How will you store and manage the best your collected data?
Collected data is very important. Data collection is the process of collecting and measuring information about specific variables in an established system, which then allows relevant questions to be answered and results to be evaluated. Data collection is a component of research in all fields of study, including the physical and social sciences , humanities and business . While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal of all data collection is to capture quality evidence that will allow analysis to lead to the formulation of compelling and credible answers to the questions that have been posed. What is meant by privacy?
The ‘right to privacy’ refers to being free from intrusions or disturbances in one’s private life or personal affairs. All research should outline strategies to protect the privacy of the subjects involved, as well as how the researcher will have access to the information.
The concepts of privacy and confidentiality are related but are not the same. Privacy refers to the individual or subject, while confidentiality refers to the actions of the researcher.
What does the management of stored information entail?
Manual collected data and analysis are time-consuming processes, so transforming data into insights is laborious and expensive without the support of automated tools.
The size and scope of the information analytics market is expanding at an increasing pace, from self-driving cars to security camera analytics and medical developments. In every industry, in every part of our lives, there is rapid change and the speed at which transformations occur is increasing.
It is a constant evolution that is based on data. That information comes from all the new and old data collected, when it is used to develop new types of knowledge.
The relevance that information management has acquired raises many questions about the requirements applicable to all data collected and information developed.
Data encryption is not a new concept, in history we can go to the ciphers that Julius Caesar used to send his orders or the famous communication encryption enigma machine that the Nazis used in the Second World War.
Nowadays, data encryption is one of the most used security options to protect personal and business data.
Data encryption works through mathematical algorithms that convert data into unreadable data. This encrypted data consists of two keys to decrypt it, an internal key that only the person who encrypts the data knows, and a key
external that the recipient of the data or the person who is going to access it must know.
Data encryption can be used to protect all types of documents, photos, videos, etc. It is a method that has many advantages for information security.
Advantages of data encryption
- Useless data : in the event of the loss of a storage device or the data is stolen by a cybercriminal, allows said data to be useless for all those who do not have the permissions and decryption key.
- Improve reputation : companies that work with encrypted data offer both clients and suppliers a secure way to protect the confidentiality of their communications and data, displaying an image of professionalism and security.
- Less exposure to sanctions : some companies or professionals are required by law to encrypt the data they handle, such as lawyers, data from police investigations, data containing information on acts of gender violence, etc. In short, all data that, due to its nature, is sensitive to being exposed, therefore requires mandatory encryption, and sanctions may be generated if it is not encrypted.
There are many advantages associated with achieving good management of stored information. Among the benefits of adequately covering the requirements of the Data Storage function and data management , the following two stand out:
- Savings: the capacity of a server to store data is limited, so storing data without a structure, without a logical order and lacking guiding principles, represents an increase in cost that could be avoided. On the contrary, when data storage responds to a plan and the decisions made are aligned with the business strategy, advantages are achieved that extend to all functions of the organization.
- Increased productivity: when has not been stored correctly the system works slower. One of the strategies often used to avoid this is to divide data into active and inactive . The latter would be kept compressed and in a different place, so that the system remains agile, but without this meaning that they remain completely inactive, since it may sometimes be necessary to access them again. Today, with cloud services it is much easier to find the most appropriate data storage approach for each type of information.
We must avoid each application deciding how to save the data , and to this end the information management policy should be uniform for all applications and respond to the following questions in each case:
- How the data is stored .
- When is the data saved ?
- What part of the data or information is collected.
In short, through a person in charge will be established who is determined by the Data Governance , which is in turn responsible for defining the standards and the way to store the information, since not all silos can be used.
And this is the way to support the common objective from this function and through the procedures, planning and organization and control that is exercised transversally and always seeking to enhance the pragmatic side of the data .
Steps of data processing in research
Data processing in research has six steps. Let’s look at why they are an imperative component of research design .
Research data collection
Data collection is the main stage of the research process. This process can be carried out through various online and offline research techniques and can be a mix of primary and secondary research methods.
The most used form of data collection is research surveys. However, with a mature market research platform , you can collect qualitative data through focus groups, discussion modules, etc.
Preparation of research
The second step in research data management is data preparation to eliminate inconsistencies, remove bad or incomplete survey data, and clean the data to maintain consensus.
This step is essential, since insufficient data can make research studies completely useless and a waste of time and effort.
Introduction of research
The next step is to enter the cleaned data into a digitally readable format consistent with organizational policies, research needs, etc. This step is essential as the data is entered into online systems that support research data management.
Research data processing
Once the data is entered into the systems, it is essential to process it to make sense of it. The information is processed based on needs, the types of data collected, the time available to process the data and many other factors. This is one of the most critical components of the research process.
Research data output
This stage of processing research data is where it becomes knowledge. This stage allows business owners, stakeholders, and other staff to view data in the form of graphs, charts, reports, and other easy-to-consume formats.
Storage of processed research
The last stage of data processing steps is storage. It is essential to keep data in a format that can be indexed, searched, and create a single source of truth. Knowledge management platforms are the most used for storing processed research data.
Benefits of data processing in research
Data processing can differentiate between actionable knowledge and its non-existence in the research process. However, the processing of research data has some specific advantages and benefits:
Streamlined processing and management
When research data is processed, there is a high probability that this data will be used for multiple purposes now and in the future. Accurate data processing helps streamline the handling and management of research data.
Better decision making
With accurate data processing, the likelihood of making sense of data to arrive at faster and better decisions becomes possible. Thus, decisions are made based on data that tells stories rather than on a whim.
Democratization of knowledge
Data processing allows raw data to be converted into a format that works for multiple teams and personnel. Easy-to-consume data enables the democratization of knowledge.
Cost reduction and high return on investment
Data-backed decisions help brands and organizations make decisions based on data backed by evidence from credible sources. This helps reduce costs as decisions are linked to data. The process also helps maintain a very high ROI on business decisions.
Easy to store, report and distribute
Processed data is easier to store and manage since the raw data is structured. This data can be consulted and accessible in the future and can be called upon when necessary.
Examples of data processing in research
Now that you know the nuances of data processing in research, let’s look at concrete examples that will help you understand its importance.
Example in a global SaaS brand
Software as a Service (Saas) brands have a global footprint and have an abundance of customers, often both B2B and B2C. Each brand and each customer has different problems that they hope to solve using the SaaS platform and therefore have different needs.
By conducting consumer research , the SaaS brand can understand their expectations, purchasing and purchasing behaviors, etc. This also helps in profiling customers, aligning product or service improvements, managing marketing spend and more based on the processed research data.
Other examples of this data processing include retail brands with a global footprint, with customers from various demographic groups, vehicle manufacturers and distributors with multiple dealerships, and more. Everyone who does market research needs to leverage data processing to make sense of it.