Published By: Aberdeen
Published Date: Jun 17, 2011
Download this paper to learn the top strategies leading executives are using to take full advantage of the insight they receive from their business intelligence (BI) systems - and turn that insight into a competitive weapon.
HR processes are changing. And, with the global skills crisis, HR Directors are struggling to attract and retain the best talent. Their departments are in flux, and 77% of organisations report a perceived lack of strategic value from HR.* If you’re going to win the war for talent, your organisation will need to make a radical change. It must transform its HR and become a People Company, utilising people data and analytics to make decisions based on real evidence. This will improve the way you interact with current and future employees, providing great workforce experiences. The result will be a more committed and engaged workforce, and increased interest in joining your organisation. From a business perspective too, you’ll benefit through higher levels of productivity, efficiency and improved performance and engagement.
Mainframes continue to provide high business value by combining efficient transaction processing with high-volume access to critical enterprise data. Business organizations are linking mobile devices to mainframe processing and data to support digital applications and drive business transformation. In this rapidly growing scenario, the importance of providing excellent end-user experience becomes critical for business success.This analyst announcement note covers how CA Technologies is addressing the need for providing high availability and a fast response time by optimizing mainframe performance with new machine learning and analytics capabilities.
Do you know your people as well as you know your customers? Your people’s expectations and the way they work is changing. Employees are more diverse, mobile and technologically-savvy than ever before. HR processes are changing from focusing on transactions to knowing and engaging people. Just as sales and marketing teams use data to develop actionable and informed insights about their customers, you need to do the same in HR to know your people. Everything, from attracting and keeping the best talent, to creating better workplace experiences and increasing employee engagement and productivity, depends on smarter decisions. These in turn rely on more actionable insights. These are only possible through accurate HR data and analytics. They are vital to address the people challenges you face, so you can make smarter decisions.
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.
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements.
To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
IBM DB2 with BLU Acceleration helps tackle the challenges presented by big data. It delivers analytics at the speed of thought, always-available transactions, future-proof versatility, disaster recovery and streamlined ease-of-use to unlock the value of data.
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
See how you can turn data into actionable insights with predictive analytics. Take our brief assessment to learn which analytical capabilities will enable you to find the greatest value in your data and make confident, accurate business decisions.
"Maximizing Operational Efficiency and Application Performance in VMware-Based Data Center
Some of the most common challenges in VMware-based virtual data center environments include:
- Lack of visibility into applications and end-user experience
- Complex and error-prone operations
- High capital and operational costs
Review our solution brief to learn how the Avi Controller, the industry’s first solution that integrates application delivery with real-time analytics, is able to solve these challenges."
Innovative data-driven strategies are enabling organizations to connect with customers and increase operational efficiency as never before. These new initiatives are built on a multitude of applications, such as big-data analytics, supply chain, and factory automation. On average, organizations are now 53% digital as they create new ways of operating and growing their businesses, according to the Computerworld 2017 Forecast Study.
As part of this transformation, enterprises rely increasingly on multivendor, multicloud environments that mix on-premise, private, and public cloud services and workloads. This shift is causing enterprises to increase network capacity; 55% of enterprises in the Computerworld study expect to add network bandwidth in the next 12 months.
Published By: LogMeIn
Published Date: Feb 27, 2018
24/7 Self-Service Support Center: Bold360 ai’s 24/7 context driven support center was implemented, allowing users to instantly discover relevant content from the smart knowledge database. Dynamic FAQs displayed trending topics in real-time to speed up customer resolution and discoverability.
Real-Time Customer Analytics highlight unanswered questions, giving Premium Credit instant visibility of missing topics, questions driving ticket volume, and more.
Published By: Workday
Published Date: Feb 27, 2018
HCM suites and their individual applications also provide reporting and data as needed by local and international regulations, and often include analytics and dashboard capabilities. Transactional employee and manager self-service have become embedded roles within these solutions, and the ability to support mobile access has also become a fundamental part of these offerings. With increasing frequency, we see the inclusion of tools and applications embedded within HCM technologies to infuse traditional processes with collaboration, and capitalize on approaches that leverage social channels in the workplace to enhance overall user engagement and productivity.
Improved availability of data and new technologies that use it are disrupting our lives, influencing the way we interact with other, and the way we gather and consume information to make decisions. Businesses too are living in a time of continuous technological upheaval. The application of key technologies such as Machine Learning and Artificial Intelligence and Optimization, are fundamentally changing the manner in which businesses make decisions.
