Quantitative Design
When you are planning on a study, you always want to ensure that the evidence obtained enables you to address the research problem effectively, logically, and as unambiguously as possible. If you begin the investigation too early, without thinking critically about what information is required, the overall problem may not be adequately addressed and the validity of the study will be undermined. Research design integrates all elements needed for the study to maximize validity and balance feasibility prior to beginning the data collection. Our quantitative design planning includes descriptive and experimental designs to provide you a blueprint for the collection, measurement, and analysis of data.
Data Governance Planning
What is it? Data governance is a system to define the rules and influence of data in order to regulate and oversee appropriate policy. These rules and policies involve decision-making and controls to ensure security (minimizing risk), accountability, management, and trustworthiness.
Data governance is different from database management, although both ensure that databases and documents are secure. Governance provides a framework and establishes why and who for data control and accessibility, whereas data management is the process that puts governance policies into action. Engaging in data governance could help companies remain responsive and properly manage their enterprise data.
There are various steps or models to enable the development of data governance structures within organizations, including determining the strategy for having an effective team to develop the data governance structure, choosing a right hierarchy for the organization, selecting data governance supporting team, developing policy and procedures, and more. All organizations need to plan how they will use data to support business outcomes. If you are unfamiliar with these processes and need guidance and support, let us help you.
How can we help you?
- Help identify data governance levels
- Provide an evaluation of data’s value and meaning to the enterprise
- Review rules and regulations for data security
- Review rules and regulations for data compliance
- Offer appropriate model and hierarchy for a good fit of an organization
- Build a reasonable data governance framework
- Identify the right governance program fit
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Learn MoreArticles and White Papers About Data Governance Planning
Trends in Data Governance Planning: Insights and Innovations for Medical Health Research
Executive Summary This content highlights emerging trends in data governance planning tailored to medical health research. As healthcare becomes increasingly data-driven, strong governance frameworks are essential for protecting sensitive patient information and avoiding ethical, legal, and financial risks. The discussion defines data governance as the policies, technologies, and regulations that...
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What Types of Data Should You Track?
Articles and White Papers About Data Governance Planning What Types of Data Should You Track? Read More 5 Strategies for Ensuring Ethical Data Handling in Nonprofit Quantitative Research Introduction With the rapid advancement of technology, ethically engaging with data is more imperative than ever, particularly in the realm of quantitative...
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5 Strategies for Ensuring Ethical Data Handling in Nonprofit Quantitative Research
Introduction With the rapid advancement of technology, ethically engaging with data is more imperative than ever, particularly in the realm of quantitative research. This is especially true for nonprofits, given the significant trust they are afforded due to the nature of their work. The Nonprofit Alliance highlights the gravity of...
Read MoreFAQ About Data Governance Planning
Ideally there is less than 5% missing data, but the importance and impact of missing data depends upon what data is missing, what type of data is collected, the intended analysis and the relationship between variables that are missing large amounts of data.
Particular assumptions about the shape of the distribution, parameters, variance and other data set factors need to be met for specific types of analyses. Failing to meet assumption requirements will severely undermine analysis potential and relevance.
Data mining seeks to discover patterns in data whereas data analysis tests a hypothesis to which the answer affects processes or phonemena.
Analytics projects generally require research design/methodology, data collection, data cleaning, analysis, results, presentation, and implementation. There are numerous intermediate processes for various steps depending on how customized and complicated the project is.
Research that is not practical or readily adaptible for implementation is wasteful. Determining the correct advanced analytics that are ready for actionable steps is a critical part of research design and methodology.
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Peggy Ostrander, DNPc, APRN, FNP-C Plano, Texas