How to best communicate the results of your data collection to stakeholders?


How to best communicate the results of your data collection to stakeholders?

data collection

Data Collection

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem.

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

How to effectively communicate the results of the employee engagement report? (with examples)

You did a survey on employee engagement, perfect! 

You are already measuring your staff’s commitment to your mission, the team, and their role within the company.

But what are you going to do with the results you get from their contributions? 

And most importantly, how will you move from reporting on employee engagement to meeting your staff’s desires for professional growth?

Are you still struggling to find the answers?

Our guide is for you. We have put together practical tips and examples that will allow you to:

  • Know exactly what to do with employee engagement survey data.
  • Make sense of what that data reveals.
  • Excellence in communicating engagement survey results.

The importance  of employee engagement  for companies

First things first: the relevance and impact of an employee engagement report goes beyond the borders of HR. Of course, measuring engagement is a People Ops function. And it’s also one of the trends in employee engagement: actively listening to determine whether workers are thriving.

But a survey report  on employee engagement  transcends concerns about team members’ aspirations. It’s a tool full of insightful information that C-suite executives need to understand how healthy and robust their workforce is.

And that is a strategic question. Because there is no way to drive business results without engaged and enthusiastic staff.

Employee engagement drives productivity, performance, and a positive workplace. 

How to analyze  your employee engagement data

Let’s consider that you already know how to design an employee engagement survey and set your goals. 

Therefore, now we will focus on analyzing the results obtained when carrying out the study.

The first important tip is to prepare the analysis in advance. To do this, put in place the mechanisms to quantify and segment the data.

Use numerical scales, scores and percentages

Use numerical scales and convert responses to numerical values ​​whenever you can in your engagement survey. 

You will see that comparing the data will not cause you a big headache. 

This is because numbers are much less prone to misinterpretation than the opinion of your staff in free text.

Consider qualitative contributions

Although quantitative data is objective, qualitative data must also be analyzed. Because, sometimes, it is not easy to put thoughts and feelings into figures. And without data, it would be impossible to draw conclusions about employee motivation, attitudes, and challenges.

For example, if you ask people to rate their satisfaction with their team from 1 to 5, a number other than five won’t tell you much. But if you ask them to write down the reason for their incomplete satisfaction, you’ll get the gist of their complaint or concern.

Segment the groups of respondents

Without a doubt, your workers are divided into different groups according to different criteria. And that translates into different perceptions of their jobs, their colleagues and their organization.

To find out, segment the engagement survey results while keeping the responses anonymous. 

Assure your employees of their anonymity and ask them to indicate their:

  • age;
  • gender;
  • department and team;
  • tenure;
  • whether they are junior, intermediate or senior;
  • your executive level (manager, director, vice president or C-level).

Read the consensus

In this step the real action of the analysis begins. 

If you’ve followed our recommendations for quantifying (and segmenting) survey data, you’ll be ready to determine precisely what they’re trying to tell you.

In general terms, if most or all of your staff express the same opinion on an issue, you need to investigate the issue and improve something. 

On the other hand, if only a few people are unhappy with the same issue, it may not be necessary to address it so thoroughly.

It all depends on the importance of the topic for the business and the proper functioning of the team and the company. And it’s up to stakeholders to decide whether it’s worth putting effort into finding out what’s behind the results.

Cross data

Engagement levels  are not always related to staff’s position, teams or your company. Instead, those levels may have to do with other factors, such as salary or benefits package, to name a couple.

And it’s up to you to combine engagement survey data with data from other sources.

Therefore, consider the information in your HR management system when reviewing the results of your engagement.

Additionally, evaluate engagement data against business information. 

This is information that you can extract from your ERP system that is closely related to business results.


Compare results

The ultimate goal of analyzing engagement survey data is to uncover critical areas for improvement. And to do this as comprehensively as possible, you should compare your current engagement survey results with

  • The results of your previous engagement surveys to understand the engagement levels of your organization, departments and teams over time.
  • The  results of national and global surveys on engagement, especially those of other companies in your sector with a similar activity to yours.

And these are the perceptions to look for:

  • Why is your organization performing better or worse than before?
  • Why do certain departments and teams perform better or worse than before?
  • Why does your company perform better or worse than similar ones in your country or abroad?

How to organize data in your  employee engagement  report

An employee engagement survey report should shed light on how engagement affects the performance of your company and your staff. 

But a report like this is useless if you do not organize the data it contains well. Let’s see how.

First of all, you must keep in mind the objective of the survey. That is developing an action plan to improve the areas with the greatest positive impact on your employee engagement levels.

So keep in mind that traditionally some areas score low in any organization. We’re talking pay and benefits,  career progression  , and workplace politics, to name a few.

But as a general rule, you should prioritize areas where your company scored poorly compared to industry benchmarks. Those are the ones most likely:

  • Generate positive ROI once you improve them.
  • Promote improvement of all other areas of the  employee experience .

