Understanding the implications of location data quality and the associated location intelligence is necessary for correct business decisions. Availability of location signals can guide audience building, audience behavior monitoring, provide insights about consumer activity in the physical world, and track, measure post-campaign activity in the physical world to name a few key use-cases. Furthermore, combining location signals with other data sources can guide broader business decisions.
Financial disruption doesn’t happen fast. There aren’t any outright winners. It would be great if there was a financial Uber or Airbnb that could flip the industry overnight, but that’s just not the nature of the space. Rather than criticizing the industry’s shortcomings or writing it off as a failure, we need to do a better job of recognizing just how much fintech has accomplished over the last few years.
Enterprise AI is the new hot topic in technology, especially as the consumer space blossoms with sales and adoption. Consumers push the expectations of AI for the business to new heights – and if not carefully prepared, solutions will inevitably fail.
Data science jobs are among the most challenging to fill, taking five days longer to find qualified candidates than the market average. Employers are willing to pay premium salaries for professionals with expertise in these areas as well. The most in-demand jobs in data science require advanced education, further driving up demand and salaries for professionals with these qualifications.
Machines are fast, impartial and relentless. This helps them do amazing things. But this side of the robot apocalypse, the real challenge of AI is how it forces us to reconceive our humanity. We have long viewed ourselves as the smartest beings on earth. Intelligent machines may knock us off this pedestal.
Has the hype around AI gone one step too far? Are we really on the brink of a rise-of-the-machines takeover, or should we view intelligent technology as a means to improve our marketing efforts and build a better relationship with consumers?
As part of intelligent automation investigations, robotic process automation (RPA) is being explored in many sectors across the globe, to increase the efficiency of operations and create new products and services. Are you exploring automation and AI for your organization?
A relation with a customer is not over after the delivery of a product or a service, in fact a relation with a customer goes way beyond making a sale. This is why businesses need customer relationship management, to help out a customer facing a problem with your service/product.
Are AI-based systems the next step in a continuous tech evolution that’s allowing us to achieve more, or something alarming that society should be concerned about? Before leaning on either side of the coin, it is important to first understand the misconceptions of AI as portrayed in popular media.
With more and more money pouring into internet companies, companies have been collectively spending billions of dollars in growing the user base on their platform and fight off the cutthroat competition to survive in their respective domain or vertical. Not many companies can survive at scale if the Churn problem is not mitigated as soon as possible.
Are you a tech geek? A business owner? Do you know how technology affects marketing? Are you constantly looking for solutions to make your processes easier? Here are some of the most interesting current and upcoming future trends in technology.
Many companies are thinking about their survival after the apocalypse that will be produced by the mix of IoT, AI and blockchain. CEOs must make decisions that prevent their companies from disappearing or worse, becoming walking dead.
There are really two meanings of “AI” and they are routinely conflated. To un-conflate what people mean by “AI,” I’m going to refer to this as “Artificial Sentience.” Then there is what people call “AI” today—basically, a variety of software that tries, tests, and auto-corrects its strategies for a given task.
If you find data science a tempting opportunity, you’ll benefit from this overview of big data basics for beginners. We’ll discuss what the requirements for jobs are and which skills you should master in order to start a successful data science career.
Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step.
As companies in a variety of industries plan and execute their digital transformation strategies, powered by the Internet of Things (IoT), they should be designing everything—products, applications, sensors, networks, services, etc.—with one all encompassing goal: to maximize the value of the data they create or ingest.
Improving the IoT device experience doesn’t have a single solution. It needs design. It probably needs new technologies and approaches that make set up and maintenance more automated. And even after all that, a lot of devices will still be useless.
We are approaching the world of artificial intelligence (AI). The world is changing; the law will change; lawyers must change. Unimaginable and exciting challenges are ahead of us.
Machine learning is not a new concept — it’s actually been around since the late 1950s. So, why all the hype? Why do businesses feel like now is the time to adopt? Before businesses start to develop a strategy around machine learning and AI, it’s important to review how machines really learn, and how this can impact your AI and machine learning strategies.
While big data has dropped off the hype cycle, it’s not going away; in fact it will only get “bigger”. Likewise, the Hadoop ecosystem has matured significantly and will continue to do so with all of the big distributions offering data science capabilities. The desire amongst enterprises to migrate big data to the cloud will continue to increase with managed services gaining momentum. There are many challenges that come with all of these things – but with the right strategy and foresight, enterprises can truly maximize big data’s value.
With an almost endless list of sources – including map and satellite data, catchment areas, service points, building and customer locations, land use data, urban data, and communication pathways – spatial data is a valuable global commodity which comes in many forms.
AI has the potential to create more jobs than it kills. In 10 years, most cognitive workers will be working in creative collaboration with intelligent machines. New industries yet unborn will almost certainly rely on the complementary strengths of artificial and human intelligence.
Machine Learning (ML) has given a whole new meaning to data. It has, in all true sense, completely transformed the way we look at data and information. It can indeed be a game changer and be very useful in proving very useful insights that can prove to be very critical for many businesses.
Machine learning is a reality that we are just waking up to. The eventual evolution of machine learning is predictive automation. Predictive automation might be a reality in the next couple of years itself. If IoT is the destination then we are on the right track.