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7 Ways to Improve Data Collection

Accurate information is the foundation of many practices in the current world. As such, the big data revolution has impacted everything - from science and business to the way we interact with each other. Thus, data collection is as crucial a practice as can be, especially in the business environment, where wrong decisions are costly and the right ones are priceless. 

 

But even when we constantly engage with data collection, we don’t always thoroughly understand this process or what makes it better. 

 

What is data collection?

 

Data collection is the process of gathering all kinds of information, usually particular variables in order to find answers or increase knowledge related to particular phenomena. The particulars of the procedure depend on the broader context and the objectives for which the data is being gathered.

 

The fields most commonly associated with data collection are academic research and business. The latter has been increasingly depending on gathering and analyzing data to improve strategizing and decision-making. Data collection is also crucial for investment intelligence and all sorts of financial risk management.

 

Collected data might come in different shapes and sizes. While numerical data is often of the utmost importance, key information can also come in textual or visual forms. What is to be considered data in a specific situation depends on the guiding goals and ideas behind data collection.

 

What are the types of data collection?

 

The categorization of data collection is also in part determined by what kind of research is being conducted and what variables we are focusing on. Data collection methods can be grouped by the sources, the type of information needed, or the particularities of procedures. Below are the main data collection types that are utilized in scientific research and business alike.

 

Surveys, questionnaires, forms

 

Such data consists of all kinds of inquiries to respondents, asking them to answer short questions. These questions are usually close-ended, which require the readers to pick an answer from several options or rate the respondent’s level of agreement with the provided statement. In other cases, they can be open-ended questions that can more often than not be answered in a short manner.

 

There are various ways to present and fill out a questionnaire. Surveys can come in written form to be filled out or respondents can be orally surveyed in person or via phone call. They can also come as online questionnaires on websites or sent out by email.

 

Valuable behavioural and opinion data can be gathered from surveys. It can be used, for example, in political polls to predict voting outcomes and to gain insight into popular opinion. For business purposes, such surveys are great to get basic customer information and public sentiment data.

 

Observations

 

Probably the oldest way of gathering information, that existed before we even had the concept of data. In its most basic form, it simply means that you look around, listen, smell and otherwise observe your environment to note its features.

 

As science developed, observations became experimentations. One no longer simply watches the environment as it naturally occurs but creates repeatable conditions to see what happens. That is what allowed people to keep inventing something new and create the kind of conditions that we want to live in.

 

Such experiments are also utilized in business. For example, retail store owners might play with different ways to arrange their assortment and observe how customers react to it. The drawback of this method is that it takes a lot of time and resources to have large enough samples to extract  reliable insights from.

 

Interviews and focus groups

 

Another way to collect data directly from the respondents is by interviewing them with more open-ended questions, giving them time to formulate their opinions. This can be done either as one-on-one interviews or by forming focus groups. The latter method has a greater potential of providing unexpected and original insights. Since focus group organizers only facilitate discussion while the members talk it out among themselves, it helps to mitigate bias.

 

Focus groups are sometimes utilized in business when a new product, project, or PR strategy is being developed. They help collect details that are more nuanced and can produce deeper insights than quantitative data collection methods. Yet, they are also more expensive and time-consuming. Additionally, one has to be careful about the skills and ability of the interviewer to conduct them right and produce actual value.

 

Online monitoring

 

The Internet is the single biggest resource of data the world has ever known. As such, collecting data from it might take various forms.

 

 The most obvious way for businesses is to track the activity on their own websites. Tracking usually includes not only the subscriptions, accounts created, and purchases made, but also visits that did not convert. Data on the shortcomings of the website could in fact be even more valuable.

 

Additionally, social media monitoring has steadily grown to be one of the most important sources of customer sentiment data. Companies should not only monitor how clients interact directly with their social media accounts but also how and in what contexts their brand is being mentioned. Review sites and blogs are also of great importance in the same regard.

 

Web scraping

 

This method of harnessing data from the internet deserves to be mentioned separately. Web scraping is especially important in the age of big data as it can efficiently collect it on a large scale and from many different publicly available online sources.

 

Web scraping is done by using a programming language to create software known as a web scraper that is capable of downloading pages from websites. It is related to web crawling which is used to find and list websites.

 

Both web crawling and scraping are used by everyone from amateur programmers to scientists and business data analysts to collect accurate data from online sources. For companies that need huge volumes of data web scraping is an indispensable option due to its efficiency and cost-effectiveness.

 

Checking archives and records

 

Tons of material that humans have gathered through the years are stored not only in digital format online but as physical objects in archives. In fact, these archives and records hold a lot of information that has not yet been digitized. That is what makes them another vital source for data collection.

 

The objects in the archive are often paper records that can provide numerical, statistical as well as textual, descriptional information. However, the archives are also full of film and sound material as well as tangible artifacts that can be of interest for some research.

 

Checking these records is slow and painstaking work that can only be sped up by increasing manual power. Such an increase, however, would come with an increase in costs as well. Thus, this method of collecting and analyzing data is best used when the data in the archives is of absolute necessity to advance the goals at hand.

 

What are the ways of improving data collection?

 

Pursuing high quality of the collected data means different things when different objectives are present. There are a few tips, however, that can help to improve data collection in nearly all cases. Generally, the following methods should ensure that collecting data will go more smoothly for you and provide all-around better results.

 

1. Define clear research objectives

 

Clearly defining what kind of questions the research aims to answer is the essential stepping-stone of beneficial data collection.

 

Data is everywhere and that is both a blessing and a curse. If you are not sure what sort of data you actually need, you are bound to be overwhelmed by it. It will lead to wasting time, money, and storage space while bringing no significant results.

 

The only way of knowing what kind of data to gather and where to look for it is by being precise about your analysis goals. Define the variables you are interested in and be sure to operationalize them clearly. This means that every vague or broad concept should be related to the data points you can get.

