Experfy Insights

Big Data has been the talk of the technological and business world for a while now. While we already see many businesses leveraging from it and its impact on processes, we decided to bring forward an expert opinion on Big Data. Here's our conversation with Dr. Rich Huebner on the same. 

In today’s world, you have a lot of Business Intelligence (BI) tools to pick from. Some are free and are open source others can cost you thousands of dollars per month. Some have automatically created dashboards while others require hours of set-up and know how to get started. If you are looking to implement a BI tool in your company or department here are some questions to ask/consider when making your decision: 

You’ve arrived here because your goal is to get your first job as a data scientist. Currently, there are more data science jobs than there are people to fill them, so these types of jobs are in big demand today. Now becoming a data scientist is not going to happen overnight, but there are some core skills and education that you will need to land that first data scientist job. Here are my thoughts on what you can do land the first role as a data scientist or data analyst.

When it comes to emerging technologies, many talk about the importance of incorporating them in day to day processes - especially when it comes to businesses. But there only a few that have hands on experience with them. So we decided to interview some experts who have spent years working on different technologies, experimenting with their use cases and guiding others who wanted to grow in that field.

Bitcoin is the leading edge of a movement I think of as Open Source money, and here are a few ways of thinking about this that might help. Here are 100 things to know for those new to bitcoin. The goal isn’t for you to understand all of these assertions, but if they contain unfamiliar words or concepts, you can then google those concepts and become informed. Understanding what is behind these 100 assertions will help you become knowledgeable about Bitcoin. If you want you can see how many of these things you already knew and give yourself a score out of 100 at the end.

What problems do fintechs need to solve to scale up and grow profits? They need building an easily scalable software product, partnering with other companies and engaging new customer segments, and complying with regulation and security standards while scaling up. In our series of articles we will dwell on how technologies can help you solve these 3 key challenges. We've collected and analysed findings from PWC, CBInsights, Forbes, etc., and fintech software development cases to elaborate a strategy on how to build a successful fintech business that generates profits, attracts investments and can achieve economies of scale.

Today we understand our customers better than ever. The data we gather and analyze determines the success of our business. Business Intelligence, big data, data science, and data analytics provide companies with illuminating insights. Those who adopt these technologies early, gain huge competitive advantages. Therefore, the demand for professional data analysts is far exceeding the supply. To deal with the scarcity of specialists and soaring prices for their services, many business leaders consider data analysis outsourcing. But how to make it right? Our article will highlight the most important aspects of data analysis outsourcing and tips on choosing the best data analysts.

Many startups invest too heavily in sales before really understanding their customers. Whether it is based on the belief that good salespeople can sell anything, spending too much money on sales personnel too early is a good way to fail fast in a bad way. Bringing on your sales talent before you are ready, can costs start-ups millions of dollars. Hiring your sales talent should instead be a natural outcome of understanding your customers, developing your segment playbooks and your learnings (wins and losses) from customer interactions.

When digital disruptors squeezed their way into financial services industry, one could think banks were doomed. Fintechs are luring customers away with better online service, lower fees, and cheaper transfers. Consider a huge cost of digital transformation for banking corporations and you see legacy players can have a hard time catching up with the newcomers. Apparently, the banks were not going to simply give up. Over the last five years, they spent tens of billions of dollars setting up innovation hubs, buying competitors, and changing operations.

Solving business problems with blockchain ensures efficient functioning of an enterprise. When an organization suffers a loss or a specific problem that manages to disrupt its rhythmic flow , authorities frantically search for options that can help them solve the problem. Blockchain is a technology that focuses on increasing transparency and introducing decentralization that will allow the technology to enable everyone on the network to view information stored on ledgers. Blockchain has many applications that contribute to its popularity amongst people. Here are four common business problems that can be solved using blockchain.

There are only two cardinal rules of cryptocurrency that arise directly from public key cryptography. As you probably know, modern cryptography is based on paired keys, a public key and a private key. Because of the blockchain, public keys and incidentally wallet addresses should be verified before sending. Due to the principle of immutability and the lack of a trusted intermediary, there’s no way to contest or reverse a transaction once it’s been made.The second law is simply that the owner of the private key is the owner of the wallet and all the funds therein.

Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Many of the real world data clustering problems arising in data mining applications are pair-wise heterogeneous in nature. Clustering problems of these kinds have two data types that need to be clustered together. In an industrial setting, despite collecting data from tens of thousands of sensors, less than 1% is actually utilized. We can move rapidly into Industry 4.0 by combining subject matter expertise, data collection methods and next-generation data science tools, beyond many of the "me too" products.

