Taking a passive approach to Business Intelligence (BI) is a mistake many companies today make. Their competitors mine data related to optimize their stake in the marketplace starting from their customers, and products all the way to market share and patterns of growth. But why are so many companies still so fearful of BI? Here are the top five myths debunked
Many of the RPA vendors have a well-rehearsed pitch and presentation clearly aimed at the global companies with tens of thousands of employees. RPA does still have impactful potential for SMB companies but the analysis and application needs to a lot more granular and explicit about avenues of costs savings beyond head-count reductions. In an SMB context, many of the process that should be automated will see FTE effort savings that will be less than 100% of 1 FTE.
One of the top challenges of IIoT is keeping valuable business data secure. Cyberattacks against IIoT systems and critical network infrastructure have severe consequences, putting world governments on high alert. Enterprises must take adequate precautions to manage and protect data related to IIoT or machine-to-machine security. By securing every necessary remote connection with VPN management, it will be possible for enterprises to stay ahead of future cybersecurity threats.
AI pioneers have provided us with a glimpse of and conditioned us to ambient AI making it hard to break up with each other. They have also set a very high bar on our expectations of what AI should do for our businesses. The point is that enterprises embarking on AI need to radically shift their approach to technology adoption and analytics. This is not a plug and play and bolt on strategy. It takes work to go from POC to a capability that comes close to our expectations of AI based on our consumer experience.
Policy approaches to education, skills training, employment, and income distribution should all now assume a post-AI perspective. This will require us to question some of our most deeply held convictions. A world where autonomous AI systems can predict and manipulate our choices will force us to rethink the meaning of freedom. Similarly, we will also have to rethink the meaning and purpose of education, skills, jobs, and wages.
Most of the traditional large system integrators focus on the higher end of the technology stack – connectivity is usually assumed to exist. But the core of their offers focusses on analytics and business process integration with legacy ERP and other systems. Newer connectivity solutions such as LPWAN are not yet fully integrated into their domain expertise. The current need for IoT network integrators is driven precisely by this need – the capability to determine the right network connectivity solution for lower tier access in terms of power consumption and cost is nascent, and this very capability is what determines whether new projects can be delivered at a reasonable ROI.
Big Data, Data Science and Analytics is the art and science of deriving business value from data. It was understood that if utilised correctly, data could remarkably elevate business’s ability to make insightful & strategic decisions.
Today’s technologies are making this easier for the savvy marketing practitioner, as there are plenty of powerful lead generation tools and marketing analytics tools that you can use to help automate labor-intensive aspects of this process and get both disciplines working in harmony.
Machine learning is great, but its really easy to quickly become disenfranchised by it if you’re not going about it the right way. But your business and understanding of machine learning needs to take occasional false positives and negatives into account.
We may want our data scientists to be superheroes, but all superheroes have their kryptonite. It would seem that the main flaw with being a data scientist is the one thing data scientists cannot change – the fact that they are human. Even Mister Fantastic was only human, after all. There is no way around this, so what is the solution? Do we throw all our data scientists out onto the street and replace them with machines?
Car manufacturers, ride-hailing services, public transportation, car washes and parking garages will all go through some major shifts for obvious reasons. Driverless cars are bound to change not only the automotive industry but revolutionize our daily lives, as well.
The data curation step involves discovering, analyzing, cleaning, transforming, combining, and de-duplicating data sources to produce target data sources that meet the requirements for input to the analysis. Every data curation step should be documented as data provenance that is then compared against the controls to determine the extent to which the appropriate data governance was followed and the required data quality was achieved.
The economy is going global. Managing a remote workforce might not be as difficult as it may sound. Communication and using the right tools remains the key to having a successful team irrespective of the geographies.
Any good friendship is based on a mutual understanding of value. The same is exceptionally true of IoT devices and IT professionals. IoT needs IT to function correctly and safely, and in some cases to have all the data that it collects understood. IT needs IoT to increase efficiency, reduce overhead and provide actionable intelligence.
In all fields new facts and knowledge are constantly being produced based on new data, discoveries, experience, and research -‐ far more than a single individual can absorb let alone put into practice. So how do professionals or how does anyone understand that they have a bias, its nature and limitations? And re-evaluate their knowledge (world view) in light of new facts (“ground truth”) and conclusions?
As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer sincere and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks.
It may be virtually impossible to develop artificially “intelligent” systems that aren’t at least somewhat contaminated and biased, in the same way, that it is impossible for a human mind to be entirely unaffected by bias. But if you want to ensure that your own machine learning tools are at least as objective as possible, then you should do your best to minimize the bias
What’s the difference between that and machine learning? or does that mean you work on artificial intelligence? The fields do have a great deal of overlap, and there’s enough hype around each of them that the choice can feel like a matter of marketing. But they’re not interchangeable: most professionals in these fields have an intuitive understanding of how particular work could be classified as data science, machine learning, or artificial intelligence, even if it’s difficult to put into words.
An aspiring data scientist needs to build useful stuff, learn new things, demonstrate that he can deliver value using data analytics and work with others using the same tools. Tips and tricks can sometimes get us ahead by a bit, but the fundamentals matter the most.
How much AI needs to mimic the brain has been debated for decades. The recent success of deep learning, which is only loosely related to brains, has bolstered the argument that AI can advance without brain theory. But that success has also brought to the fore the limits of deep learning, making it more obvious that new approaches are needed.
Most IoT deployments are cross-functional. They involve the use of both hardware and software. And IoT-enabled solutions offer several competitive advantages from a supply chain security perspective. Learn what you need to consider building a winning strategy.
Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.
We’d be better served by focusing on human’s irrational thought patterns than fearing some AI-enabled bogeyman. Doing so would shed light on our illogical biases and thought patterns that can intrude into AI algorithms. It would also help understand how AI can overcome our inherent intellectual handicaps and boost productivity
Today we are in the 4th Industrial Revolution and no doubt that this era would be driven by advancement in Artificial Intelligence and Robotics. This is the era where man and the machine are going to co-exist, collaborate and work together. So the union of man and machine is inevitable and it is up to us as to how we would exploit this situation to our advantage and live happily ever after!