Open source is a key enabler for enterprise data science, both in terms of the growing ecosystem of open-source tools and the expanding number of complementary enterprise data science platforms that incorporate and build on open source languages and tools. The challenge is identifying which of those tools is relevant and valuable to your business. Assessing the maturity of these projects, grappling with any licensing issues and making sure your team has the correct skillset to use them are challenges that many companies are now facing.
Random search is a really useful tool in a data scientist toolbox. It’s a very simple technique and can be a powerful tool to perform feature selection. It’s not meant to give the reasons why some features are more useful than other ones (as opposed to other feature selection procedures like Recursive Feature Elimination), but it can be a useful tool to reach good results in less time. Learn how to use a simple random search in Python to get good results in less time.
The Internet of Things technology provides a lot of opportunities for the education industry. Of course, the primary purpose of integrating IoT is making the learning and teaching processes easier and faster. This technology offers students and teachers various ways of communication, along with capabilities to share and edit learning materials. The education field has some issues that IoT is able to solve. In this article, we’ll discuss five solutions that can improve the learning and teaching processes along with successful projects.
It is found that half of the recruiters, HR representatives and CEOs have already recognized and anticipated the future of HR analytics. Artificial intelligence can disrupt and transform the future of HR analytics and HR operations, but the organizations will have to balance out these cognitive computing functions and advancements with complete transparency in order to implement AI successfully. Moreover, transparency in AI technology is important for employees to trust new technology. HR executives must have a clear understanding of how decisions are being made.
The future of finance is bound to be shaped by emerging technologies such as artificial intelligence (AI) and blockchain. Algorithms will play a major role in the industry, spotting money laundering schemes and helping banks better assess the creditworthiness of potential clients. Blockchain might power a real-time, cross-border payment system that’s faster and cheaper than traditional options. Winning tomorrow’s customers will depend on financial companies merging various technologies and services into a single experience that attracts and retains even the most demanding customers.
Mobile apps and Big Data have already revolutionized the world, to a great extent. The healthcare industry is no exception to this. The two big technologies, individually and combined, are sure to make healthcare cost-effective, accessible, and all the way more innovative. The goal is to not only get efficient treatments but also to need them a little less. Physicians believe that mobile health apps can improve patients’ health, and find value having a mobile health app connected to Emergency Health Services. Here are 5 ways Mobile apps and Big Data is improving Healthcare
One of the main tasks that a data scientist must face when he builds a machine learning model is the selection of the most predictive variables. Selecting predictors with low predictive power can lead, in fact, to overfitting or low model performance. This article shows you some techniques to better select the predictors of a dataset in a binary classification model, and two simple techniques in R to measure the importance of numerical and categorical variables against a binary target.
As you roll out more IoT devices, it’s time to add IoT threat modeling – a structured approach to identifying, quantifying, and addressing IoT security risks to your cybersecurity strategy. Moving to IoT threat modeling should be a cross-functional team exercise that you make part of your overall IoT development and management processes and frameworks. If your enterprise isn’t there yet, IoT threat modeling is the first step in growing your IoT security and integrating it into your overall cybersecurity strategy.
Adopting blockchain technology will become a growing priority for CTOs, and equivalent positions, in end user organisations, ranging from property to retail. Adopting blockchain, as with any new technology, will be a challenge. There are numerous suitability and adoption issues. One commonly universal benefit that blockchain technology can bring is the ability to incorporate an efficient financial inclusion mechanism into any business or utility model. Here are some of best practices towards achieving wider adoption.
The explosion of data causes problems for the way in which we currently process data. Edge computing helps solve this, by providing computational power, in the form of local computing power, such as smart phones, on the edge. Why do we need Edge computing? What is it? What are the advantages? The five pillars to edge computing provide the answers. Edge computing, by taking advantage of hardware that has already been funded, can overcome many of those disadvantages without necessarily losing the flexibility of the cloud.
While the 1st, 2nd, and 3rd Industrial Revolutions were characterized by insularity, the 4th Industrial Revolution can be considered a “foundational revolution,” that will create entirely new ways for technology to exist in the world. Instead of evolving in a linear fashion, these technologies will begin to converge with one another, the prime example of this being AI. What does that mean for the future of work and our children? Take steps ourselves to equip them with the tools they need to succeed in the age of AI.
We already discussed what makes a good data scientist and what you should learn before you set to a real project. In this post, we’ll walk you through the process of building a backbone data project in simple steps. Ask yourself: What is it that you develop, what resources do you need, and what value do you provide to the customer? For what values are customers going to pay? A nice way to do this is the business model canvas. It’s simple and cheap, you can create it on a sheet of paper.
