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How will you store and manage the best your collected data?

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How will you store and manage the best your collected data?

collected data

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

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.

 

Data encryption

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.

Data storage 

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 .

Data storage 

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.

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.  

data

What steps will you take to maintain best the confidentiality of the collected data?

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What steps will you take to maintain best the confidentiality of the collected data?

 

Collected data

 

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.

Informed consent

There are many ways to obtain consent from your research subjects. The form of consent affects not only how you conduct your research, but also who can have access to the personal data you hold.

It is called  informed consent , when before obtaining consent, the research subject is described what is going to be done with their data, who will have access to it and how it will be published.

When deciding which form of consent to use, it is worth considering who needs access to personal data and what needs to be done with the data before it can be shared publicly or with other researchers.

Anonymized data does not require consent to share or publish, but it is considered ethical to inform subjects about the use and destination of the data.

Confidentiality

Confidentiality   refers to the researcher’s agreement with the participant about how private identifying information will be handled, administered, and disseminated . The research proposal should describe strategies for maintaining the confidentiality of identifiable data, including controls over the storage, manipulation, and sharing of personal data.

To minimize the risks of disclosure of confidential information, consider the following factors when designing your research:

  • If possible, collected data the necessary data without using personally identifiable information.
  • If personally identifiable information is required, de-identify the data after collection or as soon as possible.
  • Avoid transmitting unencrypted personal data electronically.

Other considerations include retaining original collection instruments, such as questionnaires or interview recordings. Once these are transferred to an analysis package or a transcription is made and the quality is assured or validated, there may no longer be a reason to retain them.

Questions about what data to retain and for how long should be planned in advance and within the context of your abilities to maintain the confidentiality of the information.

The Data Protection Law arises as a need to protect all the information that is currently being used, and aims to safeguard the confidentiality of people and their data.

If you want to safeguard personal data, emails and other types of information, various measures can be taken to increase security levels. Next,  three methods will be described to protect the confidentiality of information,  which can be used in both personal and work settings.

Data encryption

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.

 

Data encryption

Advantages of data encryption

  • Useless data : in the event of the loss of a storage device or the data is stolen by a cybercriminal, data encryption 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.

Two-step authentication

Online authentication is   one of the simplest, but at the same time most effective, methods when it comes to protecting online identity. By activating two-step authentication for an account, you are adding another layer of security to it.

This method double checks access to the account, verifying that it is the true owner who is accessing it. Firstly, the traditional username and password method will be introduced, which once verified, will send a  code to the mobile phone  associated with the account, which must be entered to access it.

This method ensures that in addition to knowing the account username and password, you must be in possession of the associated mobile phone to be able to access it.

Currently, there are many platforms that allow you to activate this service to access them, such as Google, Facebook or Apple. They are also widely used in the video game sector, which is very prone to identity theft. Massive games like World of Warcraft or Fornite allow you to use  two-step authentication.

Although it is a very efficient system when it comes to protecting the  confidentiality of information , many users are reluctant to activate it, since the dependence on the mobile phone or simply adding one more step in authentication puts them off. backwards.

Username and Password ID

One of the traditional protection methods and no less effective, is the activation of  username and password.  It consists of creating a user identity and adding a linked password to it, without which it is impossible to access the account or platform.

To use email, access online platforms, etc., we are accustomed to using this  security method  when accessing them. That is why it is important to install this type of access in the operating systems of the computers we use, only allowing access to the equipment to those who know the username and its linked password.

It is important to create a method to recover  or change the password,  in case you forget it or suspect that the user account may be compromised by third parties. Normally, platforms use various methods to perform this recovery, such as linking to another email account or a mobile phone number, using a secret question whose answer only the user knows, etc.

Data protection example

These three methods presented are not exclusive, in fact, the ideal is to use them all together to make the protection of the confidentiality of the information more effective.

Data protection example

We can see the use of the three methods with this simple example:

We are going to send a report to the personnel manager, which includes the profiles selected in the last job interviews. We are dealing with information that must be protected to prevent it from being exposed or stolen.

To send the email, we access our computer and enter our username and password (username and password ID method). To the report, which we have in a PDF text file, we add a password using the PDFelement software (data encryption method).

To send the email, we access our Gmail account, where we enter our username and password, we receive a code on the mobile phone, which we enter to access the account (2-step authentication method ) . We compose the email for the chief of staff and attach the previously encrypted PDF file. Before sending the email, we activate Secure Mail encryption, an extension for Google Chrome that encrypts and decrypts emails sent with Gmail ( data encryption method) . We proceed to send the email.

Finally, using Whatsapp, we send  the  PDF encryption key to the chief of staff (he also uses Secure Mail to access his Gmai account), who can access the sent file securely. We use a platform other than Gmail to send the encryption password, to increase the level of security.

