How can you overcome big data and AI challenges?

As the whole world is fast shifting towards value driven data, challenges are increasingly becoming stepping stones. So, to conquer big data and AI hurdles, consider: 1. Vision First: Define clear data visions before diving deep. 2. Feedback Loops: Continuously refine AI models based on real-world feedback. 3. Ethical Grounding: Ensure AI decisions respect cultural and ethical norms. 4. Skill Investment: Prioritize continuous learning in the ever-evolving AI landscape. 5. Value Focus: Always align AI initiatives with the overarching goal of creating tangible value. Remember, in a value-driven data journey, challenges are just data points guiding us towards refined solutions.

Dashboards are Dead! With over a decade in big data at Oracle and New Relic I’m telling you more data is not better, creating critical issues in cost and confusing complexity. There is no meaningful way to aggregate disparate data or their respective sources largely due to their contextual disconnect, A has nothing to do with B, yet we divide or multiply them as if they will yield the magic code to unlock the “meaning of life”. Worse we insert them into a dashboard at the request of a Sr Leader and no one else has a clue what that number means or why they should care! Net result everyone ignores it until the next Sr Leader starts the process all over again…, ending in the same outcome. AI can overcome this if it is properly structured!

In a world filled with more data than cat videos on the internet, I've learned a thing or two at TikTok about tackling Big Data and AI challenges. It's like herding cats, only more fun! First, embrace your inner Sherlock – dive into the data and let it tell you its secrets. Then, channel your inner project manager – set clear goals and deadlines. And when things get hairy, be the 'Go-To-Expert' of your data jungle – trust your insights and craft purr-fect solutions. Remember, even AI needs a human touch. So, keep your Six Sigma cool and your sense of humor intact. After all, laughter is the best algorithm!

"Its ok if you know, What you don't know but problem arises when you don't know, What is it that you don't know" For business user it is imperative to know their enterprise's complete data landscape ie what all kind of data is being captured, retained and from which applications. This allows business user to fine tune context of the analytics that they plan to do, as well as, achieving more accuracy in the outcome. For analytics itself, business users have been using various models available and with NC apps backed by AI, they can now play around directly rather depending on analyst or IT team to assist them. This new found independence fastens the output, reduces friction between teams and allows business user to get creative with analysis

Many professionals are jumping into generative and big-data AI tools headfirst without considering the implications of publishing private data in the public cloud. As a leader in an organization that leverages AI internally, and produces AI-enabled software for data insights & analytics (around communications), I can't emphasize enough the need for a corporate AI use policy and mandated training.

It's not just about having tools; it's about having the RIGHT tools. It's like being a carpenter with a toolbox full of gadgets – some are handy, some are useless. The right tools are tailored to your specific challenge. They're efficient, accurate, and they save you time and headaches.

There are several tools and methods that can be used to manage big data. According to Coursera, big data technologies can be categorized into four main types: data storage, data mining, data analytics, and data visualization1. Each of these is associated with certain tools, and you’ll want to choose the right tool for your business needs depending on the type of big data technology required1.

It takes a deliberate approach to overcome big data and AI difficulties. Secure data governance, quality, and security should be prioritized first. For data processing and storage, make an investment in scalable infrastructure. Create AI models and continually improve them through rigorous testing. Encourage data scientists and subject-matter experts to collaborate across disciplines. Through ongoing study, stay current with industry innovations. In order to solve ethical issues, build open AI practices and ensure data privacy compliance.

Not every problem needs an AI solution. If you're jumping straight to an AI model, it's worth spending some time exploring what other techniques exist. There's decades worth of algorithms and heuristic approaches that are often quick, accurate and considerably cheaper to run than many AI models. If AI is your hammer, not every problem is a nail.

Equally significant to choosing the right tools and methods is ensuring a consistent environment for their deployment. I've found that even the best tools can falter if not integrated seamlessly with an organization's existing infrastructure. This integration allows for fluid data transfer, reduced chances of tool conflicts, and a unified approach to troubleshooting. Furthermore, dedicating time to regular training sessions ensures that your team is not just familiar with the tools but also proficient, maximizing the value derived from these resources.

