As macroeconomic headwinds continue due to budget cuts in many UK businesses, it is clear that the pressure on businesses to save money is here to stay. But organizations should be wary of the temptation to reduce investment in data technology and analytics because they risk losing a crucial competitive advantage. As data analytics and artificial intelligence (AI) become increasingly important, nearly half of companies (44%) plan to implement data modernization efforts by 2024, according to PwC. More than half of organizations therefore cannot afford to turn their backs on technologies that can deliver important business benefits, such as improved customer experiences and improved product innovations.
In the coming year, the organizations that will be most effective at navigating the economic landscape will be those focused on managing spend and increasing efficiency to drive better business results. According to IDC, the world is producing more data than ever, as much as 181 zettabytes of data per year by 2025 or the capacity of 45 trillion data DVDs. Particularly with the rise of generative AI, data will continue to be a key differentiator for those looking to benefit from AI: the more diverse and comprehensive the data, the better AI can perform. If companies want to remain competitive, harnessing the power of data insights, along with effective cost management and planning, must be top of mind for business leaders.
UK Country Manager, Snowflake.
Business value and transparency
Achieving transparency about existing costs is the first step towards data efficiency. For data managers – someone responsible for processing data into a useful data model – this means using their analytical skills to scrutinize existing workloads so they can identify those that actually deliver valuable insights. From this point, they can take a position on whether to redesign the workload, increase or decrease it, or even stop the workload that isn’t delivering results. A full understanding of data history, including where data comes from and what happens to it, can also be a useful starting point to help establish cost controls and identify costly errors.
Business transparency should also stem from the SaaS vendor and the platform they select to use when it comes to spending. This allows companies to understand what they are investing in each workload and weigh this against the return on investment. Understanding the cost per query can highlight the most expensive queries and allow administrators or IT leaders to reconsider them in terms of rewriting or refactoring. Greater visibility and control over spending will give companies the best opportunity to maximize existing resources.
Predicting future costs
Only when companies get a handle on their data costs can they actually predict future costs and implement measures to keep spending as efficient as possible. Many older data platforms are highly inflexible, with fixed costs and long-term vendor contracts, making it more difficult to implement changes during difficult times, or even when scaling back requirements during slower periods of data analysis. Such tools often require complex, time-consuming capacity planning to control data costs, which can ironically be expensive in itself.
The costs of data processing, monitoring and control mechanisms should not be an afterthought. Flexible scalability and consumption-based pricing models are a good way to avoid unnecessary overprovisioning and paying for processing and storage that don’t deliver on business. A growing number of organizations are also choosing to set budgets in advance, with spending limits, digital ‘guards’ against overspending and daily alerts and alerts. This allows companies to identify where money is being spent, how much value it is delivering and how it can be controlled.
Modern data platforms built in the cloud provide an intuitive user interface to examine usage and usage trends, with clear dashboards that visualize which teams, customers and cost centers are responsible for the majority of spend. Rather than waiting for expenses to exceed budget, companies can get a head start and see when spending limits are expected to be exceeded. In the long run, this will help tech leaders and CFOs reduce operational costs through more efficient use.
Tracking usage at a granular level (think account level, per user or per task) will be a key differentiator. However, larger companies should also consider taking control at an organizational level. This may require limiting the actions of teams or individuals to perform credit-consuming means such as creating warehouses. Such capabilities also provide deep control over factors such as the size and number of clusters, and provide granular control over when clusters are launched, to control costs now and in the future. Per-task cost allocation helps organizations manage departmental costs and maximize resources as they scale to more teams and jobs. Additionally, auto-pause and auto-resume capabilities can be enabled by default. This capability disables platforms when they are not needed, preventing payments for unnecessary use and saving customers money.
Leverage data, control costs
Even in difficult economic times, organizations should not give up their ambitions to harness the power of data. For companies in any sector, analyzing and understanding data has never been more important. The focus must instead shift to changes that actually deliver results, such as moving from legacy on-premise platforms to modern SaaS data platforms that enable better cost transparency and planning.
This will have a huge impact and enable companies to take control of their technology investments, which could be a key differentiator in today’s challenging macroeconomic landscape. Companies must avoid going down the self-defeating, retrograde path of cutting back on their data usage and embrace the potential of modern data platforms to maximize cost efficiency and control, while still paving a path to a data-driven future.
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