Navigating the path to effective data collection and selection 

In a world where success depends largely on how well you can identify, understand, and figure out how to collect data, it’s unbelievable how much of it still goes to waste. As much as two-thirds of the data created by enterprises is never used – this includes both quantitative data and qualitative data. Even then, businesses struggle to get the most out of the data they do manage to make use of.  

This wasted data represents a significant missed opportunity for businesses seeking that competitive edge. Those entities who find ways to leverage their data better are succeeding. And those stuck in data journeys beginning with only a fraction of the data they have at their disposal are looking for ways to maximize its potential.  

This article breaks down the processes behind the data journey’s opening phase. We explore the steps behind effectively identifying and selecting the right data, defining and understanding its different types and data structure, and how to collect it more efficiently.

The data journey requires a solid base from which to flow. Its success depends on using quality data that is relevant, well-understood, and gathered correctly. 

Unlocking success – harnessing the power of precise data selection  

Selecting the right data lays the foundation for the rest of the data journey. It can majorly impact decision-making, resource allocation, and competitive advantage. But how can you know which data is right for the journey and which isn’t?  

Here’s how to select the data that will lay a reliable foundation for the journey ahead: 

• Pick and obtain data with credibility and precision: Only choose and collect the data you know is reliable and accurate. This means a better ability to analyze data, improved decision-making, and higher levels of trust down the road. 
• Make insightful discoveries: Select relevant data for more insightful decisions and better strategic moves. Doing so provides a more comprehensive view of markets, trends, and challenges.    
• Ensure strategic alignment: Choosing data that aligns with organizational objectives supports strategic goals. It is far better at informing decisions that add value, maximizing its utilization potential.   
• Use resources efficiently: Working with the right data means optimized resource allocation. Efficient resource deployment saves time, energy, and money. This directs the focus towards extracting information you need and deriving insights that matter most for actionable outcomes.  
• Mitigate risks early: Ensure that the data is taken from appropriate sources. This way, organizations can pick up on early risks and manage them better. Analyzing this data exposes market shifts and operational challenges, providing an opportunity to find proactive solutions from the get-go.  

Data structure – expanding your toolkit for informed decision-making 

Understanding the different data types drives better analysis and more meaningful decision-making. Once the data’s nature and structure are determined, collecting it becomes more effective.  

Here are some of the most common types of data out there:  

• Structured data: Well-organized and often presented in table format. This data usually contains information in a categorized, numerical format and is optimized for searching and quantitative data analysis.  
• Unstructured data: This data normally lacks a fixed format and data structure. It can contain a blend of text (like answers to surveys with open-ended questions), images, audio, and video. The informal configuration means that natural language processing and computer vision are used to extract usable insights. 
• Semi-structured data: A data type usually partially organized and sometimes containing tags or attributes. Halfway between structured and unstructured data, semi-structured data lacks a rigid model, making categorization tricky. 
• Time-series data: This data is great for spotting trends and patterns. Time-series data consists of observations captured over different term intervals, sometimes spanning large information sets.  
• Sensor data: Data generated from IoT sources is useful for providing real-time insights. It can be used to explore phenomena, assist with systems monitoring, and to deliver predictive maintenance updates.  
• Transactional data: A common raw data type seen in everyday life. From sales records to user interactions and conversion rates, transactional data is ideal for identifying behavior patterns.  
• Meta data: This data comprises high-level information that delivers context around other data. Meta data is popular for understanding different data origins and how it evolves. 

Gaining a better understanding of these different data types matters. It allows you to employ collection methods that optimize how certain types of data are gathered. This, in turn, improves alignment for better exploration, discovery, and analysis of the structured data. 

Exploring effective data collection methods 

Carefully considering the data collection techniques is crucial to the data journey. It determines how well you can access external data sources and databases before leveraging them to gather the data you need.  

Data collection methods form much of the data journey launchpad, so picking the right ones is essential. Different collection methods can impact decision-making, data accuracy, and relevance.  

Here are some of the data collection methods most effective at ensuring your data is both accurate and relevant.  

• Running data queries: Data queries are effective for retrieving specific information from structured databases. It allows you to specify the data to be retrieved and from which data collection or collection group. Running data queries uses tools to extract data from columns and tables. The method delivers great insights into customer behaviors, sales trends, and various metrics related to operational activities like Firebase. 
• Application Programming Interfaces (API) integration: APIs provide an efficient means to interact with both internal and external systems. By integrating APIs into your collection processes, you gain real-time access to data. This includes tapping into fresh inventory, customer interaction, and transactional data insights. API integration also allows for communication with tools like 3rd-party apps and SaaS platforms to solicit critical information from them. 
• Data warehousing: Data warehousing is ideal for keeping business information in an organized repository. This is especially helpful for consolidating data from various departments or systems. Data warehousing also allows for advanced analytics and comprehensive trend analysis on stored data. 
• Internal data collection forms: Perfect for collecting primary data, i.e., data inside an organization. This method gathers important data points directly from your employees or departments, for example, via surveys. This allows you to collect insights on critical employee metrics. Metrics like employee satisfaction or project progress, greatly assist decision-making. 
• Organizing focus groups: You can ask your respondents open-ended questions to gather more qualitative data. This is yet another method for conducting primary research.  

