If there are pros to big data, there are cons too. The negative impact of big data is subtly hidden in the trail of digital traces we unknowingly leave. Anything that is unmonitored leaves an opportunity for exploitation. The same is the case with big data. While big data is not bad in itself, it can have undesirable effects if the people involved in its use have malicious intentions. It is time that individuals and organizations become aware of the value personal data and information holds and adopt a more transparent approach.
Where deep neural network and machine learning is headed in the larger field of artificial intelligence, and how we can get more and more sophisticated machines that can help us in our daily routines. Please note that these are not predictions of forecasts, but more a detailed analysis of the trajectory of the fields, the trends and the technical needs we have to achieve useful artificial intelligence.
This list aims to address some of the key challenges and ‘steps to success’ when implementing lab automation. Create a step-by-step implementation roadmap that includes key milestones. This should be distributed to staff to maintain an overall awareness of project timelines, allowing you to manage staff expectations and minimize disruption.
Enter Data Science as a Platform, or DSaaP (pronounced dee-sap) — a one-stop-shop of curated insights, KPIs, and knowledge specifically tailored to each business manager. DSaaP’s offer the opportunity to bring together a great User Experience (UX) with holistic insights on-demand. Business managers would log into the tool just like they do emails each day and use the DSaaP as a regular part of their job.
2018 will continue to see the continuing march of economics that drive innovation and market adoption of Big Data, Data Science, Machine Learning and Artificial Intelligence. It’s a great time to be in the data and analytics business, and 2018 will just reinforce that!
When the customer is at the store looking for a product, the retailer must have all the relevant information about the customer’s purchasing patterns and tastes within easy reach, in order to make appropriate suggestions. But this is true for all retail channels. Just-in-time context with relevant information defines the business moment for the customer. It is not just about the data itself, but about the combination of staff perspectives applied to the synthesized data.
If you look around at the real world, you see can the reality of machines working side by side with humans every day, at work, at home, in schools - pretty much everywhere you turn.
As AI continues to mature, there are many new and exciting ways companies across all industries can implement the technology. But I believe that there are three specific industries — legal, hospitality, and real estate — that will see the most impactful change through AI in the year ahead.
Just consider how connected technology typically functions right now. Even though we’ve developed a lot of cool networks and gadgets and apps, it’s certainly not all sunshine and roses in our IoT-enabled world… they are supposed to serve humanity, not the other way around.
It is crucial that data scientists and analysts take into account the existing biases and formulate remedial solutions for these. As hidden biases in big data are an impediment to accurate decision-making and can affect outcomes, it is paramount that business leaders and lead management members remain alert.
What will bring 2018 for blockchain and distributed ledger technology? How will Bitcoin and other cryptocurrencies develop? And how the acceptance of blockchain technology will evolve in 2018? In this blog, I like to share my ideas and opinions on what trends and developments to look for in 2018. Let’s go!
For a myriad of data scientists, linear regression is the starting point of many statistical modeling and predictive analysis projects. The importance of fitting (accurately and quickly) a linear model to a large data set cannot be overstated. The goal of this article is primarily to discuss the relative speed/computational complexity of these methods.
Better cybersecurity protections for IoT requires improvements in people, process and technology. So let’s not pit people issues against technology protections in a fight for dollars — nor pretend that a perfect black box is coming that will enable IoT nirvana while removing people and process from the security equation.
Digital champions utilize analytics strategically to better understand customer trends and preferences, and instantly respond to changing market conditions. Research shows that excelling in analytics cannot just accelerate time-to-market, but it can also be financially rewarding. To overcome the hurdles, organizations should focus upon organizational readiness, open data and interoperability, and well-honed orchestration of people and processes.
Far more tactical implementations of Robotic Process Automation (RPA) are more promising. RPA is not machine learning, it uses software bots to mimic human activity. Tone down your AI expectations. And get ready for the next generation of AI big words.
Apart from big data, businesses are making optimum use of open data - data that is inexpensive, easily accessible, and a profitable resource goldmine. Businesses are now starting to feel the benefits of open data in synchronization with their private data and are collaborating on a whole new level to acquire state of the art business models, improved revenue streams, innovative products and services, and a competitive advantage over their business rivals.
Organizations need to take an inside-out approach to cybersecurity assessments, looking for areas of weakness and making it a priority to safeguard systems that must be impervious to attacks to protect human life and safety. Companies should undertake scenario planning to “think the unthinkable” and run simulations to ensure that their firm is ready to withstand such attacks.
We wanted to follow up our previous piece about how to grow as a data scientist with some other skills senior data scientists should have. Our hope is to bridge the gap between business managers and technical data scientists by creating clear goals senior data scientists can aim for. Both entities have to take on very different problems. Both benefit when they are on the same page. This is why the previous post focused so highly on communication. It seems simple, but the gap between technical and business continues to grow as new technologies keep getting piled on every year. Thus, we find it important that managers and data scientists have a clear path of expectations.
Along with a few near-term predictions - so hard to resist - I'd also like to make some predictions not just about technology per se, but about related changes to organizations, processes, and the cultures around them. Here's my main prediction: By 2030 what we've come to know as "IT" today will be virtually unrecognizable.
It’s high time that organizations realize that while the long-term information security environment is likely to become better in obvious ways, it is likely to worsen in subtle ways due to technology and vested interests. The key to understanding this phenomenon lies with, how tomorrow's information systems are going to be used. This calls for an active participation in Information Security Management System activities right from the top to the very bottom of an organization.
Consumers with access to technology at their fingertips expect a digital experience taking the need for automation to newer levels positioning 2018 to be an exciting year for realizing tangible outcomes from automation. Why, you ask? Well, think about it -- this may very well be that single overarching term that brings the business and the technology mindset under the same umbrella with the common goal of enriching the experience of the end customer -- both external and internal. Let us join Business and IT in wishing Automation a Happy New Innovative 2018!
There are real-life AI applications being deployed that automate business processes to improve the customer experience. We are also hearing progressively more myths when it comes to business applications. Following are common myths, assumptions, and truths to know as you consider your AI strategy in 2018.
Creating an IoT Monetization roadmap should be the top priority for any IoT initiative. Take the time to identify, validate and prioritize those use cases with the key business stakeholders and constituents to ensure that you are focused on the right use cases in the right order. There is no value in generating and collecting the data if you don’t have a plan for how to monetize that data.
Decentralization has its flaws; the complete security and privacy are yet to be achieved. It doesn’t mean, however, that blockchains are unsafe: substantial progress has been made already in the security area and clever developers keep on improving the technology on a regular basis.