Typically, the most impactful areas are:

  • Appreciation to employees;
  • Response to proactive employees;
  • Employee participation in decision making;
  • Communication with leaders.

But it might suggest other areas to focus on.

Now, you need to conveniently organize the survey results in your employee engagement report. 

In other words, you must disclose the commitment figures:

  • for the entire company;
  • by department;
  • by team;
  • by age and sex;
  • by possession;
  • by executive level;
  • by seniority;
  • by period (current month, quarter or year versus the previous one);
  • by region (within your country, in your foreign locations and in comparison to national and global benchmarks in your sector);
  • any combination of the above that makes sense, such as by gender and team or by age and department.

And to identify areas for improvement, you should display the survey data by those areas within each of the divisions above. We recommend that you convert these divisions into distinct sections of the document.

We also recommend using media to visualize results, such as charts and graphs. 

For example, use:

  • Bar charts: to identify trends over time.
  • Line graphs: to compare this year’s data with last year’s data.
  • Callout Charts: To highlight surprising figures or conclusions.

These visuals will help stakeholders objectively understand and analyze the results of the employee engagement report. But most importantly, visualization makes it easy to prioritize areas for improvement and provides actionable results.

Good practices for communicating engagement survey results

After collecting and analyzing employee engagement survey data, it’s time to share it within the company.

And here are our tips for communicating engagement survey results to your employees and leaders.

 3 Tips for Sharing Employee Engagement Survey Results with Employees

Immediately after completing the employee engagement survey, the CEO should make a communication to the entire company. Alternatively, the VP of HR or a senior HR leader can do this.

And in that communication, they make a recognition.

Thank employees for participating in the study

Your boss – if not yourself – can do this via email or in an all-hands meeting. 

But it’s essential that you thank employees as soon as the survey closes. And in addition to saying thank you, the leader must reaffirm his commitment to take engagement to higher levels.

Advise them to appreciate employees’ dedication to helping improve your organizational culture. This will convey the message that employee opinion is valuable, which in itself has a positive impact on engagement.

Briefly present the commitment data you have obtained

One week after closing the survey, your leader should share an overview of the results with the organization. Again, an email or company-wide meeting is all it takes.

The summary should include participation statistics and a summary of the main results (best and worst figures). 

This time is also a great opportunity for your leader to explain what employees should expect next. And one way to set expectations is to outline the action plan.

However, their leader cannot provide many details at this time. 

The first communication of employee engagement results should focus on numbers with a broader impact. 

In other words, it is an occasion to focus on the effect of data at the organizational level.


Report complete engagement data and plan improvements

Three weeks after the survey closes, HR and leaders – team leaders and other executives – must get to work:

  • Carefully review the results.
  • Detail the action plan: the areas of improvement you will address and the engagement initiatives you will implement. 

Once key stakeholders have decided on the action plan, it’s time to communicate all the details to employees. 

The deadline should be no later than one or two months after the survey closes.

3 Tips for Sharing Engagement Survey Results with Leaders and Key Stakeholders

Once the results of the engagement survey are obtained, the first step is to share them with the management team. These are our main recommendations on how to approach this task.

There is no need to rush when deciding what to do with the data.

Give your leaders time to review the engagement data, digest it, and think carefully about it. 

We recommend this calendar:

  • One week after the survey closes, before communicating high-level results.
  • Three weeks after completing the survey, before we begin to thoroughly discuss the data and delve into the action plan.
  • One or two months after the survey closes, before communicating detailed results.

Increasing the engagement levels of your employees is a process of change. And as with any corporate change, internalizing it does not happen overnight. Additionally, your leaders are the ones who must steer the helm of change, so they need time to prepare.

Emphasize the end goal and fuel the dialogue

The process of scrutinizing engagement data starts with you. Introduce your leaders:

  • the overall employee engagement score;
  • company-wide trends;
  • department-specific trends;
  • strengths and weaknesses (or opportunities).

Leaders must clearly understand what organizational culture is being pursued. 

And the survey results will help them figure out what’s missing. 

So make sure you communicate this mindset to them.

Next, as you discuss the data in depth, you should promote an open dialogue. Only then will your leaders agree on an effective action plan to increase engagement levels.

Don’t sweep problems under the rug

You can’t increase employee engagement without transparency. And you play a role in it too. 

You should share the fantastic results and painful numbers from your engagement survey with your leaders.

Reporting alarming discoveries is mandatory for improvement. 

After all, how could you improve something without fully understanding and dissecting it? 

Additionally, investigating negative ratings and comments is ultimately a win-win for both workers and the company.

 Real Examples of Employee  Engagement Reports

Here are four employee engagement reports that caught our attention. We’ll investigate why they did it and what you can learn from them.

1. New Mexico Department of Environment

The New Mexico  Department of Environment’s  engagement report  : 

1. Start with a  message from a senior leader  in your organization, providing:

  • The overall response rate;
  • The overall level of commitment;
  • Some areas for improvement;
  • A reaffirmation of senior management’s commitment to addressing employee feedback.

