Quais são os desafios comuns que os profissionais de insights de negócios enfrentam?

1. Lack of awareness of what we actually do. 2. Being laden with data requests to support decisons already made. 3. Resistance to using the best tools for accessing insights. 4. Not taking to requested insghts seriously for strategic planning. 5. Not given enough time for a comprehensive analysis.

I believe the most common challenge faced by business insight professionals is the data collation. To actually collate, evaluate, clean and analyze correct and accurate data may be tasking, inaccurate data will give inaccurate outcome for business interpretation. Uncertainty factors present in some data like consumer's choice, competitor's action and so on can also be challenging for business insights expert. It takes expertise and time to navigate through this and make a business decision that will favour the business growth.

📊Data quality and availability is a fundamental challenge for business insights professionals. ❌ Poor data leads to inaccurate or misleading insights, damaging credibility and trust. ✅ To overcome this, establish and follow data quality standards and processes. 🔍 Use data validation, cleaning techniques, and collaborate with data owners to ensure high-quality, reliable datasets for analysis.

In my experience as a business consultant, ensuring data quality and accessibility remains a critical challenge for business insights professionals. Inaccurate, incomplete, or unavailable data can lead to misleading insights, ultimately eroding trust in both the data and the analysts themselves. To overcome this hurdle, a multi-pronged approach is necessary. Firstly, establishing and adhering to data quality standards and processes is paramount. This creates a consistent framework for data acquisition, storage, and utilization. Think of it as building a strong foundation for your analysis – the quality of the data directly impacts the reliability of the insights derived from it.

Ensuring data quality and availability is not just about maintaining standards; it's about fostering a culture of data excellence. As businesses increasingly rely on data-driven decisions, the role of insights professionals evolves. They must not only be adept at data management but also at driving organizational change towards valuing data integrity. This shift is crucial for businesses aiming to stay competitive in an era where data is a key asset.

Effective stakeholder communication is not just about sharing data, it's about creating a narrative that resonates. As a leader in Business Intelligence, I've seen how using storytelling and visualization can turn complex insights into compelling stories that stakeholders can easily grasp and act upon. Remember, it's not just what you say, but how you say it that drives alignment and action. Keep it clear, relevant, and engaging! 📊📈👍

In my experience, stakeholder communication is the most common, and most easily avoidable, challenge. How I've overcome this: -Set clear expectations upfront -Know your audience -Simplify as much as possible, to the point where you don't feel comfortable You can have the most earth-shattering, business redefining insights from your analysis but if not communicated properly, they have no impact.

Aligning data solutions to real business problems is a common challenge. I see data professionals quite often jumping to the solution without a clear understanding of the true problem to solve or indeed the opportunity that presents. What I finds works best is to workshop the problem statement with the stakeholder to get alignment on the problem we are setting out to solve and work from there backwards on the steps that need to be taken to solve that problem. Setting measures of success with the business helps overcome this challenge in my experience. Whether or not the problem has been solved should be clear when comparing the measures of success with the actual performance.

Realistically, everything you will ever do as a data scientist/analyst/consultant/researcher will ALWAYS begin with "what is the narrative we want to tell." In an intellectually honest world, you then conduct two analyses: one which proves your point, and the other which disproves it. Realistically, use the most-favorable analytical framework and take the least-favorable estimates, feeding the line, "even at its least favorable..." That's it, really. Also, don't get clever. E.g., if your industry doesn't actually require causality, don't waste your time proving that your model is well-specified, stick to trend lines (even if you can/do prove causality in your model, don't lose track of the plot). Make it FEEL simple and easy.

Delicare debes palos, in horreum sume pro prandio piscium, et rhoncus olera sub astra. Perfice noctem cum melle et nonnullis veterum proeliorum fabulis.

This is pretty basic. Loose lips sink ships and also lose you clients or maybe even high-risk data clearance so just zip-it-up. Otherwise, read your IT guidelines, know which federal statutes you are liable to, and hire/THANK a really solid IT team to implement the correct data infrastructure to be adherent to your DUAs, NDAs, T&Cs, DPAs, whatever it is you're rocking. Bias is a human issue that is exacerbated by algorithms. Only way around that one is hiring and training people to be more aware of this issue in themselves, their coworkers, and their black-box NLP algos. And ethically? If you're a huge company, break yourself up...data monopolies unambiguously kill competition and hurt consumers in the medium- and long-term, QED.

Business insights professionals often grapple with diverse challenges, from data overload to communication barriers. To thrive in this landscape, prioritize honing your data analysis skills and staying updated on emerging technologies. Cultivate strong interpersonal skills to effectively communicate insights to stakeholders and bridge any gaps between technical and non-technical teams. Embrace a mindset of continuous learning and adaptability to navigate the ever-evolving business landscape. Let's tackle these challenges head-on and unlock new opportunities together.

One overarching problem is that many business people fear that the finding will make them look bad or that they will have new agendas given to them. People do not like change or having to do something new. You will always encounter people who do not want the research and will often sabotage it from behind the scenes.

Just making a plug for version control, saving the original data transmission in an immutable format, and *always always always* starting from scratch when possible. Never trust any codebase or analysis to be infallible. They won't care how smart your predecessors were when they're assessing a mistake in your work, own every part of it. Also, if you're at a very junior level, develop localized knowledge on esoteric data. E.g., you can become an expert on SAP extracts or on auction data formats and make yourself invaluable to teams and to the broader company.

Balance speed of insight delivery with thoroughness... Rushing insights can compromise quality, but taking too long can make them obsolete. Finding this balance is key. A good insight is like a fine wine, it needs time to mature, but not to turn into vinegar :-)