Johannesburg, 02 Jul 2021
Is data the most valuable resource in today's business environment?
Since The Economist put data on its cover in 2017 and wrote that "data is the world's most valuable resource", organisations have been in an arms race to deploy new data tools and strategies and gain a much-needed competitive advantage.
For the third year in a row, 99% of Fortune 1000 companies made investments into data and AI.
And yet, most organisations still struggle to turn data into insights, hampered by outdated tools and processes, and old ways of thought. As one Harvard Business Review article puts it, 35 years after Robert Waterman's observation that companies are "data-rich and information-poor", little has changed.
Perhaps that is why becoming truly data-driven is still a daunting prospect for many organisations. Many simply struggle to deal with the complexities introduced during efforts at scaling analytics, which stem from changes in processes, systems and integrations of various technologies.
In fact, one McKinsey study found that 92% of organisations are failing to scale their analytics in line with the growing requirements of the business.
Common obstacles to data-driven success
A lack of data governance can create an inconsistent application of validation and business rules, which leads to inefficiencies and issues with data quality.
Data is often stuck in silos where only certain departments or users have access to certain sets of data. This can cause issues with accessibility and directly undermine efforts at building more holistic and accurate views of customers.
The rush to acquire new technologies competing with numerous outdated legacy systems and tools can further hamper efforts at gaining the analytic insights needed to make accurate decisions.
And even with the best, newest tools, the success of data strategies still depend heavily on how well users adopt and understand their new data tools.
A modern data architecture blueprint for success
So what does a successful modern data architecture, one that supports and enables the business and data strategies, even look like?
Firstly, modern data architecture must be customer-centric, and take the needs and requirements of business users into account during the design stages and on an ongoing basis. As the business changes, the data architecture should be able to adapt and evolve to meet changing customer or business user information needs.
Adaptability is critical. Data should flow freely from source systems to business users, with the data architecture managing that flow of information by creating a series of interconnected and bidirectional data pipelines that serve various business needs.
However, adaptability in today's complex business environment requires extensive automation. Data must be profiled and tagged as it is ingested, and mapped to clearly defined data sets and attributes in a process known as metadata injection.
Machine learning and artificial intelligence can help turn an automated data architecture into a smart one. Using these tools to build data objects, tables, views and models, organisations can ensure useful and accurate data keeps flowing to every part of the business that needs it.
Data architecture needs to be flexible enough to support multiple types of business users, different load operations and refresh rates such as batch processing to name one, and deliver high performance during query operations.
Focus on simplicity: the simplest architecture is usually also the best. To reduce complexity, organisations should take steps to limiting data movement and data duplication. IT leaders should strive for a uniform database platform, data assembly framework, and analytics platform.
This may require that they first ignore the noise of 'best-of-breed' advocates who insist on new tools for every component in the overall data architecture.
Finally, add an effective cyber resilience strategy that aims to ensure high availability (through disaster recovery and backup/restore capabilities) while keeping cyber criminals at bay.
By doing this, organisations may well find themselves among the hallowed 8% of modern enterprises that have broken away from the pack and have achieved true data-driven decision-making capabilities.
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