Data analysis vs data categorization: what’s the difference?
A common understanding of ‘data’ is essential before progressing to the data analysis vs data categorization prior to joining the discussion to understand their differences.
Data is defined as “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation,” by the Merriam-Webster dictionary.
Data is also understood as pieces of information or content that is stored in that particular manner for a specific purpose. Any piece of information that can be placed in a context and leveraged for extracting some future application can be considered as data. Numbers are data, as are characters of the alphabet arranged in sequences. Images of enemy territory are data, as is a video of an engineer following a sequence on the shopfloor.
Though data is also defined as ‘units of information,’ the terms ‘data’ and ‘information are often used interchangeably. However, there are many who use them to mean different things. In general, data is just data, a lot of information that may not have much meaning or use for anyone. When it starts being placed in a context or reviewed with the objective of application, it starts being information that is of use to the owner of that data.
The oWorkers advantage
With its talented pool of resources who have a deep understanding of data, oWorkers retains the ability to go beyond the data analysis vs data categorization discussion and delve deeper into all nature of work that require working with data. We have been awarded as one of the three best data services providers in the world on multiple occasions.
The rich talent pool oWorkers has access to is a result of our relationship which emanates from the deep engagement with the communities we work in. This includes a steady stream of walk-in applicants interested in a job with oWorkers which gives us a choice of talent for all our projects. Whatever the aspect of data they need to work on, our training team is equipped to polish them to deliver in the target process.
A related benefit of access to a continuous talent pool is the ability to provide for short-term ramps in client volumes arising out of planned, expected or unexpected events. Our deep supply pool enables us to meet these short-term requirements. This is a huge cost saving for clients who would normally need to hire resources on a long-term basis despite the work requirement being only for a few days or weeks in the year.
Data analysis vs data categorization: what they mean
Let us look at what data analysis and data categorization mean.
What is Data Analysis?
Data analysis can be understood as the process of making sense of information that is available, with a view to gaining knowledge and understanding about it as well as the underlying variables that have created that data, with a view to applying the learnings for the benefit of the company or individual doing the analysis.
Sounds complex, does it?
When something is put across as a formal statement, it might look daunting at first. However, if you think for a moment, data analysis is a natural process that we do all the time in our personal lives.
When you go to a doctor with an ailment, what does she do? She will look at and understand the symptoms, perhaps ask some questions, correlate the information with your past medical history as well as ailments that might be common at that time of the year and prescribe a cure. What she is doing is data analysis.
When a youngster is exploring courses and universities so that he can make up his mind on which ones to apply to, what is he doing? Isn’t he doing analysis of data? He will look for information on which universities are offering a course of his interest. He will check out their intake criteria and if he will be eligible. He will probably also check the financial requirements and establish which ones he will be in a position to afford. He is analysing the many different pieces of data that will facilitate his decision.
When I am playing tennis, I look at my opponent’s position, try to project his likely movement, and then play a shot with the intention that it is either a winning shot or is a build-up to the winning shot. What did I just do? I analyzed data.
Of course, all these elements of data analysis are perhaps one subliminally, without being called data analysis.
The same process becomes more formal when it is done in a formal setting, like that of a company, and is called data analysis.
What is data categorization?
In the modern enterprise, data is critical. This is not to suggest that data was not critical in the pre-modern enterprise, but with the growth of population and consumer franchises of global corporations, the generation and collection of data has assumed mammoth proportions. Besides, as the world has become increasingly competitive, with democratic, free-market societies becoming the norm, corporations would like to leverage every bit of data at their command to eke out an advantage over their rivals and assume dominance in the marketplace.
Generation of huge volumes of data creates a need for storing it in a manner that it can be accessed by the people and teams who need it for their requirements and are authorized to do so. Categorization of data becomes essential for its future application and useability. Categorization enables data to be stored in a manner where items to be retrieved for a particular requirement can be identified and, hence, retrieved. An organization could drown in the mass of data it has generated if for every single requirement it has to go through the entire data it has collected.
Data categorization could be defined as the process of collecting, sorting and storing data in a manner that will enable easy retrieval when needed as well as access for retrieval, editing and deleting only to a defined set of personnel, or positions, based on the policy of the company.
The oWorkers advantage
By employing the staff needed for its projects, oWorkers creates permanence in project delivery, as opposed to some competitors who choose to rely on freelancers or contract staff. In this context, data analysis vs data categorization ceases to be relevant as we can handle either with equal aplomb, the result of two-way trust built between the employer and staff. The staff trust the employer to monitor and manage career progression while the employer expects staff members to deliver their best on client contracts.
