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.
Technology enables business transformation To thrive in today’s idea economy, small and midsize companies like yours are using technology to transform their business. Like your peers, you know that mobile applications, cloud-based solutions, and advanced analytics can help you increase productivity, reduce costs, and grow your business. Older servers, storage, and networking products weren’t built to handle the exploding amount of data that is being shared today. In order to take advantage of these modern applications, many companies have found that they need to close the gap between what their business demands and what their IT systems can deliver.
This RSR custom research report explores the impact of omnichannel methods on merchandising, marketing and the supply chain; specifically, what analytical capabilities address the challenges that omnichannel selling and fulfillment pose for retailers. Consumers today routinely begin their shopping journeys online, but complete their purchases in nearby stores, in their “home” stores or delivered directly to their doors. Retail analytics enables organizations to capture data from their customers' journeys. Retailers that successfully deliver relevant omnichannel experiences while gaining a more sophisticated understanding of demand (where and how it is initiated) will enhance their brands’ value and create compelling and profitable customer relationships.
Social, Mobile, Analytics and Cloud (SMAC), have broad potential to provide huge business value, while simultaneously presenting potentially overwhelming challenges. The rapid technology changes supporting SMAC and the overall complexity involved demand a systematic approach to building out your SMAC capability.
Published By: Teradata
Published Date: Jun 22, 2015
This paper looks at the critical connection between data analytics and the future of tax compliance. It shows how the ability to pinpoint the “right” cases can bring more efficiency—and more revenue—to tax agencies. It also confirms that tax agencies already have vital data at their fingertips; the challenge is to find new and better ways to put it to use. Finally, it describes how a cohesive compliance strategy demands an agile, balanced solution of people, processes, technology, and data.
Software drives competitive advantage more than ever at an increasing velocity for releases along with higher, overwhelming levels of deployment complexity.
Dramatic growth in mobile applications, analytics, systems of engagement, and cloud demands that organizations respond adaptively, even as resource constraints make it challenging to nearly impossible to do so. As a result of these combined factors, IDC sees increased interest in, demand for, and adoption of agile approaches to development and also for business initiatives driving adoption of agile approaches to overall project, program, and portfolio management. As we receive inquiry on this area increasingly, it makes sense to assess this market.
Published By: Cognizant
Published Date: Oct 23, 2018
In the last few years, a wave of digital technologies changed the banking landscape - social/ mobile altered the way banks engage with customers, analytics enabled hyper personalized offerings by making sense of large datasets, Cloud technologies shifted the computing paradigm from CapEx to OpEx, enabling delivery of business processes as services from third-party platforms.
Now, a second wave of disruption is set to drive even more profound changes - including robotic process automation (RPA), AI, IOT instrumentation, blockchain distributed ledger and shared infrastructure, and open banking platforms controlled by application programming interfaces (API). As these technologies become commercialized, and demand increases for digitally-enabled services, we will see unprecedented disruption, as non-traditional banks and fintechs rush into all segments of the banking space. This whitepaper examines key considerations for banks as they explore value in the emerging Digital 2.0 world.
The enormous volume, velocity and variety of data flooding the enterprise, along with the push for analytics and business intelligence, is creating a massive challenge that is overwhelming traditional storage approaches. As the demand for capacity continues to escalate, companies must be able to effectively and dynamically manage the storage supply, but also the demand for storage resources. The key is to optimize the infrastructure through standardization and virtualization, and replace manual tasks with policy-based automation.
Smart on-line transaction processing systems will be able to leverage transactions and big data analytics on-demand, on an event-driven basis and in real-time for competitive advantage. Download to learn how!
Published By: Veritas
Published Date: Jan 04, 2019
The digital business continues to evolve. Investments in data analytics projects lead the way while traditional, proprietary infrastructures are being disrupted by cloud, open source and hyperconverged paradigms. These changes are forcing IT leaders to contend with greater workload diversity in the midst of tightening budgets. And while the workload [or] IT landscape is changing, the need for reliable data protection remains as crucial as ever to protect against, data corruption, human error, and malicious threats such as ransomware. Learn how Veritas can help you navigate through these obstacles. Join us to hear experts from ESG and Veritas discuss how the right data protection solution today can prepare you for tomorrow's business demands.
