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Imitation of the living and nature. That is what biomimicry means. Nature is the source of life and sustenance that nurtures, provides, and protects all living beings. But it can do more than that; it can also inspire marvelous human engineering designs and innovation. For example, if it weren’t for the study of bird flight or airflow over bird wings, the idea of inventing a plane would have probably never occurred to humans. This serves as one of the earliest examples of biomimicry and shows how nature can be a great source of inspiration for humanity and its drive to create. 

Today, biomimicry has become an essential tool for creating more sustainable designs and solutions that are inspired by nature. Let’s take a closer look at the world of biomimicry, and how it plays a transformative role in design and innovation today. 

What is Biomimicry?

The simplest way to explain biomimicry is that it is the science of examining nature’s processes, models, and elements and using them to create sustainable solutions. The concept was first popularized in 1997 by scientist Janine Benyus in her book, Biomimicry: Innovation Inspired by Nature. There are three main elements of biomimicry, which are:

Ethos:

This represents how nature works and our responsibilities toward fellow living beings

Emulate:

Leveraging natural principles to create and formulate sustainable design solutions

Reconnect:

This reinforces the fact that humans and nature are deeply intertwined 

Biomimicry begins with a system or material found in the natural world that becomes the basis for design or innovation. In fact, the main goal is to see nature as a model and design sustainable products around these standards. 

ALSO READ: Sustainable Business: Why Your Company Needs to go Green  

Examples of Biomimicry

Biomimicry finds applications across various industries, from architecture, transportation, design, and urban planning to medicine, agriculture, and technology. Here are some examples of biomimicry to help you understand its applications better. 

Wind turbines

are modeled after the tubercle-studded flippers of humpback whales

According to public-benefit corporation NYSERDA

, by 2030, the global gross domestic product from products based on beehives, whales, and seashells could reach $1.6 trillion

The shape of the kingfisher’s beak inspired the aerodynamics of the

Japanese bullet train

Antimicrobial surfaces

are created based on the presence of denticles on shark skin

How is Biomimicry Impacting Product Design and Innovation?

Biomimicry has become a viable tool for developing new sustainable products and technologies worldwide. According to a Statista report, the global biometric technology market is predicted to reach $18.5 billion by 2028. But how does this nature-inspired approach revolutionize design and drive innovation? 

Turning to nature for ideas on designing and developing products works well and is substantially cost-effective. Additionally, biomimicry takes sustainable innovation further by exploring how nature’s operating instructions can be harnessed to build and redesign products that can better support environmental sustainability. This is why many businesses worldwide are now tapping into biomimicry to bring about innovation backed by eco-friendly products that are sustainable in the long haul.

Many challenges humans face today, like depleting energy reserves, have already been explained by nature. Emulating nature’s unique ways of adapting and problem-solving can lead to developing some of the most significant product designs. Biomimetic integration, with its nature-inspired problem-solving approach, can be game-changing—even revolutionary—when applied in the right context. 

ALSO READ: Why do Sustainable Development Goals Matter to Job Seekers 

Is Biomimicry Sustainable?

Since biomimicry is based on ‘solutions’ proven by nature, it enables the designing products that are in harmony with the environment. Mimicking nature’s processes is more efficient in protecting our ecosystem and can help propel us toward creating an environmental balance that is conducive to life. In fact, biomimicry enables innovators to focus on nature’s inherent sustainable strategies to create adaptable and multifunctional products.  

Learning, the Emeritus Way

Products inspired by nature can bring forth transformative change in how we develop, design, transport, and distribute goods and services. However, if you’d like to further deepen your knowledge of biomimicry and its applications, check out product design and innovation courses at Emeritus. In fact, you can equip yourself with the right skills to build a successful career in this emerging field! 

By Neha Menon

Write to us at [email protected] 

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What Is The Role Of Orm In Digital Marketing?

The main objective of online reputation management, or ORM, is to promote a good portrayal of an item or organization.

Even though each company’s approach to content marketing strategy will be varied, ORM ought to incorporate some mixture of the following:

Staff and client tales, subscriber information (UGC), testimonials, conferences, and model materials are examples of mainstream media.

