When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? Applied data science is a team sport that’s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience. If you’re keen to make your data useful with a decision intelligence engineering approach, here’s my take on the order in which to grow your team.
Companies are sitting on some of the world’s largest reservoirs of valuable data, but many of them aren’t doing anything with it. It’s pretty hard to “do anything with it” if the data storage method doesn’t allow one dataset to talk to another. Or if tools like Hadoop and Spark process large amounts of data in a distributed fashion using commodity hardware”—can’t readily access said data to work their magic on it. Legacy institutions suffer from infrastructures predating the so-called information age. Yet these siloed institutions are the ones with the most to gain from the efficiencies and insights that data can bring.
One aspect that seems to be conspicuous by its absence is product definition of AI powered software. The reason is not too difficult to fathom. Defining AI products is hard, and there are no industry standard methodologies to speak of. As with any new paradigm shifting technology, it takes some time before the business processes can catch up. AI is no different. In the long run, I believe that a basic understanding of AI within the wider product development community is the solution.
Look at the fundamental building blocks for a flexible presentation of data. The real power of this concept lies in uncaging your data from the confines of monolithic charts and setting them free, to tell their own expressive story. Though many visualization tools today don’t adopt a grammar of graphics approach in its entirety, that seems to be the way forward. Meanwhile, there are opportunities for people to start putting this to practice. This is so important that it must be made mandatory education for anyone working with data, whether it is analysts, designers, data scientists or journalists.
Assistants and bots have reached a new adoption high. However, many businesses are finding their projects harder to scale than they expected. The disappointment with some deployments triggered controversy whether to use Artificial Intelligence (AI)-powered assistants or to stick with rule-based bots. Assistants and bots are relatively easy to set-up. Companies can start rapidly but are finding it more difficult to scale and can become disappointed. Challenges maximizing conversational AI should not be construed as the technology not being ready for large deployments. Let’s explore what needs to be done to get the most out of conversational AI.
Real world data science model is at the heart of our hedging business risk and increasing customer satisfaction. The ecommerce space is going through a major transformation like the Fintech industry, where one-to-one marketing and personalization are driving innovations through technologies like machine learning. Companies are now able to leverage this fascinating convergence of technology and consumer demand. Attaining personalized engagement with your consumer has to be on top of the list for every marketer in today’s crowded marketplace.
What exactly is data science? Data is to Data Science, as Elements are for Chemistry. The most basic thing you can have in Data Science is a data point. From data, data scientists can build a model to explain what is going on in the scenario we’re facing, validate it, and test it. But to do all this, we need a little bit of Computer Science, Math and Statistics, and Domains / Business Expertise.
Most of the AI in use now is narrow AI, meaning it is only capable of performing individual tasks. Narrow AI does a good job at executing tasks, but it comes with limitations, including the possibility of introducing biases. AI bias may come from incomplete datasets or incorrect values. Bias may also emerge through interactions overtime, skewing the machine’s learning. Moreover, a sudden business change, such as a new law or business rule, or ineffective training algorithms can also cause bias.
The Internet of Things and artificial intelligence are deeply connected. IoT systems produce big data, whereas, data is the heart of AI and machine learning. At the same time, as the rapid expansion of connected devices and sensors continues, the role of smart technologies in this space is growing too. Today, the applications of computer intelligence in IoT products vary. In this article, I’d like to focus on a specific domain of AI – Natural Language Processing.
How can machine learning help theoretical science? Machine learning can provide the mathematical scaffolding for scientific theories, to which theorists will then add meaning and the bridge to reality. However, before we can get there we will need to develop a much better understanding of machine learning. We will need to understand machine learning algorithms from general principles. Perhaps, it is time to start developing a real theory of machine learning.
Like any automation process, the most basic expectation from salesforce automation (SFA) tools is to improve efficiency and effectiveness of sales operations. Yet when you look closely, salesforce automation is usually a feature within your CRM or ERP tools to track and manage selling activities and reports, which is just not enough because not all selling are equal. While the sales numbers are not completely owned by marketing, the insights from marketing operations are a key component of sales force automation.
There is a paradigmatic shift in the way digital is becoming part of our life: from digital payments, to the way we shop or even interact with each other. Today, there are millions of connected devices, smartphones, as well as Internet users. Such is the extent of this digital revolution. This is bound to have far-reaching implications for organizations as far as cyber security is concerned. Artificial Intelligence can provide the requisite sharpness to ward off IoT security issues.
