Menden Insights: Ensuring High-Quality Data for Informed Business Decisions

Menden Insights: Ensuring High-Quality Data for Informed Business Decisions

In the realm of market research, data plays a pivotal role. It empowers businesses to connect with consumers, deliver valuable products and services, and outshine competitors. However, not all data is created equal. Inaccurate, incomplete, inconsistent, or biased data can obscure the path to intelligent and informed business decisions, introducing risks and uncertainties. That's why market researchers must prioritize data quality and demand excellence from their partners.

What is Data Quality

How successfully qualitative or quantitative information accomplishes a goal is measured by the quality of the data. To put it another way, high-quality data accurately reflects real-world constructs.

Consider a business that is striving to measure brand awareness. If survey results accurately reflect consumer attitudes, opinions, and behavior, the data is of high quality. On the other hand, if the survey generates data that dramatically skews the image, data quality is undermined.

As a result, reliability and trustworthiness are intimately related to data quality. Market researchers are comfortable using the data to make crucial business decisions when the data quality is high. In contrast, when data quality is poor, market researchers could be reluctant to use the data as a basis for business choices like increasing output or raising prices.

Why is data quality important?

Companies have frequently used intuition to make important decisions for decades. A shared understanding of what matters—the ins and outs of markets and technology—must be developed through many years of experience. But regrettably, gut instincts aren't always reliable, especially when there are market or technological changes. Therefore, many modern organizations have embraced a data-driven decision-making paradigm in an effort to get rid of the erratic nature of human emotion and prejudices.

The value proposition is evident that businesses extract insights from quantitative and qualitative information to ensure the best decision is made as well as a side benefit to improve the bottom line, as opposed to making decisions on a whim or being driven by the loudest person in the room.

Think of this illustration: A Product Managers want to introduce improvements to their accounting software for small firms. Existing clients can choose between versions A and B.

The company's market research team conducts surveys to determine the elasticity of demand for the software before devoting time and resources to product development and figuring out potential upgrade costs. More crucial than the account's stay is making sure the most recent version is recorded. Data quality is of the utmost importance because it will influence the company's next move (i.e., whether executives approve engineering's move forward or not).

Using reliable information, businesses can:

·???????Increase the value of your market research efforts.

·???????Enhance tradeoffs between choices and lower manufacturing risks and costs

·???????More precisely target your audience

·???????Create more successful marketing campaigns

·???????Improve your client service

Without a doubt, effective data management gives companies a competitive edge. By having a greater grasp of consumer thoughts and behaviors, businesses may make decisions that are efficient and effective and surpass competitors.

Consequences of poor data quality

Having accurate data enables a business to grow. The opposite is also true: A business can quickly go under due to compromised data quality. The following can happen as a result of poor data:

·???????Reduced effectiveness: Market researchers run the danger of losing time and money, two crucial resources, when they base judgements on inaccurate data. For instance, they might introduce a product for which there is no market. Or, they can start a marketing initiative that doesn't connect with the intended audience.

·???????Opportunities lost: When data quality is compromised, businesses lose chances to make money. For instance, CEOs might need to acknowledge that there is indeed a need for a specific good or service. Or, they can blame social media outreach for raising brand recognition when, in fact, out-of-home advertising is a major factor in conversions. In exchange, they might spend marketing funds on the less potent media outlet.

·???????Fragile customer relationships: The main goal of market research is to better understand your target consumer. Sadly, when data is warped or biased by outliers, comprehension must be more exact. In response, businesses can be thought to be ignoring the market, seem disjointed, or even come off as conceited.

Aspects of data

Good data may help businesses advance. However, how precisely is data quality determined? How do you decide whether to use data to make important business decisions or to completely disregard it?

There are generally six factors that affect data quality. You can use these dimensions to assess the reliability of a given dataset.

1.?????Fidelity or accuracy

The degree to which data accurately depicts reality is referred to as data fidelity. In other words, fidelity assesses the accuracy of the data collected.

Human error can affect data fidelity, as it can with most other aspects of data. For instance, when filling out a survey, a responder can unintentionally type the erroneous pin code or choose the incorrect option from a pull-down box. Although a sincere error, this blunder can impair data quality if you analyze consumer behavior based on location or brand preference. This must be distinguished from dishonest survey respondents who might deliberately lie about demographic information in order to qualify for financial prizes. The latter is an example of survey fraud and needs to be aggressively combated.

Other elements that affect the data fidelity are as follows:

·???????Data Decay: Fidelity may start out high but eventually deteriorate. A survey respondent's income or the number of dependents residing in the same home, for instance, could change.

·???????Manual Entry: As previously mentioned, a survey respondent could type a value incorrectly. Similar to this, a market researcher may swap letters or numbers while analyzing the data. That has a significant influence.

