A lack of executive support and poor data quality are just some reasons why analytics programs fail. The guide by Adam Greco, Reenergize Your Web Analytics, identifies the key reasons for program failures and provides ten ways to make your analytics program successful.
Read the guide to discover key ways to improve your analytics program, including:
• How to deal with your stakeholders
• How to set your analytics priorities
• How to reap the rewards of change
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
"Increasingly, brands are looking to differentiate based on an exceptional customer experience. The key to improving the customer experience is being able to effectively measure what’s working and what you need to improve. IBM host a webinar presenting tips on how to measure the customer experience for your brand and how to use that data to build better journeys.
Please join IBM and guest speaker Andrew Hogan from Forrester Research as we share tips on how to best measure the digital experiences customers have with your brand and how to use that information to build better journeys.
The webinar will provide attendees with:
• Best practices to measure the quality of digital customer experiences
• Guidance on the kinds of tools to use to capture the right CX metrics
• Tips for integrating metrics, including the role of customer journeys
• Techniques to drive action and improve digital experiences"
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
As big data environments ingest more data, organizations will face significant risks and threats to the repositories containing this data. Failure to balance data security and quality reduces confidence in decision making. Read this e-Book for tips on securing big data environments
"Built using the IBM® InfoSphere® Information Server, IBM BigInsights® BigIntegrate and BigInsights BigQuality provide the end-to-end information integration and governance capabilities that organizations need."
As big data environments ingest more data, organizations will face significant risks and threats to the repositories containing this data. Failure to balance data security and quality reduces confidence in decision making. Read this e-Book for tips on securing big data environments.
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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.
Many new regulations are spurring banks to rethink how data from across the enterprise flows into the aggregated risk and capital reports required by regulatory agencies. Data must be complete, correct and consistent to maintain confidence in risk reports, capital reports and analytical analyses. At the same time, banks need ways to monetize, grant access to and generate insight from data.
To keep pace with regulatory changes, many banks will need to reapportion their budgets to support the development of new systems and processes. Regulators continually indicate that the banks must be able to provide, secure and deliver high-quality information that is consistent and mature.
A powerful signal integrity analysis tool must be flexibility, easy to use and integrated into an existing EDA framework and design flow. In addition, it is important for the tool to be accurate enough. This report reviews a validation study for the Mentor Graphics HyperLynx 8.0 PI tool to establish confidence in using it for power integrity analysis.
For advanced signaling over high-loss channels, designs today are using equalization and several new measurement methods to evaluate the performance of the link. Both simulation and measurement tools support equalization and the new measurement methods, but correlation of results throughout the design flow is unclear. In this paper a high performance equalizing serial data link is measured and the performance is compared to that predicted by simulation. Then, the differences between simulation and measurements are discussed as well as methods to correlate the two.
In this document we're first going to explore the use of the insurance metaphor in terms of its most fundamental element: the broad consequences of data loss. We'll also discuss industry and regulatory consequences of data loss.
The purpose of deduplication is to provide more storage, particularly backup storage, for less money, right? Then wouldn't it be ridiculous if deduplication vendors were demanding that their customers pay more per terabyte of storage? Or if they were simply pushing the task of integrating, monitoring, and managing deduplication back onto their users?
Backup, despite the breathless hyperbole of many in the industry, is simply a form of insurance. What's important about your backup strategy is getting your systems and data protected in the most effective and affordable manner possible. Speaking plainly - you must be careful that you are in control of your backup strategy rather than your backup strategy (and vendor) being in control of you.
Clouds are all the rage right now. And the truth is that cloud-based computing can be a tremendous value. Cloud-based computing is not just an opportunity for a bunch of venture capitalists hanging out at Sand Hill Road, it's a wonderful chance for small and medium businesses to take a step toward aligning their information technology spend to increase their focus on revenue-generating opportunities.
This tongue in cheek white paper explores data loss from a contrarian point of view - exploring the top 7 shortcuts you can take to ensure that you lose your data. And since a fundamental responsibility of any information technology professional, as well as any C-level executive, is to ensure that the data upon which any company is created is protected - scrupulously following these shortcuts should also ensure that you lose not only your data but your job as well.
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
In today’s crowded analytics marketplace, who can you trust? What’s needed to deliver on the promise of transforming data into real value? And what do CIOs need to cost-effectively and successfully lead their organizations through changing technologies? For an organization to experiment with (and ultimately deploy) analytics, the responsibility falls squarely on the shoulders of IT. IT must provide secure access to lots of high-quality data, a friendly environment for experimentation and discovery, and a method for rapidly deploying and governing models. SAS can support an organization's journey toward becoming a data- and analytics-driven organization. We can help unlock the value by enabling with choices that make sense. Plus, we can show organizations how to get the most out of technology investments.
Learn how to increase your landing page conversion rate by 10-30% using data validation, which will help you to get the most out of your landing page optimization.
When it comes to storing data, the concepts of cost-effectiveness and efficiency are often at odds with each other. What we all seek is that elusive deal where we don’t affect the quality of service, yet are able to store a lot more (without spending a lot more). Read this white paper to learn how IBM Real-time Compression provides the data center with the flexibility to increase capacity and maintain performance for all kinds of data, including your most active data.
Modern enterprise businesses need the right IT tools to engage
customers online and keep them engaged until they buy. In
short, they need marketing automation software.