International dropshipping is a very promising business. Dropshipping entrepreneurs will find that there are lots of other untapped markets in other regions too. However, developing a profitable international dropshipping business is going to be much more complex than one that solely serves domestic customers. You will want to take advantage of predictive analytics to develop a lucrative funnel for your international customers. Here are some ways that predictive analytics can be invaluable.
People view the concept of artificial intelligence as one of humanity trying to play the role of God and being doomed to extinction rooted in hubris once the creation surpasses the creator. The reality, of course, is that we are using AI all the time now, at least in some form or another. AI has revolutionized almost every industry and will continue to do so. In this article, we will examine how AI has changed the app development, travel, debt, retail, and IT industries so that we may better understand how AI interacts with business.
If your organization is planning to leverage the Internet of Things (IoT) to gather data from products and systems, see how goods are performing in the field, enhance factory production, or any other reason, it needs to become familiar with the concept of the “digital twin.” A digital twin is a digital replica of a physical asset, process, or system that can be used for a variety of purposes. The digital twin is intended to be an up-to-date and accurate replica of all elements of a physical object for which sensor data is available.
Big data may be a good tool in helping increase employees productivity, but it’s the managers that wield those tools who have the biggest impact. It takes a gifted leader to truly listen and appreciate people. Balancing the needs of the business with the needs of the individual is always challenging. Data can be used to tear people down who aren’t performing, or it can be used to empower and improve performance. Management needs to take care of employees and create a positive environment by using data for good.
We can outline the typical process used in Machine Learning. This process is designed to maximize the chances of learning success and to effectively measure the error of the algorithm. A subset of real data is provided to the data scientist, and he experiments with a number of algorithms before deciding on those which best fit the training data. A further subset of real data is provided to the data scientist with similar properties to the training data. This is called the validation set. The algorithm is run on the test set and the error is calculated.
Those who control the data control the future not just of humanity, but the future of life itself because today, data is the most important asset in the world. The combination of data and computation creates a power that surpasses that of the most powerful spy agencies of past centuries. This is changing thanks to the rise of machine learning and deep learning, smart artificial intelligence software that can mine huge sets of data and find meaningful patterns that would go unnoticed to the biologically limited minds of human beings.
The divide between consumer health and fitness wearables, and medical-grade devices is slowly being bridged as technology evolves to offer advanced sensors and form factors that combine the best of two worlds. The result is vast amounts of higher quality data to feed the complex algorithms which not only deliver the personalized results the growing market of medical-grade devices, designed for consumer/patient access without the need of a prescription, is enabling a new category of remote patient monitoring.
It’s time to take our identity into our own hands. As social creatures, we are the sum total of our interactions with others. Blockchain brings new opportunities to the field of identity management. It does so via the qualities of immutability and distributed access (anyone, anywhere in the world, can verify that information exists). Together, these enable a new paradigm of trustlessness: I don’t need to trust you, a stranger, because I trust the immutability of the blockchain. Blockchain-based peer-to-peer marketplaces are the future.
The convergence of IT and OT dramatically alters investing activities in corporate development. We’ve been seeing a new type of acquisition by large, vertical-specific OT players acquiring ventures focused on vertical industries. The new digital trajectory of OT affects the strategic investment considerations of a corporate development leader in OT or IT and the strategy of an entrepreneur. How do you align the new target’s investments with internal business unit’s goals? Is the new technology enabling multiple internal businesses? How should it be structured and measured internally if acquired?
Data science is the discipline of making data useful. Data science is a ‘concept to unify statistics, data analysis, machine learning and their related methods’ in order to ‘understand and analyze actual phenomena’ with data. When all the facts you need are visible to you, you can use descriptive analytics for making as many decisions as you please. It’s through our actions — our decisions — that we affect the world around us. So it is making data useful.
Many enterprises that scrambled to put a minimally GDPR-compliant set of privacy policies in place are now lulling themselves into complacency. A closer look at the steps taken by many of these companies reveals a GDPR strategy that it is only skin deep and fails to identify, monitor or delete all of the Personally Identifiable Information (PII) data they have stored. To address these risks, companies need a holistic strategy to manage their data—one that automates the process of profiling, indexing, discovering, monitoring, moving and deleting all of their data as necessary.
Algorithms have the potential to help us overcome rampant human bias. They also have the potential of magnifying and propagating that bias. I firmly believe this is an issue and it is the duty of data scientists to audit their algorithms to avoid bias. However, even for the most careful practitioner, there is no clear-cut definition of what makes an algorithm “fair.” In fact, there are many competing notions of fairness among which there are trade-offs when it comes to dealing with real world data.