Article by Steve Singer, A/NZ country manager at Talend
In today’s highly competitive, always-on business world, organisations are seeking to gain an edge through digital transformation.
As a result, many feel a sense of urgency to change everything they do - from manufacturing to customer service - in a constant pursuit of continuous innovation and process efficiency.
Data sits at the heart of all these digital transformation projects. It’s the critical component that helps generate smarter, improved decision making by empowering business users to eliminate gut feelings, unclear hypotheses, and false assumptions.
For this reason, many organisations believe building a massive data lake is the ‘silver bullet’ for attaining real-time business insights.
In fact, according to a study by CIO from IDG, 75% of business leaders believe their future success will be driven by their organisation’s ability to make the most of their information assets.
However, only 4% of these organisations say they have a data-driven approach that will allow them to benefit from their data.
The reality is that the new initiatives and technologies being rolled out come with a unique set of generated data which, in turn, creates additional complexity in the decision-making process.
To cope, a growing number of organisations are migrating to the cloud. However, this ends up creating other issues. For example, once data is made more broadly available via the cloud, more employees want access to it.
They’re keen to extract value from increasingly diverse data sets, faster than ever before.
This desire puts pressure on IT teams to deliver real-time, data access that serves the diverse needs of users looking to apply real-time analytics to support their roles.
Those users are also looking for ways to prepare, share, and manage data more easily.
To achieve this, many organisations decided to move raw data to one place where everybody can access it, thus creating a data lake.
In essence, a data lake is a large body of raw data held in a natural state where different users can examine it, delve into it, or extract samples from it.
However, organisations are beginning to realise that all the time and effort spent building massive data lakes has frequently made things worse due to poor data governance and management. All too often the result can become a ‘data swamp’.
In the same way data warehouses failed to manage data analytics a decade ago, data lakes will undoubtedly become data swamps if not correctly managed. Indeed, putting all data in a single place won’t by itself solve the broader data access problem.
Leaving data uncontrolled, un-enriched, unqualified and unmanaged will dramatically hamper the benefits of a data lake as it will still have the ability to only be utilised properly by a limited number of experts with a unique set of skills.
The long-term value of an enterprise data lake depends on the level of trust employees have in the data it contains.
Failing to control data accuracy and quality will create mistrust amongst employees, seed doubt about the competency of IT, and jeopardise the whole data value chain.
Increasing numbers of firms are deciding that a governed cloud data lake represent an adequate solution to overcoming some of the traditional stumbling blocks.
The four key steps to follow to adopt this approach are:
Unite all data sources
Ensure your organisation has the capacity to integrate a wide array of data sources, formats and sizes.
Storing a wide variety of data in one place is the first step, but it’s not enough. Bridging data pipelines and reconciling them is another way to gain the capacity to manage insights.
Accelerate trusted insights
Efficiently manage data with cloud data integration solutions that help prepare, profile, cleanse, and mask data while monitoring data quality over time regardless of file format and size.
When coupled with cloud data warehouse capabilities, data integration can enable companies to create trusted data for access, reporting, and analytics in a fraction of the time and cost of traditional data warehouses.
Embrace collaborative data governance
The old schema of a data value chain, where data is produced solely by IT in data warehouses and consumed by business users, is no longer valid.
Now everyone wants to create content, add context, enrich data, and share it with others.
Companies should encourage a collaborative governance by delegating appropriate role-based, authority or access rights to citizen data scientists, line-of-business experts, and data analysts.
Democratise data access
Without making people accountable for what they’re doing, analysing, and operating, there is little chance organisations will succeed in implementing the right data strategy across business lines.
Thus, you need to build a continuous ‘data value chain’ where business users contribute, share, and enrich the data flow in combination with a cloud architecture that will accelerate data usage by load balancing data processing across diverse audiences.
Data has very much become a strategic asset, however, it’s all too often more like a hidden treasure at the bottom of many companies.
Once modernised, shared and processed using AI and the cloud, data will reveal its true value and deliver better and faster insights to help companies get ahead of the competition.