How can you gather data quickly and accurately

There are no predefined answers. There may be expected answers, based on your knowledge of who you are surveying. Open-ended surveys give them the freedom and flexibility required when answering. When you create open-ended surveys, the length and complexity of the questions also plays a critical role.

Try to determine the optimal number of questions and the correct answer length single line vs. Open-ended surveys typically deliver qualitative data while closed-ended surveys deliver quantitatively. One-on-one interviews are a great way to collect data from customers or conduct qualitative research. In interviews, the interviewer is collecting data directly from the interviewee. This approach is much more personalized and can sometimes result in better data collection.

Often, interview data is highly-personalized and can provide unique insights that you would otherwise not have access to through traditional surveys.

Interviews can range in formality. They can be conversational or more structured. They can even be off-the-cuff chat sessions that can still provide valuable insights. Some companies that regularly conduct interviews opt for a more structured approach, while others want the interviewee to feel less restrained and more comfortable.

Focus groups are a popular method for collecting data as well. Focus groups will always be a popular data collection method, despite some flaws in the approach.

Most focus groups will have people in them, with one person moderating the discussion. How the focus group is handled will depend on your research goals and what the data will be used for.

There will likely be some points of commonality for people in the focus group, but sometimes people can share opinions that will sway others. That means that the opinions of people within a focus group can change over the course of an interview.

Most often, focus groups are a tool used for collecting qualitative data, not quantitative. They are excellent for collecting opinions and not always the best for collecting facts. Simply observing your customers and prospects can be another great way to collect data. Direct observation represents a passive way to collect qualitative data. Those that are conducting the observation may be participating while they collect the data or simply observing. For instance, if you recorded your product demos, you could collect observational data from the person conducting the demo participating or allowing a third-party to view the product demo video and draw their own conclusions.

Direct observation is prone to bias when the observer is participating. If you track the performance of offline ads by, for example, asking customers how they heard about your brand, you can import that data into your DMP. Social media is another excellent source of customer data.

You can look through your follower list to see who follows you and what characteristics they have in common to enhance your understanding of who your target audience should be.

Many social media sites will also provide you with analytics about how your posts perform. Third-party tools may be able to offer you even more in-depth insights. Offering customers something in return for providing information about themselves can help you gather valuable customer data. You can do this by requiring some basic information from customers or site visitors who want to sign up for your email list, rewards program or another similar program. One benefit of this method is that the leads you get are likely to convert because they have actively demonstrated an interest in your brand.

If you have a brick-and-mortar store, you can also gather insights from monitoring the foot traffic there. The most straightforward way to do this is with a traffic counter on the door to help you keep track of how many people come into your store throughout the day. This data will reveal what your busiest days and hours are. It may also help give you an idea about what is drawing customers to your store at certain times.

Collecting data is valuable because you can use it to make informed decisions. The more relevant, high-quality data you have, the more likely you are to make good choices when it comes to marketing, sales, customer service, product development and many other areas of your business. Some specific uses of customer data include the following. It can be difficult or impossible to get to know every one of your customers personally, especially if you run a large business or an online business.

The better you understand your customers, though, the easier it will be for you to meet their expectations. Data collection enables you to improve your understanding of who your audience is and disseminate that information throughout your organization. Collecting and analyzing data helps you see where your company is doing well and where there is room for improvement. It can also reveal opportunities for expanding your business.

Looking at transactional data, for example, can show you which of your products are the most popular and which ones do not sell as well. This information might lead you to focus more on your bestsellers, and develop other similar products. You could also look at customer complaints about a product to see which aspects are causing problems.

Data is also useful for identifying opportunities for expansion. For example, say you run an e-commerce business and are considering opening a brick-and-mortar store. If you look at your customer data, you can see where your customers are and launch your first store in an area with a high concentration of existing customers. You could then expand to other similar areas. Analyzing the data you collect can help you predict future trends, enabling you to prepare for them.

As you look at the data for your new website, for instance, you may discover videos are consistently increasing in popularity, as opposed to articles. This observation would lead you to put more resources into your videos. You might also be able to predict more temporary patterns and react to them accordingly.

If you run a clothing store, you might discover pastel colors are popular during spring and summer, while people gravitate toward darker shades in the fall and winter. Once you realize this, you can introduce the right colors to your stores at the right times to boost your sales. You can even make predictions on the level of the individual customer. Say you sell business software. Your data might show companies with a particular job title often have questions for tech support when it comes time to update their software.

Knowing this in advance allows you to offer support proactively, making for an excellent customer experience. The data can be recorded in many of the same ways as interviews taking notes, audio, video and through pictures, photos or drawings.

Each source of data used to collect information has its strengths and weaknesses. Some of the more common potential strengths and weaknesses identified above have been highlighted. Analyzing data from multiple perspectives and relying on data from different sources can strengthen the conclusions drawn from research. Data can be collected and analyzed on a short-term or project basis in response to situations or needs that arise from time to time. A short-term data collection project would include a start and a finish date, with set deliverables to be carried out over a certain period of time.

