¿Cuáles son los peligros de depender demasiado de los datos en los negocios?

In and IT system data output is only as good as the data input, you can have as many BI or AI systems as possible, if the data input is not correct then the data output or the AI interpretation of the data is going to be wrong. You have a BI / AI dataset for sales, and you are making decisions based on the zipcode. Let’s now argue that your best customer is based in 1000. Suddenly the customer decides to move to 2000. First your master data must be fixed so that all transactions are correctly addressed to the new zipcode. But more importantly if you do not also update all your historic transactions, the AI and BI system will take a while before it understands that you now must focus on 2000. Based on real example, though simplified

It's not the quantity of data that determines the accuracy of outcomes, but its quality. Raw data containing inconsistencies and errors can negatively impact the analytical process. So, ensure you're using processed, clean, and accurate data.

Any amount of data is valuable to generate insights. The question is, how do we really convert the data into piece of information which could timely and efficiently become insightful. Its on us to move the jargon out of data and make it relevant for business. Off course the interpretation of data is to be done by human to generate value out of it which always keeps human in front for any business insights. So I suggest to keep generating as much data for your business and keep relying on the information generated out of it.

It's important to consider all 'sides' of the data. Think about how the data could be interpreted by other business disciplines or departments. And, view the data wholistically. Also, when determining the course of action, based on the data being presented, make sure you're aligned on how the action will impact future results.

To avoid misleading metrics, there are good habits that can help. For example, evaluate and form the key question we want to answer using the metric. The more specific, the better. As a business analyst, it is important to put effort into collecting all of the data that can provide a picture of the Key Performance Indicator (KPI) and then define some goal or benchmark to compare it againt.

Focusing solely on lagging indicators (outcomes that have already occurred, like sales revenue) can prevent you from identifying early warning signs or opportunities for proactive intervention. Leading indicators (predictive metrics, like customer satisfaction scores or lead generation rates) are crucial for course correction and future planning.

Metrics are crucial but can be deceptive, if not chosen or interpreted correctly. High website traffic, for example, is meaningless without conversions. Misleading metrics can cause detrimental harm because of the issues they could be hiding behind them, like poor UX or irrelevant traffic. Always compare your metrics with business goals and adjust as needed.

Building data quality assessments into your data practice helps you understand where you have weak spots. Doing this before you use data in analytics and reporting let’s you focus on building reports and analysis with reliable data while putting corrective measures in place for unreliable data. Knowing your data and data quantity gives you confidence in your data and your stakeholders confidence in you.

Data quality is most important across all pillars. Approaching wrong target audience for any brands / products will always lead to low data quality. The target audience plays an important role while generating data. Right approach to correct people / samples leads to the best quality of data and findings.

Dentro da auditoria, é importante garantir que os dados sejam completos, corretos e atualizados, assegurando a integridade dos nossos relatórios. O processo de data cleaning é essencial para a precisão e rapidez das análises. Problemas como dados incompletos, erros e informações desatualizadas podem comprometer a qualidade das análises, mas também oferecem oportunidades para a implementação de controles e melhorias contínuas. Estima-se que até 80% do tempo de um projeto de análise de dados seja dedicado ao data cleaning. Com dados bem preparados, economiza-se tempo na fase de análise, resultando em insights mais rápidos e confiáveis.

Too much data, too many options, and too many perspectives can make it difficult to process information effectively and make confident decisions. The pressure to make the "perfect" decision, especially with high stakes, can lead to indecision and procrastination. Perfectionism: Striving for an unattainable level of certainty and completeness can delay action indefinitely. Without well-defined goals and priorities, it's easy to get lost in a sea of data without a clear direction.

The pitfall of relying heavily on data lies in how good your data is, and by good data, it means data is clean, usable, and informative, I saw a couple of situations where people were relying heavily on data, but there was no quality in the data they used to rely on, so the better the data, the great is the outcome, also, keep in mind that the past data always teach you what has been done wrong, and how to avoid it, they are the foundation of your business decision, so give it a good care, and it will gift you amazing plans.

The organizations need to strive for a balance between leveraging data-driven insights and relying on people-focused perspective such as customer service, innovation, and organizational culture. Delivering customer experiences and building connections cannot be fully captured through data analysis alone. Innovation and creativity often comes through experimentation, out-of-the-box thinking, and risk taking that goes beyond what only data can predict. While employee feedback can define future job satisfaction measures, the sense of purpose and trust can only be fostered through interpersonal leadership, communication, and good leadership. In summary, relying on the data-driven analysis alone is not a guarantee of business success.

Les données peuvent vous dire pourquoi ou le peut-être via une probabilité de prédiction. Cependant, elles ne vous disent pas le comment. Le comment provient de la créativité et de la coopération d'une équipe motivée

Humans are the key players in the business, otherwise, let the machines rule the universe, the human factor adds the missing piece that the machine learning and other AI tools lack, and planning a good business shouldn't just be in how much we make, it should be, what we have to do to keep people interested, because interested people means flowing transactions, more data, good business decisions, so to me, the human factor is as important as good quality data!

Data shows past trends, but cannot predict sudden shifts in market conditions- because of this it is crucial to plan ahead and: 1. Develop multiple potential future scenarios (and update them as condiitons change), you could do so through the use of predictive analytics. 2. Implement technologies that foster innovation like collaborative tools, cloud computing and CRM systems. 3. Train your employees; remember "a captain is as good as his team". Receiving training on the latest tools and best practices will increase your team's adaptability, efficiency and foster innovation.

In my experience Data used properly is an asset and can only help in getting insights.Relying heavily on data is the way to help decision making if used properly.

Most importantly, we remember that past behaviours do not always predict the future—an important principle to never lose sight of. While we all want to rely heavily on historical data, we must appreciate and understand that there are constant evolutions across industries, and it is an uneasy business that never comes as a sure bet when predicting behaviour. Therefore, we must keep an open mind and be flexible, even prepared to adjust if outcomes are unexpected. While data does provide clear and fine-tuned truths, it is not infallible. Knowing this helps us accept change better and sharpens our strategies over time.

Data is essential for good decision making. However, a completely different strategy can completely change performance in a business. Said another way, using reporting or models to predict future performance can't tell you much about something you've significantly changed, that data has no account of. The same goes for marked changes in political, economic, social, technological, legal or environmental factors (PESTLE analysis). Data is essential in so many ways... operations, CRM, analytics...but for this latter element, accuracy is dependent on lack of significant change. I know this well because I was working for SAS when Covid19 happened and that brought global change that broke all the models! Creativity and strategy is still required.

Use caution when utilizing data to answer a question. Often times, results can be manipulated to answer pre-defined questions. Don't fall into the trap of making the data support the question you have by manipulating what the analysis is telling you.

Data often reflects past trends and behaviors. However, the world is dynamic, and relying solely on historical data can create blind spots to emerging trends, changing customer preferences, or unforeseen events (like economic shifts or global pandemics). A core principle of data science is that models should be continuously evaluated and improved. The same applies to business strategies. As new data becomes available and the business environment changes, being adaptable allows you to refine your approach.