2. Use  graphs  to highlight the most interesting conclusions.

3. Breaks down the figures from an  overview to  year-on-year  highs  and lows  by department and survey section

4.  Compare  your employees’ level of engagement  to a national benchmark.

5. Includes information on the   organization’s  commitment actions throughout the year prior to the survey.

6.  Demographic breakdown of respondents .

7. Clarify  next steps for  your  leaders  and how employees can participate.

8. Discloses an appendix containing  year-over-year scores  for all survey questions that used a numerical scale.

Note:  The year-over-year comparison allows this organization to identify trends in employee engagement.

2. GitLab

The  GitLab Commit Report :

  • Explain how survey responses   will  be kept  confidential .
  • List  the areas of interest for the survey.
  • Shows a  chronology of the actions that the company carried out around the survey.
  • Clarify the  steps that will follow after closing the survey.
  • It presents the  global response rate, the global commitment level and an industry benchmark.
  • Thank you  employees for  participating  in the survey.
  • It reveals the  top-ranked responses in the three main areas of interest  and compares them to the industry benchmark.
  • Highlight  areas that require improvement.

👀  Note:  The calendar with the survey actions may seem insignificant. However, it is an element of  transparency that generates trust among readers of the report.

3. UCI Irvine Human Resources

The  University of California  HR  Employee Engagement Report  :

  1. Start by  justifying why employee engagement is important  to workplace culture and various stakeholders.
  2. Defines the  responsibilities of everyone  involved in engaging those stakeholders.
  3. Remember the  results of the previous employee engagement survey  and set them as  a reference .
  4. Compare the  most recent survey results with the baseline figures.
  5. Distinguishes engaged, disengaged, and actively disengaged staff members  between previous and most recent data by organizational unit.
  6. Lists new opportunities  that the department should address  and strengths that it should continue to explore.
  7. It presents a  timeline of the phased engagement program  and some planned actions.
  8. Describes the next steps  leaders should take with their team members.

👀 Note:  The report notes that the figures vary between the two editions of the survey because the HR department encouraged staff participation instead of forcing it.

4. UCI Riverside Chancellor’s Office

The  University of California,  Riverside  Office of the Chancellor’s  Employee Engagement Report :

  1. Contains  instructions on  how scores were calculated .
  2. Compare employee engagement survey results  to different types of references , from previous survey results to national numbers.
  3. Highlights issues  that represent a  priority  for the organization.
  4. Distinguish the  level of statistical significance that each number has , clarifying the extent to which they are significant.
  5. Describe  the suggested actions  in some detail.
  6. It groups scores by category – such as professional development or performance management -, role – such as manager or director -, gender, ethnicity, seniority and salary range.
  7. Break down the scores  within each category.
  8. Shows the  total percentage of employees at each engagement level , from highly engaged, empowered and energized to disengaged.
  9. It concludes with the  main drivers of commitment , such as the promotion of social well-being.

 Note:  The document is very visual and relies on colors to present data. While this appeals to most readers, it makes it less inclusive and compromises organization-wide interpretation.

Now that you know how to analyze your survey data and organize your engagement report, learn how to create an  employee engagement program .


How to better manage data validation and cleaning processes?


How to better manage data validation and cleaning processes?   Data Data is a collection of facts, figures, objects, symbols, and events gathered from different sources. Organizations collect data with various data collection methods to make better decisions. Without data, it would be difficult for organizations to make appropriate decisions, so data is collected from … Read more

How to best handle data storage and archiving after the project is finished?

Big Data

How to best handle data storage and archiving after the project is finished?



What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

To collect data, we must first identify what information we need and how we will collect it. We can also evaluate a hypothesis based on collected data. In most cases, data collection is the primary and most important step for research. The approach to data collection is different for different fields of study, depending on the required information.

Research Data Management  RDM) is present in all phases of research and encompasses the collection, documentation, storage and preservation of data used or generated during a research project. Data management helps researchers:  organize it,  locate it,  preserve it,  reuse it.

Additionally, data management allows:

  • Save time  and make efficient use of available resources : You will be able to find, understand and use data whenever you need.
  • Facilitate the  reuse of the data  you have generated or collected: Correct management and documentation of data throughout its life cycle will allow it to remain accurate, complete, authentic and reliable. These attributes will allow them to be understood and used by other people.
  • Comply with the requirements of funding agencies : More and more agencies require the presentation of data management plans and/or the deposit of data in repositories as requirements for research funding.
  • Protect and preserve data : By managing and depositing data in appropriate repositories, you can safely safeguard it over time, protecting your investment of time and resources and allowing it to serve new research and discoveries in the future.

Research data  is  “all that material that serves to certify the results of the research that is carried out, that has been recorded during it and that has been recognized by the scientific community” (Torres-Salinas; Robinson-García; Cabezas-Clavijo, 2012), that is, it is  any information  collected, used or generated in experimentation, observation, measurement, simulation, calculation, analysis, interpretation, study or any other inquiry process  that supports and justifies the scientific contributions  that are disseminated in research publications.