Publicity consists of corporate communications, consumer promotions, and corporate communications.

Service learning and collaborations, cross efforts, and spontaneous interactions on social media are examples of computer networks.

Shifting the Story

Restoring your internet identity is really difficult.

An unusual experience with your company may not only deter a single consumer from coming, but then if they chose to publish a nasty evaluation on Amazon, LinkedIn, or anywhere else for that, you might soon see a decline in sales.

According to a Khoros 2023 survey, 83% of customers are more loyal to firms who listen to and solutions to their problems.

Knowing Your Clients

If your company does not have its foot just on the pace of developing brand discussion, you may have to allocate considerable resources to new marketing campaigns that failed to solve the first impediment because you did not address underlying problems earlier.

Although the terms “confidence administration” and “corporate communications” are frequently used similarly, they really mean entirely distinct things.

They can both be commonly utilized as a component of your comprehensive product marketing, but it’s critical to grasp the distinctions before spending on either one or both of them.

Although ORM focuses only on the requirements of the company and sustaining a favorable internet presence, PR fosters a more beneficial relationship between this kind of company as well as other organizations, notably the press.

Public relations initiatives may and ought to be focused on enhancing a company’s public perception.

Yet, the purpose is usually to present messages concerning something additional specialized, such as a recently introduced service or product.

ORM and Website Development

Brands have practically no influence over customer reviews and nowhere is this more evident and less on online platforms.

Indeed, commanding the listings on search engine outcomes pages (SERPs) with quality brand narratives is an important aspect of ORM.

But, efficiently controlling and answering inquiries on different parts of the internet is also important. It is critical to be proactive and take part in online dialogues regarding your business.

Several of them have opinion analysis techniques, allowing you to readily track if your discussions are skewed more upward or downward.

What Exactly is ORM in SEO?

To uphold an excellent impression for its consumers, a competent online reputation management business uses SEO as well as other strategies.

ORM and SEO allow you to organize information and webpages with an eye towards web placement. Somebody, will most probably have searched you up on social media prior to starting a trade with you. As a consequence, the connections and material at the beginning of the results page will reflect how possible stakeholders see you. It is additionally the reason why it’s critical to keep a positive internet presence.

Individuals look on the World Wide Web for data concerning themselves and are impacted by what they find. It is critical to ensure that only uplifting material performs well.

The Relevance of ORM in Internet Advertising

Internet reputation management is critical for keeping a good impression on the public and establishing your company in the industry. Given the prominence of third-party evaluations, having a solid internet presence is crucial for any organization.

Negative reviews can be combated via ORM. It can assist you in keeping a record of your industry’s or manufacturer’s online reputation. Furthermore, the notion of the management of reputation (ORM) may assist you in removing negative material from search engine results by introducing fresh and meaningful information.

Employ Anyone else to Assist you with ORM

Dialogue about company business is continually occurring, regardless of whether you realize it or not.

Once it involves acquiring new clients and maintaining existing buildings, a targeted ORM campaign may have a substantial effect.

Create a positive first appearance and reclaim ownership of your company’s online narrative.

Conclusion

Social media is becoming an increasingly important aspect of just how companies promote themselves in the digital sphere. Potential clients, present consumers, and experts are all looking at your public interactions to decide the degree to which people want to collaborate alongside you.

The simplest and greatest way to remain on top of such matters is through online reputation management, which ensures that everything is stated appropriately and represents the brand recognition you want to build and uphold.

What Is A Research Design

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall research objectives and approach

Whether you’ll rely on primary research or secondary research

Your sampling methods or criteria for selecting subjects

Your data collection methods

The procedures you’ll follow to collect data

Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Step 1: Consider your aims and approach

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

Research question exampleHow can teachers adapt their lessons for effective remote learning?

