Quality Assurance Since quality assurance precedes data collection, its main focus is 'prevention' i. Prevention is the most cost-effective activity to ensure the integrity of data collection.
This proactive measure is best demonstrated by the standardization of protocol developed in a comprehensive and detailed procedures manual for data collection.
Poorly written manuals increase the risk of failing to identify problems and errors early in the research endeavor. These failures may be demonstrated in a number of ways:.
An important component of quality assurance is developing a rigorous and detailed recruitment and training plan. Implicit in training is the need to effectively communicate the value of accurate data collection to trainees Knatterud, Rockhold, George, Barton, Davis, Fairweather, Honohan, Mowery, O'Neill, The training aspect is particularly important to address the potential problem of staff who may unintentionally deviate from the original protocol.
Since the researcher is the main measurement device in a study, many times there are little or no other data collecting instruments. Indeed, instruments may need to be developed on the spot to accommodate unanticipated findings. A clearly defined communication structure is a necessary pre-condition for establishing monitoring systems.
There should not be any uncertainty about the flow of information between principal investigators and staff members following the detection of errors in data collection.
A poorly developed communication structure encourages lax monitoring and limits opportunities for detecting errors. Detection or monitoring can take the form of direct staff observation during site visits, conference calls, or regular and frequent reviews of data reports to identify inconsistencies, extreme values or invalid codes.
While site visits may not be appropriate for all disciplines, failure to regularly audit records, whether quantitative or quantitative, will make it difficult for investigators to verify that data collection is proceeding according to procedures established in the manual. Data collection instead allows them to stay on top of trends, provide answers to problems, and analyze new insights to great effect.
After data collection, all that data needs to be processed, researched , and interpreted by someone before it can be used for insights. Data scientists are now one of the most sought after positions.
To become a data scientist you need a solid foundation in computer science, modeling, statistics, analytics and math. What sets them apart from traditional job titles is an understanding of business processes and an ability to communicate quality findings to both business management and IT leaders in a way that can influence how an organization approaches a business challenge and answer problems along the way.
What is data, and why is it important? June 28, Import. Humans vs Machines Human-readable also known as unstructured data refers to information that only humans can interpret and study , such as an image or the meaning of a block of text. Data in the news When it comes to the types of structured data that are in Forbes articles and McKinsey reports, there are a few different types which tend to get the most attention… Personal data Personal data is anything that is specific to you.
Transactional data Transactional data is anything that requires an action to collect. Web data Web data is a collective term which refers to any type of data you might pull from the internet, whether to study for research purposes or otherwise. Sensor data Sensor data is produced by objects and is often referred to as the Internet of Things. When does data become Big Data? The importance of data collection Data collection differs from data mining in that it is a process by which data is gathered and measured.
The sexiest job of the 21st century? Data blogs Flowing Data — run by Dr. Nathan Yau, PhD, it has tutorials, visualizations, resources, book recommendations and humorous discussions on challenges faced by the industry FiveThirtyEight — run by data-wiz Nate Silver, it offers data analysis on popular news topics in politics, culture, sports and economics Edwin Chen — the self-named blog from the head data scientist at Dropbox, this blog offers hand-on tips for using algorithms and analysis Data Science Weekly — for the latest news in data science, this is the ultimate email newsletter No Free Hunch Kaggle — hosts a number of predictive modeling competitions.
Their competition and data science blog, covers all things related to the sport of data science. SmartData Collective — an online community moderated by Social Media Today that provides information on the latest trends in business intelligence, data management , and data collection.
KDnuggets — is a comprehensive resource for anyone with a vested interest in the data science community. Data Elixir — is a great roundup of data news across the web, you can get a weekly digest sent straight to your inbox. Extract — San Francisco, CA October 30 — bringing together more than of the best minds in data science to combine growth hacking with data analysis to equip you to be the best data scientist in the field. Data Summit — New York, NY May — brings together government agencies, public institutions, and leading businesses to harness new technologies and strategies for further incorporating data into your day-to-day experience.
Data courses Udemy — free and paid for online courses to teach you everything you need to know Code School — learn coding online by following these simple step by step tutorials and courses Decoded — essential introduction to code that unlocks the immense potential of the digital world Data Camp — build a solid foundation in data science, and strengthen your R programming skills.
One thing that is important to note is that since secondary data is essentially secondhand information, the process for collecting secondary data is often less time consuming and relatively easier than the process that one must go through for the collection of primary data. Mosaic data refers to data and information that is collected by putting together bits and pieces of information that is already publicly available. The major difference between mosaic data and secondary data is that while the sources of both of these types of data are already publicly available, collectors of mosaic data need to reach their own conclusions with the data.
Now that you understand the importance of data collection and the different technique that can be used, it will be easier for you to get the results that you need.
To get the most out of your collected data, connect or upload it into AnswerMiner and explore your data in the easiest way. Resources Data Visualisation Catalogue. December 11, 3 min read. Why is Data Collection Important?
What Are the Different Types of Data? Primary Data Primary data is data that is collected through firsthand experiences, studies or research. Secondary Data Unlike primary data, secondary data is data or information that has been collected from other sources. Mosaic Data Mosaic data refers to data and information that is collected by putting together bits and pieces of information that is already publicly available.
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