Digital transformation (DX) is a must for midsize firms (those with 100 to 999 employees) to thrive in the digital economy. DX enables firms to increase competitive advantage through initiatives such as automating business processes, creating greater operational efficiencies, building deeper customer relationships, and creating new revenue streams based on technology-enabled products and services. DX is a journey, and it starts with firms embracing an IT-centric vision that guides a data-driven, analytics-first strategy. The outcome of DX initiatives depends on the ability of a firm to efficiently leverage people (talent), process, platforms, and governance to meet the firm’s business objectives.
The data center is central to IT strategy and houses the computational power, storage resources, and applications
necessary to support an enterprise business. A flexible data center infrastructure than can support and quickly deploy new applications can result in significant competitive advantage, but designing such a data center requires solid initial planning and thoughtful consideration of port density, access-layer uplink bandwidth, true server capacity, oversubscription, mobility, and other details.
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally.
Learn more about key findings, including:
Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics.
Return on investment for analytics stems from the governing and sharing of data throughout the organization.
Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
This paper introduces the practice of Model Management, an organizational capability to develop and deliver models that create a competitive advantage.
Today, the best-run companies run their business on models, and those that don’t face existential threat. The paper explains why companies that fail to run on models are falling for the Model Myth—the assumption that models can be managed like software or data. Models are different and need a new organizational capability: Model Management.
Defining a model
Why models matter for businesses
Why companies fall for the Model Myth
A framework for Model Management
Practical steps to get started
The paper is intended for anyone in a data science organization, or anyone who hopes to use data science as a key source of competitive advantage for their business.
For most enterprises, 60 to 73 percent of enterprise data goes unused for business-intelligence (BI) and analytics efforts, according to Forrester.1
Data that is out of sight or out of date creates a competitive blind spot for businesses today. With customer demands, economic changes, and new trends and technologies evolving at a dizzying pace, staying relevant — not to mention competitive — requires that businesses access all available BI to be ?exible and agile. Businesses must have quick access to data that is comprehensive, accurate, current, and consumable in real time. A traditional infrastructure, where the online analytical processing (OLAP) platform and the online transaction processing (OLTP) platform are separate, makes ?exibility and agility difficult to achieve.
When a business has accurate, current data in hand, it can make real-time data-driven business decisions so that it can stay relevant and competitive, or even be a disruptor in its industry. One way that a busines
In competitive business environments, speed is a key differentiator. IT organizations worldwide have realized significant value in transitioning from traditional disk to flash-based storage architectures, paving the path to making businesses more responsive and ultimately more competitive. Learn how to transform traditional IT with high-speed data delivery while reducing the costs and risks.
Published By: Anixter
Published Date: Jun 16, 2015
Faster, denser technology is driving costs, and the right high performance cabling is needed to provide stability in the data center. Unsuitable infrastructure can become an expensive problem, delaying necessary upgrades and creating other potential obstacles needed to stay competitive. This report contains the four best practices needed to achieve a high performance, future-ready structured cabling solution for a data center.
Today’s customers are empowered with information – if they want to know something, they can look it up in an instant. Having information they need at their fingertips is expected. Having information at the ready – whether your sales or service personnel are in the field or in the office – is no longer a competitive advantage, it’s expected and required. In this whitepaper, you will learn how Scribe’s customer data integration solutions and connectors let you put customer information where it needs to go quickly and easily. Don’t be that business who makes their customers wait for answers!
Today’s leading-edge organizations differentiate themselves through analytics to further their competitive advantage by extracting value from all their data sources. Other companies are looking to become data-driven through the modernization of their data management deployments. These strategies do include challenges, such as the management of large growing volumes of data. Today’s digital world is already creating data at an explosive rate, and the next wave is on the horizon, driven by the emergence of IoT data sources. The physical data warehouses of the past were great for collecting data from across the enterprise for analysis, but the storage and compute resources needed to support them are not able to keep pace with the explosive growth. In addition, the manual cumbersome task of patch, update, upgrade poses risks to data due to human errors. To reduce risks, costs, complexity, and time to value, many organizations are taking their data warehouses to the cloud. Whether hosted lo
s your information technology (IT) organization pressured to get more work done with fewer people or on a constricted budget? Do you need to make IT a competitive asset rather than a cost center? Does your business struggle with slow software applications or data that's too often unavailable? If you answered "yes" to any of these questions, it's time to take a close look at Oracle Exadata, the world's fastest database machine exclusively designed to run Oracle Database. It is the first database machine optimized for data warehousing, online transaction processing (OLTP), and database consolidation workloads as well as in-memory databases and database as a service (DBaaS).
To succeed in today’s competitive reality, businesses need to free themselves from the limitations of legacy IT infrastructure. The days of purchasing hardware and maintaining massive data centers to run IT must come to an end. Managing and maintaining your infrastructure is simply too expensive.
Explanation of the Oracle IaaS solutions and cases studies.
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
Does your current software give your business the visibility and flexibility you need to be responsive and competitive, or are you using an outdated software to manage your farm and agriculture retail business?
The retail world is becoming more complex. eCommerce has grown in the U.S.—from $42 billion to $236 billion in the last decade*—and lifestyle shopping is raising customer expectations. Having the right retail management software solution—one that can keep up with your business needs and the increasing pace of technology—is critical.
Given the recent advancements in technology—and higher customer expectations—perhaps it’s time to ask if your retail management software is up to the task of nurturing and managing your growth over the coming decade.