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach Quantitative approach

Understand subjective experiences, beliefs, and concepts

Gain in-depth knowledge of a specific context or culture

Explore under-researched problems and generate new ideas

Measure different types of variables and describe frequencies, averages, and correlations

Test hypotheses about relationships between variables

Test the effectiveness of a new treatment, program or product

Qualitative research designs tend to be more flexible and inductive, allowing you to adjust your approach based on what you find throughout the research process.

Qualitative research exampleIf you want to generate new ideas for online teaching strategies, a qualitative approach would make the most sense. You can use this type of research to explore exactly what teachers and students struggle with in remote classes. Quantitative research exampleIf you want to test the effectiveness of an online teaching method, a quantitative approach is most suitable. You can use this type of research to measure learning outcomes like grades and test scores.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics.

How much time do you have to collect data and write up the research?

Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?

Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?

Will you need ethical approval?

At each stage of the research design process, make sure that your choices are practically feasible.

Step 2: Choose a type of research design

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and

quasi-experimental

designs allow you to test cause-and-effect relationships

Descriptive

and

correlational

designs allow you to measure variables and describe relationships between them.

Type of design Purpose and characteristics

Experimental

Used to test causal relationships

Involves manipulating an independent variable and measuring its effect on a dependent variable

Subjects are randomly assigned to groups

Usually conducted in a controlled environment (e.g., a lab)

Quasi-experimental

Used to test causal relationships

Similar to experimental design, but without random assignment

Often involves comparing the outcomes of pre-existing groups

Often conducted in a natural environment (higher ecological validity)

Correlational

Used to test whether (and how strongly) variables are related

Variables are measured without influencing them

Descriptive

Used to describe characteristics, averages, trends, etc

Variables are measured without influencing them

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation).

Correlational design exampleYou could use a correlational design to find out if the rise in online teaching in the past year correlates with any change in test scores.

But this design can’t confirm a causal relationship between the two variables. Any change in test scores could have been influenced by many other variables, such as increased stress and health issues among students and teachers.

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Experimental design exampleIn an experimental design, you could gather a sample of students and then randomly assign half of them to be taught online and the other half to be taught in person, while controlling all other relevant variables.

By comparing their outcomes in test scores, you can be more confident that it was the method of teaching (and not other variables) that caused any change in scores.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design Purpose and characteristics

Case study

Detailed study of a specific subject (e.g., a place, event, organization, etc).

Data can be collected using a variety of sources and methods.

Focuses on gaining a holistic understanding of the case.

Ethnography

Detailed study of the culture of a specific community or group.

Data is collected by extended immersion and close observation.

Focuses on describing and interpreting beliefs, conventions, social dynamics, etc.

Grounded theory

Aims to develop a theory inductively by systematically analyzing qualitative data.

Phenomenology

Aims to understand a phenomenon or event by describing participants’ lived experiences.

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Step 3: Identify your population and sampling method

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Population exampleIf you’re studying the effectiveness of online teaching in the US, it would be very difficult to get a sample that’s representative of all high school students in the country.

To make the research more manageable, and to draw more precise conclusions, you could focus on a narrower population—for example, 9th-grade students in low-income areas of New York.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling. The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling Non-probability sampling

Sample is selected using random methods

Mainly used in quantitative research

Allows you to make strong statistical inferences about the population

Sample selected in a non-random way

Used in both qualitative and quantitative research

Easier to achieve, but more risk of research bias

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Step 4: Choose your data collection methods

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Questionnaires Interviews

More common in quantitative research

May be distributed online, by phone, by mail or in person

Usually offer closed questions with limited options

Consistent data can be collected from many people

More common in qualitative research

Conducted by researcher in person, by phone or online

Usually allow participants to answer in their own words

Ideas can be explored in-depth with a smaller group (e.g., focus group)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation Qualitative observation

Systematically counting or measuring

Taking detailed notes and writing rich descriptions

All relevant observations can be recorded

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field Examples of data collection methods

Media & communication Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives

Psychology Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time

Education Using tests or assignments to collect data on knowledge and skills

Physical sciences Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

Step 5: Plan your data collection procedures

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations, which events or actions will you count?

ExampleTo measure student participation in an online course, you could record the number of times students ask and answer questions.