·???????Data Integration and Movement: The data When data is transferred from one system to another where the formatting could be different, it may potentially unintentionally change.

2.?????Completeness

Measures whether each data entry is "full." In other words, this measure looks to see if any records, fields, rows, or columns are missing.

In general, there are two categories of missing records:

·???????Unit nonresponse: This happens when a participant in the sample for the survey declines to respond to it.

·???????Item nonresponse: It is when a survey respondent omits to respond to one or more survey questions.

Both of these events have the potential to lower the quality of your survey's results, potentially leaving insufficient information to do useful analyses (such cross-tabulation).

It is crucial to remember that a project's required degree of completion is arbitrary. Depending on the study's objectives. The acceptable response rate is something that market researchers (in collaboration with their survey partner) decide. Data science methodologies evaluate if the missing data exhibits particular patterns. It is also the responsibility of market researchers to discern between crucial and non-critical data, or information that is essential to the study. It is important to understand data imputation methods and their correct degree before implying.

3.?????Consistency

The degree to which a survey would provide comparable results if repeated under the same circumstances is referred to as consistency. This also connects to the statistical ideas of confidence intervals and outcomes. In other words, it's a determination of whether the survey collects the data you want to collect as a market researcher.

Consistency can also mean that certain data points obtained through your questionnaire are consistent with data obtained from other sources. For instance, during a pre-screening survey, a responder might mention making a specific income. However, they can specify a far lesser income when the study is actually conducted.

4.?????Timeliness

Timeliness is a term used to describe how current the data is. In other words, when was the information gathered?

Companies should generally use the most recent information available when making choices. Otherwise, decisions might be made incorrectly as a result of outdated facts. For instance: Imagine that a business studied consumer purchasing patterns before to the pandemic. This information is outdated in light of COVID-19's changes to consumer behavior. Therefore, more research should be done to more accurately assess the target audience.

5.?????Validity

Valid data is data that has been formatted appropriately in accordance with specified guidelines established by market researchers.

For instance, a survey can request that participants provide the day, month, and year of their birthdays in British English. Month, day, and year responses given in American English would be regarded as invalid. There are also numbers as examples. The respondent's phone number may be requested in a survey using only numbers and no symbols. Symbolic submissions of responses would not be accepted.

6.?????Uniqueness

In a dataset, unique data only happens once. That is to say, there aren't any duplicates.

Data duplication, regrettably, occurs frequently. Furthermore, dishonest and fraudulent survey respondents may purposefully misrepresent their identities to obtain incentives. Of course, there is a chance that these respondents require additional information about your target market. And to make matters worse, dishonest survey takers are frequently highly skilled and difficult to spot. Anti-fraud software is necessary because of this.

Data fidelity and completeness as they are preserved over time and between formats are referred to as data integrity. Unfortunately, there are many risks to data integrity, including data degradation and human error (such as when a market researcher accidentally deletes a row in Excel). Consequently, preserving data integrity is a continuing, ongoing task.

How to improve your data quality

With the help of high-quality data, market researchers can gain a deeper understanding of the motivations of the target market. Unfortunately, corrupted data can ruin outcomes and cost a corporation a fortune.

Fortunately, there are three?basic approaches to enhance data quality.

·???????Know your niche audience

Market researchers use panel surveys to learn more about the attitudes, behaviors, and thoughts of potential clients. For these insights to be useful, market researchers must first identify their target niche audience.

A group of people who are most likely to buy a product or service is known as a niche audience or target audience. These people frequently have similar racial, ethnic, geographic, educational, and socioeconomic characteristics.

Prior to conducting a survey, it is imperative to have a firm understanding of your target population. Why? Because polling this particular group of people improves the accuracy of your statistics. The dataset will be more accurate if you poll middle-aged women if, for instance, the majority of your company's customers are middle-aged women.

·???????Engage the people you're surveying

The enemy of the market researcher is boredom. It causes panelists to rush through questions, answer them in a straight line, fill blank sections with nonsense, and stop filling out questionnaires completely. Unfortunately, by doing these things, you risk degrading the quality of your data, leaving you with a dataset that requires greater precision and thoroughness.

·???????Reduce Fraud

38% of the data they gather are invalid due to panel fraud and issues about quality. Fortunately, market researchers can thwart dishonest and sluggish panelists with good survey design. For instance, you can cut the survey's duration to under 10 minutes by eliminating items that are unnecessary. You may also employ iconography to keep poll respondents interested.

Despite these attempts, as long as there is a financial incentive, dishonest panelists will be a problem. These clever scammers, who are frequently based abroad, are highly skilled at hiding their IP address, device kind, and other telltale signs of their identity. Their objective is to obtain as much money as possible immediately. Consequently, they are often quite forceful in their approach.

If your business wants to make smart, data-informed decisions, the first step is to partner with Menden Insights.

Contact us at [email protected]