This paper is your first step in understanding:
• how you can leverage and operationalize analytics in your everyday business processes
• improve customer relationships
• grow revenue in an increasingly competitive world
The role of analytics in managing, improving and ultimately transforming supply chains cannot be understated. But what about the analytics themselves? FICO’s Zahir Balaporia and renowned author Tom Davenport use the term “The Analytics Supply Chain” to reflect that the actual analytics themselves parallel supply chains, with inherent challenges and problems if things “get stuck.” Rethinking analytics in these terms can not only improve supply chain performance, but also any other business problems you seek to solve.
This article targets:
· Steps in the analytics supply chain and the vital role of data and analytic models
· How your predictions, recommendations and insights need to rely on similar attributes to finished manufactured products;
· Key questions to ask yourself in determining where you need to fix your analytics supply chain.
Published By: Snowflake
Published Date: Jan 25, 2018
To thrive in today’s world of data, knowing how to manage and derive value from of semi-structured data like JSON is crucial to delivering valuable insight to your organization. One of the key differentiators in Snowflake is the ability to natively ingest semi-structured data such as JSON, store it efficiently and then access it quickly using simple extensions to standard SQL.
This eBook will give you a modern approach to produce analytics from JSON data using SQL, easily and affordably.
With decisions riding on the timeliness and quality of analytics, business stakeholders are
less patient with delays in the development of new applications that provide reports, analysis,
and access to diverse data itself. Executives, managers, and frontline personnel fear that
decisions based on old and incomplete data or formulated using slow, outmoded, and limited
reporting functionality will be bad decisions. A deficient information supply chain hinders quick
responses to shifting situations and increases exposure to financial and regulatory risk—putting
a business at a competitive disadvantage. Stakeholders are demanding better access to data,
faster development of business intelligence (BI) and analytics applications, and agile solutions in
sync with requirements.
Better health care at lower costs, for everyone – how do health care providers get there? Understanding the gaps in patient care, patient needs, and the geographic distribution of the patient population are important elements to consider when making decisions about improving the quality of care and reducing its costs.
To effectively analyze gaps in patient care, the data needs to be in a single place or system. However, in many organizations, data is spread across a myriad of spreadsheets and database systems. Data not organized for visual exploration and coherent analysis isn’t useful for decision making. Hence the need for visually appealing and scalable analytical tools to help organizations be more efficient, effective and economically successful.
Known for its industry-leading analytics, data management and business intelligence solutions, SAS is focused on helping organizations use data and analytics to make better decisions, faster. The combination of self-service BI and analytics positions you for improved productivity and smarter business decisions. So you can become more competitive as you use all your data to take better actions. Instead of depending on hunch-based choices, you can make decisions that are truly rooted in discovery and
analytics. And you can do it through an interface that anyone can use.
At last, your business users can get close enough to the data to manipulate it and draw their own reliable, fact-based conclusions. And they can do it in seconds or minutes, not hours or days.
Equally important, IT remains in control of data access and security by providing trusted data sets and defined processes that promote the valuable, user-generated content for reuse and consistency. But, they are no longer forced
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for
the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and
discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the
right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it
removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility.
SAS adheres to five data management best practices that support advanced analytics
and deeper insights:
• Simplify access to traditional and emerging data.
• Strengthen the data scientist’s arsenal with advanced analytics techniques.
• Scrub data to build quality into existing processes.
• Shape data using flexible manipulation techniques.
• Share metadata across data management and analytics domains.
The 2016 ACFE Report to the Nations on Occupational Fraud and Abuse analyzed 2,410 occupational fraud cases that caused a total loss of more than $6.3 billion.8 Victim organizations that lacked anti-fraud controls suffered double the amount of median losses.
SAS’ unique, hybrid approach to insider threat deterrence – which combines traditional detection methods and investigative methodologies with behavioral analysis – enables complete, continuous monitoring. As a result, government agencies and companies can take pre-emptive action before damaging incidents occur. Equally important, SAS solutions are powerful yet simple to use, reducing the need to hire a cadre of high-end data modelers and analytics specialists. Automation of data integration and analytics processing makes it easy to deploy into daily operations.
The Internet of Things enables retailers to do three basics better
1) Sensing who customers are and what they’re doing,
2) Understanding customer behavior and preferences, and
3)Acting on that insight to create a more engaging customer
- There are high-potential IoT applications in supply chain, in
“smart store” operations, and especially in providing an engaging
experience to the “connected customer.” IoT data can anticipate
where the customer is headed and how to meet her there.
- Much of the IoT ground, in both data management and analytics,
may be unfamiliar. Retailers and their IT organizations have to be
realistic about the technological challenges, their own capabilities,
and where they need assistance.
- To differentiate through IoT, focus on the analytics. Devices and
their data — and even their platforms — are commodities.
Advantage goes to the retailer who does the most with the data to
engage the connected customer.