If you’re using surveys, which questions will you ask and what range of responses will be offered?

ExampleTo measure teachers’ satisfaction with online learning tools, you could create a questionnaire with a 5-point rating scale.

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability Validity

Does your measure capture the same concept consistently over time?

Does it produce the same results in different contexts?

Do all questions measure the exact same concept?

Do your measurement materials test all aspects of the concept? (content validity)

Does it correlate with different measures of the same concept? (criterion validity)

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?

What inclusion and exclusion criteria will you use to identify eligible participants?

How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability).

Step 6: Decide on your data analysis strategies

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis. With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics, you can summarize your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)

The central tendency of the data (e.g., the mean to describe the average score)

The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics, you can:

Make estimates about the population based on your sample data.

Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis.

Approach Characteristics

Thematic analysis

Focuses on the content of the data

Involves coding and organizing the data to identify key themes

Discourse analysis

Focuses on putting the data in context

Involves analyzing different levels of communication (language, structure, tone, etc)

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

Other interesting articles

If you want to know more about the research process, methodology, research bias, or statistics, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about research design Cite this Scribbr article

McCombes, S. Retrieved July 16, 2023,

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The Role Of Chatbots In Online Casino Platforms

“Hi, I’m Jack. Welcome to the XYZ website. What do you need?” is a commonly spotted message on different platforms.

Some people misinterpret this to mean that they’re chatting with a human customer care agent. When you see this type of message pop-up, you need to understand that you’re communicating with an online robot or chatbot.

The job of this chatbot is to ensure that all users have quick answers to the questions they seek. That’s why online platforms like Wildz Casino leverage these bots in several ways.

In the sections below are the types of chatbots found on online casino platforms.

Command-based chatbots

Command-based chatbots depend on an existing database of questions and their associated solutions. This type of bot can only provide solutions to questions that are similar to those that have been pre-programmed.

You can liken it to making a query on a search engine. A search engine like Google can only return results based on the results that closely match your query.

Similarly, a command-based chatbot cannot answer questions that don’t match the existing ones. If this chatbot doesn’t understand the query, it can enlist the help of a human customer service official.

Artificial Intelligence-based chatbots

An Artificial Intelligence-based chatbot doesn’t need to have questions and answers pre-programmed. You can even give the chatbot unclear questions and receive a decent answer.

This chatbot leverages natural language processing, a field in deep learning to give human-like replies. Like other machine learning algorithms, chatbots improve over time as they learn from increased data.

Read on about the benefits of chatbots to online casinos.

Customer support

One of the biggest benefits of online casinos is that you don’t have to travel miles away from home to engage in gaming. Nevertheless, this convenience is countered by the fact that you must understand many things in the casino, which could prove difficult for a new gamer.

The quality of answers given to a wide range of questions is heavily contingent on the deep learning algorithm the chatbot uses.

Gives information on promotions

The casino industry is highly competitive, with new platforms entering the sector monthly. The size of the target market is increasing at an arithmetic rate, and the platforms are increasing geometrically.

This means the supply is outstripping demand in the sector. That’s why casino operators are using promotions and bonuses to beat their rivals and attract customers.

The promotion offered could be contingent on the time spent on the website. The chatbot could also study the gamer’s playing pattern.

Points of communication

Chatbots are sometimes leveraged as points of communication for a casino’s customers. Crucial information like a policy modification or a new product can be passed through chatbots.

Chatbots typically pass this information when the user visits the website. Nevertheless, gamers can receive the information on other communication channels as long as they have accounts with the casino.

Money-saving tool

Quality customer support is highly vital to the success of any online casino. However, the cost associated with hiring human customer support is typically high.

Leveraging a chatbot saves money and even cuts down on the human customer support required. The only cost associated with the chatbot is the initial expense of buying and integrating the tool.

Chatbots also go further than humans in their service, allowing users access to 24/7 service.

Chatbots work by allowing users to get quick help on a website. Command-based chatbots are the ones that have pre-programmed questions and answers. However, artificial intelligence-based chats leverage natural language processing to find solutions to vague queries.

