This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
A recent survey by insightsoftware and Hanover Research reported 63% of IT decision makers find that finance is either very- or over-reliant on the IT department for operationalreporting. Both statistics are alarming considering market uncertainty increases the demand for more frequent, more accurate forecasts and reporting.
These are end-to-end, high volume applications that are used for general purpose data processing, Business Intelligence, operationalreporting, dashboarding, and ad hoc exploration. But an important caveat is that ingest speed, semantic richness for developers, data freshness, and query latency are paramount.
Some cloud applications can even provide new benchmarks based on customer data. Advanced Analytics Some apps provide a unique value proposition through the development of advanced (and often proprietary) statistical models. Advanced Analytics Provide the unique benefit of advanced (and often proprietary) statistical models in your app.
Batch processing pipelines are designed to decrease workloads by handling large volumes of data efficiently and can be useful for tasks such as data transformation, data aggregation, dataintegration , and data loading into a destination system. What is the difference between ETL and data pipeline?
These statistics underscore the importance of addressing transparency issues, implementing effective data cleansing processes, and proactively closing the skills gap in SAP data management to ensure data reliability and effectiveness in decision-making.
Administrators will also appreciate the addition of “usage statistics” for each report layout. They can see at a glance which reports are used routinely, and which have not been accessed in months or years.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content