PCA vs Linear Regression – Two statistical methods that run very similarly. However, they differ in one important respect. What the two methods actually are and what this difference is, we explain to you in the following article. Principal Component Analysis (PCA) is a multivariate statistical method for structuring or simplifying a large data set. The main goal here is the discovery of relationships in 2 or 3 dimensional domain.

Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, in its efforts to continue to transform the data discovery market, today announced it has been named a Leader in the use of artificial intelligence (AI) and machine learning (ML) for data management in a new research report and decision guide from Enterprise Management Associates (EMA). The research report, which names Io-Tahoe a Leader, says companies which deploy AI-enabled analytics and data management solutions can potentially save up to $5,000,000 a year. EMA research also finds that they can create more value through enhancements such as increased speed of innovation; the report claims that 83 per cent of the companies surveyed are already seeing cost savings, along with a significant reduction in annual person-hours required to complete analysis of the data. "AI enablement signifies a major shift from passive to active use of metadata," said John Santaferraro, EMA's Research Director, Analytics, Business Intelligence, and Data Management. "The passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services."

Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, in its efforts to continue to transform the data discovery market, today announced it has been named a Leader in the use of artificial intelligence (AI) and machine learning (ML) for data management in a new research report and decision guide from Enterprise Management Associates (EMA). The research report, which names Io-Tahoe a Leader, says companies which deploy AI-enabled analytics and data management solutions can potentially save up to $5,000,000 a year. EMA research also finds that they can create more value through enhancements such as increased speed of innovation; the report claims that 83 per cent of the companies surveyed are already seeing cost savings, along with a significant reduction in annual person-hours required to complete analysis of the data. "AI enablement signifies a major shift from passive to active use of metadata," said John Santaferraro, EMA's Research Director, Analytics, Business Intelligence, and Data Management. "The passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services."

As we've talked about "Going Broad" and "Embracing Fuzziness," we've mentioned cause-and-effect relationships, and understanding upstream and downstream impacts, and correlating "fuzzy" inputs. So by this point, you understand that linking data is more important than just collecting data. So maybe this will be a very short chapter. There it is, 4 sentences and we're all done. Just within the last few weeks you've heard someone say "We'll collect the data, and then we'll figure out what to do with it."

Information security, data science and cloud computing skills are the most sought-after talents in the marketplace today. Security operations center (SOC) resources -- typically analysts and threat hunters -- are increasingly needed to combat the growing threat of adversaries launching aggressive campaigns with the latest techniques and technologies. While there are several products to identify, detect and contain known threats and any indicator of compromise (IOC), there is very little protection against unknown threats, zero-day exploits and newly identified vulnerabilities. With the explosion of enriched security log data from thousands of servers, devices, databases and applications, managing this highly complex puddle of structured and unstructured data is a humongous task. Enter the security data scientist.

"Big data" grows bigger every year, but today's enterprise leaders don't only need to manage larger volumes of data, but they critically need to generate insight from their existing data. Businesses need to stop merely collecting data points, and start connecting them. In other words, the relationships between data points matter almost more than the individual points themselves. In order to leverage those data relationships, your organization needs a database technology that stores relationship information as a first-class entity. That technology is a graph database. While traditional relational databases have served the industry well in the past in enabling service and process models that tread upon these complexities, in most deployments they still demand significant overhead and expert levels of administration to adapt to change. Relational databases require cumbersome indexing when faced with the non-hierarchic relationships that are becoming yet more persistent in complex IT ecosystems, with partners and/or suppliers and service providers, as well as more dynamic infrastructures associated with cloud and agile. Unlike relational databases, graph databases are designed to store interconnected data that's not purely hierarchic, make it easier to make sense of that data by not forcing intermediate indexing at every turn, and also making it easier to evolve models of real-world infrastructures, business services, social relationships, or business behaviors that are both fluid and multi-dimensional.

Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.

In a 2016 The New York Times article about the challenges facing data scientists, one was quoted as saying, "We really need better tools so we can spend less time on data wrangling and get to the sexy stuff."1 Manually converting or mapping raw data into data that can generate actionable insights is an important but repetitive task that takes a huge amount of time. Even simple terminology used to define the task of dealing with colossal amounts of modern data makes it sound like something of a chore; a data wrangler or data janitor. The "sexy stuff" is analysis and modeling to enrich the data relationship. The key to any good relationship is understanding--prompted by, based on, or demonstrating comprehension, intelligence, discernment, and sentiment.

Having herself held senior roles in IT at Wall Street companies including Deutsche Bank and Morgan Stanley Smith Barney, Oksana Sokolovsky is quite familiar with the challenge of Data Management and data discovery. As co-founder and CEO of ROKITT, her goal was "to build a product that solves that challenge," she says. The challenge exists across large enterprises in multiple industries, but is often especially acute in those dealing with regulatory pressures and compliance requirements – healthcare, for instance, and of course, the financial sector. Basel Committee on Banking Supervision (BCBS) 239 compliance for effective risk data aggregation and reporting, for example, is a big driver of improved Data Management for global systemically important banks. In fact, a McKinsey & Company and Institute of International Finance survey showed that more than half of the world's biggest banks faced significant challenges meeting the January 1, 2016 deadline for compliance, with the Global Association of Risk Professionals commenting that "many institutions continue to struggle to fully implement the requirements across the business under the most demanding interpretation of those requirements."

With all of the discussion about Big Data these days, there is frequest reference to the 3 V's that represent the top big data challenges: Volume, Velocity, and Variety. These 3 V's generally refer to the size of the dataset (Volume), the rate at which data is flowing into (or out of) your systems (Velocity), and the complexity (dimensionality) of the data (Variety). Most practitioners agree that big data volume is indeed huge, but that is not necessarily big data's biggest challenge, at least not in terms of data storage capacities, which are growing rapidly also and keeping pace with data volume. The velocity of big data is also a very big challenge, though primarily for applications and use cases that specifically demand near-real-time analysis and response to dynamic data streams. However, unlike volume and velocity, most will agree that the variety (complexity) of the data is truly big data's biggest mega-challenge at all scales and in most applications.