You will learn:
The key trends that are driving change in the digital business
The most common causes of data loss in tomorrow’s multi-cloud data centers
How to protect an increasingly diverse environment with minimal operational overhead
In the new age of big data, applications are leveraging large farms of powerful servers and extremely fast networks to access petabytes of data served for everything from data analytics to scientific discovery to movie rendering. These new applications demand fast and efficient storage, which legacy solutions are no longer capable of providing.
Published By: Exabeam
Published Date: Sep 25, 2017
In evaluating UEBA solutions’ ability to detect, prioritize, and respond, it is important to understand the full potential of data sciencedriven analytics. Organizations should ask their vendors if they can support the following Top 12 UEBA use cases, and most importantly, demand that the vendor demonstrate this support within the POC or pilot.
"Today, enormous amounts of data are being created from a variety of sources, such as applications, new mobile devices, big data analytics, and the cloud. This is changing the speed with which business gets conducted and the scale in which it occurs. Moreover, the digital explosion shows no sign of slowing and IDC expects the amount of data being stored to grow in excess of 50% per year over the next few years.
Discover how flash technology is being deployed to improve performance and efficiency for these demanding environments. Access this IDC whitepaper to learn more about the role of flash technology and the importance of flash solutions that are designed specifically for the enterprise."
Predictive IT is a powerful new approach that uses machine learning and artificial intelligence (AI) to predict incidents before they impact customers and end users. By using AI and predictive analytics, IT organizations are able to deliver seamless customer experiences that meet changing customer behavior and business demands. Discover the critical steps required to build your IT strategy, and learn how to harness predictive analytics to reduce operational inefficiencies and improve digital experiences.
Download this executive brief from CIO to learn:
5 steps to an effective predictive IT strategy
Where AI can help, and where it can’t
How to drive revenue and exceptional customer experiences with predictive analytics
As organizations develop next-generation applications for the digital era, many are using cognitive computing ushered in by IBM Watson® technology. Cognitive applications can learn and react to customer preferences, and then use that information to support capabilities such as confidence-weighted outcomes with data transparency, systematic learning and natural language processing.
To make the most of these next-generation applications, you need a next-generation database. It must handle a massive volume of data while delivering high performance to support real-time analytics. At the same time, it must provide data availability for demanding applications, scalability for growth and flexibility for responding to changes.
It's true that deploying B2B e-commerce platform involves many unique requirements not commonly found in B2C operations, such as incorporating a complex product port-folio, multiple distribution channels, and integrating with third party systems
And yet, it's possible to complete an initial out-of-the-box B2B implementation within three months, if all the right critical path steps are followed. Additional features and functionality can be added after the initial launch, provided that the chosen out-of-the-box platform is designed to be used over the long run.
Ordinary analytics tools can’t keep up with today’s digital, multichannel and demanding customers. This guide highlights three major challenges associated with traditional analytics and how innovative strategies combined with IBM’s Customer Experience Analytics solution can solve them.
Cloud-based solutions are revolutionizing the way that enterprises conduct business. These web-based versions of common business tools, like analytics or document management tools, retain most or all of the functionality of their desktop versions and provide significant access, customization, and utility to end users. More organizations are ditching on-premises solutions and adopting cloud-based tools, also known as Software-as-a-Service (SaaS), "hosted," or "on-demand" solutions. The are becoming invaluable assets in today's agile and mobile workforce.
Advancements in analytics have sparked renewed excitement in the consumer products (CP) industry. Maturing analytics capabilities are now delivering greater value across major CP functions including marketing, sales, merchandising, supply chain, and more. Thus helping CP companies become more consumer and customer focused in all aspects of their businesses. Read this research report conducted by 'Consumer Goods Technology' to understand how, next-gen analytics capabilities is key in unlocking a deeper understanding of customers, help create successful new products, foster brand advocates and optimize distribution channels and ultimately shape demand
Energy companies are being challenged on multiple fronts. Core expectations require the delivery of safe, reliable, affordable and sustainable energy. Business models are being challenged by alternatives such as renewables and consumers demanding more sophisticated interactions through social and mobile.
Energy companies need to:
Disruptively innovate business processes through analytics driven operational excellence to increase agility and responsiveness, reduce operational costs and improve asset reliability
Assume the role of energy integrator to optimally balance supply and demand points
Deliver a 360-degree customer-of-one experience to increase customer satisfaction and loyalty, reduce costs, and improve management of energy demand
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