Insight development is based on gathering information, then data integration and analysis. However, organisations often find this challenging due to multiple sources, formats and time scales. Do you?
Many companies struggle to benefit from all their data and information because they don’t know how to turn it into insight, or their insights remain interesting but not actionable. There are many reasons for this.
From data quality issues to technological limitations and resistance to change, organizations must navigate a complex landscape to unlock the full potential of their data.
This comprehensive guide delves into the ten most common challenges in insight development, offering detailed analysis and strategies to overcome each obstacle, ensuring your organization can harness data for strategic advantage.
What an Actionable Insight Really is
I get so frustrated when people refer to numbers, data, or the findings from research projects as insights. None of these are!
In addition, developing actionable insights from a single survey is rare.
The reason is that insight development, getting to that “aha” moment that everyone immediately understands and wonders why no one thought of before, needs a 360 perspective of the challenge or opportunity under investigation and uses information from multiple sources.
There are many definitions of insight, but the one that I use, and that resonates with my clients, is a statement that impacts the attitudes or behaviours of current or potential customers/shoppers of a brand or category based on a human truth that results in an emotional response.
At first glance, this may seem like quite a mouthful, so to simplify retention, I refer to it as ABCDE:
A = Attitudes and Actions
B = Brand or Category
C = Customer, consumer, client or shopper
D = Deep human truth
E = Emotional response
To fast-track your understanding, here are some great examples of the insights behind some of the best-known brands:
- Heineken Jillz: I want to drink alcohol on a night out, but I don’t like beer and wine is too variable in quality.
- Kraft Philadelphia: Food is delicious, but I don’t want to eat too much fat (butter versus cream cheese).
- DTC Diamonds: I want to stand out (shine), but as a modern woman, I also want to be seen as gentle and feminine.
- Unilever Dove: I want to be admired for my beauty on the inside, not for what I look like on the outside.
- AXE (Lynx in UK): I (young men) want to attract as many beautiful and sexy women as possible.
- Haribo Starmix: There’s a child inside every adult.
- Dulux sample paint pots: I love to decorate my home, but I don’t want to look stupid by choosing the wrong colour.
You’ll notice that most are written in the first person as if the target audience is speaking. This makes it much easier to understand and resonate with the reader without much effort since we can immediately put ourselves in the other person’s shoes.
If you’re interested in learning more details about the insight development process my clients use with great success, I suggest you read: “Five Brilliant Ideas to Boost Your Insight Development.”
Now, to discuss the solutions to the challenges often faced when trying to develop actionable insights from all the information you have available and the data integration you have accomplished:
1. Data Quality Issues
Data quality issues are a cornerstone challenge in insight development. Organisations often grapple with inaccuracies, incomplete data, and duplication, leading to flawed analyses and misguided decisions. This is all amplified by the fact that the data comes from multiple sources and departments within the organisation.
According to Gartner, poor data quality costs organizations an average of $15 million annually due to operational inefficiencies and lost revenue (Gartner, 2021).
Issues such as unstructured data and format inconsistencies further complicate matters, making it difficult to maintain data integrity.
Solution: Implement robust data governance frameworks, leverage advanced data quality management tools, and establish central data procurement teams.
According to McKinsey, these are all essential to enhancing data quality. Continuous monitoring and validation at the data entry point can also prevent the proliferation of errors, ensuring reliable and accurate data for analysis.
2. Data Overload
The exponential growth of data, projected to reach 175 zettabytes by 2025, presents a significant challenge in managing data overload (Source – IDC, 2018).
This overwhelming volume can hinder the identification of relevant information, with data scientists spending up to 80% of their time on data preparation rather than analysis (Source – Pragmatic Institute).
Solution: To address this, organizations must invest in advanced data management tools that automate profiling and filtering, enabling efficient sorting of relevant datasets.
Machine learning algorithms can further streamline the process by highlighting significant patterns and reducing the time spent on data preparation, thus enhancing the overall efficiency of insight development (Source – LakeFS, 2022).
3. Lack of Analytical Skills
The scarcity of skilled professionals capable of analyzing complex datasets is a major barrier to effective insight development.
With data engineering job postings growing by 35% annually, there is a clear demand for analytical talent (Source – LinkedIn, 2020).
Solution: Organizations need to invest in training programs and foster a culture of continuous learning to build internal capabilities.
Encouraging collaboration between data engineers and business users ensures analytical efforts align with business needs.
Additionally, advanced analytics platforms with user-friendly interfaces can democratize data integration and analysis, allowing non-technical staff to contribute to data-driven decision-making processes (Metaplane, 2023).
If you are interested in our Actionable Insight Development training program, a big hit with students, and our most popular course, check it out HERE.
4. Data Integration from Multiple Sources
Integrating data from diverse sources poses significant challenges due to differing formats and structures.