Chatbots benefit casinos since they allow for reduced expenditure, dissipate useful information, and are quality customer support tools. Also, they are available 24/7. They provide a quick and easy way for users to get help without having to wait for a human customer service representative. In addition, chatbots can be used by anyone.

Reason Why Innovation Is The Most Critical Aspect Of Big Data?

Introduction to Why Innovation is The Most Critical Aspect of Big Data?

Innovation and creativity have always played prime roles in helping brands and companies succeed in the short and long term. The need for creative problems has increased dramatically because the kinds and challenges that brand managers face are getting more complex and complicated daily. When brands are innovative in their approach to solving hurdles, then they, more often than not, can resolve their issues faster and easier manner.

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Creativity is one of the most significant driving forces behind innovation because it allows individuals to challenge the existing situation and develop innovative, unique, and effective solutions. It will enable brands to break the shackles of normality, uniquely expand their brands, and set new rules and benchmarks within the industry.

Why is Creativity such a Critical Element in Organizations?

Innovation and creativity are the most essential elements in any organization and will result in gains and success. By embracing creativity and exploring new territories, brands can reach new levels of productivity and growth within a company. When brands encourage employees to think outside the box and allow them to explore their talents, companies can continue to grow and discover solutions to many of their internal and external conflicts. Therefore, creativity is a sure-shot way brands can solve their problems. So whether brands want to develop a new strategy, creative thinking will get them ahead. Creativity, without a doubt, sets organizations apart from others and gives them a competitive edge in all aspects of the operation.

Creative ideas and innovative approaches can help brands in all possible manners from the point of view of customers, target groups, employees, or partners. Bringing a unique perspective to their plans not just encourages communication but also brings a unique manner of functioning and management within the company. Some of how companies can develop a culture of innovation include the following:

Failing is a Part of the Learning Curve

Many brands are afraid to fail in any manner. This is because failure is considered a sign of weakness, and even when there is an opportunity, many companies hesitate to take a leap of faith. As against popular notion, successful brands have often failed several times before eventually achieving success in the true sense. Further, failure is also a form of freedom. This is because now the worst has happened. While failure on a large scale can be avoided, it is essential to face failure strategically.

As J.K. Rowling says, There is no way to live life without failing occasionally. You won’t fail only if you live so guardedly that you barely live, wherein you fail by default. Overcoming failure is possible when brand managers accept, annoy, dissect, and learn from it. As they say, failure is the stepping stone to success and growth. The bottom line is that brands must learn from their failure and, even if there are obstacles, remain positive, as a good attitude can go a long way in helping brands gain the success they wish to achieve eventually.

Brand Managers must Learn to Lead from the Front, In all Situations

Running a big brand or company is no easy task, as it requires constant attention and hard work. So while there may be many highs and successes, management will also face many lows and challenges. Often, a brand is successful because of the efforts and hard work of the entire team rather than just a single individual. A brand manager, therefore, needs to act in a manner that will enable them to distribute the required tasks according to the capabilities and talents of the employees.

Another thing that brand managers must learn to do is learn from past experiences. Share stories about your campaigns so that you learn about what went right and what went wrong. Good communication between team members and employees will keep the team motivated, encouraged, and keen to perform despite all obstacles. In short, bring everyone together so that everyone participates in the success story of the brand/company.

Always Remember to Reward Innovation and Incentives at Every Stage

Innovation is, therefore, the basis of success for brands and companies across sectors. Additionally, when companies are responsive and resilient, they have all the ingredients to stay ahead of their competition successfully. This is where big data analytics can help companies strategically achieve goals. By analyzing the information, brands can deal with change effectively. By gathering data on various aspects of the organization and making required changes, brands can move ahead on the path of progress in a much more successful fashion.

This has always been the goal of brand managers who want to take their companies ahead on the path of development, profitability, and progress. Data analytics that helps support decision-making is, therefore, an essential element in any company, though it has one flaw is that it still involves human intervention and interaction. As Big Data gets bigger and bigger, the human element cannot be ignored. Still, the fact remains that human beings have a limited span of attention, especially when it comes to processing vast amounts of data.

