A&BI platforms are transitioning from delivering simple, manual self-service to supporting more advanced, automated analytic use cases via growing, augmented, ML-driven capabilities. Data and analytics leaders should enable broader use cases to increase their investments’ business impact.
Analytics and business intelligence (A&BI) platforms exhibit differences in functional capabilities, particularly in their support for advanced analytics, data source connectivity and embedded functionality.
As the market matures, the capabilities required to build and deliver user-friendly analytics dashboards are the least differentiated.
The trend toward assisting users with augmented data discovery functionality continues, but many products still lack enough support for this critical capability.
When viewed across the whole span of capabilities, significant differences remain between competing platforms and, therefore, also between which are most appropriate for a given use case. In some cases, th
Research shows that organizations that can increase BI adoption will come out ahead in their industry, but most enterprises report that adoption remains at around 30%. How can they overcome challenges like data silos, education gaps, and more? With pervasive experiences, scalability, and an open architecture—and MicroStrategy can deliver it all in our newest platform.
MicroStrategy 2019 is the industry's first modern analytics platform, featuring:
Federated Analytics—guaranteeing a single version of the truth, no matter what BI tools you use
Transformational Mobility—bringing analytics to every device and every constituent
HyperIntelligence™—the breakthrough technology that delivers insights with zero clicks
Read the MicroStrategy 2019 white paper to learn more!
Gartner’s annual Predicts report is out for 2019—and it includes recommended solutions to issues like incorporating analytics into corporate strategy, measuring the value of data assets, the rapidly increasing volume of data, the lack of data literacy, and more. Some of the valuable findings include:
Today, fewer than 50% of documented corporate strategies mention data and analytics as key components for delivering enterprise value, per Gartner’s “How Infosavvy Are You? Study."
Organizations that fail to develop and enforce such codes of conduct are at a greater risk of liability and misuse of data science and AI.
Few organizations have implemented continuous intelligence capabilities, spanning multiple applications and business functions, because they lack the relevant skills.
Today, despite massive investments in data, IT infrastructure, and analytics software, the adoption of analytics continues to lag behind. In fact, according to Gartner, most organizations fail to hit the 30% mark—meaning more than 70% of people at most
organizations are going without access to the critical information needed to perform to the best of their abilities.
What’s stopping organizations from breaking through the 30% barrier and driving the pervasive adoption of intelligence? Simple. The majority of existing tools only cater to users who are analytically inclined—the analysts, data scientists, and architects of the world. The other 70%—the people making the operational decisions daily within a business—simply lack the time, skill, or desire to seek out data and intelligence on their own.
HyperIntelligenceTM helps organizations operationalize their existing investments and arm everyone across the organization with intelligence. Whether it’s a salesperson looking to close a
The use of analytics has exploded across business, and the value it already has delivered has heightened executives' expectations. Now data can be processed in real time to meet a constantly widening range of analytic needs. How your organization utilizes them in the next decade will be essential to your success.
These developments come at an opportune time. Organizations are being over-whelmed by the rivers of data generated by applications and systems on-premises or flowing in via the cloud. At the same time, the cost of computational power has declined dramatically, making it practical to apply analytics and generate information on just about anything.
But no advance comes without challenges. While the widespread availability of analytics has created seemingly valuable insights, executives and managers are finding that those insights are not easily linked to steps that will improve business outcomes or optimize actions. So the challenge is to make analytics impactful in eve
For the second year, MicroStrategy has surveyed business intelligence and analytics decision makers from around the world about the current state of their organization’s analytics initiatives and their plans for the future. Respondents were asked about benefits realized, challenges to success, priorities, and investments—and most importantly, if current initiatives to create and deliver on a data-driven culture and business were moving forward.
As in the previous year’s survey, respondents had no doubt about the importance of data and analytics when it came to digital transformation. Yet, this year’s analysis uncovered
that, as the reality of 2020 and a new decade of accelerated innovation set in, a smaller set of leaders were confident in their progress to date.
Published By: Workday
Published Date: Jul 30, 2019
When your organization moves quickly to drive growth and profitability, you can risk missing business targets due to inaccurate data, gaps in processes, and limited visibility into real-time metrics and analytics. Download the report to learn how you can get the most out of your data.
Published By: Workday
Published Date: Oct 22, 2019
Whether informing talent strategy or building more effective teams, data-driven insights about your workforce can set you up for success. This eBook from Human Capital Institute explores how people analytics can empower your entire organization.
Published By: Genesys
Published Date: Jun 19, 2019
Successfully managing a contact center requires a collaborative, multidisciplinary approach to handle a broad range of operational and tactical tasks. Planning, day-to-day operations and quality management must be seamlessly orchestrated, along with human resources functions like recruitment, learning and development, and employee scheduling.
Read this executive brief to learn how to transition to an AI strategy that can take your team – and business results – to the next level. See how you can:
Create an AI strategy with a single data model that includes routing, interaction analytics, forecasting/scheduling and predictive engagement
Harness the power of your data to align customers with the best resource
Drive employee effectiveness by ensuring you hire the right people and manage their performance to drive their success over the long term
Organizations realize they need analytics to run, grow, and differentiate their products
and services. More than just driving strategic decision making, they need to incorporate
analytics into their high-volume operational and transactional decisions every minute
of every day. This means that analytics is no longer just the responsibility of the data
science team. Organizations have to continuously deliver analytics into operational
systems in a systemic, high-quality and dependable way.
