Big data is, or soon will be, a reality in professional life for the vast majority of people in the modern economy. Here I’ve explored just three strategies you can use to help yourself be successful in that world. 1984 wasn’t just the year of Big Brother, it was the underwater tremor of what would—by 2017 —become an earthquake of data. The tsunami is on its way.
Deep Learning has generated the biggest breakthroughs over the last five years. You can almost always download a “pre-trained” model and apply it to your data — for example, you can download pre-trained image classifiers that you can feed your data through to either classify new images or draw boxes around the objects in images. Because much of this work has been done for you, the work necessary to use these cutting edge techniques is not in “doing the deep learning” itself — the researchers have largely figured that part out for you — but rather in doing the “dev” work to get the models others have developed to work for your problem.
FinTechs are dramatically changing the perception and function of banking. Banks find themselves having to transform into trusted brands and get to know their customers anew by getting close to their needs and understanding their working style. They also need to start providing real help in their areas of greatest need. Also, besides merely focusing on meeting the customer’s financial needs, they should additionally offer real-time, on-demand financial troubleshooting.
When it comes to big data, challenges derive from the nature and the volume of the data. Whether it is a data leak or a financial company’s internal data, the amount of data we are dealing with is considerable. To complicate things, investigations usually start from raw, unstructured data. And it’s impossible to automate or scale the investigation without a predefined-data model or any kind of organizational logic.
With blockchain-based technology like bitcoin taking up news headlines, awareness and excitement about other potential uses for blockchain is increasing. One of the best use cases for blockchain may end up being healthcare and medical records. By having a distributed database for healthcare-related information, healthcare providers can benefit from increased accessibility, accuracy, and safety, all of which will result in better healthcare outcomes for all.
Today’s telecommunication operators are facing greater competition and increasing challenges within the marketplace. That means that everyone needs to find a new way of minimising costs, and enhancing revenue if they want to succeed. When it comes to performance, the blockchain has a lot to offer, including better trust and transparency. The distributed nature of the blockchain means that there are no single points of failure, no worries about hacking attacks, and no stress caused by control from a single entity.
The marketing and advertising industries are going through a paradigm shift as technology progresses exponentially. It was not long ago when big data was the talk of the town. The challenge was in collecting and manipulating it. In the next three years, machine learning, Artificial Intelligence (AI) and the Internet of Things (IoT) will force us to deal with a large amount of data. The more people are going to use technology the more they will leave a footprint data, that will be available for marketers and advertisers to be leveraged for gaining deep insights.
The definition of Big Data will continue to change. And, “Big” today is very different than it was just a few years ago. As technology progresses, the definition of what is being collected as big data will change. No matter what your definition of Big Data is, when it comes to a conversion project the keys to success are having a project plan, and the right people to work with.
Data now informs organisations about trends and problems they never knew existed. It shapes how people interact, share information, purchase goods, and how they’re entertained and how they work. It dictates political decisions and economic cycles. Data is the raw power that helps us optimise decisions and processes to iron out inefficiencies through use of analytics. Analytics can be utterly transformative.
Blockchain adds some important elements to the peer-to-peer network. This allows the participants to verify and audit transactions. The network relies on mass collaboration driven by the shared interests of the participants, and the result is (or should be) a shared data set where there is little if any, uncertainty regarding data security.
With all the talk on “the street” about disruption in the various markets, from the auto industry to retail, it can be easy to act reactively and change too fast too soon rather then to be strategic and do what is best for your line of business
Smart data analysis can help a team recognize and use its strengths and weaknesses to achieve a competitive advantage. It can help corporations make savvy business decisions to propel them to the top of their market. The key here, however, is to recognize which data is helpful and which should be ignored. Too much data can lead to an information overload, prompting decisions to be over analyzed and delayed.
You might have witnessed how these fintech companies are contributing to the transformation of the financial services sector in the United States. But what’s happening in the global south? There, “inclusive fintech”, or fintech products and services that serve the bottom of the pyramid, is thriving in its own right with the likes of Tala and Branch. The sector is growing – enough to compile a list of our top 100 inclusive fintech companies by funding raised in emerging markets.
With insurance companies at the forefront of automation, we see that this technology can help regain the erosion of trust, serving to ease the regulatory, communication and staffing challenges large carriers face. Automation can help insurance companies become the responsive, helpful, and reliable resources that customers seek. We see four primary benefits of automated intelligent interactions for insurance companies.
Over half of the world’s population are carrying personal devices that have powerful processors, sensors, cameras, high speed communications and intelligent applications. In the next ten years, an Ambient Intelligence (AmI) revolution is coming where all the above technologies will be embedded in our homes, grocery stores, offices, hospitals and transportation services.
More recently, the internet and high levels of smartphone adoption have totally changed the way we do business. Now, it’s the turn of artificial intelligence (AI). Here are five of the best reasons why ‘thinking’ computer systems can help your business sell more.
Data that is available for collection in every moment in our lives is a rich resource just waiting to be collected by businesses that are strategically positioned and enabled to do so. Now, what are some things you can start doing this quarter to make sure your business is more of an economic driver, with a data driven co-pilot?
What did FinTech bring into wealth management exactly? The short answer is AI. Thompson Reuters suggests that cognitive software platforms, which provide the tools to analyze, organize, access, and provide advisory services based on a range of structured and unstructured data, are set to attract investment of nearly $2.5 billion in this year alone.
In many of these developing nations less than 20–30% of the citizenry have bank accounts. The other 70% of the population is without access to a bank or financial services and have been deemed “the unbanked”.
This article is the second of a two-part series on Robotic Automation for customer engagement. Part one provides an overview of the technology. It explains how it can be used to assist customer service representatives and automate tasks of customer-facing processes. Part two explores the art of the possible and dig into the three most common models for leveraging this style of automation. But first, we need to do away with two misconceptions.
Artificial intelligence (AI) is the ability of machines or computers to emulate human thinking or decision-making. After years of speculation into the technology and its possibilities, AI is starting to deliver on its promise. Here are eight stats that prove natural language processing, machine learning and cognitive computing are helping businesses to deliver excellent customer service.
Data governance means understanding the data assets of the organization – knowing that they are of good quality, accessible and secure and that using them does not put the company at legal, reputational or financial risk while also enabling it to be agile. Now, the age of Data Governance 2.0 is dawning. It’s an age that must be marked by everyone within the business collaborating in data governance.
The new generation of fintech tools offers the potential to help consumers manage their increasingly complicated financial lives, but also poses risks that will need to be managed as the marketplace matures.
Global financial companies enhance digital services to attract new customers across the whole financial ecosystem. Fintechs and software giants successfully exploit their corresponding niches. In turn, the vast majority of community banks have no distinct advantages over their competitors.