I disagree with assumption that people can develop critical thinking or problem solving using data as an asset. That is aptitude of individuals and some individuals have been very successful repeatedly because of their intuition based decision making.

Pablo Picasso once said, "The problem with computers is that they give you answers." Advanced AI tools expedite our search for information, but it's your ability to ask the right questions that unlocks the full potential of using AI tools. Let AI outputs serve as springboards for inspiration and refining ideas, rather than conclusive answers. Shift your focus to tougher challenges and pursue broader perspectives to sharpen these essential skills.

In the world of AI, where the landscape is constantly shifting, critical thinking is your North Star. It helps you adapt, evolve, and thrive in the face of uncertainty.

In the realm of big data and AI, cultivating strong critical thinking and problem-solving skills is a vital step forward. These skills enable us to define problems clearly, identify relevant data, evaluate evidence, draw conclusions, and communicate results effectively. Moreover, they empower us to question our assumptions, recognize biases, and challenge the validity of AI outputs. AI is a powerful tool, but it's equally vital to consider users' ability to ask the right questions, often leading to unique and valuable outcomes. Regular practice through case studies and real-world problems hones these skills, ensuring our ability to navigate this data-driven world and extract meaningful insights.

In terms of developing a structured problem-solving approach, we need to clearly define the problem, gather relevant data, choose appropriate analysis methods, and interpret the results in the context of the problem.

Whenever learning something new, remember to implement it. Even if it’s a very simple project with limited scope, the learning gained would stay with you for a very long time.

It's not just about sharing knowledge and working together. It's about pushing each other to think differently, challenge assumptions, and see the bigger picture. Collaboration isn't a one-way street; it's a dynamic exchange where everyone elevates each other.

One of the most invaluable insights I've gleaned from collaborating with peers is that sometimes the most innovative solutions to big data and AI challenges come from outside one's immediate industry or discipline. By actively seeking interdisciplinary collaborations, you often stumble upon fresh perspectives and methodologies that you might not have considered otherwise. These cross-disciplinary interactions can be a goldmine for breaking through bottlenecks and reimagining solutions.

Some factors to keep in mind include compliance and regulations of laws and others. To do this, everyone must be well-educated on the subject. There is no way to avoid it than to learn about it, as it carries financial and reputational risks for the business. As mentioned, efforts must be made to avoid inaccurate or low-quality data. If data is not properly maintained or recorded, it is as if it doesn’t exist. To do this, we must define the necessary data that aligns with the required information and business objectives. Regular data cleaning is necessary, and when collecting data from different sources, it must be organized before loading it into any analysis tool. Once data is cleaned and uniform, it can be segmented for better analysis.

In the day, competition was the name of the game. Nowadays it is all about people and collaboration. You dont always have to make money on everything, relationships and learning has much more value than a quick buck here and there.

To stay current with the trends, consider watching AI-related startups closely. Observe their innovations and how they are applied in the real world. Analyzing these startups can guide your investment decisions, shape your skill development, and inform your company's adoption of new technologies. This proactive approach can protect both your company's and your own relevance.

Trends reveal where the currents of consumer behavior are headed. They help you anticipate market shifts and adapt your strategy accordingly.

Beyond just staying updated with the latest trends, it's equally important to discern which innovations align with your organization's goals and infrastructure. I've observed that jumping on every new trend can lead to a scattered approach, wasting resources on tools or methods that might not bring tangible benefits. Thus, while it's vital to be aware of the evolving landscape, it's equally crucial to adopt a strategic approach, ensuring that every innovation incorporated offers genuine value to your big data and AI endeavors.

I like the analogy of music. I do like my old timers like Alan Parsons, Kansas, Boston, etc... but I hear the radio and I go with my daughter to concerts to hear the new groups... I hear everything. So being in knowledge of the new trends gives you perspective to be always connected to the majority of teammates today.