The best practice is to collect data on an ongoing, permanent basis, and to analyze this data as often as is needed to identify, address and monitor barriers to Code -protected persons or other persons based on non- Code grounds. Data collected in a time-limited study may be less complete than data collected through ongoing monitoring.

This is because short-term studies do not allow for the assessment of trends, patterns or changes over time. However, where costs, time and resources are a factor, short-term studies may be the preferred choice to fulfil a need and project goals. Other factors may also influence the reliability of the data. For example, people may modify behaviour while under scrutiny during the data collection period.

When planning on how best to collect data in Step 4, it is important to be aware of the practical considerations and best practices for addressing logistical challenges organizations often face at this stage of the process. Implementing a data collection plan requires attention to matters such as:. Step 5 involves analyzing and interpreting the data collected. Explaining the technical steps involved in analyzing and interpreting data is beyond the scope of this guide.

An organization will have to determine whether it has the internal capacity and expertise to analyze and interpret data itself, or whether it will need the help of an external consultant. A smaller organization that has basic data collection needs may be able to rely on internal expertise and existing resources to interpret the meaning of gathered data. Example: An organization with 50 employees wants to find out if it has enough women working in management positions, and if there are barriers to equal opportunity and advancement.

The organization counts the number of female employees it has 25 , and determines how many of these employees are working in supervisory and management positions two. A few motivated employees identify some issues of concern, like gender discrimination, that may have broader implications for the organization as a whole. Efforts are made to work with female employees, human resources and other staff to address these barriers.

The organization makes a commitment to foster a more equitable, inclusive work environment for all employees. Once an organization has analyzed and interpreted the results of the data collected, it may decide to act on the data, collect more of the same type of data or modify its approach.

Quantitative and qualitative information can provide a solid basis for creating an effective action plan designed to achieve strategic organizational human resources, human rights, equity and diversity goals identified through the data collection process. If an organization feels it has enough information to develop an action plan, it should consider including the following elements:.

In some cases, an organization may decide that it needs to collect more information because there are gaps in the data collected, or areas where the data is unclear or inconclusive.

This may prompt them to conduct a more detailed internal and external assessment go back to Step 1 or try another approach. How long will the data be collected the scope of data collection?

See City of Toronto, Publications and reports , online: www. Comparison is made between a group claiming discrimination and another group that shares the relevant characteristics, to determine if disadvantage, denial, devaluation, oppression or marginalization has been experienced.

A comparator group must share relevant characteristics with the group of interest in the area being questioned for comparison to be meaningful. Who the appropriate comparator group is will depend on the context and is often contested between litigants.

Often the comparator group is a more privileged group in society, often the dominant group. In comparison, data collection on other grounds, such as sexual orientation, has not been done much in the past.

Notably, the national Census does not include a question about sexual orientation, although sexual orientation has been included on other non-mandatory surveys and has been the subject of testing. Icart, M. Labelle, R. Skip to main content Skip to local navigation Skip to global navigation Skip to footer.

You are here Home » Count me in! Collecting human rights-based data » 6. What is involved in collecting data — six steps to success. Page content If an organization is considering whether to collect data on its own or get help from an external consultant, it will need to have enough information to make an informed decision about how to proceed.

Conduct a review of all policies, practices and procedures applicable to employees, service users or another appropriate audience: Does the organization have human resources and human rights policies, practices and procedures that are accessible to all employees or to the people they serve?

Does the organization have clear, transparent and fair complaint procedures in place to deal with allegations of discrimination, harassment or systemic barriers? Have any claims, grievances or allegations been made or received relating to discrimination, harassment or systemic barriers?

Have any been dealt with appropriately and in accordance with existing polices, practices and procedures? Explore organizational culture from a human rights, diversity and equity-inclusion lens: What are the organization's mandate, goals and core values?

What is the history of the organization? Are equity, diversity and inclusiveness supported, reflected and promoted by senior leaders throughout the organization? Do employees feel that the organization is diverse, inclusive, and provides equal opportunity for learning and advancement? How are decisions made? Issues related to maintaining integrity of data collection :.

The primary rationale for preserving data integrity is to support the detection of errors in the data collection process, whether they are made intentionally deliberate falsifications or not systematic or random errors.

Each approach is implemented at different points in the research timeline Whitney, Lind, Wahl, :. Quality Assurance Since quality assurance precedes data collection, its main focus is 'prevention' i. Prevention is the most cost-effective activity to ensure the integrity of data collection.

This proactive measure is best demonstrated by the standardization of protocol developed in a comprehensive and detailed procedures manual for data collection. Poorly written manuals increase the risk of failing to identify problems and errors early in the research endeavor.

These failures may be demonstrated in a number of ways:. An important component of quality assurance is developing a rigorous and detailed recruitment and training plan. Implicit in training is the need to effectively communicate the value of accurate data collection to trainees Knatterud, Rockhold, George, Barton, Davis, Fairweather, Honohan, Mowery, O'Neill, The training aspect is particularly important to address the potential problem of staff who may unintentionally deviate from the original protocol.