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Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
As the information age matures, data has become the most powerful resource enterprises have at their disposal. Businesses have embraced digital transformation, often staking their reputations on insights extracted from collected data. While decision-makers hone in on hot topics like AI and the potential of data to drive businesses into the future, many underestimate the pitfalls of poor data governance. If business decision-makers can’t trust the data within their organization, how can stakeholders and customers know they are in good hands? Information that is not correctly distributed, or abandoned within an IT silo, can prove harmful to the integrity of business decisions.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
In our 29-criteria evaluation of machine learning data catalogs (MLDCs) providers, we identified the 12 most significant ones — Alation, Cambridge Semantics, Cloudera, Collibra, Hortonworks, IBM, Infogix, Informatica, Oracle, Reltio, Unifi Software, and Waterline Data — and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise architecture (EA) professionals make the right choice.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
The growing need for data governance, risk and compliance, data analysis and data value still drives strategic requirements in metadata management and the growth of its solutions. Data and analytics leaders can use this vendor evaluation to find the most appropriate solution for their organization.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
As the foundation for most critical business decisions, today's data environments are not just a vital piece of IT infrastructure, but key component of corporate strategy.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
In this report, we''ll analyze the many challenges that organizations face when it comes to building and managing modern IT infrastructure. We'll also look at how many businesses are taking advantage of a hybrid cloud and on-premise approach, which comes with some significant benefits.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
One of the biggest changes faces organizations making purchasing and deployment decisions about analytic databases -- including relational data warehouses -- is whether to opt for a cloud solution.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
In Forrester's evaluation of the emerging market for conversational computing platforms, we identified the seven most significant providers — Amazon, Google, IBM, Microsoft, Nuance Communications, Oracle, and Rulai — in the category and evaluated them. This report details our findings about how each vendor scored against nine criteria and where they stand in relation to each other. Application developers should use this review to select the right partners for their conversational computing platform needs.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
Delivering personalized customer experience remains the top business challenge for communications service providers (CSPs). Ovum's recently published 2018 ICT Enterprise survey saw almost all CSP IT executives interviewed identify delivering personalized customer experience as one of their three most important business challenges for the next 18 months. This trend emphasizes the high priority CSPs place on how customer relationships are managed. However, several factors have an impact on CSPs' ability to identify and then deliver customers' core needs. These include understanding the data sets they should focus on; collecting, cleansing, and consolidating these data sets; and having the right expertise to mine the data sets.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
IDC strongly believes that the days of homogenous compute, in which a single architecture dominates all compute in the datacenter, are over. This truth has become increasingly evident as more and more businesses have started to launch artificial intelligence (AI) initiatives. Many of them are in an experimental stage with AI and a few have reached production readiness, but all of them are cycling unusually fast through infrastructure options to run their newly developed AI applications and services on.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
Power Systems are built for the most demanding, data-intensive, computing on earth. Our cloud-ready servers help you unleash insight from your data pipeline—from managing mission-critical data, to managing your operational data stores and data lakes, to delivering the best server for cognitive computing.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
POWER9 provides the infrastructure foundation for a future-looking organization that is ready to meet today’s business challenges and tomorrow’s advancements. By updating your foundation with the latest POWER9-based servers, you can effectively run your mission-critical requirements alongside modern, dataintensive workloads. POWER9 gives you the reliability you’ve come to trust from IBM Power Systems, the security you need in today’s high-risk environment, and the innovation to propel your business into the future.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
The data maturity curve As companies invest more and more in data access and organization, business leaders seek ways to extract more business value from their organization’s data. 92 percent of business leaders say that to compete in the future, their organization must be able to exploit information much more quickly than it can today.1 Chief Information Officers (CIO) need solutions that will allow them to evolve their organization’s approach to data and drive real value with strategic decisions. This journey can be depicted in a data maturity curve.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
IBM Cloud Private for Data is an integrated data science, data engineering and app building platform built on top of IBM Cloud Private (ICP). The latter is intended to a) provide all the benefits of cloud computing but inside your firewall and b) provide a stepping-stone, should you want one, to broader (public) cloud deployments. Further, ICP has a micro-services architecture, which has additional benefits, which we will discuss. Going beyond this, ICP for Data itself is intended to provide an environment that will make it easier to implement datadriven processes and operations and, more particularly, to support both the development of AI and machine learning capabilities, and their deployment. This last point is important because there can easily be a disconnect Executive summary between data scientists (who often work for business departments) and the people (usually IT) who need to operationalise the work of those data scientists
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Mar 29, 2019
The vast increase in dark data—all of the unstructured data from the Internet, social media, voice and information from connected devices—is overwhelming many executives and leaving them completely unprepared for the challenges their businesses face.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Mar 29, 2019
Banking engagement is about to come full circle. Over the past few decades, banks have pushed customers to digital and self-service channels, with significant consequences. As customers take their transactions from the branches to the digital channels, banking has become less and less personal. Will this trend continue? To predict the future let’s first review how we got here.
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Group M_IBM Q2'19
Published By: Group M_IBM Q119     Published Date: Mar 13, 2019
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
This report explores a new breed of data warehouse that can operate in a world of legacy on-premise systems while exploiting the potential of cutting edge technologies and deployment styles
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
In this report, we'll analyze the many challenges that organizations face when it comes to building and managing a modern IT infrastructure
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
The days when avoiding the Cloud was a defensible position for a business are gone. By taking advantage of the Cloud, organizations not only gain its direct benefits, but are also better able to leverage other new technologies and become more efficient and innovative
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
In this paper, we focus on the DWA and how it has evolved over the years since its introduction. The XDW architecture is then described, in which the need to maintain the data warehouse is documented while adding new components and capabilities to extend the analytical capabilities. This section also discusses the appropriate usage of appliances within the XDW. The rest of the paper covers the benefits from implementing the DWA, the selection considerations for them and what the future holds for them.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
The life of an enterprise architect is becoming busy and difficult. Before the era of big data, the enterprise architect “only” had to worry about the data and systems within their own data center. However, over the past decade there were revolutionary changes to the way information is used by businesses and how data management platforms support the information available from modern data sources.
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Group M_IBM Q119
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