Avoiding Big Data Problems and Pitfalls

Avoiding Big Data Problems and Pitfalls

Big data problems pervade market research despite the technological advances that enhance and simplify access to consumer data. As such, big data incurs various difficulties. 

This is because the many types of Martech and SaaS solutions designed for market research yield massive quantities of data. Many times, this data is of no use to organizations. On the contrary, large pools of data hamper progress. 

While a whopping 90% of companies say that data is key to their organization, only 3% of company respondents say they are able to act on all of the customer data they collect.

Many businesses have caught up already implementing big data, along with AI, as 97.2% of organizations invest in AI and big data. 95% of businesses say that managing unstructured data is a problem for their business.

Big data is undoubtedly a source of contention for many businesses, including the kind that provides market research. Despite its many conveniences, big data carries various issues.  

This article expounds on big data problems, their implications and how to avoid them using the correct market research techniques and online survey software. 

Understanding Big Data 

Before delving into big data problems, you should first understand the meaning of big data. As its name implies, this term refers to the large volume of structured and unstructured data that overloads a business on a daily basis. 

Big data is composed of larger, more complex sets of data, especially from new data sources. These ample data sets are often too big for traditional data processing software. Or, when they aren’t, they are still too large and complex for a business’s personnel to fully process.

When data meets “big data” status within a business, it has reached the point of being unmanageable. Although these massive volumes of data can be used to address and resolve other business problems you wouldn’t have been able to before, their sheer volume is the root of the problem. 

As such, businesses struggle with big data, often leading them to render the data that they wind up collecting as unwieldy and, in the worst case, useless.   

The Three V’s of Big Data 

There’s more to the concept of big data aside from the high-level notion of voluminous data sets that are difficult to access and understand. There is also more to this concept than the fleshed-out definition of the previous section.  

To understand the makeup of big data, you must understand its three main factors, called the Three V’s of Big Data. These represent the following:

  1. Volume
    1. Big data entails high volumes of low-density, unstructured (and sometimes structured) data. This can include data with unknown value, such as Twitter data feeds, clickstreams on a site page or a mobile app, or sensor-enabled equipment. This can equate to terabytes of data for some companies or hundreds of petabytes for others.
  2. Velocity
    1. Velocity refers to the quick rate at which data is received and acted upon. Typically, high-velocity data goes directly to memory instead of being written to disk. There are certain internet-enabled smart products that work in either real-time or near real-time that will require real-time evaluation and action on this.
  3. Variety
    1. This involves the many kinds of data available to an organization. Unlike in traditional data, big data types are usually unstructured, whereas transitional data sets are structured and neatly fit in a relational database. Big data also includes semistructured data types, such as audio, video and text. This requires additional preprocessing to obtain meaning and support metadata.

Understanding Big Data Problems 

Big data problems involve a wide variety of issues that spring from big data and its nuances. To tackle these problems properly, you must first be aware of them and how they can affect your business. 

While some of these problems involve the nature of the data itself, others deal with how the data is perceived, along with how workers engage with it. As such, these problems are typically twofold in nature, stemming from either the big data features or the way that they are dealt with. 

The following considers both of these kinds of big data problems:

Internal Big Data Challenges

These challenges originate from big data and its source. As such, they often deal with the features and capabilities of big data and its operating system. 

  1. Inaccurate Data: Possibly the biggest detriment to your data, inaccurate data renders all campaigns void, whether it is a marketing email drip campaign, an ABM campaign, or a survey campaign. Accuracy is the strength in any campaign, as it will prevent workers from sending messages to the wrong people, along with reeling in the wrong consumer info, the kind that defeats the purpose of market research, which is to understand your target market at a closer level.
  2. Lack of Integration: Not all sources of data are designed to be integrated with other systems. This is a major problem, as it creates data silos, thereby averting teams from accessing different data sets and projects simultaneously. It becomes exceedingly difficult to interpret different data sets when they cannot be integrated. As such, data silos keep much of the data unused.
  3. Unorganized: Scores of big data that belong to one source are plagued by disorganization. Whether it is in the way it is stored or presented, a lack of organization makes big data difficult to read and evaluate, forestalling the use of any data for decision-making efforts. In turn, this slows down any actions that businesses could have otherwise taken based on the use of big data.
  4. Imprecise: While big data can be insightful and information-rich, it offers little value when it is imprecise. This means the customer insights are either too vague or lack data filtering options. For example, your big data platform may have imprecisely targeted your target market or its particular segments. It may also offer few options when it comes to filtering pre or post-results data.   

External Big Data Challenges

These challenges usually result from the internal challenges of big data. However, they can also originate in purely external ways, meaning how workers and other users react to it and engage with it. 

