MHD magazine article Moving with the times Snowflake

Moving with the times – from MHD magazine

Peter O’Connor

Data warehouses are far from new. The term itself was coined back in the 1970s, and repositories for data amassed from a range of sources that can be used to inform business decisions have been part of Australia’s corporate high-tech landscape for almost as long.
With data analytics and artificial intelligence (AI) the primary foci for organisations of all stripes and sizes, it’s an opportune time for businesses to review their data warehousing strategies, to ensure they’re well positioned to support burgeoning demand for insights.
Here are some tips for maximising the value of the data warehouse.

  1. Adopt an ‘as-a-service’ model

What’s the primary role of your data warehousing team? Is it to manage infrastructure or to support the development of data programs that drive efficiency and profitability across the enterprise? If you haven’t yet adopted an ‘as a service’ model, you’ll likely find it’s the former. Management and administrative tasks – think partitioning, scaling, maintenance, back-up scripts and the like – are likely to consume a significant portion of your team’s time. As a result, there’s less time for them to work on projects that add value to the enterprise. Adopt an as a service model and all that changes. Data and service protection, database management and security are taken care of, leaving staff free to focus on exploiting data to help the organisation succeed.

  1. Broaden your horizons

Data no longer comes in two flavours – structured and semi-structured. If your organisation is not ingesting data from a wide variety of sources – enterprise applications, mobile applications, the internet, APIs and the Internet of Things (IoT) – then you’re at the back of the pack. Today’s data integration technology makes pulling these data sources together to exploit the insights they contain a much more straightforward matter than once it was.

  1. Implement self-service data analytics

Historically, data analytics was the remit of small specialised teams within organisations. If business units wanted queries run or information analysed, it was a matter of lodging a request and waiting for the results to be returned. Delays were a common but unavoidable occurrence.
User-friendly data analytics software has turned this model on its head. An increasing number of Australian enterprises are adopting a self-service model, whereby employees are given access to the data warehouse and provided with tools to extract insights for themselves.
The benefits of democratising data in this way can be significant. Bottlenecks can be minimised and results extracted and acted upon more quickly. Conversely, businesses that fail to throw open the data warehouse risk being left behind, as their more nimble counterparts gain a competitive ‘information advantage’.

  1. Foster a data-driven corporate culture

Empowering employees to access and manipulate data is a vital step towards the establishment of a data-driven corporate culture, in which up-to-date information is used to inform every business decision.

“Exploiting the full potential of the corporate data warehouse is vital to the process.”

Data-driven decision making has superseded decision making based on intuition or gut feeling in at least a third of Australian boardrooms, according to 2016 research by PwC.
ICT staff can play a vital role in fostering this culture at all levels of the enterprise. Instead of acting as gatekeepers for the data warehouse, they should be regarded as trusted guides for their colleagues throughout the organisation.

  1. Control the quality

If leveraging data across all areas of the enterprise is the aim – and it should be – it’s vital to ensure its quality and integrity. Establishing a rigorous data governance regime will ensure what’s extracted from the data warehouse is clean, trustworthy and correct.

  1. Embrace concurrency

Old-school data warehouse professionals were required to be masters of scheduling. Their challenges invariably included finding time slots when large jobs could be run without monopolising finite processing resources and disrupting other activities.
Migrating to a cloud-based, as-a-service data warehousing model puts paid to this issue. Eliminating the competition for resources allows IT staff to run multiple jobs concurrently and deliver results and insights more efficiently to stakeholders.

  1. Measure performance

Improving the efficiency of the data warehouse begins with finding the right metrics with which to measure its performance and its cost to the organisation. It makes sense to include management overhead in the calculations. Many tasks and procedures which require management intervention under an in-house model can be executed automatically under an as a service model. The time savings can be considerable, over time, and should be included in any reckoning of the relative costs and benefits of the two models.

  1. Doing more with data

In today’s digitally driven business environment, data has been dubbed the new oil. The insights it contains can help organisations become more efficient and profitable. Exploiting the full potential of the corporate data warehouse is vital to the process and Australian organisations which fail to do so may find themselves struggling to keep up with their data-driven competitors.
Peter O’Connor is the vice president of sales, Asia Pacific and Japan, at Snowflake. For more information visit www.snowflake.com.
 

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