Top RPA platforms for Python developers in 2020: AIMultiple Guide

It is practically impossible to teach good programming to students that have had a prior exposure to BASIC: as potential programmers they are mentally mutilated beyond hope of regeneration.

Edsger W. Dijkstra

In Dikstra’s days, computing was an emerging niche, like the RPA of today. From the perspective of computer scientists, BASIC or its latest incarnation, Visual Basic are mutilating young minds in both cases. However, there are emerging RPA platforms emerging on Python so Python developers no longer need to use .Net to develop RPA solutions to benefit from this fast growing market. Read more


How to be a Successful RPA developer in 2020: In-Depth Guide

What you need to do to excel as an RPA developer depends on what you know about RPA:

For those undecided about becoming an RPA developer:

RPA is the fastest growing enterprise technology

According to Gartner, RPA is the fastest-growing segment of the global enterprise software market. Unsuprisingly, Interest in RPA has been steadily increasing in the past 5 years. Read more to see even more evidence on RPA’s growth

Source: Google TrendsWe summarized all major industry analysts’ estimates about the RPA market and there is consensus about RPA retaining its momentum into the next few years. Read more


45 RPA Case Studies: Explore RPA in your Industry & Function

Case studies are one of the most effective methods to learn about a new technology. RPA is no different. Therefore, we decided to aggregate case studies about RPA from numerous sources so you can filter/sort them by industry (e.g. telecom, financial services) or business function (e.g. marketing) to identify how your company can implement RPA. To prioritize these use cases and select vendors for your business, you can also examine the reported benefits and the vendors in the list below.

A trend we observed in the list is that in more than half of the cases where the process was specified, documents (e.g. invoices, receipts, annual reports) were involved in the process. This was also verified by our numerous interviews with RPA practitioners who mentioned that companies generally start their RPA journey with structured data since these are easier to automate. However, as companies expand their RPA deployment, they start automating document based processes which yield higher levels of savings. Most RPA tools do not have best-in-class tools for extracting data from documents. For the best document extraction tools and an overview of document extraction, feel free to read our articles on the topic: Read more


Synthetic Data: An Introduction & 10 Tools [2020 Update]

Synthetic data, as the name suggests, is data that is artificially created rather than being generated by actual events. It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI needs.

The questions that this post sets out to answer include:

Why is synthetic data important and what are some use cases for it?

How does synthetic data perform compared to real data?

What are some benefits associated with synthetic data?

What are some basics of synthetic data creation?

What are some challenges associated with synthetic data?

What are some tools related to synthetic data?


Data Governance in 2020: A Comprehensive Guide

Businesses around the world are striving every day to become more data-driven, and as such, how they collect and manage this data is evolving. One important topic that has arisen out of this shift is data governance. In this post, we set out to answer the following questions:

What is data governance?

Why is it important?

What are the benefits of effective data governance?

What are some key tasks associated with a data governance strategy?

What are some best practices for data governance?

What are the related challenges and pitfalls?

What are some common data governance tools?

What does it all have to do with the recent GDPR?