Monthly Archives: December 2019

Postal services could avoid this seasonal complaint with data and AI

The IBM Data Science and AI Elite team showed that PostNord can predict non-deliveries of traceable items depending on address, weather condition, sizes and time of delivery. By leveraging AI, it’s possible to reduce non-deliveries by 50 percent annually, beneficial for both customers and PostNord as operator.

Reality and misconceptions about big data analytics, data lakes and the future of AI

With the amount of choices surrounding big data analytics, data lakes and AI, it can sometimes be difficult to tell fact from fiction. With more than 40% of organizations expecting AI to be a “game changer,” it’s important to have a complete picture of the capabilities and opportunities available.

Implementing DataOps across a banking enterprise

Imagine a day in the life of Sarah, a hypothetical Chief Data Officer at a major bank in South Africa. There are many expectations on her shoulders. She struggles to deliver business-ready data to fuel her organization and support the decision makers within the bank. It is her job to put in place a team that will make sense of the myriad of data sources and different representations of data, multiple formats and technologies used to store and move that data.

6 steps to start your DataOps practice

DataOps is the orchestration of people, process, and technology to accelerate the quick delivery of high-quality data to data citizens. When done right, DataOps creates business value because users know what data they have, can trust the quality and its meaning, and use it without violating compliance or privacy laws. 

Components of the DataOps toolchain and best practices to make it successful

High-quality data is the core requirement for any successful, business-critical analytics project. It is the key to unlock and generate business value and deliver insights in a timely fashion. However,  stakeholders across the board are responsible for data delivery, quickly evolving requirements, and processes. Their preference towards technology is deflating traditional methods of responding to inconsistent data and consequently disappointing users.

The difference between DataOps and DevOps and other emerging technology practices.

The expectation to achieve faster results continues to rise. Businesses everywhere are looking for ways to improve their operational efficiency and effectiveness to enable the best decision-making. The need to optimize typically comes to a head with the reality that there are many silos within any company.

What is DataOps?

Most businesses collect data but are unable to use it to generate business value or deliver insights in a timely fashion. Data volume and data types continue to grow, as do the different types of data citizens—ranging from business users to data scientists. As a result, data management and delivery often become critical bottlenecks. Enter DataOps.

Why DecisionBrain is taking notice of IBM Watson Studio Premium for Cloud Pak for Data

When planning for a day of business, how do you calculate the numerous factors that may affect your bottom-line revenue? For Serco, a company which operates a bike-sharing service throughout London, the answer was in their data.

Why is a data catalog essential to making your data lakes successful?

All industries—from healthcare to retail to banking—are digitally transforming themselves every day to become more agile and stay competitive. However, all industries depend on data  to be successful, and this impacts the way enterprises plan and execute their operations.

3 steps to effective data classification for business-ready data

Global data privacy compliance regulations like the General Data Protection Regulation (GDPR),California Consumer Privacy Act (CCPA) and Brazil’s LGPD have created scrutiny around personal, customer and employee data.