They come  in any format and support,  for example:

  • Numerical files,  spreadsheets, tables, etc.
  • Text documents  in different versions
  • Images,  graphics, audio files, video, etc.
  • Software code  or records, databases, etc.
  • Geospatial data , georeferenced information

Joint Statement on Research Data from STM, DataCite and Crossref

In 2012, DataCite and STM drafted an initial joint statement on linking and citing research data. 

The signatories of this statement recommend the following as best practices in research data sharing:

  1. When publishing their results, researchers deposit the related research data and results in a trusted data repository that assigns persistent identifiers (DOIs when available). Researchers link to research data using persistent identifiers.
  2. When using research data created by others, researchers provide attribution by citing the data sets in the references section using persistent identifiers.
  3. Data repositories facilitate the sharing of research results in a FAIR manner, including support for metadata quality and completeness.
  4. Editors establish appropriate data policies for journals, outlining how data will be shared along with the published article.
  5. The editors establish instructions for authors to include Data Citations with persistent identifiers in the references section of articles.
  6. Publishers include Data Citations and links to data in Data Availability Statements with persistent identifiers (DOIs when available) in the article metadata recorded in Crossref.
  7. In addition to Data Citations, Data Availability Statements (human and machine readable) are included in published articles where applicable.
  8. Repositories and publishers connect articles and data sets through persistent identifier connections in metadata and reference lists.
  9. Funders and research organizations provide researchers with guidance on open science practices, track compliance with open science policies where possible, and promote and incentivize researchers to openly share, cite, and link research data.
  10. Funders, policy-making institutions, publishers, and research organizations collaborate to align FAIR research data policies and guidelines.
  11. All stakeholders collaborate to develop tools, processes and incentives throughout the research cycle to facilitate the sharing of high-quality research data, making all steps in the process clear, easy and efficient for researchers through provision of support and guidance.
  12. Stakeholders responsible for research evaluation factor data sharing and data citation into their reward and recognition system structures.


The first phase of an investigation requires  designing and planning  your project. To do this, you must:

  • Know the  requirements and programs  of the financing agencies
  • Search  research data
  • Prepare a  Data Management Plan .

Other prior considerations:

  •     If your research involves working with humans, informed consent must be obtained.
  •     If you are involved in a collaborative research project with other academic institutions, industry partners or citizen science partners, you will need to ensure that your partners agree to the data sharing.
  •     Think about whether you are going to work with confidential personal or commercial data.
  •     Think about what systems or tools you will use to make data accessible and what people will need access to it.

During the project…

This is the phase of the project where the researcher  organizes, documents, processes and  stores  the data.

Is required :

  • Update the Data Management Plan
  • Organize and document data
  • Process the data
  • Store data for security and preservation

The  description of data  must provide a context for its interpretation and use, since the data itself lacks this information, unlike scientific publications. It is about being able to understand and reuse them .

The following information should be  included:

  • The context: history of the project, objectives and hypotheses.
  • Origin of the data: if the data is generated within the project or if it is collected (in this case, indicate the source from which it was extracted).
  • Collection methods, instruments used.
  • Typology and format of data (observational, experimental, computational data, etc.)
  • Description standards: what metadata standard to use.
  • Structure of data files and relationships between files.
  • Data validation, verification, cleaning and procedures carried out to ensure its quality.
  • Changes made to the data over time since its original creation and identification of the different versions.
  • Information about access, conditions of use or confidentiality.
  • Names, labels and description of variables and values.



 The data must be clean and correctly structured and ordered:

A data set is structured if:

  •     Each variable forms a column
  •     Each observation forms a row
  •     Each cell is a simple measurement

Some recommendations :

  •    Structure the  data in TIDY (vertical) format  i.e. each value is a row, rather than horizontally. Non-TIDY (horizontal) data.
  •    Columns  are used for variables  and their names can be up to 8 characters long without spaces or special signs.
  •    Avoid text values ​​to encode variables, better  encode them with numbers .
  •    In  each cell, a single value
  •    If you do not have  a value available , provide the missing value codes.
  •    Provide  data tables , which collect all the data encodings and denominations used.
  •    Use data dictionary or separate list of these short variable names and their full meaning


Ordered data  or  “TIDY DATA” are those obtained from a process called “DATA TIDYING” or data ordering. It is one of the important cleaning processes during big data processing.

Ordered data sets have a structure that makes work easier; They are easy to manipulate, model and visualize. ‘Tidy’ data sets are arranged in such a way that each variable is a column and each observation (or case) is a row.” (Wikipedia).

There may be  exceptions  to open dissemination, based on reasons of confidentiality, privacy, security, industrial exploitation, etc. (H2020, Work Programme, Annexes, L Conditions related to open access to research data).