Data analysis vs data categorization: their purpose
Data analysis is a key input process for business leaders. They expect the collective wisdom of past experiences to be distilled out and used as the bedrock for future decision-making and direction.
It can provide key insights about customer behavior, the reason for the existence of the business. The business not only gets information on buying behavior, but also a wholesome view including what the customers are saying about you.
It serves to measure the efficacy of initiatives like marketing and promotional programs; which ones are working and which ones are not, and take decisions on the fly.
It can even serve as a barometer for internal evaluations based on key metrics of the business, either for teams and departments, or for individuals.
In short, data analysis serves as a key input for managerial decision-making.
The main aim of data categorization is arranging data for easy access to authorized users. The stored data becomes easy to understand once categorized and improves its utility manifold. It also serves as an input for data analysis as it would be impossible to analyse raw data that is unstructured and undifferentiated. The exercise also serves as a validation of the data by ensuring that it fits into one of the expected categories, based on characteristics of each individual piece.
Data categorization also serves the purpose of regulatory compliance. Many jurisdictions have laws pertaining to storage, searchability and retrievability of data.
With several unicorn marketplaces as longtime clients, oWorkers understand the challenges of this work and is equipped to handle them. With centers in three of the most sought-after delivery locations in the world, oWorkers employs a multicultural team which enables it to offer services in 22 languages.
Our leadership team comes with hands-on experience of over 20 years in the industry. They lead the company on its various projects and ensure client requirements are fulfilled. Through an Internal Quality (IQ) team that serves as their eyes and ears, they stay abreast of developments on the shop floor and are able to intervene when the requirement arises.
The IQ team also leads improvement initiatives and keeps a check on output to ensure the client does not receive sub-par quality. They monitor transactions and provide feedback and inputs to the operating units.
Data analysis vs data categorization: how they are done
It is difficult to put a boundary around how data can be analysed. There are many different ways of looking at data analysis methods. At the highest level, one school of thought is to break data down into qualitative and quantitative sets.
Qualitative data, as the name suggests, is data that is anecdotal, like visitors at an exhibition showing interest in one product and not the other, or textual, like comments on a feedback form, or verbal, which is like an unwritten version of textual data. As it cannot be numerically measured, there is some amount of subjectivity in drawing conclusions from it. Though some people are chary of handling unstructured data, as conclusions can be questioned, it is an important source of information for decision-making.
Quantitative data, on the other hand, is often numbers, or at least surrogate numbers that can be processed through standard mathematical or statistical techniques. While average and deviation might be the most common, a host of other techniques like Conjoint Analysis, Cluster Analysis, Regression, Factor, Time Series and Cohort Analysis and many others come into play.
Of course, it is not a simple exercise of finding a tool and passing data through it. The analysis is contextual and needs to be done keeping in focus the objective of the organization as well as what it expects from that analysis.
Categorization of data can be done on a variety of parameters.
Traditional data categorization systems were driven more by the need for securing data based on its sensitivity. Data tended to be placed into categories such as ‘Restricted,’ ‘Confidential,’ ‘Classified,’, ‘Public,’ ‘Sensitive’ or ‘Private.’
Thought processes have evolved. The preference now is towards multidimensional tagging that can categorize a particular piece of data on multiple parameters at the same time. Of course, data storage in digital formats also facilitates multi-dimensional categorization that could be affixed as tags. Once this is done, the level of availability of data and access rights can be determined.
Some common types used by complex organizations:
- Based on value
- Based on usefulness timeframe
- Based on information type
- Based on who it pertains to – clients, employees, etc.
- Based on requirements to refresh
- Based on retrieval rights
The oWorkers advantage
oWorkers is GDPR compliant, ISO (27001:2013 & 9001:2015) certified and operates from super secure facilities in each of its three delivery locations. oWorkers has also emerged stronger from the global emergency created by the Covid-19 pandemic. We have been amongst the first to create an environment for their staff to work from the safety of home in times of the epidemic, as and when required. With our technology, all staff can operate fully either from home or office, as dictated by the unfolding situation. In addition to trained human resources, oWorkers is able to access the latest technology tools suitable for this activity, thanks to its enduring partnership with leading providers of technology. Data analysis vs data categorization ceases to be relevant when we can operate with equal facility on both.