As we have seen, we can use various methods, both to protect the privacy of identities and the confidentiality of data. A  combined use of all methods  offers greater guarantees that the data travels safely through the network until it reaches the recipient.

What is your best data collection timeline?

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What is your best data collection timeline?

data collection

 

Data collection

Data collection is very important. Is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.

The importance of ensuring accurate and appropriate data collection


Regardless of the field of study or preference for defining data (quantitative, qualitative), n is essential to maintaining the integrity of research. Both the selection of appropriate data collection instruments (existing, modified, or newly developed) and clearly delineated instructions for their correct use reduce the likelihood of errors occurring.

Consequences from improperly collected data include

  • inability to answer research questions accurately
  • inability to repeat and validate the study
  • distorted findings resulting in wasted resources
  • misleading other researchers to pursue fruitless avenues of investigation
  • compromising decisions for public policy
  • causing harm to human participants and animal subjects

While the degree of impact from faulty data collection may vary by discipline and the nature of investigation, there is the potential to cause disproportionate harm when these research results are used to support public policy recommendations.

collected data

Several plans come together to create a strong, comprehensive, and generally successful market research initiative, and one of the most important pieces is the  data collection plan .

A data collection plan describes how your organization’s data will flow from its source to actionable information. The process of creating this plan will reveal where the data comes from, who has access to it, and how it is collected and stored.

Below we explain why you need to have a plan and how you will use it. We also go over the key steps to creating a data collection plan that ensures your data is on track to produce actionable insights that drive your business.

What is a data collection plan?

Is a detailed document that outlines the exact steps and sequences for collecting data for a project. It is a statistical approach to achieve significant improvements by reducing variation and defects.

A collection plan ensures that data is accurately sent to the organization’s key stakeholders, who will help you meet your data needs. The plan aims to ensure that the data collected is valid and meaningful.

We need a data collection plan to avoid wasting resources on irrelevant or useless data. When developing a data collection plan, we can focus on answering specific questions related to the company.

Why is a data collection plan necessary?

Collecting and analyzing a bunch of different data isn’t much use if you don’t know what it means. A good  plan helps save money and time, as collecting data without a plan can be time-consuming. Additionally, it may not be possible to obtain all the data when it is needed. 

These are the most important reasons why your company needs a collection plan. When creating a data collection plan, you can focus on answering specific questions important to your business.

When and how to use a data collection plan?

A comprehensive data collection plan ensures that the data collected is useful and well organized. The plan is used to evaluate the current state of a process or to improve a project. In addition, it is useful during the last phase of a project when generating new metrics and the necessary evaluation procedures.

An adequate data collection plan involves taking a systematic approach, including:

  • Identify the data to be collected.
  • How the data will be collected.
  • Collect the data

Discover some  data collection techniques  that will be useful to you.

Steps to create a data collection plan

Next, we will explain the steps of a data collection plan to explain how to create one. The plan consists of 8 steps:

Identify the questions

The first step in developing a data collection plan is to decide what questions we want to answer. Our information has to be useful for the project. These questions should be based on what our process is actually like in its current state.

The best way to collect data is to use the  SIPOC diagram  as a guide. We also have to decide what measurements or metrics we want to use.

Identify accessible data

The second step in developing a data collection plan is to determine what types of data can be collected. Sometimes, a specific piece of information can provide us with many solutions. Be sure to list all the data you need to answer the questions underlying the project.

Determine how much data is needed

The third step of a data collection plan is to determine the data needed. Write down how much data is needed for each item on the list. The goal is to collect enough information to perform proper analysis of the data and identify patterns and trends.

Decide how to measure the data

The fourth step in developing a data collection plan is to determine how we measure the data. Data can be measured in various ways, such as check sheets, survey responses, etc. The  type of data  we seek will determine how we measure it. 

Determine who will collect the data

The fifth step in developing a data collection plan is determining who will collect the data. Currently, data can be collected using automated software. We may need to contact the person in charge of the software to ensure that the data is in the proper format.

Choose the data source

The sixth step is to determine the  data sources . Location does not always mean a physical location. It is the place where the process is located. The collection plan should specifically indicate where data should be collected throughout the process.

Choose whether to measure a sample or the entire population

The seventh step is to decide whether to sample the data or not. It is often impractical to measure an entire population of data. In this situation, a sample is then collected. 

The team may need to investigate the following question: What should be our  sampling method  and  sample size  to produce statistically accurate judgments?

Determine data display format

The eighth step is to decide how to display the data. We can display data in many ways, such as  Pareto charts , scatter plots, etc.

Identify accessible data

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