is a term used to describe a set of technologies and practices that enable organizations to manage and access data across multiple platforms and environments. This includes supporting an organization’s need to break down , gain more insight into , across and data platforms. Organizations are starting to explore more flexible ways of managing their data ecosystems and ensuring they can leverage data more effectively. This blog looks at pros and cons of a .
Pros include -, governance, agility, scalability and cost savings. Each of these require more than software to succeed.
Cons include - complexity, integration challenges,, potential lack of vendor support, and limited integration options.
These pros and cons are not exhaustive but provide a good starting point for organizations evaluating their options.
Why The Discussion Aboutand Are Important
Lately I have talked a lot about and manage it over time. Whether or not, the reality is that organizations and also discussing . This blog isn't going into the differences but discusses the pros and cons of because the reality is that organizations are struggling and selecting an or over a approach isn't the right conversation. Organizations most likely need a diversified approach to their because they have varying needs. Operational storage and analytics consumption require different platforms and tools to enable successful business outcomes.and whether or not an organization can actually create a successful
Therefore, when people talk aboutand , they are looking for answers to the challenges they have surrounding the ability to leverage data across without sacrificing visibility into the business.
Before moving on, let's provide a general definition of. For the purposes of this blog, is a term used to describe a set of technologies and practices that let organizations manage and access data across multiple platforms and environments. The goal is flexibility and agility.
Some of the Pros and Cons of
: An advantage of is the ability to integrate data from different and platforms. This includes structured and , as well as data stored in the cloud or . By integrating data from multiple sources, organizations can gain a more complete view of their data and make better use of it.
: enables better , , and data lineage. These tools and practices help organizations ensure that their data is accurate, secure, and compliant with regulatory requirements. At the same time, requires a framework, processes, people, etc. to be successful and not only a approach. It is important to make sure that, although easier to maintain, using this approach does not guarantee better unless there is commitment to a full program.
Data agility: Organizations can be more agile in their approach to. Companies can quickly and easily access and move data between different platforms and environments, enabling them to respond quickly to changing business needs. This helps ensure proactive and limits the potential for shadow IT.
Scalability:allows organizations to scale their infrastructure as data volumes increase. It is designed to work with large and complex , and can handle the high volumes of data that are generated by modern applications and systems.
Cost savings: Organizations can reduce costs by eliminating the need for multipletools and platforms. Organizations can manage and access data using a single set of tools, which can be more cost-effective over time. It also supports better visibility into the data across sources.
Complexity:a can be complex. It requires a high level of expertise and the right resources to set up and manage, which can be challenging for organizations with limited IT and data skill sets.
Integration challenges: Integrating data from multiple sources and platforms can be challenging, especially when dealing with data that is stored in differentor has different structures. This requires organizations to have a high level of expertise in and data mapping.
: requires organizations to have a high level of expertise in . Organizations need to ensure that data is protected from unauthorized access and potential security breaches. This includes taking into account and putting in measures to address data encryption, data masking, and data lineage.
Limited vendor support:is a relatively new concept, and there are a limited number of vendors that offer . This can make it difficult for organizations to find a vendor that meets their specific needs. Additionally, organizations rely too heavily on vendors to provide the answers without realizing that for , organizations need to develop the right framework to support any software . This requires stakeholder and executive support across .
Limited integration with existing tools: tangible business value.may not be able to integrate with existing tools and platforms. This may limit overall effectiveness and make it difficult for organizations to adopt it and gain
are complex. Organizations are leveraging diverse across , ecommerce, and other industries. is required to make sense of and deliver data to while making sure that support and strong outcomes. Adding and to the mix creates added interest in adoption because the assumption is that the creation of a framework will support and visibility to support .