A continuación, le indicamos cómo puede sortear los riesgos de confiar únicamente en los datos en Data Analytics.

Context is the key for data Analytics. You need to understand thoroughly how the data is collected, where it is collected and by whom. So, a lot of sectors have seasonality factors, a lot of sectors have age factors,etc. So, if you understand this then you would know better than whether your data is apportioned across seasons and years to represent the correct picture. It would help in presenting better analysis.

When relying on data in analytics, understanding its context is essential. Data reflects real-world events, behaviors, and trends, and does not exist independently. Always consider the conditions under which the data was collected. Was there an event that might have influenced the results? Could cultural or time-related factors impact how the data is interpreted? By examining these contextual elements, you can reduce the risk of drawing misleading conclusions and ensure that your analysis remains accurate and meaningful.

While data can illuminate hidden patterns, it's not infallible—think of it as a powerful spotlight that can sometimes cast misleading shadows. A savvy data analyst must be part detective, part philosopher, questioning not just the data itself, but the assumptions and biases baked into its collection and interpretation. They should view data through the lens of robust business knowledge, using their understanding of industry dynamics and organizational processes as a reality check against statistical anomalies. Sometimes, data whispers counterintuitive secrets that challenge conventional wisdom—here's where the analyst becomes a tightrope walker, balancing healthy skepticism with open-mindedness.

It is crucial to complement data with human judgment and contextual understanding. Data may not always capture the nuances of business scenarios, customer behavior, or market trends. Validate your data regularly and ensure it reflects current realities. Engage with experts from other fields to provide perspective and interpret findings holistically. Additionally, build flexibility into your decision-making, using data as a guide rather than the sole authority, ensuring you consider broader implications and insights beyond the numbers.

There are more than 100+ biases exist and knowingly/unknowingly we all are affected by a few of those. As an example, Loss Aversion is one such Bias that avoids you investing in a few options where you have sustained losses in the last. Confirmation bias is again one such bias where you look out for articles, people who confirm your views. Similarly, there are many biases and one should be aware of such bases for a better analytics.

When relying on data in analytics, it's crucial to recognize the risks associated with potential biases. Both the data and your perspective as an analyst can introduce bias. For example, confirmation bias may lead you to favor data that aligns with your pre-existing beliefs or assumptions. Additionally, data can be inherently biased depending on how it was collected, processed, or interpreted. It's important to acknowledge these biases and actively seek diverse datasets and perspectives to challenge your initial assumptions. By doing so, you foster a more balanced and objective approach, which ultimately leads to more accurate and reliable insights in your analysis.

One thing, i learnt is data is never stable. Any pipeline you wrote for data processing is never going to be perfect, it always needs tuning and bug fixing. But to identify that errors you need to always have fool proof quality checks embedded into the data flow after every step to make sure all corner cases are covered. Quality check is a must for every data scientist, to become one.

Since the entire analytics is based on data, hence it is important to maintain the quality of data. Corrupted data, incomplete data, biased data, etc. could harm the generated analytics. Ensure that you are able to maintain the quality and remember quality is not one time process but an ongoing process.

Always scrutinize the quality of your data, as errors like corruption, incompleteness, or obsolescence can severely affect your analysis. Establish thorough data-cleaning procedures to maintain accuracy and routinely verify your data against multiple sources. Regular updates are essential to ensure it reflects the most current information. Reliable analytics start with high-quality data, which is crucial for making sound decisions based on your findings.

The experience, expertise and business sense of the people operating the process are essential to validate the veracity and plausibility of both the findings and the recommendations. Generating conclusions collaboratively is important to mitigate the risks of bias, impressions or misinterpretations.

I worked with a large chemical plant where industry experts were skeptical about the value of data analytics, leading to a cold war between them and the AI team. To bridge the gap, I focused on leveraging the experts knowledge to improve the AI models. I collaborated with them to develop the right features and refine the models, making their expertise a crucial part of the process. I also ensured their contributions were recognized and praised by their leadership, helping to build trust and align both teams towards a common goal. In short, human insights and expert feedback are crucial for the success of any data analytics project.

Never overlook the importance of human insight in data analytics. While data can reveal patterns and trends, it should work alongside human expertise and intuition rather than replace it. Collaborate with subject matter experts who can provide valuable context and interpret subtleties that raw data may miss. Their perspectives help you grasp the deeper meaning behind the numbers, enabling you to develop more nuanced and effective strategies.

While systems, tools, and products are becoming much more effective day by day, there is nothing that can replace the human touch. Humans perceive information in a very different way and could present a very different analysis than systems. Hence keeping human in loop is very important.

Planning a bit ahead is essential. So, anticipating various possible outcomes and planning a roadmap for those outcomes helps you to deal with surprises. So, always think a bit ahead and plan upfront.

Incorporate scenario planning to prepare for various potential outcomes. While data may indicate a likely scenario, it's crucial to explore alternative possibilities. Consider how changes in trends or the introduction of new variables might affect your results. By planning for different scenarios, you can develop adaptable strategies that remain robust despite uncertainty and shifting conditions.

Lastly, always address the ethical implications of your data analytics practices. Prioritize privacy, data security, and the potential impact of your decisions. Ensure compliance with regulations and respect the ethical aspects of data use. This approach helps build trust and upholds integrity in your analytical work.