Staying updated on trends and innovations is essential in the fast-paced worlds of big data and AI. Rapid advances can quickly render today's best practices obsolete. Beyond technical aspects, it's also crucial to keep an eye on ethical, social, and legal considerations, as they can significantly impact the field and its public perception. Utilizing multiple channels for learning—like articles, conferences, and following industry experts—ensures a holistic understanding. The key to remaining relevant is a commitment to continuous learning and adaptability.

AI Challenges is collateral damage from bad data. If we already know 60-80% of your time spends on getting data prepared, why we keep on the loop thinking as more cleansing will solve the problem when indeed it's a PAINKILLER and DOES NOT solve the root cause problem? Nowadays, organizations are producing more data than ever. A meaningful question is not about "How many data are we producing?" instead "How many of these data it's in their right format to deliver business value?" OWNERSHIP is a key word on producing data, this is the REAL root cause. How we can make sure we're measuring what matters? Are the data we're storing scalable and useful from a business perspective?

The management and handling of data are crucial as it must be flawless within an AI environment, as AI can control the entire management from a single system. If integration capability is included, consideration can also be given to where the data comes from and its nature to avoid unnecessary resource expenses. As studied in “The lack of qualified resources can be compensated in two ways. Data scientists and database administrators (DBAs) gain valuable time by including languages and development frameworks and automating routine tasks such as query optimization.”

Aks for advice, ask for feedback, look for new ideas, look for an image to reflect yourself and learn. Look for people and companies who are doing better than you. Put your aim higher. Lookout for self complacency.

Seeking feedback is a cornerstone of continual improvement, especially in fields as dynamic as big data and AI. The iterative nature of data projects makes them ideal for applying constructive criticism. Whether it's from peers, mentors, or stakeholders, external perspectives can provide valuable insights into both technical and soft skills. Self-assessment is equally crucial. Knowing your strengths and weaknesses allows you to target areas for growth, be it through new certifications or advanced education. The path to analytical mastery is a journey, not a destination, and feedback is your navigational guide.

We should seek feedback on our work and be open to constructive criticism. This means that we should regularly assess our performance and identify areas for improvement.

Big data and AI are transforming various industries, but they also present challenges for professionals in terms of analyzing, interpreting, and communicating complex information. To overcome these challenges and enhance our analytical skills, we could consider the following strategies: - Continuous Learning and Skill Development - Statistical and Mathematical Proficiency - Coding Skills - Master Data Visualization - Machine Learning algorithms and AI techniques - Deepening Domain Knowledge - Data Management best practices - Applying practical experience in Real-World Projects Additionally, Soft Skills like critical thinking, problem-solving, and attention to detail, are essential for practical analysis.

I agree… Ethical Grounding: As AI’s impact grows, so does its ethical footprint. Engage in dialogues on fairness, transparency, and responsible AI. It’s not just about what AI can do, but what it should do. Diverse Data: Embrace data’s diverse nature. Understand its sources, biases, and implications. It’s the raw material – ensuring its quality directly impacts the output. Resilience & Adaptability: In a field that’s always evolving, resilience isn’t just an advantage; it’s a necessity. Embrace failures as learning steps, and adapt swiftly to technological shifts. Mastering AI is a multifaceted journey. Ethics, data quality, and adaptability form its backbone. Chart your path wisely!

Overcoming challenges in Big Data and AI starts with a robust data governance strategy. Ensure data quality and integrity by implementing checks and validation processes. For AI, focus on explainability; opt for models that provide insights into their decision-making. Talent is key; invest in skilled data scientists and AI specialists. Also, consider partnerships with academic institutions for R&D. Scalability is another concern; opt for cloud-based solutions that can grow with your needs. Security is paramount; employ encryption and multi-factor authentication to protect data. Lastly, stay compliant with data privacy regulations like GDPR. By addressing these areas, you can mitigate risks and leverage Big Data and AI effectively.

First we need to analyze the nature of the problem. Then we need to deep dive into that in order to get more insights. In case it seems that you need assistance, then always seek from professionals, community groups to get more ideas on that in case you are not aware of the possible solution to the problem as AI and big data is a vast field. Knowing everything is not important but the keen to learn and follow strategies shared by experts after proper research plays a vital role for handing issues. So follow this process to tackle issues.