Sentiment plays a very important role in decision making and the ability of a machine to convert human language into machine readable code and convert it into actionable insights, the capability offered by natural language processing (NLP). The topic of sentiment brings us to affective computing. While NLP is capable of reading or converting words into a stream of logic that can be used as an input to a computation devise, there are subtle nuances that humans use to communicate.
A trap many businesses fall into is to try and automate as many processes as possible. However, automation for the sake of automation is a real problem, because digitising everything is not a realistic goal. Businesses need to prioritise in order to establish a road map for transformation. The question is – how to identify which processes to automate in order to achieve the greatest value? That’s where process mining comes to the fore. Process mining applies the concepts of data mining to business process. The technology is largely used for process discovery; compliance auditing and process enhancement.
Need to know how to select your Continuous Testing Solution? Focus on existing processes that are being followed within the organization that is evaluating a new tool. Try focusing on how your process needs to look like, see what objectives the business and management has for your projects, and then see how tools can fit into your processes. Features are critical for the success and enable your test automation coverage, however not every tool also fits your entire processes that starts from the creation of tests through analysis and maintenance. Happy Transformation and Testing!
CTOs and tech leaders need a blockchain guide. From what is the blockchain, to adoption issues and advice surrounding regulation, compliance and more, blockchain guide is an invaluable tool for CTOs and other tech leaders looking at blockchain for their business. When blockchain adoption is no longer an issue and the technology becomes fully mature, various forms of new ideas and business models can be instantly generated, tested and realised with rapid adjustment via this autonomous DMS structure, where collaboration can be automated. The possibility is infinite with blockchain!
If you are not a huge fan of the robot, and would not trust something if you don’t see where it keeps its brain, you can still embrace automation in the old-fashioned way. What should I automate? This is the first question you will need to answer, as you wouldn’t want to invest a lot of time into automating something that should actually be done manually. Ask yourself some questions regarding if the process you are trying to automate fits your defined criteria? If so you can move on to automation.
The trend of new technology being used by businesses around the world isn’t slowing down any time soon. It is critical to adapt and implement data management strategies that ensure an optimal customer experience for your digital properties. You have to invest in resources, infrastructures, and new processes to get a holistic customer view of the data coming from your digital touchpoints. If you want to keep up with the competition, and eventually surpass them, it’s time to start using the power of data to your advantage to build a better user experience.
With the advent of the software, high speed communications, AI and the internet, there are new horizons of innovation, which bring new competitive advantages to those that embrace them. Every company needs to have a strategy for all three horizons: continue to optimize the core business, but also create subscription-based digital offerings and build a digital platform with network effect. Failing to do so will mean yielding market value, customers, and employees to companies who do.
AI is the biggest commercial opportunity for companies and industries over the next 10-15 years. Yet, despite the promise of AI, many organizations’ efforts with it are falling short. Most firms are only using AI in ad hoc pilots or applying it to a single business process. Only a few firms are engaged in practices that support widespread adoption. Why the slow progress? To support the widespread adoption of AI, companies must make three fundamental shifts.
Economic and trade uncertainty is the new certainty. Every manufacturer needs to start taking a more data-driven approach to defining the initiatives and strategies that will keep their businesses growing. The best countermeasures capitalize on and scale the data manufacturers have been accumulating in some cases for decades. The following strategies enable manufacturers to capitalize on the data they’ve been aggregating and analyzing on suppliers, pricing, production and operations, quality, and service. In short, these strategies have an insight track on succeeding during challenging, uncertain economic times by delivering quicker results and immediate payout.
It’s important to reiterate the growing connectedness of consumer demands that will play a much more significant role in changing manufacturing for the better in the coming year than those technologies themselves. Today, every company is here to serve the customer, no matter how far away from the customer they may have functioned in the past. Transformational trends such as the (A)IoT and 5G will force them to do that even more in the coming decade. It will also make that level of accountability possible.
Following AI trends and understanding the benefits of technology have become a necessity for business leaders. Early adopters of AI can gain a competitive advantage over other businesses. Also, AI coupled with big data can help business leaders predict market trends and create effective strategies for adapting to changes in the market. Hence, organizations can incorporate AI in sales departments to augment the growth of their business. The integration of AI in sales can help businesses in scoring leads, optimizing product prices, and developing personalized marketing campaigns.
What you need to know about virtual support agents. The more time the IT help desk spends putting out fires by phone, through email, or in person, the less time they have to focus on resolving the bigger issues and applying their cognitive skills to more meaningful projects. Are chatbots or virtual support agents the answer? The success of virtual support depends on several key factors. Here’s how to identify those factors and evaluate whether or not VSAs are right for your organization.