Bots are considered the next big thing in the digital age. The new retail frontier is all about smart voice assistants such as Amazon Echo, Google Home and Co. and self-learning and intelligent chatbots. We’re surrounded by them. They are transforming the customer experience, significantly raising customers’ expectations and increasing demands on Product Information Management.
The interactive future of digital commerce and its master data requirements
Interactive shopping – on the internet. Two things that, at first glance, are difficult to combine. E-commerce platforms and users/customers usually interact “statelessly”.
Many websites are already offering their customers ways to interact with a customer service team and ask questions through hotlines and live chats. These services are usually labour and cost-intensive. Thus, they are often limited to very expensive and complex products and services. Bots are going to change that.
Just before Christmas is traditionally the time when industry colleagues and market criers throw around predictions. Of course we, the experts, have to be part of it, too. A quick survey among our colleagues showed that the issues and technologies we will deal with in our customer projects don’t really require a gaze into the crystal ball. Our customers and we are already working on topics and areas that, for most people, will be only be relevant in the near future.
These are, in a nutshell, our new and old themes for 2017
In the first two articles of this series we discussed what steps need to be taken in order to be able to calculate the ROI of product data quality. This part will focus on how to define ROI metrics based on the previously outlined technical metrics to prove with hard figures if data quality will be benefiting the business and whether it will be worth the investment.
In the first part of this series of articles about the “ROI of Product Data Quality” we talked about data quality and the reasons for its increasingly important role. This part will focus on parsionate’s general approach in DQ projects – prerequisites that are necessary in order to be able to reliably measure the ROI of product data quality.
We live in the Information Age, an age characterized mainly by an increasing digitalization and exponential data growth. If we are to believe a well-known American market intelligence company, the amount of data being generated annually will grow to 44 zettabytes by 2020 (IDC, 2014). This is about ten times more than in 2013 and corresponds to the staggering amount of 44 billion 1 TB hard disks. We certainly notice that, in our everyday life, more and more devices are equipped with sensors and probably our refrigerators will soon be able to automatically detect when to order new groceries.
This incredible amount of data will need a more efficient data management – not only on an individual level. More and more companies are appointing a CDO (Chief Data Officer) to drive overall data management strategy, implement enterprise-wide data standards and set up data governance initiatives. This central role is supposed to help firms to harness today’s flood of available data and to protect sensitive information from prying eyes.