Robotic Process Automation Hype - Some facts

Robotic Process Automation

This  data was shared by #Pega Systems on webinar. https://www.pega.com/rpahttps://www.pega.com/rpa

Why private and government sector in #India needs to re-evaluate their Data Strategy?

                       

Data Strategy – Time to re-evaluate?

It seems a long time ago that the  3 V’s of volume, variety and velocity was unleashed on the world to describe the evolution of Big Data that organizations were about to see. For years we have been told that we needed to get ready for a new Data Tsunami . We need to be ready to store more data, take data that might not look like we had traditionally from operational systems (such as textual unstructured data) and handle data arriving more quickly.  This was in the web era before mobile and social media took off. Then we had the Big Data storm where all the V’s got bigger, faster and more diverse. When Social Media arrived and the use of external data to help make decisions became a norm the Big Data is everywhere, so much that we seem to have stopped talking about it.

An emerging ecosystem of options

 To deal with big data we needed new ways to store data. This led to the emergence of a new ecosystem of database options to support different needs. New model/schema databases were created with new query approaches to overcome gaps in what was available. Over the time most companies adopted a modified data landscape including NoSQL databases rather than adapting “Hadoop Based Data lake”.What seems to be lacking is a sound understanding of the new COMP-LEXER landscape of data-sources and databases and urgent a need to have a fresh Vision and a new road map for Enterprises Data Strategy.

When Big Data is everywhere, Big Data is just another Data

Today most organizations have stopped thinking about “Big Data” as a challenge that need to be addressed. Now it is just the data that they have to handle to meet different business requirements. Importantly many of those organizations are moving the discussion on to how they get value from that most valuable of assets.  It is no coincidence that focus of enterprises is to get Insights from the data rather than the handling 3Vs of Big Data. It is great that the focus is on deriving value from data. But I wonder if things happening too fast and some enterprise seem to over simplify their database landscape?

Understanding the Complexity Of Data Landscape

The rapid evolution of business requirements has resulted in organizations ending up with an data landscape that has become incredibly complex.  Many organizations are significantly overspending on managing that complex bloated data landscape. The European Data Protection Regulation became applicable on May 25th, 2018 in all member states to harmonize data privacy laws across Europe.Organizations have a huge variety of databases including tabular relational databases, columnar databases, NoSQL databases and the list just goes on.  Organizations have reached this point because they had to meet their business needs. The databases they had were not able to support what they needed to do when they needed to do it.

Tackling the Complexity Of Data Landscape

I believe it is time Organizations should STOP overhauling their data landscape and look for an approach that drives towards a new Data Architecture Vision. It is time to take stock. Think simplification of the data landscape while continuing to meet the business needs today and of the future. Defining a fresh Data Vision and simplification of Data Landscape will help with costs and manageability and help adhere to new Data Protection Laws.  By reducing complexity at source organizations will be better set to use data to create value rather than passing on chaos and complexity to value creators! The evolution of database technologies has been almost as relentless as the progress in other areas of software. Today SQL Server can run on Linux.  Would that make you consider if an open source database is really better than an enterprise grade best in class equivalent you can now use when security and reliability around data is going to underpin everything you do? Look at the fact Graph processing is available in SQL Server and that machine learning capabilities are now pervasive in databases with SQL supporting Python and R.  Would that change the need to create separate data marts for analytics processing reducing complexity and data sprawl?

New deployment options

Finally lets look at the new deployment options.

• Flexible agreements that let you move to the cloud incrementally
• Moving from on premise to  the cloud unchecked lets you reduce the overhead of hardware and having to deal with Capital Expenditure
• Using managed services in the cloud with powerful SLAs to reduce administration overhead while enabling new modes of data storage to support emerging business needs
• Building Hybrid solutions that span into the cloud as needed
• The capability to stand up what you want when you want it and have all that handled with super clear SLAs.

The modern data estate is available on-demand. It spans all deployment modes, offers almost every type of database you might need and helps you find the right ones to meet your business needs. Options abound for simplification, consolidation, modernization and agility within your data landscape all without compromising on meeting your business needs.

Moving forwards

The forwards momentum in database capability and their deployment options  is staggering. Many organizations are not on top of that. Previous decisions, even from as little as 12-18 months ago, can now be revisited to see if your data landscape is running as efficiently as possible.
It is a known fact that progressive organizations, some already because of GDPR, are busy documenting their data assets. In most cases better than ever before. Most of them are focused on what data is where though and how to secure it and ensure it is used appropriately.
Many are not looking at which database it is being stored and if migration and/or consolidation could make life much easier. Be sure to think about your data landscape and consider how it can evolve.
Here are some questions:

1. Have you recently looked at where you are storing your data and do you understand why you have it there? Have you evaluated if there a better option today?
2. Do you know how much it is costing you to manage and maintain your data estate and could reduced complexity reduce that? If lowering IT costs is on your radar this is a sure fire way to find ways to do that.
3. Have you considered if your GDPR compliance would be easier with a less complex environment to manage? Is database consolidation an option you considered on your GDPR journey? If not why not?
4. When did you last evaluate which databases need to be on-premise, which can be deployed in a hybrid mode and which should be able to be totally moved to the cloud? If not recently you may be constraining your potential based on old options and adding additional costs you do not need.
5.  

In Conclusion

A modern data estate will provide options to meet you where you need it to. As you consider your data landscape moving forwards you might want to think about if you are missing a trick by not thinking big picture and looking for vendors who can, perhaps together with partners, cover the entire data estate and all that entails.I have written about need for a Vision & a Road Map for an enterprise and that applies for Data Strategy. as well. The speed at which technologies are evolving and the rate at which new technology get adopted every CTO and CIO should review the Enterprise Data Vision every year and do the necessary change to the Road Map.