There are some  reasons why certain types of data cannot and/or should not be shared , either in whole or in part, for example:

  • When the data constitutes or contains sensitive information . There may be national and even institutional regulations on data protection that will need to be taken into account. In these cases, precautions must be taken to anonymize the data and, in this way, make its access and reuse possible without any errors in the ethical use of the information.

  • When the data is not the property of those who collected it or when it is shared by more than one party, be they people or institutions . In these cases, you must have the necessary permissions from the owners to share and/or reuse the data.

  • When the data has a financial value associated with its intellectual property , which makes it unwise to share the data early. Before sharing them, you must verify whether these types of limits exist and, according to each case, determine the time that must pass before these restrictions cease to apply.  

How to best verify the accuracy of self-reported data?

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How to best verify the accuracy of self-reported data?

shutterstock 252668938 1280x720 1



What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

Put simply, data collection is the process of gathering information for a specific purpose. It can be used to answer research questions, make informed business decisions, or improve products and services.

To collect data, we must first identify what information we need and how we will collect it. We can also evaluate a hypothesis based on collected data. In most cases, data collection is the primary and most important step for research. The approach to data collection is different for different fields of study, depending on the required information.

collect data


A significant number of scientific investigations denote a lack of rigor,  and this is largely due to the non-validation of the instruments used. This is much more evident in the behavioral sciences, where the most frequent methodology is qualitative,  a type of research where an indiscriminate use of instruments is observed, which are not typical of this methodology. This responds to an interest in the search for contextualization and homogeneity.

In an analysis carried out on 102 doctoral theses developed in the last 10 years, it was detected that the most used instrument is the survey; that each investigation designed its own instrument; and that, in the best of cases, responded to the objectives set (Conference in postdoctoral course: Analysis of the use of instruments in doctoral research, presented in 2014 by Tomás Crespo Borges, at the Pedagogical University of Villa Clara).

Due to its importance and complexity of application, instrument validation is considered a type of study within intervention studies, that is, at the same level as experimental, quasi-experimental, among others.

The questionnaire is an instrument for collecting information, designed to quantify and universalize it. For this reason, the moment of validation is of great importance, since the results obtained from its application can falsify the research, and thus, lead to fatal consequences in robust studies, in the social, constructive, and life of a patient. , among others.

In this work, dividing sections will be used; in practice it is a process that is presented as a system, where all its elements have an important function.

A first conception that has two phases is described below:

Phase 1: Generalities of validation

An instrument must meet two fundamental elements: validity and reliability, to match the gold standard instrument. If it does not exist, then it must meet a series of requirements to be reliable enough to accept the results in scientific research.

First: Validation involves two fundamental concepts, what has been applied up to this point? Is it good, surely? Second: How accurate is the new instrument to compare it with the one accepted by the scientific community, as correct in its measurements?

Phase 2: Internal validity

Validity is the degree to which an instrument measures what it is intended to measure. To obtain it, the instrument to be used must be compared with the ideal, gold standard or  Gold Standard .

Reaffirmed as a process, five sources of evidence have been postulated for it: according to the content, the internal structure, in relation to other variables, in the consequences of the instrument and in the response processes.

Reliability is the degree of congruence with which an instrument measures the variable. It is obtained by evaluating reproducibility, which is when there is a good correlation in the measurements at different times; and on the other hand, reliability, which is the accuracy of the measurements at different times. The application of both concepts is revealed in a recent article, where an instrument is validated with the purpose of being used in a study on tourist destinations in the province of El Oro, Ecuador.

When exploring the state of the art, the first thing to do is verify the existence of instruments applied in previous research, used for the same purpose, that have been validated at the time, as part of the investigative process. The most used tests, depending on the measurements of the variables, can be Student’s t  or Anova, if the data follow a normal distribution; otherwise, their non-parametric counterparts; Wilcoxon or Kruskal Wallis, in the case of two or three measurements, respectively, in both situations.

When there is no instrument that fits the objectives of the research, then it must be formed and contrasted with the ideal or gold standard.

In the second option, validity is very difficult to prove, since it has been decided to use an instrument different from those existing in the literature consulted.

Next, reliability is verified. For this, reproducibility is measured .  The instrument is applied several times (two or more) in samples that belong to the same universe or population where the research is carried out. To obtain a correlation considered good in the results (according to the Pearson, Spearman coefficients or the CCC coefficient of agreement) between the measurements, a value greater than 0.7 is accepted, although the ideal is 0.9.

Generalities of validation

For reliability, it is proven that in the different measurements, taken in the same universe or population, the responses of the subjects do not differ significantly, that is, there is accuracy in the instrument measurements at different times. The most used statistical tests are Aiken’s V and Dahlberg’s error. Therefore, validity is measured with another instrument, and reliability with the same one.

Other authors include the term optimization. It is associated with minimizing the error when providing a criterion, at the time of decision-making, based on the results obtained from the instrument.

