Quantitative Maximization
Companies and organizations are collecting an increasing amount of data in this rapidly expanding digital age. However, there is not always a direct correlation between the amount of information/data collected and the value of the data. Our multi-disciplinarian team has extensive experience across all prominent analytic platforms, software packages, and data collection programs to maximize the value from your quantitative data.
Data Process
What is it? Data processes are as important as the data itself and provides valuable insight into operations. Companies seeking to be truly data driven often focus on the results they desire rather than the complete process required to achieve that goal. Maximizing the potential of data requires an iterative and multifaceted approach consisting of half a dozen steps, including design, collection, input and preparation, processing/analytics, output, and storage. The design state of data processes is absolutely critical to unlock the power of data. How strong is your data design? How is it iterative and does it facilitate accurate data collection, cleaning and analysis? Analyzed data must also be interpreted and then communicated visually in some capacity. Examining the processes and metrics collected and assessed go a long way into maximizing the potential of your data. Machine learning provides iterative feedback that is constantly optimizing the process of collecting data, analyzing data, evaluating and responding to data. It is a crucial tool to automate data issue identification and problem solving in order to speed up data process into valuable information.
Elite Research has thorough experience in each of the individual stages of the data processes required to maximize your data collection. More than that, they have the insight and capacity to focus on the design components and overall efficiency of the process to facilitate more agile, adaptive and rapid data collection, analysis, and data-driven strategic management.
How can we help you?
- Audit your research design state to maximize your data analysis potential
- Build a comprehensive data process strategy and model from design to implementation and iteration.
- Implement predictive modeling with advanced analytic tools and iterative processes to streamline the entire data processes.
- Provide coaching on different areas of weakness and data opportunities within your data processes.
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Learn MoreArticles and White Papers About Data Processes
Why You Should Go Beyond Microsoft Excel (Part 1)
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Which Six Assumptions Of Multiple Regression Should You Always Test?
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How You Can Use Statistics to Help Predict Trends in the Market
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5 Best Practices for Data Cleaning and Preprocessing in Independent Research Projects
Introduction Achieving data integrity is, in part, a result of engaging in data cleaning and data preprocessing before ever running those first statistical analyses to test research questions and hypotheses. While data cleaning and data preprocessing are technically separate terms, data cleaning is nested within the various steps involved in...
Read MoreFAQ About Data Processes
There are many academic, philanthropic, governmental, and professional sources of data that are available for free, at low costs as well as higher costs. Understanding the benefit, need, and fit for these alternate data sources will help determine which sources can be practical and profitable for your needs.
Data reviews will generally save time and money that reasonably and affordably replicate research that is helpful, necessary, or enlightening in areas of a program or project that will impede, expedite, or ignore critical components of whatever you are trying to do.
Predictive analytics and machine learning take information that is readily available to you and make predictions about future events based upon your data, existing theories, algorithms, and probability. These approaches may help identify future opportunities and threats in ways that allow you to have as much control and time to react to imporant events as possible.
Good research design is well balanced, usually incorporates a mixed method approach combining quantitative and qualitative processes and is fully informed by known assumptions and limitations given constraints, targeted outcomes, and informed input by requisite stakeholders.
Data processes benefit from a multidisciplinarian approach across qualitative and quantitative skillsets. Assessing the research design, data collection, data cleaning, analysis, results and reporting steps are all important components of robust data processes. Sometimes a fresh perspective or third party consultant or coach can help shore up potential weaknesses in process.
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Peggy Ostrander, DNPc, APRN, FNP-C Plano, Texas