data analysis

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Published By: Mitto     Published Date: Jun 06, 2017
APQC reports that 30% of CFOs that are unhappy with their current planning solution.1 Most FP&A professionals want a planning and forecast process that is fast and repeatable. They want the financial data to be accurate and transparent to the deepest levels. They need planning tools that provide structure to streamline the process and flexibility to accommodate changes in the business. And they require analysis and reporting that bring visibility to the state of the business and lead to actionable insights. This paper details five best practices that Finance teams can follow to improve their planning and forecasting and influence the strategy of an organization.
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veritas, backup, recovery, netbackup
    
Mitto
Published By: Pure Storage     Published Date: Jul 03, 2019
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
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Pure Storage
Published By: Pure Storage     Published Date: Jul 03, 2019
Financial services businesses face unprecedented market challenges. Disruption from Fintech firms, increased local and international regulation, geo- political upheavals and wavering customer loyalty. The need to fully understand the market, to innovate, to reduce costs and be more competitive has never been greater, and this is where AI can help. According to one fintech research company, by 2030 the financial services sector could reduce operational costs using AI, by as much as 22%. It suggests that will equate to around $1 trillion in efficiencies. So, from a purely operational point of view, doing nothing is not really an option for companies that want to remain competitive. Today, financial services firms across the board need to rejuvenate customer experience to protect against client attrition, and protect those customers against risk. While data analysis and visualization are key to making sense of data, the fundamental challenge for all businesses is building an infrastructur
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Pure Storage
Published By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes.
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TIBCO Software
Published By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes. Download now to read on.
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TIBCO Software
Published By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes. Download now to read on.
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TIBCO Software
Published By: Group M_IBM Q2'19     Published Date: Jul 01, 2019
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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Group M_IBM Q2'19
Published By: Expert System     Published Date: Jul 26, 2019
As companies increasingly recognize the business implications and actionable benefits of AI, the question becomes: How will you use AI for your business? Thanks to the Cogito platform based on AI algorithms, organizations can effectively support and improve unstructured information management and text analytics in order to: Leverage all information, combining internal knowledge with other information sources to extract relevant data Provide effective and real-time insight on strategic initiatives, partners and any third parties Mitigate and even completely avoid risks for operations, reputation, etc. through information analysis and monitoring Know what competitors are doing and intercept market trends Implement automation for the broader, more complex set of processes that involve data Free up teams to focus on more creative or critical activities inside the organization See the entire business through a different perspective
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Expert System
Published By: ttec     Published Date: Jul 24, 2019
Data drives decision making across the enterprise. For sales executives, itís critical to have information about where to focus outreach and understand what potential customers are looking for. But having data for its own sake wonít do much good. With advanced tools and a customerfocused mindset, companies are learning things about prospects never before possible. Thanks to advanced insights and machine learning that process algorithms and crunch millions of data points, new purchase patterns and propensity models are emerging to guide sales leaders as to what will work best for their business. Read this paper to learn how to act on advanced insight in the sales and marketing process. Highlights include: The enormous potential of new data tools and analysis Resources needed to act on the insight Company examples Strategic and operational recommendations
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ttec
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