As organizations continue to adopt predictive analytics, many are struggling to make it stick. Challenges include skills, executive and organizational support, and data infrastructure issues. Many organizations have not considered how to practically put analytics to work, given the organizational, technology, process, and deployment issues they face. Addressing these issues involves a combination of traditional and new technologies and practices. Some practical considerations highlighted in this report include: Model Development, Infrastructure and Operational Process.
Published By: Mindfire
Published Date: May 07, 2010
In this report, results from well over 650 real-life cross-media marketing campaigns across 27 vertical markets are analyzed and compared to industry benchmarks for response rates of static direct mail campaigns, to provide a solid base of actual performance data and information.
Artificial intelligence (AI) and machine learning (ML) are emerging technologies that will transform organizations faster than ever before. In the digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Historically, these insights were discovered through manually intensive data analytics—but the amount of data continues to grow, as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology
Why your data catalog won’t deliver significant ROI
According to Gartner, organizations that provide access to a curated catalog of internal and external data assets will derive twice as much business value from their analytics investments by 2020 than those that do not.
That’s a ringing endorsement of data catalogs, and a growing number of enterprises seem to agree. In fact, the global data catalog market is expected to grow from US$210.0 million in 2017 to US$620.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 24.2%.
Why such large and intensifying demand for data catalogs? The primary driver is that many organizations are working to modernize their data platforms with data lakes, cloud-based data warehouses, advanced analytics and various SaaS applications in order to grow profitable digital initiatives. To support these digital initiatives and other business imperatives, organizations need more reliable, faster access to their data.
However, modernizing data plat
With digital IT transformation solutions and new business
models driven by the democratization of data, advanced
analytics, and a citizen-centric approach, HPE helps cities
like yours get smarter—and continue to evolve into great
places for your citizens to live.
Visit hpe.com/info/public-sector to learn how you can
take the next step on your journey.
Using customer location data to drive foot traffic and sales is a given for today’s smart marketers. But digital data alone isn’t enough to design and execute optimal campaigns.
The future of location-based marketing incorporates new technology that provides a data stream of polygons - granular information on the size and shape of buildings - that identifies precisely where customers are when shown relevant offers, and helps track whether those offers convert to sales.
This Industry Dive eBook endorsing HERE Technologies explains how to leverage such new, powerful and precise matrices to measure campaign efficacy and success. It also includes:
• An explanation of how new granular audience and footfall data can provide impactful attribution insights
• A specific list of value adds a proprietary polygon database partner can provide
• A case study of a company that used continually-updated polygon data to boost their segmentation, behavior analytics and attribution capabilities.
Published By: Cisco EMEA
Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
Published By: Cisco EMEA
Published Date: Mar 05, 2018
The competitive advantages and value of BDA are now widely acknowledged and have led to the shifting of focus at many firms from “if and when” to “where and how.” With BDA applications requiring more from IT infrastructures and lines of business demanding higher-quality insights in less time, choosing the right infrastructure platform for Big Data applications represents a core component of maximizing value. This IDC study considered the experiences of firms using Cisco UCS as an infrastructure platform for their BDA applications. The study found that Cisco UCS contributed to the strong value the firms are achieving with their business operations through scalability, performance, time to market, and cost effectiveness. As a result, these firms directly attributed business benefits to the manner in which Cisco UCS is deployed in the infrastructure.
The success of every business is driven by the quality of its connections, whether with clients, employees, investors, suppliers, manufacturers or other key stakeholders. Increasingly, these relationships are measured through data-driven analytics, enhanced through video communication, and empowered through cloud computing and collaboration. As the volume of data grows, so do bandwidth requirements.
Consider the key trends driving the modernization of the data infrastructure: focus on governance, mobilization and analytics. And take a look at the technologies that make up modern data infrastructure, including artificial intelligence (AI), flash storage, converged and hyperconverged platforms, and software-defined infrastructures.
Read this e-book to observe the key trends driving
the modernization of data infrastructure and see how
organizations are adapting and flourishing in a data-driven world.
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.
If your business is like most, you are grappling with data storage. In an annual Frost & Sullivan survey of IT decision-makers, storage growth has been listed among top data center challenges for the past five years.2 With businesses collecting, replicating, and storing exponentially more data than ever before, simply acquiring sufficient storage capacity is a problem.
Even more challenging is that businesses expect more from their stored data. Data is now recognized as a precious corporate asset and competitive differentiator: spawning new business models, new revenue streams, greater intelligence, streamlined operations, and lower costs. Booming market trends such as Internet of Things and Big Data analytics are generating new opportunities faster than IT organizations can prepare for them.
Nimble Secondary Flash array represents a new type of data storage, designed to maximize both capacity and performance. By adding high-performance flash storage to a capacity-optimized architecture, it provides a unique backup platform that lets you put your backup data to work.
Nimble Secondary Flash array uses flash performance to provide both near-instant backup and recovery from any primary storage system. It is a single device for backup, disaster recovery, and even local archiving. By using flash, you can accomplish real work such as dev/test, QA, and analytics.
Deep integration with Veeam’s leading backup software simplifies data lifecycle management and provides a path to cloud archiving.
The performance of enterprise applications will have a direct impact on business activities and outcomes. The quality of the delivery of applications will depend on how smoothly the underlying data infrastructure operates.
? Optimal application performance and delivery is difficult to achieve in complex environments.
? Many IT infrastructure and operations teams are stretched to the breaking point.
? Predictive analytics and machine learning can be applied to great effect
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