The Ledger
Curated content foranalytical business leaders
Ending The Financial Close Nightmare
October 31, 2017
“Improving your financial close requires a cohesive and well-planned strategy to manage the systems, people, and processes, from transactional accounting processes to entity close, corporate, financial and management reporting, and disclosure. It requires strong governance, from centralizing compliance processes and controls to managing master data. Transforming the financial close is not a one-time project; it’s a continuous incremental journey, and the dividends to making even the first few steps to improvement can be substantial.“
Read More at The Digitalist by SAP >
Preparing for The Data-Driven Future
October 30, 2017
Real-time tools are starting to replace old-fashioned ones and are impacting change management. Companies are finally realizing the benefits of using data science by collecting the right kind of data and investing in their analytics capacity. The key is building predicative models by knowing what it is that you want to predict and collecting large and diverse data sets that enable you to get those answers. The companies best positioned to change in the next decade will be the ones that start preparing now.
Read More at The Harvard Business Review >
AI Feeds the Prediction Machine
October 26, 2017
One of the most common questions from executives dealing with a finance/IT transformation is, “How will AI change our strategy?” Predictive analytics have completely changed the way companies do business, and AI allows retail companies, like Amazon, to predict what their customers want to buy about 5% of the time. AI allows companies to have access to more customer data, which in turn equals stronger predictions, and stronger predictions lead to more profit. The key insight here is that turning up the prediction machine has a significant impact on strategy.
Read More at The Harvard Business Review >
Machine Learning is Necessary for Continuous Data Innovation
October 25, 2017
Automation has made some huge plays in the finance world. It has improved efficiency and given employees more bandwidth for value-added tasks. Machine learning is the next logical step to use this enriched, validated, and accurate data to liberate finance professionals from at least five kinds of redundant, yet necessary tasks. These tasks include digital business, fp&a, finance operations, and enterprise governance, risk and compliance. Through machine learning, finance organization can do more than ever before with lower or current support levels, innovate new ways to work, and increase efficiency, output, and, ultimately, profitability.
Read More at The Digitalist by SAP >
