How will you deal with unexpected challenges or obstacles during data collection?

How will you deal with unexpected challenges or obstacles during data collection?

data collection


What is data collection?

Data collection is the process of gathering data for use in business decision-making, strategic planning, research and other purposes. It’s a crucial part of data analytics applications and research projects: Effective data collection provides the information that’s needed to answer questions, analyze business performance or other outcomes, and predict future trends, actions and scenarios.

In businesses, data collection happens on multiple levels. IT systems regularly collect data on customers, employees, sales and other aspects of business operations when transactions are processed and data is entered. Companies also conduct surveys and track social media to get feedback from customers. Data scientists, other analysts and business users then collect relevant data to analyze from internal systems, plus external data sources if needed. The latter task is the first step in data preparation, which involves gathering data and preparing it for use in business intelligence (BI) and analytics applications.

For research in science, medicine, higher education and other fields, data collection is often a more specialized process, in which researchers create and implement measures to collect specific sets of data. In both the business and research contexts, though, the collected data must be accurate to ensure that analytics findings and research results are valid.

Some observations on the challenges of digital transformation research in the business sector

Since digital transformation is an applied field and not purely theoretical, collaboration with companies during research is essential. However, such research activities are typically subject to two main types of challenges, one arising from the data collection process and another from the publication process. Below, I will take a closer look at these two obstacles and offer solutions.

Challenges in the data collection process

Trust is the fundamental basis for successful collaboration between companies and researchers. However, creating the trust necessary to establish that initial connection can be difficult, especially when the parties do not know each other. Companies tend to refuse to collaborate with external researchers when the benefit and/or form of collaboration is unclear.

However, even in cases where a minimum trust has been established, companies often have reservations about disclosing their most sensitive and specific data. They may want to avoid falling into the hands of competitors or may not want to speak publicly about their failures. This resistance represents a big problem for researchers, since these insights are important for the general understanding of the underlying problem and would allow other professionals to learn from them. Withholding certain data also prevents general understanding of the object of research.

Another key challenge in terms of collaboration is often creating a common timeline. In the business context, decisions can sometimes be made randomly, and deadlines are usually short. This does not always correspond to the requirements that researchers must meet in their environment. For example, for professionals without academic training, it is often problematic to understand that publication processes can take several years.

Matrix 3


Challenges in the publishing process

For many researchers, publishing studies on digital transformation is often a difficult process due to the lack of theoretical foundations and development. While conclusions may be practically relevant, their integration into the body of knowledge and their implications for research are not always clearly defined.

As research with companies is often carried out on a small scale, it can be difficult to ensure its generalisability or replicability. At this point, therefore, it would be necessary to anticipate a possible selection bias that could call into question the representativeness of the results, which is normally associated with a concern for the suitability of interviewees who, for various reasons, could not give opinions on the different functions of the company or on the company as a whole.

Some suggestions

In view of these frequent problems in the data collection and publication processes, some recommendations are made below:

In general, researchers should strive to establish long-term collaborations with companies, not only because it can reinforce mutual trust, but also because it could improve the efficiency of many collaborative processes. To this end, it might be useful to jointly create a long-term plan. Larger collaborative initiatives can be complemented by a more institutionalized approach, for example through regular stakeholder meetings.

Certainly, the key to success in establishing such partnerships is to highlight the benefits that the company can obtain. Only by sharing the benefits will companies commit to supporting researchers in the long term and assuming the additional costs that this may entail. The potential benefits of collaboration can be justified, not only by providing external expertise and methodological support, but also, for example, by facilitating better access to universities’ knowledge resources or to high-potential students.

Transparency is also crucial to establishing a relationship of trust. This should apply not only to operational matters, but also to the objectives pursued by both parties, including clear definition of roles and responsibilities and open and reliable communication between the parties. Researchers should inform companies of interim results and proactively share other issues of interest or potential project ideas that could also stimulate collaboration.

Whenever sensitive data is involved, a confidentiality and non-disclosure agreement can be advantageous for both parties. In this way, researchers will have a more complete and reliable view of the object of the investigation, while the company will ensure the protection of its sensitive data. From the researcher’s perspective, although access to that sensitive data may be crucial, not all information needs to be published, and data anonymity or publication embargo periods may mean that it can be published without violating the agreement. However, since unexpected changes in the research environment are common in companies, researchers must have a Plan B.

When considering publication, it is advisable to develop a clear theoretical basis during an early stage of research planning, without neglecting the generalizability of the practical problem. Researchers must also identify the most appropriate publication options. Journals that are more practitioner-oriented may offer advantages in terms of the length of the publication process, as well as a potentially more suitable target audience.

To ensure the scientific rigor of the research, it is advisable to select an adequate number of respondents within the companies. It is especially recommended to triangulate results with external sources (for example, annual reports or newspaper articles) to reduce potential respondent bias. Researchers should also strive to make the selection of their respondents and companies as transparent and legitimate as possible. Detailed documentation of the research process and underlying methodology will further increase examiners’ confidence. While small-scale exploratory studies are particularly suitable for new areas of research, large-scale quantitative studies could be a good opportunity to verify the generalizability of the promise of initial results.

salud medico cerebro realidad virtual medicina robot


Research on the digital transformation of “living objects” can sometimes be fraught with difficulties, but if well prepared and the above recommendations are taken into account, researchers can overcome the key double challenge of such efforts.

Table of Contents