Epicor has been a part of successful retail businesses for years. Here are some risks you may face from using outdated software:
1. Excessive labor costs and hiring gaps
2. Operational inefficiencies
3. Insufficient data and insight
Published By: CIC Plus
Published Date: Sep 23, 2014
Learn three reasons that being a competitive employer of choice demands that you take advantage of a cloud-based, paperless system for generating and storing pay stubs and the increasing amount of data they contain.
Companies that hope to stay competitive in the marketplace must begin to consider alternatives to x86, or else they will find themselves at a serious infrastructure disadvantage. Performance and flexibility embedded in the architecture can scale in a way that the number of servers can’t, as applications and data grow in depth and complexity.
Did you know that the average health of marketers’ data is only “questionable?”
Dun & Bradstreet analyzed 695M customer contact records and surveyed more than 500 B2B marketers to provide the truth about the state of B2B marketing data. Download the fourth annual report to discover how you can turn improved data quality into a competitive advantage.
• The trends and pitfalls every data-driven marketer needs to know
• The true state of B2B marketing data quality
• Benchmarks for assessing your own data quality
• Dun & Bradstreet’s research of B2B marketers’ data-fueled priorities for 2016
Do you have the data strategy you need to deliver on your marketing goals? Download the B2B Marketing Data Report to find out!
Cloud-based data presents a wealth of potential information for organizations seeking to build and maintain a competitive advantage in their industry. However, as discussed in “The truth about information governance and the cloud,” most organizations will be confronted with the challenging task of reconciling their legacy on-premises data with new, third-party cloud-based data. It is within these “hybrid” environments that people will look for insights to make critical decisions.
Cloud-based data presents a wealth of potential information for organizations seeking to build and maintain a competitive advantage in their industry. However, most organizations will be confronted with the challenging task of reconciling their legacy on-premises data with new, third-party cloud-based data. It is within these “hybrid” environments that people will look for insights to make critical decisions.
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
Published By: Oracle CX
Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s
databases, accessing and using the right information at the right time has
become increasingly critical. Real-time access and analysis of operational
data is key to making faster and better business decisions, providing
enterprises with unique competitive advantages. Running analytics on
operational data has been difficult because operational data is stored in row
format, which is best for online transaction processing (OLTP) databases,
while storing data in column format is much better for analytics processing.
Therefore, companies normally have both an operational database with data
in row format and a separate data warehouse with data in column format,
which leads to reliance on “stale data” for business decisions. With Oracle’s
Database In-Memory and Oracle servers based on the SPARC S7 and
SPARC M7 processors companies can now store data in memory in both
row and data formats, and run analytics on their operatio
Mountains of data promise valuable insights and innovation for businesses that rethink and redesign their system architectures. But companies that don’t re-architect might find themselves scrambling just to keep from being buried in the avalanche of data.
The problem is not just in storing raw data, though. For businesses to stay competitive, they need to quickly and cost-effectively access and process all that data for business insights, research, artificial intelligence (AI), and other uses. Both memory and storage are required to enable this level of processing, and companies struggle to balance high costs against limited capacities and performance constraints.
The challenge is even more daunting because different types of memory and storage are required for different workloads. Furthermore, multiple technologies might be used together to achieve the optimal tradeoff in cost versus performance.
Intel is addressing these challenges with new memory and storage technologies that emp
GDPR has prompted banks to re-evaluate their data protection policies. Going beyond pure compliance can help establish consumer trust as a point of differentiation.
Read this report to find out:
• the ethical challenges and risks arising from the use of customer data
• how to start embedding principles for ethical data handling in your organisation
• the competitive advantages that come from getting data ethics right.
For decades, the financial services industry has endured constant change
and uncertainty, from the depths of a financial crisis to widespread
regulation overhauls. With the advent of more advanced cybersecurity
threats, the industry has responded with rapid digital transformation to
remain competitive while also pushing the envelope. Today, managing
and mitigating cyber-related risks not only draws government scrutiny, but
increased consumer scrutiny as well, with longstanding brand reputations
anchored to institutions’ ability to protect its most sensitive data. In a
recent survey of Americans, financial information was considered by
consumers to be their most valuable personal information, worth even
more than personal or family photos and videos. For consumers, failing
to protect their data is a grave violation of trust, to the point where 72%
would consider leaving their current financial institution if their sensitive
information was taken hostage by ransomware.1
Not only does the
Data overload have burdened practically every largescale enterprise today. It’s vital for competitive enterprise not only to find solutions that will deliver the right data at the right time, but also be confident that the data they are looking at is governed and trusted. Sage Business Cloud Enterprise Management enables businesses to gain real-time access to data and make critical business decisions; achieve a better ROI while reducing IT overhead; automate data integration and deliver a 360-degree view of the businesses; mitigate data security risks and comply with legal regulations. Download this whitepaper and discover how to overcome some common challenges of information overload to increase your business insight and visibility.
Envision this situation at a growing bank. Its competitive landscape demands an agile
response to evolving customer needs. Fortunately, analytically minded professionals in
different divisions are seeing results that positively affect the bottom line.
• A data scientist in the business development team analyzes data to create customized
• experiences for premium customers.
• A digital marketer tracks and influences the customer journey for prospective
• mortgage customers.
• A risk analyst builds risk models for the bank’s loan portfolios.
• A data analyst examines data about local customers.
• A technical architect defines a new system to protect bank data from internal and
• external cyberthreats.
• An application developer builds a new mobile app for online customer portfolio
Between them, these employees might be using more than a dozen packages for
analytics and data management.
DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
Our portfolio of live events, online and print publishing, business intelligence and professional development brands are centred on the complexities of technology convergence. Operating in 42 different countries, we have developed a unique global knowledge and networking platform, which is trusted by over 30,000 ICT, engineering and technology professionals.
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