Unless and until brands can make sense of the vast amounts of data available, techniques and methods of Big Data are redundant. That is why professionals who can help companies to use Big Data properly are in prime demand across companies and sectors. It is these people who can help companies use the insight that Big Data has generated so that it can be used to create campaigns that not just help brands reach their goals and objectives but also empower and strengthen brand power among their customers as well.

As humans lack attention, removing the human element is the only way to make Big Data seamless. This is to say that the entire big data process has to become completely automated. So if a business deals with equipment, everything from manufacturing to delivery is done through machines. Machines will fix these hurdles despite errors, making human interaction minimum and needed only in extreme situations.

Being more responsive to the needs of the brand needs is an essential requirement if brands are to stay in business, especially with rising competition and increasing consumer demands on the other hand. There are many areas in that brands can optimize their data in real time. These include optimizing salaries based on profit outcomes and corresponding security policies that align with the risk of loss. Brands can make automated and data-driven decisions by continually updating the company’s data.

Data is an essential part of almost every sector around the globe. It has become an integral part of how brands and companies function. It is as important as capital and labor in today’s economy. As the amount of data increases daily, it is estimated that 5 quintillion bytes of data are generated every minute, which is set to increase daily. By making sense of this vast data, companies can expand and grow in many ways that would have seemed impossible until yesterday. The rate of expansion and growth has grown tremendously after the introduction of Big Data, and add to that the angle of innovation, companies can quickly gain a competitive edge. Investing in innovative big data technology is how brands can increase and improve profitability in the coming years.

Big data can be used to create value in five distinct ways. The first way is to ensure that data is transparent in all manners. By providing that data is transparent throughout the organization, it becomes possible for brand managers to gain insights from them simply and effectively. Data transparency is essential; this is the first step towards creating value through big data.

The second way to generate value in big data is to create and store transactional data in the digital medium. By storing information in this manner, brand managers can collect accurate and detailed information; this, in turn, helps them optimize details for many things, including product inventories to employees’ sick days. By doing this, companies can keep a tab on all the company’s functioning and, in the process, boost their performance and use existing opportunities. Through the use of Big data, companies are successful in making better decisions and managing their inventories in a better manner. By adjusting their goals and objectives to align with the big data objectives, brands can successfully empower and strengthen their brand image.

The third way big data can add value to a company is by helping them personalize their products and services according to their target audience. Personalization of products is essential in this competitive age, so it is important to tailor products and services to their needs. Every customer loves having a personal touch when buying products and services. Always remember that an engaged and loyal customer is one of the most significant assets of any company. Developing ingenious engagement methods is the need of the hour for all brands and companies that want to succeed. The bottom line is that the personalization of services will help brands retain their existing customer base and expand it profitably.

The fourth and final way big data can help companies is by assisting them to make better decisions. It can help them provide not just practical and superior services but also enhance after-sales support as well. For instance, with Big data, manufacturers can embed their products with sensors to understand customer needs and provide better after-sales service offerings, helping them stand out.

“Convergence of social, mobile, cloud and big data technologies presents new requirements – getting the right information to the consumer quickly, ensuring reliability of external data you don’t have control over, validating the relationships among data elements, looking for data synergies and gaps, creating provenance of the data you provide to others, spotting skewed and biased data.”

As technology develops, brands will need new methods to keep up with this rapid change to remain viable and effective. In short, brands can stay ahead of the competitive curve by effectively combining powerful analytics with the correct data. So the earlier they start down this road, the more chances they have for learning and using this medium effectively.

When coupled with innovation, big data can help bring a new era of productivity and consumer growth. Many studies have proven that investing in good analytical techniques can help brands to increase their operating gains by more than 60 percent. By offering numerous benefits to consumers and companies, innovation and big data are here to stay long. In addition, with a host of personal location devices that are gaining a lot of prominence, big data can open a host of opportunities for brands to connect and engage with clients personally and intimately.