Inconsistent data definitions and the lack of standardized models can lead to discrepancies, undermining the accuracy of insights (Source – Data Ladder, 2023).
Solution: Harmonising definitions before data integration from multiple data sets is essential. It sounds obvious, I know, but many organisations face this issue when they are already well into their integration project, so make sure you start with it!
Employing data integration tools that harmonize various data types and establishing robust data governance practices are crucial steps to mitigate these issues. Standardizing data models and enforcing relationship constraints can ensure consistency across integrated data, enhancing the reliability of insights.
5. Technological Limitations of Data Integration
Technological limitations, often stemming from outdated systems, can significantly impede data analysis. Legacy systems may be unable to process large data volumes efficiently, resulting in delays and inaccuracies.
Solution: Invest in modern data infrastructure, such as cloud-based platforms and advanced analytics tools, to overcome these limitations.
These technologies offer scalable solutions that can handle growing data demands. Today’s opportunity to integrate machine learning and AI into data workflows further enhances analytical capabilities, enabling sophisticated data analysis and faster insight generation (Source – BMC Medical Research Methodology, 2023).
6. Bias in Data Interpretation
Bias in data interpretation can distort insights and lead to poor decision-making.
Personal biases, cultural influences, and preconceived notions can all impact how data is analyzed and understood.
Solution: Organizations should employ diverse teams and objective, data-driven methodologies to mitigate this risk. Implementing rigorous validation and testing procedures, such as cross-validation and blind testing, can enhance the objectivity of data analysis.
Fostering a culture of critical thinking and continuous evaluation of analytical methods helps ensure insights are unbiased and representative of reality (Source – McKinsey, 2023).
7. Communication Gaps
Effective communication of the analysis results to stakeholders is crucial for decision-making but often presents significant challenges.
The analysis must be presented clearly and concisely to be actionable. According to a McKinsey survey, organizations that effectively communicate data insights are 1.5 times more likely to achieve top-quartile financial performance.
Solution: Data visualization tools, like interactive dashboards, can bridge communication gaps and make complex data more accessible.
Combining these with stakeholder training in data literacy empowers them to understand and use the information more effectively, maximizing the impact of analytical efforts (Source – LakeFS, 2022).
8. Time Constraints
Time constraints often pressure data teams to develop analyses quickly, leading to rushed and potentially inaccurate conclusions.
Balancing the need for timely insights with thorough analysis is a significant challenge. Implementing agile data processing techniques and leveraging automation can streamline the analysis process.
Solution: Automated data pipelines expedite data preparation, while machine learning algorithms accelerate the identification of key patterns.
Adopting a collaborative approach ensures that analyses are relevant and timely, enhancing the organization’s ability to produce accurate insights within tight timeframes (Source – Data Ladder, 2023).
9. Resistance to Change
Resistance to change is a common organizational challenge when insights suggest significant shifts in strategy or processes.
Overcoming this resistance requires effective change management practices and clear communication of the benefits of data-driven decisions.
Solution: Involve stakeholders early in the insight development process and provide training and support to facilitate smoother transitions.
It is vital to demonstrate quick wins and tangible benefits from data-driven initiatives as they build momentum and encourage broader acceptance of change.
Addressing resistance proactively fosters a culture of continuous improvement and enhances the organization’s ability to leverage insights for strategic decision-making (Source – Metaplane).
10. Privacy and Ethical Concerns
Navigating privacy and ethical issues in data collection and analysis is increasingly complex, especially with stringent regulations like the European GDPR and recent stricter Californian regulations.
Ensuring compliance with data protection laws and maintaining high ethical standards is crucial to building trust and avoiding legal repercussions.
Solution: Implement robust data governance policies, including strict access controls and data anonymization techniques. These will protect all the sensitive information you may have gathered.
Fostering a culture of transparency and ethical responsibility ensures data is used appropriately.
Regular audits and assessments of data practices further ensure compliance and identify areas for improvement. Organizations can enhance their reputation and build stronger stakeholder relationships by prioritising privacy and ethics and supporting effective and responsible data use (Source – BMC Medical Research Methodology).
Conclusion
Data integration and effective insight development are essential for organisations to navigate the complexities of today’s data-rich environment and maintain a competitive edge.
While the challenges are numerous and multifaceted, strategic investments in technology, skills, and governance can significantly enhance the ability to integrate data, which is the first step to developing actionable insights.
Organizations can unlock the full potential of their data by addressing data quality issues, managing data overload, building analytical skills, integrating diverse data sources, overcoming technological limitations, mitigating bias, bridging communication gaps, managing time constraints, and addressing resistance to change and privacy concerns.
Embracing these strategies ensures that insights are based on accurate and reliable data, which is essential for driving informed decision-making and fostering innovation and growth in an increasingly data-driven world.
