The banking and finance industries are enjoying greater productivity and profitability than ever, not to mention finding new ways to serve customers and protect their interests. Some of these innovations are visible and obvious while others work quietly behind the scenes. In both cases, we can expect a better experience for clients and shareholders alike as new technologies come of age. Let's look at a few of the ways this revolution is coming about.
We’re addicted to data. The addiction to data crosses a line when organizations focus on uncovering massive volumes of information with too little focus on what it all really means. It’s not enough to just share data—it has to be the right data for the right person, with clear next steps rooted in best practices. To reveal the truth and truly drive change, data must be customized and actionable. Here’s why both are vital.
Bad Data or Poor Data means false information or inaccurate data that can be created by duplication of data, wrong formatting or by an uncomplicated error of typos. Bad Data can turn into an expensive mistake for your business. It can be a difficult problem to deal with. A recent survey shows that bad data costs companies an average of $9.7 million per year. Bad data and the problems that come with it are here to stay for now, and the only thing that we can do is deal with it.
The concept of Clickless Analytics is one that will be happily embraced by business users and by the business enterprise. Clickless Analytics incorporates Natural Language Processing (NLP) and takes Augmented Analytics to the next level with Machine Learning and NLP in a self-serve environment that is easy enough for every business user. Business users can leverage sophisticated Business Intelligence (BI) tools to perform Advanced Data Discovery by asking questions using natural language. In this, the first of a three-part series we discuss Clickless Analytics and how it can simplify user adoption of Augmented Analytics.
The AI market specifically for oil and gas is expected to reach USD 2.85 billion by 2022. It's growing fast as more companies realize the potential of the technology. Artificial intelligence is being used to discover new gas and crude oil sources, optimize various industrial processes such as the transport of raw oil and even make more positive environmental decisions. How are oil and gas companies putting AI technologies to use in today’s market? To break it down, we're going to take a look at the three most important sectors in oil and gas: upstream, midstream and downstream applications.
Here we discuss why retail is positioned to reap the biggest benefits of data analytics today. Organizations are using advanced analytics to do everything from understanding their customers to improving forecasting, driving better, faster results. While the impact of these approaches is being felt across nearly every industry, retail stands to reap the biggest benefits. With more big box retailers announcing layoffs, store closures, and bankruptcy, data science may just be the secret weapon for success.
Today's marketers must compete against smart devices, geo-targeted advertising and a plethora of brand messages, in effect making consumer attention a limited and invaluable resource. Because of this, successful contemporary business leaders use technology and innovative practices to engage the right consumers at the right time. As the information universe expands, the value of consumer attention rises. Today's marketers capture the attention of consumers using every available touch point, especially consumer mobile devices. The following sections highlight five tactics that marketers can deploy to influence consumers in the highly competitive business marketplace.
Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. Within those areas there are dozens of languages, frameworks, and technologies data scientists could learn. How should data scientists who want to be in demand by employers spend their learning budget? Which skills are most in demand for data scientists?
We have more data at our fingertips than entire generations before us. But due to data mismanagement issues, for many companies that don’t mean very much. Data Management is a serious obstacle for companies who want to increase productivity, collaborate more efficiently and generate data-driven decisions. The bright side is that executives are recognizing the need for improved Data Intelligence strategies. So, how can enterprises identify and fix their Data Management challenges? There are three major signs that your data isn’t being leveraged fully.
The diagnosis will start with us seeing how Blockchain technology and Healthcare fit together and what are the different use cases of the technology in the industry? Next, the symptoms of Blockchain will be studied and a clear diagnosis will be made in terms of the technology future. Lastly, the measures will be shared to help the businesses offering Healthcare Apps utilize the technology, rightly. Let’s start with the Medical Evaluation of Blockchain Technology.
Data lakes quickly emerged as a technology front-runner in the race to make data more digestible – and to finally get it in one place. Data lakes are flexible, scalable and offer an easy solution to store data. Here are strategies to ensure that data moves beyond the raw material to take its rightful place as a valuable business asset. The article outlines common problems with data lakes, strategies for how business can avoid those problems, and how governance enables a data lake to become more than just a data repository.
Blockchain is the next step for data-driven Industries where Centralized databases are updated on a moment-to-moment basis, capturing the system’s status at a specific moment in time. Think of it as a timestamp. You can take any data entry and trace it back to its origin. You know who created it and when, and that it’s been vetted. This leaves little doubt as to the legitimacy of any entry. It’s the kind of provenance coveted by data scientists and marketers, and it may inspire consumer confidence, especially in light of all the scandal around data misuse.
