What data and analytics trends are you learning?

Scalability: Choose platforms that grow with your data needs to avoid obsolescence. Governance: Don't overlook regulatory compliance, especially for data privacy. Cost: Monitor expenses closely, as cloud services can become costly if not managed well. Interoperability: Ensure easy integration with your existing tools to avoid data silos. Community: A strong user community can expedite problem-solving and learning. Moving to a cloud-based data platform is a strategic decision that offers flexibility and scalability but requires careful planning around these key aspects.

The biggest advantage to cloud based data platforms is that it provides flexibility and easy to use access with the other team members. I have recently started using a similar platform and the ease and speed of it just made the collaboration with other team members very easy.

Biggest flexibility offered by cloud is that anyone with idea can now try to build its own without worrying too much about Infra scaling. Infact the whole idea of virtualization was to disconnect Software development from Hardware Configurations.

I’ve really enjoyed diving into cloud based data platforms with databricks being one of my current favorites as it offers a community edition to explore their platform and see if it’s a fit for your organization.

Data scientists should learn trends such as advanced machine learning and AI, focusing on deep learning. Real-time data processing and analytics are crucial, using tools like Apache Kafka. Embrace cloud computing with AWS, Azure, or Google Cloud for flexible data management. Natural language processing (NLP) is essential for unstructured data analysis. Augmented analytics automate insight generation using AI. Understanding ethical AI and responsible data practices is vital for privacy and unbiased algorithms. Proficiency in data visualization tools is also important for effective data communication. Staying updated with these trends is crucial for data scientists in the evolving field.

Data storytelling is a very crucial part of any analytics process because your analysis is nothing if your data doesn’t tell a compelling story that drives stakeholders to action and not just any action impactful data driven business decisions. It’s easy to visualize data but using those visualizations to tell a story is just as important as the analysis as well and when the data can’t tell a story it becomes difficult for stakeholders to really understand what’s happening or even make any proper decision.

The most impactful analytics projects are not necessarily the most complex or cutting-edge. Rather, the best insights are those that lead to real change through adoption across an organization. For data and models to drive value, analytics teams must go beyond crunching numbers to become master storytellers. Using compelling narratives and visuals, they need to translate complex findings into simple, actionable business recommendations. By improving data literacy through effective communication and storytelling, analytics teams build understanding, trust, and buy-in. This catalyzes the adoption of analytics, creating a flywheel effect for continuous optimization and impact.

I think the best way to share your insights to the audience is through the power of storytelling. However, it is very crucial to have the foundation knowledge of the data such as the problems, the solutions and the understanding of the audience. Crafting a story for your data will be extremely easy when you have knowledge of the data background. Creating a good story for your data will also give an edge on the best way to communicate with your audience. Telling a story bring audience understanding, focus, and attention. Learn to use it!

I strongly believe Data insights is like story telling to kids. It should be simple enough to understand in a glance yet powerful enough to change whole perspective. The way currently this works needs to be simplified to the point where all you would need is to ask the right question and you should get an answer covering all aspects. Person should not have to go through 10 widgets to make sense of his/her data.

The most important part here is to build a data literate culture in the entire org. itself which starts with data democratization and then moves to measurement science and then ends with driving strategic business decisions using data. A data point is as good as its relevance and impact on business, so a good storytelling needs awareness building among all the stakeholders and then leveraging that to drive decision making using data.

Implementation of AI and machine learning to solve a sustainable solution using the data is a game changer in telling your story. Stating your story from the problem and ending it with a prefer solution that will address the complex challenges shows create a positive atmosphere when telling your story to the audience. Overall, AI and Machine is two parts of data analytics which give a final verdict of your story.

AI and ML had been very important part of whole Data insights business. With the introduction of Generative AI I think this would take this whole landscape to another level. As we said if its about story telling then LLM does that job far better than any other media currently available.

In today's modern world when everything seems to revolve around data, Its very easy to introduce the bias if Data is not filtered correctly. Modern articles written by AI bots could be manipulative and can introduce a Bias among people in society. This could rather become uncontrollable and could lead to severe outcomes.

European companies should seriously consider migrating to piwik pro. All data is hosted on European servers and thereby comply with the GDPR. Whereas Google Analytics data is still stored in the US. Trying to make your Google Analytics usage compliant with GDPR is a bit like pissing in your pants - it’s warm in the beginning but after a while it gets cold. It’s awkward and you need to change your pants “analytics platform”.

'A picture is more than a thousand words' seems cliched? but the same applies in Analytics as well where 'A visualization is more than a thousand data points'. While there is a lot of advancements in the area of visualizations including the upcoming spur of more flashy, interactive and complex ones, the philosophy is to always stick to the basics, the main purpose of a great visualization more than anything else is the impact and understanding of data it creates at the first look without the strenuous effort of skimming through hundreds of data points. Like a good story has been the fundamental success driver for movies irrespective of how much we made advancements in visual effects, the same is the case with data visualizations a well.

It has become a yearly tradition, and lots of new vendors (and users) say "Dashboards are dead", but the hard true we can't make decisions without seeing it in our own eyes. Data visualization is an un-written contract of how to conclude a given behavior, see the yield of a set of activities, or just monitor our KPIs. With the new AI revolution, it's still dashboards that are here to stay, but with a secret (not) sauce, adding an LLM layer to better serve natural language business questions, and having our private analyst in hand to draw conclusions faster than before. Keep adding your point of view (analysis), highlight anomalies and outliers, make dashboards customized and personalized, and keep increasing the numbers trust.

Data visualizations and Dashboards are very important because it’s the easiest way to get your message across with little to no words. The difference between helping stakeholders and business owners make prompt data driven decisions depends on well visualizations are used to present the data or how well dashboards summarizes and communicates the business and key data points. For me, visualizations and dashboards require constant improvement so it’s I’m always learning and trying to improve on.

In a technical sense, this plane seems to be rapidly changing from static dashboards to dynamic dashboards, and now to self servable platforms providing data and even insights (using LLMs). However, without a strong understanding of data literacy, data visualizations and dashboarding may simply be viewed as technical abilities rather than critical analytical skills, leading to overlooked insights.

I am very critical about the use of the word "Data" in this context. It makes seem like "Data" is an object. Data are elements of the process by which we discover new things about the world. But they are just an element of a more complex process.

In today’s age, data is the new oil and leveraging data to unravel actionable insights has become a very important skill. In this backdrop, to stay ahead of the curve, having a data-driven culture and mindset is a necessity. Analyzing data provides a comprehensive understanding of trends, patterns, and correlations, enabling informed decision-making. This approach minimizes uncertainties, enhances accuracy, and aligns actions with real-world insights, ultimately fostering more effective and successful outcomes in an increasingly data-driven landscape.

Explainability and Robustness of any AI model is crucial. It should be clear how the results came to be and adding noise, drifts or out-of-domain data should not confuse the model. Or even better: it must tell that it is unable to make a reasonable prediction.

Two big trends after the 9/11 attacks related to the rise of analytics from open source data (#OSINT), and in the removal of data silos. Now, global privacy and national security legislation, the Ukraine war, AI, and Covid are converging to influence new trends. These include the idea of “transparent conflict”, where signals information (#SIGINT) and satellite data (#GEOINT) are increasingly available and processed for new signals. Other trends include “disinformation” and detection of AI lies and hallucinations. Likewise, barriers are going up around the world relating to personal identifying information (#PII) and anonymity.