Finding the data collection method that suits your business needs and insight objectives means tapping into available first-party data. Leveraging these methods correctly helps you uncover beneficial insights. This ultimately leads to improved strategies, optimized operations, and enhanced innovation.  

Types of data collection methods – unveiling 7 best practices for excellence 

Here are some of the ways that can facilitate collecting information for your business. 

1. Define clear objectives for your data collection activities and specify the insights you’re trying to gather. Establishing a clear purpose for how to gather the data you need will help optimize your collection strategy.  

2. Use technology wisely to streamline your data collection efforts. Pick the custom software, cloud-based solutions, and 3rd-party tools that suit your collection needs. Deploy business intelligence tools that help non-technical users create customized reports and dashboards, too. Consider opting for platforms that consolidate data from different internal sources as well. This ensures that KPIs are unmistakable, clearing the way for you to track progress toward business objectives.  

3. Data privacy and security must be prioritized to protect sensitive data for your customers and the business. This means following relevant regulations (such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA) rules) and implementing effective safeguards.  

4. Data collection experts or data scientists are vital to success. These experts specialize in the design and execution of effective data collection strategies. They help to ensure that the methods used are appropriate and that they can provide the required insights. Experts at C&F possess the tools, expertise, and know-how to elevate your data collection capacity. Our industry-leading capabilities translate to getting the most out of your data collection efforts while crystallizing every insight along the way. 

5. Clear documentation of your data collection process efforts is essential. This includes the methodologies you use and assumptions you make. Clear documentation allows you to maintain a capacity to reproduce methods, structured data outcomes, and processes that work while ensuring adequate quality control. 

6. Regular monitoring and updates of your data collection processes are important. These observations allow you to make necessary adjustments that boost effectiveness. While frequent updates in response to changes in your business goals or technological advances can help you stay ahead of any major shifts.  

7. Integration and compatibility of your data collection methods with your systems and tools are critical to efficient analysis and reporting.  

Good data collection is an ongoing process. Regularly evaluating your methods to improve them maximizes the value of your collected data, helping you to make better business decisions.  

7 data collection challenges that you might face  

Data collection challenges can affect the quality of the data, its accuracy, and how effectively it can be used. Overcoming them begins with knowing what they are. 

Here are some of the most common data collection challenges you’re likely to face: 

1. Compromised data quality and accuracy: This can lead to data that is unreliable and full of errors. The resulting flawed analyses and faulty insights are then sure to hurt decision-making.  

2. Insufficient or incomplete data: These problems are caused by compromised data sources. Ineffective collection methods or limited sample sizes are also to blame. Spotty data impacts an organization’s ability to extract useful insights and make informed decisions.  

3. Data integration: Integrating contrasting data sets can be difficult. When data is collected from various sources, it can often lead to inconsistencies. These can end up impacting your ability to gain a comprehensive view of everything.  

4. Data privacy and security: This common challenge has become more critical than ever. Securing data matters most regarding customer and employee information. Protecting sensitive and confidential data in line with regulations is complex and constantly evolving. This can be a difficult challenge to overcome.   

5. Changing data sources and formats: Constantly evolving technologies mean new data sources and formats are emerging. This can cause processing and analysis problems. The need to constantly adapt means a higher risk of issues arising, requiring additional resource allocation.   

6. Lack of standardization: Data gathered from various sources often comes in different shapes and sizes. Varying formats, units, or measurement types can lead to problems in data analysis and comparison.  

7. Data volume and velocity: This problem comes with any large data set. Data today is generated at high speed and in massive volumes. This can quickly overwhelm businesses, challenging their ability to manage data quickly and in real time. 

Using the right methods of data collection and selection – the groundwork for a successful data journey 

The first step in the data journey is a very important one. Determining which structured data and unstructured data you need and how to find it is one thing. Collecting it right is another. Good raw data collection lays the platform for how the rest of the journey unfolds. It allows organizations to bolster data-driven decision-making, improve innovation, and extract the insights that can make all the difference.  

Choosing the right data structure and type from the outset can transform the data journey into an efficient, fruitful process. Choosing the wrong data leads to a polluted data outcome with gaps and inconsistencies. 

Curating your data to be accurate, reliable, relevant, and usable is crucial to helping your business extract maximum value from it. The success of your data efforts depends heavily on this important early step, so make sure you have the tools, expertise, and platform to define, find, and learn how to gather data right.