Hypothesis Tests, or Statistical Hypothesis Testing, is a technique used to compare two datasets or a sample from a dataset. It is a statistical inference method so, in the end of the test, you'll draw a conclusion — you'll infer something — about the characteristics of what you're comparing. Before even thinking about what test you are going to use, you need to define your hypothesis to set the significance level of the statistical test, and then you're good to pick the statistical test!
Surviving in the Deep Learning world means understanding and navigating through the jungle of technical terms. Use this guide as a reference to freshen up your memory when you stumble upon a term that you safely parked in a dusty corner in the back of your mind. This dictionary aims to briefly explain the most important terms of the Deep Learning. It contains short explanations of the terms, accompanied by links to follow-up posts, images, and original papers. The post aims to be equally useful for Deep Learning beginners and practitioners.
Self-serve data preparation is the next generation of business analytics and business intelligence. Self-serve data preparation makes advanced data discovery accessible to team members and business users no matter their skills or technical knowledge. Supporting business users with powerful tools that are meaningful to their role and goals is critical to every organization. Self-serve data preparation takes the complexity out of the data prep and analytical process and results in better data discovery.
There are distinct differences in the way business executives and IT professionals think about their company’s data. The disconnect is a result of perspective and manifests itself in a lack of communication that produces further confusion as IT systems evolve to meet the needs of a rapidly forward-charging business. The fruit of this misunderstanding often includes friction between the business and IT, but require a decoder ring to help them understand each other. The decoder ring is an application created by the right people with the right experience to confront and address this known problem.
The four “Vs” of data are well known – volume, velocity, variety and veracity. However, Data Warehousing infrastructure in many organizations is no longer equipped to handle these. The fifth elusive “V” – value – is even more evasive. Meeting these challenges at the scale of data that modern organizations have requires a new approach – and automation is the bedrock. Creating a successful Data Warehouse, then, is critical for CDOs to succeed in monetizing data within their organization.
As organizations continue to evolve their information strategy and find innovation opportunities, many seek to generate value through data monetization. Self-Service Data Prep technology allows data-to-information process to take place in the line of business where most of the knowledge about data, its context and understanding resides. This enables organizations to turn data into monetizable information assets – rapidly and seamlessly. While data monetization at its core is tied to a tangible financial value, it does not always translate to a commercial product. Companies monetize their data in one of four ways.
It is heady days for deep learning with the stellar advances and infinite promises. But, to translate this unbridled power into business benefits on the ground, one must watch out for these five pitfalls. Ensure availability of data, feasibility of labeling them for training, and validate the total cost of ownership for business. You may wonder when deep learning must be used vis-a-vis other techniques. Always start with simple analysis, then probe deeper with statistics, and apply machine learning only when relevant. When all these fall short, and the ground is ripe for some alternate, expert toolsets, dial in deep learning.
Pointers have been in and out of data models. From the advent of the rotating disk drive in the 60s and until around 1990, pointers were all over the place together with “hierarchies”, which were early versions of aggregates of co-located data. But relational and SQL made them go away, only to reappear around year 2000 as parts of Graph Databases. Here is the fascinating journey of the history of pointers in data models.
Smart Data Visualization can radically improve your Business Intelligence, Data Discovery and Analytics. It can streamline the work process of business users, improve the accuracy of planning and forecasting and ensure better, more timely, more accurate business decisions. Smart Visualization tools allow users to gather various data components and tell a story. Revealing results in this manner makes it easier for business users and the organization to identify the cause of a problem, see trends and patterns and find those elusive nuggets of information that will provide a competitive edge.
Young Data Scientists provide tremendous value to companies. They’re fresh off taking online courses and can provide immediate help. They’re often self-taught, as few universities offer Data Science degrees, and thus show tremendous commitment and curiosity. They’re enthusiastic about the field they’ve chosen and are eager to learn more. Beware of the mentioned pitfalls to succeed in your first Data Science job. This article examines 5 common mistakes of early Data Scientists. This post aims to help you better prepare for your work in real-life.
Linear regression is a linear approach to modelling the relationship between a dependent variable and one or more explanatory variables. In simple linear regression, a single independent variable is used to predict the value of a dependent variable. In multiple linear regressions, two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. In a situation where you need to estimate a quantity based on a number of factors that can be described by a straight line — you know you can use a Linear Regression Model.
The journey to Reinforcement learning continues… It’s time to analyze the Q-learning and see how it became the new standard in the field of AI with a little help from neural networks. We will learn the Q value from trial and error? We initialize the Q, we choose an action and perform it, we evaluate it by measuring the reward and we update the Q accordingly. In first, randomness will be a key player but as the agent explores the environment, the algorithm will find the best Q value for each state and action.