights_global_practice/2015/12/10/analytics-centre-of-excellence-whats-the-value/
2. Friedman T, Tapadinhas J, Judah S, Heudecker N, Herschel G, White A.  (2018, August 1).  Leadership Vision for 2019: Data and Analytics Leader.  Gartner research article.
3. Heudecker N, Edjlali R.  (2018, January 8). Data Management Strategies Primer for 2018.  Gartner research article.
4. Edjlali R, Friedman T.  (2017, October 23).  Modern Data Management Requires a Balance Between Collecting Data and Connecting to Data.  Gartner research article.
5. White A, O’Kane B.  (2017, November 16).  Mastering Master Data Management.  Gartner research article.
6. Rouse M.  (2012, April).  Master Data.  Retrieved from https://searchdatamanagement.techtarget.com/definition/master-data
7. Rouse M.  (2018, March).  Master Data Management (MDM).  Retrieved from https://searchdatamanagement.techtarget.com/definition/master-data-management
8. Rouse M.  (2005, September).  Data Dictionary.  Retrieved from https://searchmicroservices.techtarget.com/definition/data-dictionary
9. Metadata.  Retrieved from http://www.businessdictionary.com/definition/metadata.html
10. De Simoni G, Edjlali R.  (2018, January 11).  Develop Valuable Metadata to Exploit Digital Business.  Gartner research article.
11. Zaidi E, De Simoni G, Edjlali R, Duncan AD.  (2017, December 13).  Data Catalogs are the New Black in Data Management and Analytics.  Gartner research article.
12. Tapadinhas J.  (2016, April 12).  Select the Right Architecture Model for Your Modern BI and Analytics Platform.  Gartner research article.
13. Duncan AD.  (2018, January 22).  Data-Centric Facilitators are Crucial for Enabling Data Literacy in Digital Business.  Gartner research article.
14. Adnams S. (2017, October 12).  CIOs Need Organizational Change Management and Change Leadership for Digital Business.  Gartner research article.