The Ledger

Building Agility Across Your Business

In today’s turbulent markets, where established companies are furiously battling assaults from start-ups and other competitors, the prospect of a fast-moving, adaptive organization is highly appealing. But as enticing as this vision is, turning it into a reality can be challenging- which is where agile teams come in. Agile teams are best suited for the profitable application of creativity to improve products and services, processes, or business models. Expanding the number of agile teams is an important step toward increasing the agility of a business. But equally important is how those teams interact with the rest of the organization. To ensure that administrative functions don’t hamper the work of agile teams or fail to adopt and commercialize the innovations developed by those teams, such companies constantly push for greater change in at least four areas.

Read More at The Harvard Business Review >

Predictive Analytics Can Play a Big Part in Emerging Economy Growth

Predictive analytics uses historical data to predict future events and has been proven to transform business data into actionable insights that help companies with strategic decision making. But can developing countries also springboard off it and apply the technology-enabled assumptions for new areas of development? As the emphasis on digitalization takes root across services and sectors in emerging economies, analytics is becoming more relevant and more usable. By building forecast models that are economy-based and multidimensional, developing countries can understand country-level indicators including risks, metrics for investment, inflation, and opportunities to build a plan for growth.

Read More at The Digitalist by SAP >

Data Quality Directly Affects the Quality of Your Predictive Model

“Poor data quality is enemy number one to the widespread, profitable use of machine learning. “

Data is absolutely key to the success of any machine learning process. If there is bad data in the process, the bad effects will be seen twice-  first in the historical data used to train the predictive model and second in the new data used by that model to make future decisions. The recipe for a strong predictive model is accurate, quality data and having the right data. The right data for the model is lots of unbiased data, over the entire range of inputs for which one aims to develop the predictive model. Most data quality work focuses on one criterion or the other, but for machine learning, you must work on both simultaneously.

Read More at The Harvard Business Review >

Management Accountants: Succeeding in The Digital Age

“Having a view of a business structured according to financial reporting rules and standards is important. Yet having accounting systems that deliver only such information is insufficient for providing the guidance enterprises need to succeed. In order to succeed in today’s increasingly competitive environment, organizations will need to understand the need for cost modeling that adequately supports its managers’ decision-making requirements.”

Read More at Strategic Finance Magazine >