In general sense, in the studies discussed it can be seen that there are several ways to carry out the validation of measurement instruments. The one that the researcher considers most appropriate can be used, but keeping in mind that the one selected meets all the necessary scientific rigor.

Below, a methodology will be shown to validate a measurement instrument, which is a hybrid between the conception of two different groups of authors, who are essentially similar.

Qualitative, which coincides with content analysis, is part of internal validity. To this are added the reliability and the construct, which belong to the quantitative, as well as the criterion, stability and performance. These last three correspond to external validity.

A second conception, which has six phases, in correspondence with  Supo ‘s idea , is described below:

Phase 1 : qualitative or content validation. It is part of internal validity. It is the creation of the instrument. It is divided into three moments, which do not have to follow an order, but are mandatory. It coincides with a type of diagnostic investigation.

  • Approach to the population: its purpose is to investigate the problem being addressed, approach the units of analysis or variables that should be used in the research. To do this, interviews, population survey studies and others can be carried out to provide this information.
  • Expert judgment: the selected experts are responsible for assessing whether the items in the instrument are clear, precise, relevant, coherent and exhaustive.
  • Rational validity (knowledge): they must be concepts that have been searched in the literature. It is assumed that the researcher is knowledgeable about the topic being studied.
qualitative or content validation

Phase 2 : quantitative or reliability. It is within the internal validity of the instrument.

This phase was detailed previously. According to  Aiken : “…strictly speaking, rather than being a characteristic of a test, reliability is a property of the scores obtained when the test is administered to a particular group of people, on a particular occasion, and under specific conditions.”



How to best handle outliers or anomalous data points?

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How to best handle outliers or anomalous data points?




Data is a collection of facts, figures, objects, symbols, and events gathered from different sources. Organizations collect data with various data collection methods to make better decisions. Without data, it would be difficult for organizations to make appropriate decisions, so data is collected from different audiences at various points in time.

Outlier detection in the field of data mining (DM) and knowledge discovery from data (KDD) is of great interest in areas that require decision support systems, such as, for example, in the financial area, where through DM you can detect financial fraud or find errors produced by users. Therefore, it is essential to evaluate the veracity of the information, through methods for detecting unusual behavior in the data.

This article proposes a method to detect values ​​that are considered outliers in a database of nominal type data. The method implements a global “k” nearest neighbors algorithm, a clustering algorithm called  k-means  , and a statistical method called chi-square. The application of these techniques has been implemented on a database of clients who have requested financial credit. The experiment was performed on a data set with 1180 tuples, where outliers were deliberately introduced. The results demonstrated that the proposed method is capable of detecting all introduced outliers.

Detecting outliers represents a challenge in data mining techniques. Outliers or also called anomalous values ​​have different properties with respect to generality, since due to the nature of their values ​​and therefore, their behavior, they are not data that maintain a behavior similar to the majority. Anomalous  are susceptible to being introduced by malicious mechanisms ( Atkinson, 1981 ). 

Mandhare and Idate (2017)  consider this type of data to be a threat and define it as irrelevant or malicious. Additionally, this data creates conflicts during the analysis process, resulting in unreliable and inconsistent information. However, although anomalous data are irrelevant for finding patterns in everyday data, they are useful as an object of study in cases where, through them, it is possible to identify problems such as financial fraud through an uncontrolled process.

What is anomaly detection?

Anomaly detection examines specific data points and detects unusual occurrences that appear suspicious because they are different from established patterns of behavior. Anomaly detection is not new, but as the volume of data increases, manual tracking is no longer practical.

Why is anomaly detection important?

Anomaly detection is especially important in industries such as finance, retail, and cybersecurity, but all businesses should consider implementing an anomaly detection solution. Such a solution provides an automated means to detect harmful outliers and protects data. For example, banking is a sector that benefits from anomaly detection. Thanks to it, banks can identify fraudulent activity and inconsistent patterns, and protect data. 

Data is the lifeline of your business and compromising it can put your operation at risk. Without anomaly detection, you could lose revenue and brand value, which takes years to cultivate. Your company is facing security breaches and the loss of confidential customer information. If this happens, you risk losing a level of customer trust that may be irretrievable. 

anomaly detection


The detection process using data mining techniques facilitates the search for anomalous values ​​ ( Arce, Lima, Orellana, Ortega and Sellers, 2018 ). Several studies show that most of this type of data also originates from domains such as credit cards ( Bansal, Gaur, & Singh, 2016 ), security systems ( Khan, Pradhan, & Fatima, 2017 ), and electronic health information ( Zhang & Wang, 2018 ).

The detection process includes a data mining process that uses tools based on unsupervised algorithms (Onan, 2017). The detection process consists of two approaches depending on its form: local and global ( Monamo, Marivate, & Twala, 2017 ). Global approaches include a set of techniques in which each anomaly is assigned a score relative to the global data set. On the other hand, local approaches represent the anomalies in a given data with respect to its direct neighborhood; that is, to the data that are close in terms of the similarity of their characteristics.

