Companies across the world are employing lean enterprise practices to eliminate “wasteful” activities, i.e. activities that are not adding value. However, one is often left with a number of indispensable, rote tasks that consume staff resources but do not easily lend themselves to further optimizations. Should we as managers be satisfied with this? No — with any kind of repetitive task we should be looking for a way to automate it.
Consider how much time employees waste every day keeping systems in sync because no proper APIs or integration points exist between these systems. Take for instance the US Army Corps of Engineers that was facing the challenge of coordinating payments to contractors and employees across 13 districts, each with its own project management system. Using the Kapow Katalyst platform for process automation, they set up automated synchronization between the local systems and the central systems, resulting in savings of $2 million.
Similarly, the Spanish call center provider Atento had an army of people employed to type in data from printed forms, as this had to be done each time a new customer account was set up. Again, with the Kapow Katalyst platform for process automation, the account processing could be done automatically — leading to both the re-allocation of staff to more productive tasks as well as an improvement in the data quality due to the elimination of human errors in the data entry.
This ties in nicely with iSixSigma’s top four metrics of success of a lean project:
- Customer Satisfaction
As illustrated by the cases above, process automation can contribute to all of these key areas. It enables the organization to scale; growing the top line without incurring proportional costs in staffing to meet back-office demands. Employees can be freed to spend the bulk of their time on activities that yield core business value, leading to a more focused and agile enterprise. This is lean in its purest form.
By: Anne-Sofie Nielsen
Everyone talks about Big Data, and specifically about the three “V” drivers for Big Data: Volume, Velocity and Variety.
But most forget about a fourth and equally important dimension of Big Data, which is the spread of data across many different sources. It’s not how much or how fast or what type the data is, as in the three Vs above, but WHERE the data is.
Just look at all the data posted on social media, forums, and blogs. And then consider the thousands of new websites popping up every day. We are facing the largest “data silo” problem ever, and the situation is only going to get worse.
The challenge of “data spread” is how can businesses access, transform, and integrate the data from these sources to turn it into actionable information for competitive advantage? Because, as pointed out in the recent Forrester research report Enterprise Hadoop: The Emerging Core Of Big Data, “This growing tsunami of intelligence feeds downstream business processes in both the front and back office, helping organizations optimize their interactions and operations through powerful analytics.” Not if they can’t communicate with the source!
I recently posted an article entitled The Fourth Dimension of Big Data Nobody Writes About to start a discussion on how to best address these challenges. I encourage you to read it and to add your insights along with the other perspectives already posted.
I believe that the Kapow Katalyst Application Integration Platform for Big Data is the way to deal with this exploding data silo problem. Our approach is already tested and proven by more than 500 companies around the world. It solves the data spread problem like no other approach.
If you have “data spread” problems you need to address, I encourage you to check out Kapow Katalyst, whether it’s for large-scale web data collection from social media websites, or API enabling of data hidden in applications all over your business network, intranet, and the Internet.
By: Stefan Andreasen
Many of you have asked for a deeper dive on the meaning of “Web Data Services”, so let me answer it here.
First, it’s important to understand the terms “Data Integration” and “Application Integration”.
Data Integration (DI) and (Enterprise) Application Integration (EAI) are not the same, though many vendors often confuse the two. Application Integration focuses on managing transactions or messages between applications while Data Integration focuses on managing the flow of data and providing standardized API’s to access the information. For more details, refer to Mark Madsen’s blog, Key Differences Between Data Integration and App Integration.
There are essentially three different types of Data Integration:
- Consolidation means moving all the data from the original data sources to a new repository, much like an ETL tool.
- Propagation means moving only the necessary data to a local storage for each application consuming the data.
- Federation means leaving the data at the original source and accessing it as needed in real-time.
Web Data Services is in reality all forms of Data Integration as well as Application Integration, with two distinct differences. With Web Data Services:
- You primarily access data and business logic residing on the web (any application or data source you can access from a Web browser like Internet Explorer, Firefox or Safari). This includes applications inside your own organization and even at your business partners.
- You do not need to recode or have programmatic access to any of the data sources. As long as you have access from a Web Browser, you can access the data with no coding and be up and running in a matter of hours rather than weeks or months.
Web Data Services is the new highly productive way to access almost any of the data you need for Business Intelligence (BI), Data Validation and Acquisition, Enterprise Mashups, Partner Integration, or basically any solution that needs agile access to data or business logic. Web Data Services gives unheard of business agility and competitive advantage compared to traditional Data Integration or Enterprise Application Integration methods.
Try it free with our Kapow Web Data Server Trial Offer
Also check the Wikipedia entry on Web Data Services to read the definition from leading industry analysts.
By: Stefan Andreasen