Data Federation: A Single Simplified Data Silo

Data Federation: A Single Simplified Data Silo

The need for all data under one centralised roof – here’s what you should know about data federation

Data is gold to a good business – and proper data management the cornerstone in managing said business in taking advantage of the available growth opportunities. The biggest challenge of today is, however, understanding an organisation’s complex data needs. While most organisations today are eager to apply advanced algorithms, analytics and machine learning to generate insights from data at scale, the struggle to modernise existing legacy systems is quite the challenge.

In fact, a study conducted by US research organisation Forrester commissioned by Capital One confirmed the challenges in understanding and using data – nearly 80% of data management decision makers cited a lack of data cataloguing, and 75% saw a lack of data observability as primary concerns. VentureBeat writes, in this regard:

“Data that’s out of sight doesn’t generate value for your organisation. That’s why it’s so important to bring data out of the darkness and make it more visible and usable. For example, data cataloguing plays a critical role in understanding data, its use and ownership. When data professionals adopt more holistic approaches to cataloguing, observability and governance, they can better unlock the data’s value to improve business outcomes.”

What is needed is simplification

There are already several global organisations in data quality, ETL, classification, data loading and cataloguing – what is needed is simplification. The major ‘pain point’ is that data analysts and engineers facing complexities in getting specific jobs done, such as publishing, finding or trusting a dataset usually involve going through multiple tools owned by different teams with their own required approvals.

What is needed is a simplified experience layer – so that users can answer only a few questions and data can be published without backend integration.VentureBeat writes:

“if that experience can happen seamlessly and comply with policy guidelines, working with data won’t be a burden. All kinds of great experiences will emerge, including faster time-to-market and fewer duplicative efforts within the organisation.”

US-based consulting firm Gartner opines that the first stage is to migrate towards the cloud. According to their forecasts, cloud end-user spending could hit up to $600 billion next year, up nearly 50% from 2021. Companies today can do so much more with their data on the cloud – not only in relieving pressure of several centralised teams managing critical data components, but also in alleviating data bottlenecks and increasing the variety of incoming data and from a greater number of sources.

Image source: Tibco

On the need for the federation of Data

There is a pivotal need to centralise data to allow for better management – with a central tool to manage risks and costs. Business teams can move at their own pace while central shared services teams can ensure the platform is centrally serviced, well managed and highly observable. Salim Syed, VP and head of engineering at Capital One Software,writes:

“It’s important to consider the different ways business teams produce and use data. You need to build flexibility into the tools. If you don’t, you risk these teams finding another channel to do the work. When that happens, you lose visibility and cannot guarantee all business teams are complying with governance policies. A federated data approach with centralised tooling and policy avoids excessively centralised control, without decentralising everything to the point where you run the possibility of cost overrun and data security risks.”

Data federationgives consumers, risk managers, data producers as well as the underlying platform managers a single simplified silo where data analysts and scientists can find everythingthey need under the same UI layer, policies and tools – so that they can ensure that all policies are being adhered to.

Another crucial aspect in this regard is to ensure that a firm’s data scientists and analysts have a clear ‘productisation’ path. If analytics result in something worthwhile, they must have an easy and streamlined path towards wrapping that work in proper data governance policies while getting it into production; else, organisations run the risk of ‘shadowy, ungoverned pseudo-production datasets running in unstable environments.’

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