Virtual Reality in Data Science & Analytics
XR Immersive Technology will change the way we look, feel, act, think, and live. XR is Virtual reality, Augmented Reality and Mixed Reality. Think Oculus, Microsoft HoloLens and so on. Now what if Virtual and Augmented Reality was integrated with Machine Learning, Data science & Analytics ? Tableau in Virtual Reality ? Spark ML in Augmented Reality ? Imagine that.
VR has empowered gamers to be immersed in an actual virtual universe where they participate, visit, explore, and play. Now VR or AR in marketing, filters, masks, and barcodes can lead people to explore and interact with new products and services.
We are living in an interesting time. Covid-19. Quarantine. WOrld-wide protests to improve the lives of everybody. Immersive Intelligeeent and Creative Information technology is changing basically every part of our society: how we work, play, learn and talk. The pace of the change is remarkable with the significant changes occurring on the size of years instead of decades or hundreds of years.
Another major result of this unrest is an exponential growth of data rates and data volumes, mirroring Moore’s law that depicts the rapidly evolving technology that produces the data. Just as significant is the growth of information quality and data multifaceted nature.
Before IoT and analytics tools, data fit pleasantly on a spreadsheet and KPI dashboard. Big Data, hadoop, Spark, the Cloud, mobile apps, digital transformation and more pushed that level activity from just reading the data and trying to make gut decisions with minimal analytics to a more programmatic statistical process left in the hands of data scientists who apply models to remove helpful insights from large data sets.
Humans have always wanted to predict the future. We still look to Nostradamus and the Mayans and religious texts to predict the future. Imagine a future where we can visualize huge data sets that show not tell significant patterns and trends and recommendations and simulations. Consider the possibility that you would associate with the information, move it around, truly stroll around it. Mixed Reality Data Science Proactive Analytics is the future.
Mixed Reality 3D visualizations are the future that will guide and assist decision-makers comprehend and gather insights from big data sets. The innovation will open the intensity of big data and advanced analtyics in realms as unique as health & medicine, manufacturing, public safety, retail, agriculture, board rooms, and governments, and it could push the adoption of enterprise AR/VR.
It’s been proven that when we are completely submerged in such a immersive data space, taking a look at the data from the back to front as opposed to from the outside glancing in, just like the case in all traditional visualization approaches, pushes boundaries and limits. The Show not tell theory of Data science, Analytics, and more. We are visitors and actors, not just viewers. Our brains are best if we are looking for patterns in such a space regardless of whether space itself is abstract in nature.
Virtual Reality is a characteristic platform for collaborative community data visualization, analysis, story telling and story living and visual exploration. Visitors and Actors can interface with the data and with one another in a common virtual space regardless of whether they are in San Diego, Atlanta, Hong Kong, or Auckland or on Mars in the physical world. Such connections are incomprehensibly better than any remotely coordinating experience. Virtual is the new physical. Simply saving time, cost, and effort of travel is a great advantage in and of itself.
Sorting big unstructured data is essential. Self-service Business Intelligence and Analytics has been touted for years, but with Virtual Reality Data Science Visualizations, it is the ultimate self-service immersive technology for business. Conventional visual diagrams and pie outlines on tablets, laptops, mobile phones are basically 2D screens with sever limitations. The Data Warehouse and Business Intelligence Dimensional Modeling slicing and dicing and pivot tables in tableau or excel or power BI are still 2D. No matter how we slice and dice the data, it’s on a flat 2d Screen. Graphs and maps and animation have improved upon this limitation,, but it’s still really a flat 2D world. You’re not immersed in the simulation.
VR and AR provide this Visitor and Actor Simulated world where information actually comes alive, the story is alive, and the simulations you may want to run, you can interact, create, change, and participate, not just watch on a small mobile phone. Imagine Big data visualization interactive experiences where the information and data and simulation models encompass you. That’s immersive. That’s the future.
Smart mapping with better mapbox, openstreetmap and google maps integrations. Intelligent smart contracts and automation for physical manufacturing or retail, machine learning and natural language processing to find significant patterns and conversation semantics and show it all in the virtual realm, which would also be personalized and customized by visitors and actors. Mixed Reality doesn’t have users. It has Visitors and Actors.
VR and AR with big data and data science will improve the slow arduous data integration and processing and transformation challenges. Extreme Analytics on steroids in an immersive realm.
Pagarba Solutions combines artificial intelligence, Virtual Reality, Augmented Reality, blockchain, gamification and Big data(Spark like tools and the cloud) to make solutions which permits visitors to analyze up to 10 elements of immersed sliced data.
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Once you know how to analyze everything, your information can be gold. The value and importance of leveraging machine learning to create a true competitive advantage is known to most successful startups & businesses. Artificial intelligence provides actionable insights, which lead to a stronger business strategy and plan of action. It is essential to have usable data and actionable predictive analytics to make the most of your analytics and data science pipelines, and for a business person to understand the typical product life-cycle flow.
Artificial Intelligence Drives Innovation
Retail Case Study
For several years, inventory and sales information has been obtained by a network of integrated but silo department store systems. Mergers, Acquisitions, various consultancies and data warehouses turned this business intelligence and ‘big data’ platform into a data swamp, not a data lake. Nobody wanted to use it because it was like a scary swamp monster.
