Beginning the Data Journey
We’re all engaged in some sort of data analysis every day of our lives. This is especially true in our highly technological times. Many of the daily decisions we make are driven by the analytical process. For example, when you choose a product based on online reviews or star ratings, or use your smartwatch to track your exercise or sleep and then make decisions based on results, you are doing data analysis.

The Alliance for Healthier Communities, Ontario’s provincial Community Health Centres association, has many member organizations that are very skilled at getting a holistic view of the services they provide and the populations they serve. However, as a member-driven association, there is significant value for the Alliance to be able to show the impact that either a subset of or all our member organizations are having. Since the scope of this impact can be at the centre, community, regional or even provincial level it is important for us to fully understand and be able to articulate this.

Setting the Groundwork for Shared Data Analysis Work
As an alliance of like-minded organizations, there were several fundamental considerations that guided the development of our holistic view of how to do data analysis among multiple community health centres via the Alliance.

Heterogenous environment: As all of our member community health centres are autonomous, they are free to use different software to manage the electronic health record or any other data they need to capture. This means that we needed to develop a deep understanding of workflows and best practices for data collection within an interdisciplinary care environment.

Accessibility: Each of our member organizations is a Health Information Custodian and responsible for the privacy and security of their clients’ health records. As owners of their own data, centres had to establish common ground for data sharing and analysis.

Common classification system: We needed all of our member centres to collect key client data using a common encounter classification system. For our members, this meant using the Electronic Nomenclature of Disorders and Encounters for Family Medicine (ENCODE-FM). This is a systematic and hierarchical clinical terminology system for family medicine that simplifies comparability.

Evaluation framework: Ontario CHCs use a robust evaluation framework which serves to further strengthen and contextualize the data collected through ENCODE-FM. The socio-demographic information they collect adds another deeper layer of analysis.

Data governance: It was a substantial achievement to get 80+ organizations to agree on policies and procedures related to the collection and quality assurance of a common data set. Establishing a robust, data governance process that was managed by a committee comprised of member organizations was a crucial step to success.

Addressing and aligning these various factors helped the Alliance and our member health centres to articulate, cultivate and continuously grow a shared vision of achieving better health care throughout Ontario through advanced analytics.

Quantity or Quality
Data analytics can be separated into quantitative data analysis and qualitative data analysis. It is important to be clear on which type of analysis you will engage in.

Quantitative data analysis involves the evaluation of numerical data with quantifiable variables that can be compared or measured statistically. It often describes a situation or event, answering the ‘what’ and ‘how many’ questions you may have about something. This is research which involves measuring or counting attributes (i.e. quantities).

A quantitative approach is often concerned with finding evidence to either support or contradict an idea or assumption you might have.

Qualitative data analysis is more interpretive — it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. Qualitative data does not simply count things but is a way of recording people’s attitudes, feelings and behaviours in greater depth. Qualitative data analysis is:

• Often based on grounded theory practices
• Answers the ‘why?’ questions
• Pays greater attention to individual cases

Data analytics applications involve more than just analyzing data. Particularly on advanced analytics projects, much of the required work takes place upfront, in collecting, integrating and preparing data and then developing, testing and revising analytical models to ensure that they produce accurate results.