In summary, achieving success in a competitive environment is challenging. But at the same time, it is incredibly fulfilling and satisfying. And one way a company can continue on the path of success is by investing in techniques and innovation that will help them strengthen their internal communication and external relationships with clients, customers, and stakeholders.

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Learn The Role And Basic Features Of Scrum Software

Introduction to Scrum Software

Scrum is the widely used methodology in the agile framework, and the main aim is to reduce the workload and distribute the work between the team members so that work should be completed on time. The other feature of this method is concerned has more pros than cons, which we will discuss further. Now if we talk about the scrum software which is the essential part of this method and used at large in the industries which work on the agile framework, the scrum software is a set of instruction which help the scrum master as well as the whole team to reduce the time of work and also help to increase the quality of work, so this whole collectively defines the scrum software by and large. So now we will discuss the other aspect of the scrum software.

Roles of Scrum Software

Scrum software is a tool that we can say is the panacea in the field of agile. As far as the role of scrum software is concerned, it plays a vital role by helping the entire team provide easy tools and an interface compatible with work.

The main problem of scrum master, which is handling of the entire process, is also sorted out with this particular software. With the help of this software, one can easily handle on or more than one process at one time, and also team management and allotment of work is also very easy. So, by and large, it has a very crucial role in the work.

Basic Features of Scrum

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Scrum provides the facility of multiuser, so a total of 12 Users may access the scrum software as a whole.

If we talk about the storage of the scrum software, so it comes with the storage of 5-10GB, which is partially the online storage and internal storage.

Scrum also provides the facility of multitasking; with this, a person can access or work on two or more tasks at one time, which eventually saves time and as well as resources.

It comes with a very good feature called the employee workload management tool, which is nothing but a tool that helps the scrum master keep vigil over the team and help him check the work status of the team members.

A tool which is a Task report, from time to time, update the user that how much work is completed and also shows the time required to complete the residue work.

Pros and Cons of Software

Below are some of the pros and cons of the scrum software, which are as follows:

Pros:

Scrum software puts more emphasis on the workload, and the management tools of this software are quite work-centric and bind the user to work effectively.

The work distribution method and team management are two basic pros of the scrum software; these two tools make the work effective and also enhance the quality of work and reduce the time for work as actual required.

Scrum software comes with many updates, and also, if one wants to increase the storage capacity and user limit, he can easily subscribe to this facility by paying a nominal amount.

Team management and workload are the biggest challenges every manager faces, so this software eliminates this problem and provides an enhanced experience.

If we talk about productivity, it is more than good, and also, eventually, it helps the company to increase its profit.

Cons:

The upper version of the scrum software comes with less user facility and less storage, so the user has to pay extra to enhance the user capacity and increase the storage of this software.

The facility of the auto tool is there, but the productivity goes decreases when the user uses this tool for some time. So this is not a good tool to use.

Every upgraded software comes with a debugging facility, but this particular software is the one that did not have this facility, which put the data under the threat of spying and Phishing.

The bundle of tools available in the software sometimes confused the user that which tool is to use or not to. So this basically is a negative thing for the work.

If we talk about the resolution and other display aspects, it is not as good as one expects per se.

List of Some Effective Tools

Now we will see some of the tools which are used in the scrum software and are very effective from the work perspective, so some tools are given below:

2. Jira: This tool is used basically for the debugging process and also tracks the bugs in the process so that the project should be saved from theft and spying.

3. nTask: This is a very effective tool in the scrum software, which is basically used to manage tasks; with the help of this tool, users can manage the no. Of task at one time and devote this time to other essential work.

5. Quick scrum: Quick scrum is the tool that helps the user find the effective tool for the process because due to lots of tools, users get confused, so mainly it enhances the efficiency of work.

Conclusion – Scrum Software

As we discuss above, Scrum software is very important software and helps a lot of the industries earn profit by quickly completing the projects. The market of scrum software is a very wide market, and the developers who create this software are really genius because it is very hard to arrange so many programs and algorithms to build Software. Also, it is not exaggerated that scrum software nowadays is the backbone of the industries and companies that rely on it.

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