  1. Lack of proper data understanding: This often deals with accurate data being evaluated improperly, which yields incorrect findings and actions thereof. Employees may not understand the significance of their data, how to organize it or what to make of it. They can also simply misinterpret it. This is more common with big data given its vast amount and variety. 
  2. Faulty Data Storage: Storing large sets of data correctly is a major external challenge, especially as it increases with the progression of time and campaigns. This makes it extremely difficult to handle this data, let alone fully assess it and put it to actionable use. Given that most of the big data is unstructured and comes from various sources such as documents, videos, audios, text files and more, you cannot find it in databases.
  3. Confusion with Data Tool Selection: Whether it comes from choosing between platforms, or features within one tool, confusion over these key choices can easily arise. 
  4. Securing the big data: Securing a massive set of data is not only challenging, but a lack of it leads to data breaches, a dangerous and costly repercussion for businesses. Unfortunately, many companies are too busy to understand, store and analyze their data that they delay data security until later stages. This leaves the data in unprotected repositories that hackers can easily take advantage of.
  5. Lack of data professionals: Many companies simply lack data professionals, whether they are data scientists, analysts, engineers or IT professionals. This leaves companies with workers who lack the experience in working with hefty data sets. This can be problemsome even with the emergence of data handling tools, as some employees simply don’t have the aptitude for dealing with data. 
  6. Slow acclimation: Not all big data providers offer platforms inclined toward the democratization of data. As such, this creates slower acclimation across teams, given that only a select few employees learn how to use the data. This also contributes to silos, which impede acclimation to the new team projects and actions taken after certain workers have analyzed the data. 

The Importance of Avoiding Big Data Problems 

Given the many utilities that big data offers, it is important to steer clear of big data problems. When you evade these problems, you can reap the benefits of big data to its fullest advantage. Essentially, the more big data problems your company circumvents, the greater your benefits will be. 

Here are some of the foremost benefits that big data offers for market research and general business purposes:

  1. Machine learning and AI: 
    1. Streamlines operations, removes fraud from research, reduces manual labor
    2. Not all big data providers implement AI and machine learning in their systems.
  2. Product development:
    1. Big data provides insights on demographics and their relation to and opinions on products.
    2. It helps build predictive models for new products by classifying the key features of past and current products and comparing the relationship between those attributes and the products’ commercial success. 
  3. Innovation:
    1. Big data can help you determine new ways to use your insights to drive innovations. For example, using data for financial considerations.
    2. It helps discover trends that can inspire new ideas for products and experiences.
  4. Operational Efficiency:
    1. With big data, you can analyze and evaluate production, customer feedback and returns, which anticipate future demands and reduce outages. 
    2. It can be used to improve decision-making in line with current market demand.
  5. Customer Experience:
    1. Companies compete on CX every bit as much as they do on the products themselves. Big data aids all CX decisions with critical customer insights.
    2. Big data allows you to correctly perform marketing personalization, a crucial strategy that removes the monotony from marketing and shows customers that you pay attention to their needs and preferences.   

How Proper Market Research Solutions Ward Off Big Data Problems

Using the correct market research solution will ward off big data problems. This is why it is important to use a provider that offers features that ward off big data problems. First off, a strong market research platform, such as an online survey tool should offer various means of data visualizations and exports.

This makes the data far more accessible for various team members to view and analyze. It also provides more options to group the data differently for the purpose of conducting different analyses. 

Along with accessibility, this kind of platform should offer an advanced data filtering system, the kind that allows researchers to neatly organize and view only the bits of data most relevant to their study. A strong data filtering system allows researchers to filter data across the entire platform. 

For example, this includes filtering options in the screener, the questionnaire and the post-survey data results. This is especially useful to ward off big data problems, as it removes irrelevant data from view, along with irrelevant segments of the population from taking part in the study. 

With this capability, researchers do not have to confront massive sets of irrelevant data, or even the kind that does not perfectly align with what they are currently studying. 

In addition, this kind of platform should use a variety of mechanisms that constantly perform quality checks that protect against survey fraud. These mechanisms include bot removal, disqualifying respondents on a VPN, checking carrier consistency, testing whether respondents are paying attention, eliminating survey fraud and much more. These kinds of mechanisms remove low-quality data, eradicating various kinds of survey bias from your research.

Such a platform should cut through the noise of big data by offering extremely user-friendly interfaces, the kind that makes it possible to make your own survey in just three quick steps. After all, to scale back on big data, you would need a simplified interface.

Making the Most of Your Data

The data that you can extract from market research is a commodity in a competitive market, whether you operate a startup or a long-established business. Readily accessible consumer data allows market researchers to form large data sets to mine for all kinds of consumer insights. 

However, large sets of data often hamper any market research progress, as they yield various big data problems, from inaccurate data to imprecise information, to unorganized data presentations and more. 

Sampling size alone does not establish a quality set of data, nor does it reduce big data problems. As such, there is far more than the amount of collected data that makes for a strong market research project. The most important factor for reducing big data problems and conducting a strong market research campaign is the market research platform you use to conduct your research and extract the data. 

A strong online survey platform offers all of the capabilities aforementioned in the previous section, along with being agile and offering a mobile-first platform, as mobile use dominates the digital space. It should engage respondents in their natural digital environments via random device engagement (RDE) sampling, as this too reduces biases.

When market researchers use such an online survey platform, they are equipped with tackling and avoiding big data problems. 

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