According to the aforementioned concepts, the local approach detects outliers that are ignored when a global approach is used, especially those with variable density (Amer and Goldstein, 2012). Examples of such algorithms are those based on i) clustering and ii) nearest neighbor. The first category algorithm considers outliers to be in sparse neighborhoods, which are far from the nearest neighbors. While the second category operates in grouped algorithms ( Onan, 2017 ).

There are several approaches related to the detection of outliers, in this context,  Hassanat, Abbadi, Altarawneh, and Alhasanat (2015) , carried out a survey where a summary of the different studies of detection of outliers is presented, these being: statistics-based approach, the distance-based approach and the density-based approach. The authors present a discussion related to outliers,  and conclude that the k-mean algorithm is the most popular in clustering a data set.

Furthermore, in other studies (Dang et al., 2015; Ganji, 2012; Gu et al., 2017; Malini and Pushpa, 2017; Mandhare and Idate, 2017; Sumaiya Thaseen and Aswani Kumar, 2017; Yan et al., 2016 ) data mining techniques, statistical methods or both are used. For outlier detection, nearest neighbor (KNN) techniques have commonly been applied along with others to find unusual patterns during data behavior or to improve process performance. What’s up. (2017) present an efficient grid-based method for finding outlier data patterns in large data sets.

Similarly, Yan et al. (2016) propose an outlier detection method with KNN and data pruning, which takes successive samples of tuples and columns, and applies a KNN algorithm to reduce dimensionality without losing relevant information.

Classification of significant columns

To classify the significant columns, the chi-square statistic was used. Chi-square is a non-parametric test used to determine whether a distribution of observed frequencies differs from expected theoretical frequencies ( Gol and Abur, 2015 ). The weight of the input column (columns that determine the customer profile) is calculated in relation to the output column (credit amount). The higher the weight of a column corresponding to the input columns on a scale of zero to one, the more relevant it is considered.

That is, the closer the weight value is to one, the more important the relationship with respect to the output column will be. The statistic can only be applied to nominal type columns and has been selected as a method to define relevance. Chi-square reports a level of significance of the associations or dependencies and was used as a hypothesis test on the weight or importance of each of the columns with respect to the output  column S. The resulting value is stored in a column called  weight , which together with the anomaly score is reported at the end of the process.

Nearest local neighbor rating

To obtain the values ​​with suspected abnormality, K-NN Global Anomaly Score is used. KNN is based on the k-nearest neighbor algorithm, which calculates the anomaly score of the data relative to the neighborhood. Usually, outliers are far from their neighbors or their neighborhood is sparse. In the first case, it is known as global anomaly detection and is identified with KNN; The second refers to an approach based on local density.

The score comes by default from the average of the distance to the nearest neighbors ( Amer and Goldstein, 2012 ). In the  k  nearest neighbor classification, the output column  S  of the nearest neighbor of the training dataset is related to a new data not classified in the prediction, this implies a linear decision line.


To obtain a correct prediction, the value k (number of neighbors to be considered around the analyzed value) must be carefully configured. A high value of k represents a poor solution with respect to prediction, while low values ​​tend to generate noise ( Bhattacharyya, Jha, Tharakunnel, & Westland, 2011 ).

Frequently, the parameter k is chosen empirically and depends on each problem. Hassanat, Abbadi, Altarawneh, and Alhasanat (2014)  propose carrying out tests with different numbers of close neighbors until reaching the one with the best precision. Their proposal starts with values ​​from k=1 to k= square root of the number of tuples in the training dataset. The general rule is often to map k with the square root of the number of tuples in dataset D.


If we have confirmed that these outliers are not due to an error when constructing the database or in measuring the variable,  eliminating them is not the solution. If it is not due to an error, eliminating or replacing it can modify the inferences made from that information, because it introduces a bias, reduces the sample size, and can affect both the distribution and the variances.

Furthermore,  the treasure of our research lies in the variability of the data!

That is, variability (differences in the behavior of a phenomenon) must be explained, not eliminated. And if you still can’t explain it, you should at least be able to reduce the influence of these outliers on your data.

The best option is to remove weight from these atypical observations using robust techniques .

Robust statistical methods are modern techniques that address these problems. They are similar to the classic ones but are less affected by the presence of outliers or small variations with respect to the models’ hypotheses.


If we calculate the  median (the center value of an ordered sample) for the second data set we have a value of 14 (the same as for the first data set). We see that this centrality statistic has not been disturbed by the presence of an extreme value, therefore, it is more robust.

Let’s look at other alternatives…

The  trimmed mean (trimming)  “discards” extreme values. That is, it eliminates a fraction of the extreme data from the analysis (eg 20%) and calculates the mean of the new data set. The trimmed mean for our case would be worth 13.67.

The  winsorized mean  progressively replaces a percentage of the extreme values ​​(eg 20%) with less extreme ones. In our case, the winsorized mean of the second sample would be the same 13.62.

