Artificial Intelligence is expected to permanently change the banking industry in profound ways during the coming months and years. Companies want to seek a competitive edge by implementing more technology to achieve improvements in speed, cost, accuracy and efficiency. 

As enterprises become more complex, and success increasingly depends on streamlining operations, it becomes imperative to invest in emerging technologies that enhance efficiency and facilitate improved decision-making in the back-office. Robotics Process Automation (RPA) brings you one step closer to this goal by significantly reducing turnaround time, interacting with multiple applications in a non-intrusive manner, and enhancing accuracy and reliability. With its ability to stitch an automation story across multiple application environments, it streamlines your back-office operations, making it possible to realign your value proposition to meet changing customer expectations and thrive in a dynamic business environment.

Digital transformation trends have resulted in a data explosion across industries. Enterprises are increasingly leveraging Artificial Intelligence (AI) and Machine Learning (ML) to identify trends, harness insights based on data, and make critical business decisions to gain a competitive advantage in the market. This paper explores the key trends and insights in advanced machine learning.

In this white paper, you will gain understanding of the simple steps required to build a business case for IA, including the benefits of using a Business Case Tool to quickly and easily model many processes, understand the potential savings and prioritise accordingly in order to build momentum and maximise automation value.

Chief Data Officers (CDOs) can drive data strategy, support machine learning analysis, and generate business value with data. We live in a world rife with rich data and open-source tool support. However, there are many obstacles between CDOs - or any data leader - and successful organizational integration of data insights (lack of executive buy-in, shortages of data scientists, and dated, disorganized data, to name a few). We wanted to explore the intricacies of the role and uncover the path to data leader success. Dataiku collaborated with Caroline Carruthers, of Carruthers and Jackson, a CDO who literally wrote the book on it, (The Chief Data Officer Playbook, Facet, 2017) to provide new datasets and insights into ways to ensure CDO success.

This white paper includes:

• An expose into the different types of CDOs and what kind of support and knowledge each needs to succeed;
  
• Insights from more than 50 top cross-industry CDOs on the challenges they’ve faced and the solutions they’ve driven in their organizations;
• Context on the Data Revolution and tips on how organizations can drive it forward; and
• Exploration into the future of the data leader community and insights into why joining is beneficial.
  

You are the CXO of a company that serves as a sales platform for thousands of different clients. You and your management team have identified a list of processes that could be improved via better use of your data and advanced analytics. For instance, in order to help your clients increase sales (and thus increase the stickiness of the platform), you decide that the development team should surface a custom recommendation module offering three product recommendations per client.
Requirements include that:

• The recommendations be available in real time and
•  The recommendation engine can be updated without causing platform downtime

At the same time, there are internal requests from different teams (like sales and marketing) who want to make data-driven decisions (for example, key trending products with positive reviews from social networks or how transformation rates are influenced by the historical browsing behavior of a visitor), but their old dashboards are static and don’t address their needs. Even though the data exists internally, they can’t get insights for themselves because they don’t have direct, regular, monitored access to data that can help them do their jobs.

Which need should be prioritized? And how do you even begin to tackle these projects with an approach that will be sustainable and reproducible for other projects and requests down the road?

There is a revolution happening in analytics and that is the move towards self-service. TDWI research indicates strong interest in self-service business intelligence (BI), analytics, and data preparation solutions. For instance, as far back as 2013, close to 80 percent of respondents in a TDWI survey said it is important to implement analytics solutions that do not require significant IT involvement. Companies are still very interested in this today. Self-service analytics technologies are an important trend for democratizing BI and analytics, which is about giving more users better tools for interacting with and analyzing data. Companies want to evolve their analytics strategies beyond spreadsheets or simple dashboards; many seek to build a broad “analytics culture” in which analysis plays an important role in decisions and is fundamental to business collaboration.