The Healthcare Data Language Crisis
Healthcare organizations have been capturing, collecting, transforming, accessing, analyzing, securing, and storing large amounts of data for many years, and yet there is still doubt in trusting and/or understanding their data and how to utilize it. For many healthcare organizations, data is still a vast, field of information and definitions, with no common understanding and unrealized potential.
And this is why it’s necessary for healthcare organizations, and for those working on digital healthcare projects, to assure that everyone understands the language of data. Lack of common understanding regarding data terms used extensively in day-to-day business conversations results in delays, confusion, and chaos.
All healthcare models spanning payer, provider and life sciences will need to understand that success and growth is dependent on everyone in the organization understanding the language of data.
In this post, we’ll first look at what data literacy means and why it’s important for the culture of healthcare organizations working in the digital space. We’ll then define for you some of the most used data-related terms.
Let’s Define Data Literacy
Data literacy is the ability to read, understand, create, and communicate data as information. Gartner defines data literacy as, “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, application, and resulting value.”
Meanwhile, data literacy describes the ability to read, work with, analyze, and argue with data, according to Raul Bhargava and Catherine D’ignazio from MIT and Emerson College.
Successful organizations must be literate in data, specifically how they communicate the information and operationalize the meaning of their data. To get to the deeper meaning of their data, data understanding and literacy is essential.
Data Literacy is the Way Forward
Gartner Inc.
One leading data executive recently defined the need for data literacy as the need to have data as a second language throughout your organization. While data dictionaries, business glossaries, and documented definitions are the foundational beginning, data literacy represents more than that. It comes from a place of understanding, that is, not just understanding ‘data speak,’ but being able to converse with one another about the lifecycle of data in the data ecosystem.
Simply put, data literacy is a culture change with roots in better understanding of one another. Perhaps most importantly, data literacy is also the cornerstone to data-driven decision-making throughout your organization. Want a digital transformation? Then you need to start by making sure people can understand data, what that is—and know how to speak to it.
Defining the language of data within your organization is necessary for several reasons. It allows for more trust, better conversations, and less confusion around data.
Also, large volumes of data are now available, if employees cannot access, use, and interpret it the data value is not realized. Plus, poor data literacy impedes an organization’s digital transformation growth.
Meanwhile, understanding data definitions helps to communicate clear expectations and enables quicker deliverables with minimum iterations. It allows organizations to have a common understanding of a word or subject—you’re on the same page when discussing or reading business cases with others in your organization.
To connect on a personal level with customers, it is important to develop a specific linguistic style and talk the same language across the organization.
Let’s Define the Data
It begins with defining the right terms within your organization and at Emids we recommend you define the following terms within your organization. Take a look at the table below.
Term | Definition |
Data |
Discreet entities that are described objectively without interpretation. |
Information |
Data that are interpreted, organized, or structured. |
Knowledge |
Information that is synthesized so that relationships are identified and formalized. |
Descriptive Analytics |
The examination of data or content to answer the question “What happened?” It is typically characterized by traditional business intelligence (BI) and data visualization. |
Diagnostic Analytics |
A form of advanced analytics that examines data to answer the question “Why did it happen?” You can achieve it with the help of techniques such as data mining, statistics, and machine learning. |
Predictive Analytics |
A form of advanced analytics that examines data to answer the question “What is likely to happen?” You can achieve it with the help of techniques such as machine learning and Artificial Intelligence (AI). |
Digital |
Refers to new ways of doing business (new business models) coupled with emerging technologies (Big Data, Cloud, RPA, IoT, Mobility, AI/ML/NLP, Blockchain, etc.), creating new consumer experiences, values, revenue & business results. |
Digital – Consulting |
Enablement Strategy, Consumer Experience Design, Architecture Design, Business Transformation. |
Transformation |
The process of using digital technologies to create new—or modify existing —business processes, culture, and customer experiences to meet changing business and market requirements. It deals with reimagining of business in the digital age. (Ref: Salesforce) |
Profiling |
It’s the technology for discovering and investigating data quality issues, such as duplication, lack of consistency, and lack of accuracy and completeness. |
Migration |
The process of moving a company’s digital assets, services, databases, IT resources, and applications either partially, or wholly, into the cloud. Cloud migration is also about moving from one cloud to another. |
Integration Bridge |
A software component installed on the customer system to mediate between Agile Manager and on-premises applications located behind firewalls, such as ALM, enabling two-way communication between the two. |
Data Science |
The discipline of applying advanced analytics techniques to extract valuable information from data for business decision-making and strategic planning. It brings together fields such as data mining, statistics, mathematics, machine learning, data visualization, and software programming. |
Data Scientist |
A person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics. |
Data Distribution |
A data distribution is a function or a listing which shows all the possible values (or intervals) of the data. It also (and this is important) tells you how often each value occurs. |
Interface |
The place at which independent and often unrelated systems meet and act on or communicate with each other. |
Interface Engine |
An interface engine/integration engine is a software program that processes data between numerous Healthcare IT systems. |
Mapping |
Data mapping is the process of matching fields from one database to another. It’s the first step to facilitate data migration, data integration, and other data management tasks. (Ref: Talend) |
Data Management |
Consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes. |
Data Governance |
A framework and a set of practices to help all stakeholders across an organization identify and meet their information needs. |
Data Literacy |
The ability to read, write and communicate data in context. It includes an understanding of data sources, analytical techniques, business applications, and resulting value. |
Data Culture |
Refers to values, behavior, and norms shared by most individuals within an organization regarding data-related issues. Broadly, it refers to the ability of an organization to use data for informed decision-making. |