Business units need to work together, and companies need to know their consumers. By integrating data across teams, enterprises make it easier to analyze consumer information and complete marketing tasks. Big data and data-driven marketing is the new status quo. Marketers need the reports generated by big data to find new business, optimize campaigns and help companies make a profit, and each business has its own needs and its own way of making the most of big data technology. Big data technology, cross-functional integration and social media adoption are powerful and effective ways to reduce costs, discover new opportunities and launch new services and products.

Banking runs on a set of regulatory guidelines and deals with numbers, it was only about time that it would board the AI bus. Secondly, there is this deviation angle. As we fully realize the fact that anything handled by a human is prone to deviation or personal discretion; so to be vigilant, all inputs are to be taken with a generous pinch of salt. In other words, everything must undergo a reviewing pair of eyes. How about implementing a system that can auto-understand and auto-function and auto-verify without or with minimal human intervention barring some very delicate cases?

Few industries will see as big an impact from the internet of things as the insurance sector. Indeed, IoT has the potential to touch nearly every facet of insurance, with the promises of both benefits and risks for carriers as well as their customers. IoT will impact how insurance underwriting and pricing are done for markets including transportation, home, life, healthcare, workers’ compensation and commercial. And it will transform the way insurers gather information about customers and their environments to process claims, determine risks and calculate costs.

Are you taking your social media strategy as seriously as you should? Your customers are driven to your social media pages—but once they get there, they don’t find 24×7 empathy-building communication or proud, positive brand representation, and a fast, solution-focused approach. Instead they are faced with thread after thread of negative customer feedback and lacklustre product support. Simply put, you’re doing it wrong. Think smart about your social media strategy, and start with understanding the three ways today’s fashionable feedback tool can make (or break) your CX

In the near future, digital assistants will help with all kinds of mundane work tasks -- from setting up conference calls to replenishing office supplies. The majority of these digital assistants use voice recognition technology as their primary interface, which means they are always listening, even when they are not in use. With hacker activity and state-sponsored surveillance also on the rise, will digital assistants become the proverbial Trojan horse that allows attackers to sneak past our defenses unnoticed? While digital assistants are all very convenient, will using them be at the expense of our privacy and security?

Although data visualization as a topic has been around for a while, in the recent era of data science, it has received increased attention. To be a successful data scientist today, not only is it necessary to be skilled in storing, managing, and analyzing large amounts of data, but also the ability to visualize the data effectively. To that end while there are excellent tools that can aid in that process, it is important to highlight the characteristics that make for an effective visualization. Learn how to create rich, compelling stories that perfectly visualize your data.

While some things are easy to measure, intelligence is not one of them. Intelligence is a very abstract and complex thing to measure. How do we, people, perceive artificial intelligence and what are our expectations from it? It seems when people judge AI’s ability, we are harsh. We want AI to be perfect. We do not show the flexibility we provide to human mistakes proportionally to AIs. We want AI to be “like a human”. Whatever the reason is, it is a common pattern. We expect artificial intelligence to be comparable to human intelligence.

Blockchain technology alone cannot provide freshness, safety, provenance, and recall capabilities. That requires data and capabilities from outside the blockchain. It seems that the best emerging approach will be a hybrid consisting of 1) a centralized networked SaaS platform providing economical scalability and deep algorithmic and process capabilities, combined with 2) blockchain and smart contracts for transparency and validation. Blockchains are attractive because of their ability to create a shared, trusted single-version-of-the-truth between trading partners. However, a networked SaaS platform can provide a shared, trusted single-version-of-the-truth at a much lower cost.

If you have a single data scientist and you already think they should be delivering more to your bottom line than they are news flash: "They suck" and you hired the wrong caliber individual for the job. You may still be able to keep them if they are good, but you need to bring in a type-E rockstar to cement your data arm and redirect the unstoppable ship. A type-E individual doesn't settle anywhere. If you ask an individual where do you see yourself in 5 years and they respond "Not working here" you have found a real winner.

Industry 4.0 promises to combine digital technologies — such as big data, data analytics, artificial intelligence and machine learning — with all-pervasive internet connectivity to produce vast quantities of valuable data. Companies mine, analyze and convert the information into a wealth of insights and then use the knowledge to boost factory productivity, increase supply chain efficiency and make substantial cost savings. As always, new trends bring about new security challenges. Though connecting industrial machinery to the outside world can be risky, the deployment of virtual private networks (VPNs) can ensure that Industry 4.0’s data treasures stay hidden from unwelcome observers.