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
In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.
COVID-19 and the related economic fallout has pushed organizations to extreme cost optimization decision making with uncertainty. As a result, Data, Analytics and AI are in even greater demand. Demand from all these organizations lead to yet more data and analytics. With data comes quality issues. Everything Changes.
While some see digital transformation as a trend that has existed since the 1950s, an alternative view is that today’s digitalisation is a distinct phase because it describes the way technology and data now define rather than merely support operations. However, these problems have also encouraged new thinking and problem solving.
With advanced analytics, flexible dashboarding and effective data visualization, FP&A storytelling has become both an art and science. You can watch the webinar here (registration required) to learn how to conduct FP&A storytelling in order to enhance fact-based decision making. First, because uncertainty exploded.
1] The need to improve real-time decision-making is contributing to a growing demand for event-driven solutions and their ability to help businesses achieve continuous intelligence and situation awareness. An event-driven architecture focuses on the publication, capture, processing and storage of events.
In that role, I frequent webinars, podcasts, partnerships with various organizations, and consulting work in the field of finance and accounting. . The goal of AI in accounting and finance is to get professionals to focus less on tactical aspects like data collection, mining, and aggregation.
Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” Nick Elprin, CEO and co-founder of Domino Data Lab. First item on our checklist: did Rev 2 address how to lead data teams?
It’s what allows them to unlock the full power of their data and make informed decisions. But, many don’t know where to begin or how exactly to work with their data to their optimal benefit. Data gives insight into user demographics, habits, preferences, and more. What is business intelligence?
Recognizing the need to harness real-time data, businesses are increasingly turning to event-driven architecture (EDA) as a strategic approach to stay ahead of the curve. This trend grows stronger as organizations realize the benefits that come from the power of real-time data streaming.
We are currently operating in an environment with a very high (if not the highest ever) level of VUCA, (Volatility, Uncertainty, Complexity, Ambiguity). The way you mitigate uncertainty is with planning, planning, and more planning. Not to mention eventually leveraging the 70% – 80% of data that is considered “dark”.
This data is gleaned from a report from insightsoftware and Hanover Research: The Operational Reporting Global Trends Report. The issues finance and operations teams face are twofold: the nature and increasing demands for operational data and analytics, and the fact that tools on hand are not optimized to handle the data or analysis.
Many teams accept long hours and repetitive data entry as a necessary evil, the hard work required to get a clear and comprehensive portrait of financial performance. Spreadsheet Server puts an end to tedious, manual data dumps from NetSuite into Excel. The question is, does financial reporting have to be this way?
Every organization wants to better serve its customers, and that goal is often achieved through data. Situationally, it was a really good time to deploy a data mesh architecture and its principles and invest in this space because we were doing so much tech modernization,” Lavorini says. “So So why not make data a part of it?”
You help companies adapt to a changing, tech-driven economy. Among several services my organization provides; we help individuals, enterprises, and public agencies plan, prepare, and manage through the uncertainty, demands, and challenges of the future. How quickly do companies need to become “data-driven”?
By getting a large enough sample size of companies of each type within the supply chain, I can also compile enough data on the scope of the industry. Analytics helps with that uncertainty because you really are paving the way. For more from Ryan MacCarrigan, read his take on dashboards and informed data-driven decision making.
More than ever before, business leaders recognize that top-performing organizations are driven by data. In a fast-moving world where virtually every business is struggling to meet customer demand amid supply-chain uncertainty, rapid delivery times are more important than ever. On-Time Delivery.
Three of the most important of these are: cloud migration, data standardization, and interoperability. In the case of data standardization, silos of information held by different teams are being replaced by single common datasets that underpin every process and are updated in real-time. Transfer Pricing at an Inflexion Point.
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