We see that all of these robust estimates better represent the sample and are less affected by extreme data.

What measures are in place to ensure the security of your data?


What measures are in place to ensure the security of your data?



Data is a collection of facts, figures, objects, symbols, and events gathered from different sources. Organizations collect data with various data collection methods to make better decisions. Without data, it would be difficult for organizations to make appropriate decisions, so data is collected from different audiences at various points in time.

For instance, an organization must collect data on product demand, customer preferences, and competitors before launching a new product. If data is not collected beforehand, the organization’s newly launched product may fail for many reasons, such as less demand and inability to meet customer needs. 

Although data is a valuable asset for every organization, it does not serve any purpose until analyzed or processed to get the desired results.

A phrase attributed to the creator of the Internet, Tim Berners-Lee, is that “data is precious and will last longer than the systems themselves.”

The computer scientist was referring to the fact that information is something highly coveted, one of the most valuable assets that companies have, so it must be protected. The loss of sensitive data can represent the bankruptcy of a company.

Faced with an increasing number of threats, it is necessary to implement measures to protect information in companies. And before doing so, it is necessary to classify the data  you have and the risks to which you are exposed: the price list of the products that are marketed is not the same as the estimated sales figure that is planned to be achieved in the year or base. of customer data.

To talk about the cloud these days is to talk about a need for storage, flexibility, connectivity and decision making in real time. Information is a constantly growing asset and needs to be managed by work teams, and platforms such as  Claro Drive Negocio  offer, in addition to that storage space, collaboration tools to manage an organization’s data.

In cloud storage, the user, instead of saving the data on their computers or hard drives, does so somewhere in the remote location, which can be accessed through the internet service. There are several  providers  of these services that sell space on the network for different ranges, but few offer true security and protection of that gold that you have in your company called: data.

To give you context, more than a third of companies have consolidated  flexible and scalable cloud models  as an alternative to execute their workload and achieve their digital transformation, reducing costs. Hosted information management services allow IT to maintain control and administrators to monitor access and hierarchies by business units.

Five key security measures

Below are five security recommendations to protect information in companies:

  1. Make backup copies or backups . Replicating or having a copy of the information outside the company’s facilities can save your operation in the event of an attack. In this case, options can be sought  in the cloud or in data centers so that the protected information is available at any time. It is also important that the frequency with which the backup is made can be configured, so that the most recent data is backed up.
  2. Foster a culture of strong passwords . Kaspersky recommends that passwords be longer than eight characters, including uppercase, lowercase, numbers, and special characters. The manufacturer also suggests not including personal information or common words; use a password for each service ; change them periodically; Do not share them, write them on paper or store them in the web browser. Every year, Nordpass publishes a ranking of the 200 worst passwords used in the world. The worst four are “123456”, “123456789”, “picture1” and “password”.
  3. Protect email.  Now that most of the communication is done through this medium, it is advisable to have anti-spam filters and message encryption systems to protect and take care of the privacy of the data. Spam filters help control the receipt of unsolicited emails, which may be  infected with viruses  and potentially compromise the security of company data.
  4. Use antivirus.  This tool should provide protection against security threats such as zero-day attacks, ransomware, and cryptojacking. And it must also be installed on cell phones that contain company information.
  5. Control access to information.  One way to minimize the risk and consequent impact of errors on data security is to provide access to data according to the profile of each user. With the principle of least privilege, it is considered that, if a person does not have access to certain vital company information, he cannot put it at risk.

security measures

In security, nothing is too much

In a synthetic way, the National Cybersecurity Institute of Spain, INCIBE, recommends the following “basic security measures”:

  • Keep systems updated  free of viruses and vulnerabilities
  • Raise awareness among employees about the correct use of corporate systems
  • Use secure networks to communicate with customers, encrypting information when necessary
  • Include customer information in annual risk analyses, perform regular backups, and verify your restore procedures
  • Implement correct authentication mechanisms, communicate passwords to clients securely and store them encrypted, ensuring that only they can recover and change them

The first time a company or business faces the decision to automate a process it can be somewhat intimidating, however, taking into account the following points, it is a simple task.

1.- Start with the easy processes

Many companies start  considering automation  because they have a large, inflexible process that they know takes up too much time and money. So they start with their most complex problem and work backwards. This strategy is generally expensive and time-consuming, what you should do is review your most basic processes and automate them first. For example, are you emailing a document with revisions when you should be building an automated workflow? There are probably dozens, if not hundreds of these simple processes that you can address and automate before taking on your “giant” process.

2.- Make sure your employees lose their fear of automation

Many times an employee who is not familiar with an automated process is afraid of it. Because? In general he is afraid that automation will eliminate his position. That’s why it’s important to build a supportive culture around automation and get your employees to understand that just because  some of their work is now being assisted by an automated process , it doesn’t mean they are any less valuable.

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

what is object storage 1080x630 1

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.


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.  


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