This system acted as the analytic and AI center of excellence used to train a machine learning model by the Pagarba Solutions data science team. The team helped the founders, directors and C-Level executives identify strategic actionable priorities and act on them in a timely manner. The current system was the method used to create a demand forecasting model to make future predictable trends and forecasts.
By analyzing data from the past five years of retail sales, an adaptive selective model approximated demand for the coming month. Vector auto-regression models coupled with external data sources are often used in machine learning. That made it possible at any time to forecast demand for any particular product. The company was able to maximize inventory and warehouse management by using this solution to provide alternatives available.
BioTech Smart Medicine Inventory Management Hardware Case Study
The Pagarba Solutions client was advised to use multi-modal bio-metric authentication object recognition system that required a user to pass both a facial and voice recognition to gain appropriate authorized access.
Pagarba Solutions Artificial Intelligence experts suggested introducing enhanced security features to the system, such as image and object recognition technology like optical character recognition (OCR). This was for driver’s license or passport ID authentication. Another strategy and plan was to reduce outliers and overfitting by using deep learning algorithms to prevent fake or biased facial recognition. And finally using Natural Language Processing technology( NLP) for security based written quiz like challenges. These changes also culminated in the development of an event-as-a-Service (EVaaS) platform.
Artificial Intelligence Process Flow & Pipeline
Executives, Directors, & Business owners want to know how to use AI and emerging technology to achieve actionable and realistic business goals. What’s the Return on Investment? They are less interested in the details of how the technology works.
Steps of a Artificial Intelligence Strategy & Development project
1> User Experience (UX) is Key
How will your customers and end users use this Artificial Intelligence (AI) product ? Think of the “old school” recommendation engines we see on Amazon, Wal-mart, any ecommerce site or even youtube and netflix or Hulu. A recommendation engine is really a subclass of information filtering systems that predict the “rating” or “preference” a end user would give to an item.
Recommendation systems work best with unbiased explicit user feedback like Amazon and shopping cart purchases or watching a video on Youtube or Netflix or listening to a song on Pandora or Spotify or Apple Music. But in many use cases the application and platform offers mostly implicit feedback scenarios. Think click through rates, page views, google search queires, like or dislikes, up or down voting and so on. This data is heavily biased and we’ve seen people cheat these systems or bully younger users. Click-through rate is heavily dependent on the position of content on a page. Poor UX/UI and web design and it’s like putting Milk at the front of a grocery store. Not ideal if you think with a data-heavy analytics scope. Implicit feedback also tends to perform worse. Google search results are heavily sponsored and ad based on page one now, so a lot of the content may show sponsored results and/or clickbait headlines. And then the site is all spam. This results in high bounce rates after initially high click through rates. This may not matter to a spam site, but a legit business might not be getting the results they desired.
The user experience (UX) can be broken down into three parts:
- Initial Impression
- What is the user trying to achieve?
- How does the user arrive at this experience?
- Where do they go?
- What should they expect?
- Interacting Stage
- What should they see ?
- Is it clear what to do next?
- How are they guided through errors and processes ?
- Feedback Loop
- Did the end user achieve their goal?
- Is there a clear “end” to the experience? Call to Action ?
- What are the follow-up steps (if any)?
Knowing what an end user should see during the start, into the interacting stage and feedback after of your app or software will ensure the data science and AI team pick the right features and variables for the models and the data science team trains and continuously improves and updates the AI models on accurate proper data from the start. And it helps the engineering and devops teams to automate the MLOps process flow. And they can build and design valuable automated feedback loops for analytics that offers the best solution for end users.
The Wyoming Banking Board just voted to approve Kraken, a San Francisco-based cryptocurrency exchange, as a special purpose depository institution (SPDI).
Technically it’s the first crypto exchange in the United States to become a bank. “By becoming a bank we get direct access to federal payments infrastructure, and we can more seamlessly integrate banking and funding options for customers,” David Kinitsky, a managing director at Kraken and the CEO of the newly formed Kraken Financial, told CoinDesk.
If this plays out as it seems , good and bad in a sense , Kraken Financial will no longer have to deal with a variety of different rules for regulating digital assets, depending on which state.
SPDI banks in Wyoming, started in November 2019, are still different than regular national banks. They never actually hold full legal ownership over any digital assets, but are legally allowed to hold them. Any assets following a bankruptcy must be returned to customers.
What about crypto debit ?
Kraken has plans now that it has been approved. “We would expect to offer a host of new products as we get established,” Kinitsky told CoinDesk. “Those will range from things like qualified custody for institutions, digital-asset debit cards and savings accounts all the way to new types of asset classes.”
In the wake of a July letter from the U.S. Office of the Comptroller of the Currency giving national banks the go-ahead to custody crypto, the Division of Banking also announced it has been working with Promontory Financial Group, a prominent Washington, D.C.-based consulting firm made up of lawyers and former government regulators. In October, the division along with Promontory will publish the first manual for banks regarding procedures and policies for handling digital assets, Land said.
In addition to more products, Kraken Financial will give Kraken the ability to operate in more jurisdictions, Kinitsky said. As a state-chartered bank, Kraken now has a regulatory passport into other states without having to deal with a patchwork state-by-state compliance plan.
“By becoming a bank we get direct access to federal payments infrastructure, and we can more seamlessly integrate banking and funding options for customers,” David Kinitsky, a managing director at Kraken and the CEO of the newly formed Kraken Financial, told CoinDesk.