 

For example, if you want to figure out the success of your product release, define what is success for you in this case. Whether these are initial profits, an increase in traffic, a surge in buyers, or the acquisition of new target markets. When you know what you are looking for and how you can recognize it, your data collection will certainly not go to waste.

 

2. Set a reasonable schedule for data collection and analysis

 

Another important instance of pre-planning related to data collection is deciding upon a schedule. One has to set a limit to when the collection needs to end, otherwise, it might go on forever with no useful results in sight.

 

Thus, one has to have an initial approximation of how much time will be dedicated to data collection before at least the first stages of analysis begin. Also, define how much time should be spent on analyzing the collected data and preparing the report.

 

Not everything is going to go according to plan and some adjustments to the schedule might be needed. But having deadlines as to when one stage needs to end and another begins will help avoid significant delays. With no set schedule, it is easy to get lost in uncertainty and keep collecting data just because it is out there. At some point, one has to start delivering and it is better to have this point pre-established.

 

Additionally, it would be wise to set a schedule of intervals at which new data should be collected and other research conducted. The world does not stand still, thus knowledge gets old if it is not updated with new accurate data at regular intervals.

 

3. Automate everything you can

 

The only way to deal with the vastness of information is by turning to all the technology that has been created to manage and analyze the collected data. There are of course some methods of data collection that require a lot of manual labor, but certainly not all of them.

 

A lot can be done by AI tools, especially when dealing with web analytics. Well-written software programs will scrape, parse, and move data to storage efficiently. Additionally, automated data collection systems will be better at integrating external data with the internal company data such as from the inventory system.

 

There will always be some things that require human intervention and decision-making. But precisely because these things are the most important and the most interesting, it is better to reserve humans for the tasks most worthy of them. 

 

Meanwhile, data collection systems should be as automated as possible. It will increase efficiency, remove risks of human error, and make sure that the resources are distributed in an effective way.

 

4. Prioritize data quality

 

Data collection in the current era reminds us of one of the oldest pieces of wisdom known to mankind - quality is more important than quantity. Whatever the memory space available to them, anyone can easily fill it up with data if they want to. But ensuring that the collected data will be valuable and not just pieces of irrelevant information is the actual goal.

 

In order to ensure that only quality data is used in research, always consider the reliability of sources for your data collection. Suspicious sources might only be able to provide outdated, incorrect, or incomplete data, which can damage the outcome of the research.

 

Prioritizing data quality will immediately improve data collection, as high-quality data will be easier to organize and manage. Collecting information that does not conform to reasonable standards of quality is not only a waste of time and resources but might lead to harmful decisions.

 

To find accurate data, look for constantly updated sources that have no indication of providing false information. When collecting data yourself, it is all about the skill of the researchers and the discipline of the process. If everyone involved is good at their job and the tools used are reliable, getting high-quality data is a very achievable goal.

 

5. Keep in mind who is going to read the reports

 

Just like when releasing a product one has to think about who is going to be their customers, when reporting on research, you should consider the future readers. The primary readers of the report might be other data analysts or they could be managers of particular departments and even company heads.

 

Whichever it is, the report should be tailored to particular skills and needs of those for whose benefit the data analysis is primarily being done. Data analysts will likely prefer more numbers and statistics while managers will need more interpretations and insights.

 

It is crucial to understand that this matters not only at the level of presentation of the results but at the data collection level as well. For example, it might be wise to gather more contextual information in order to make it clear why something matters for a particular department. Additionally, the types of the collected data should depend on whose attention you are going to need to capture.

 

6. Compare results with previous research

 

To know how much you can trust the insights of your analysis you should look at what has been done before with a similar research agenda. If you get different results, it is a good idea to compare the data collected by other analysts as errors might be apparent.

 

7. Adjust and repeat

 

The work of a data analyst is never finished as there is no end to data. Thus, finally, when all is said and done… It is time to start all over again.

 

Data analysis might reveal that you need more information to make adequate conclusions. On the other hand, additional data collection could be necessary to reach one or more of the research objectives. It might also turn out that the answers you have successfully provided to the initial problems are now posing new and not any less pressing questions.

 

Proxies for improved data collection

 

In many cases, the largest chunk of data collection, if not all of it, is done on the internet. As automation becomes more popular, using proxies becomes a necessity.

 

When collecting data online, one is in constant danger of being blocked or stumbling on geo-restricted content. Proxies are the best ways to mitigate such issues of web scraping. By hiding your IP address and redirecting your traffic, proxies will make bypassing bans and geo-restrictions much easier.

 

The best proxies to use for web scraping are datacenter proxies. Data centers can provide services that create many proxy subnetworks with numerous IP addresses in each of them. This allows datacenter proxies to offer the best balance between traffic speed and low costs.

 

Additionally, the best proxy service providers will often offer free proxies to try out without commitment. Take advantage of such an option as there is nothing to lose and much to gain in terms of improving data collection.

 

Online data collection without proxies is not only dangerous but inefficient as well as constant delays in data collection can snowball into bigger problems within the organization. Proxies can be a simple and vital way to avoid all such issues and improve data collection.

 

In conclusion

 

Data collection is an art and a science. Doing it right takes both practical skills and theoretical knowledge. But there are simple tips that everyone can follow and that will help to mitigate risks, avoid problems and get the results. But with attentive preparation and execution, data collection will surely be beneficial to anyone seeking to find knowledge in the sea of information.

 

By Oliver Jones
Oliver is someone you would call a tech-wizard. Fascinated with everything computer and machine related, he has been involved in the industry for ages. Proxies and data are his two newest interests that have carried him to the field of writing. Oliver believes that all the knowledge in the world is worth nothing if it can’t be shared!