Gathering information for data collection
The process of gathering and quantifying information, data collection enables researchers to acquire the relevant information from different sources to answer various questions, evaluate outcomes, or even test hypotheses. As corporations gather information from different sources to collect data, most of the information becomes components of research to understand different operational aspects including customer retention, service feedback, customer billing, a business model based customer service, etc.
Regardless of the industry, data collection requires accurate methodologies to maintain research integrity.
Consequently, improper collection of data can result in
- Inability to answer research questions
- Wastage of resources
- Misguided information provided to researchers
- Inaccurate public policy decisions
In order to understand if the company is gathering useful data, data analysts should look at two main components that assure the integrity of data collection namely quality assurance, that is based on activities that take place before data collection; and quality control, which refers to those events during and after data collection. The importance of quality check allows the users to understand the reliability of these data points, and also analyze if the errors that may have been made are intention or random.
Quality assurance
Focusing primarily on preventing problems or issues with the quality of data, quality assurance standardizes the protocol carried out to collect information using qualitative research strategies. The protocol is often in the form of a manual that helps prevent errors in data collection including Partial collection of items Uncertainty over reviewing data Content identification failure due to improper training Lack of instruction for calibrating data collection equipment
Quality control
Quality control is the presence of openly defined communication structure that helps pre-condition monitoring systems. This process helps eliminate uncertainty in the flow of information thus reducing errors. Detection can be organized in different forms including site visits, conference calls or regular data reports that help understands various aspects of data. This process of records order help identify several problems including
- Errors in data items
- System errors
- Protocol violation
- Poor site performance
- Fraudulent or scientific errors Typically, data collection has now been made possible through several methods. With the rise of technology, several new methods of data collection have helped data analysts over the years. Some of the accessible means of data collection include
1. Enterprise Resource Planning (ERP)
Data collection is brought through the usage of several modules that focus on multiple business figures including marketing, human resources, product planning, inventory, etc.
2. Enterprise Data Warehouse (EDW)
Using long-term operational systems, EDW is a central repository of integrated data. Being an integral business intelligence component, EDW helps with data analysis and reporting.
3. Department Data Marts
A sub-sect of data warehouse, department data mart allows users to develop and manage their data based on the department’s needs and uses. Data marts are used in an organization that does not have an organized system of database thus making the understandability of information complicated.
4. BI reporting
BI reporting is the art of collecting data from various sources and compiling them as a report that helps the end-user to understand and analyze the data quickly. This system of reporting can be customised based on the end user. For instance, ad-hoc reporting can be given to nontechnical user whereas technical personnel can use managed reporting. However, as with every system, these above methods also have problems that restrict the data collection. Some of the most commonly faced issues include Time is taken to create reports based on operational systems Improper structuring of operational system leads to missing data points Data gaps that create production issue tickets Unsatisfied business users due to inadequate data analysis The recent emergence of Big Data has proven to transform the possibilities of using information. The next generation of data collection has introduced Big Data Hadoop. Hadoop is an open-source software framework that stores data on commodity hardware. Providing a massive storage space up to 50PB for any kind of data, Big Data Hadoop can virtually handle a countless number of concurrent jobs. With the massive volume of data available, analysts can streamline their search based on the requirement whenever needed. Owing to the recent development of Hadoop, it can also be observed that analysts can face several challenges while capitalizing the new technology. From understanding the technology to analyzing the extensive data, here are some of the drawbacks of Big Data Hadoop that data analysts face Lack of technical skills to perform basic operations Ability to break down the usability of data collected Inability to classify structured and unstructured data Failure to understand the crux of data Given the situation, it can be observed that business users have found it difficult to capitalize Big Data effectively. The limitations of newer technology have restricted the usage and perception of information. Being on the constant lookout for personalized data collection and analysis, the business user prefers a more flexible system that caters to their individual analysis needs. With the help of Bizstats, business users and data analysts find it easy to break down and understand the large pool of information. The launch of Bizstats Analytics has been a game-changer in the business community. The simplicity of the Bizstats Analytics tool has helped business capitalize the information gathered from various sources. Some of the critical aspects of Bizstats analytics that has helped businesses include 100% data-based decisions making offering Extended analytics capability Decreased risk of data gaps Ability to make timely decisions
