By Gary Allemann, MD at Master Data Management

Now more than ever, businesses are looking to cut down on costs while optimising their cloud usage. However, the cloud is not a one-size-fits-all solution for all applications, and cloud costs are determined by each business’s unique needs. 

Hybrid cloud will dominate as companies continue to optimise infrastructure costs

Ongoing cost containment will continue to drive decision-making. In many cases, companies are looking at cloud options to provide more flexibility at a lower cost. Yet, in practice, the cloud is not suitable for all applications, and cloud costs can vary widely depending on the data types, access frequencies, and data movements.

From a data management perspective, companies need to untangle existing complex data landscapes to understand where critical data is stored and how it flows to enable critical business processes and analytics products. This understanding is critical to assessing the impact of proposed shifts to the cloud, and to understanding business logic that may be hardcoded in the data layer. 

Lift-and-shift movements also offer limited value – so reverse engineering existing data stores can be a great way to identify opportunities to optimise designs to improve performance and improve insights.

From a governance perspective, it is important to begin to classify data in terms of frequency of access to select cost-appropriate storage and access options.

Finally, businesses must plan for consistent data access management across the data landscape, particularly with hybrid- or multi-cloud solutions. This can be simplified through the use of centralised, fine-grained access policies that can be easily deployed across environments.

More organisations will move away from a single, monolithic ERP solution toward a more flexible and agile composite architecture

Over the last several decades, many businesses implemented mega-Enterprise resource planning (ERP) applications, from vendors like SAP or Oracle, to run their core business. However, as businesses seek more agility and better efficiency, these monolithic architectures are increasingly presenting unacceptable levels of risk and specialist alternatives are increasingly being sought. According to Gartner, at least 50% of ERP clients will evaluate alternatives to upgrades of their existing ERP by the year 2024, and this is particularly true for companies attempting to modernise analytics. Gartner suggests that profound mind-shifts will be necessary to exploit the change to composable ERP.

However, one challenge is that ERP mega-vendors have often made it difficult to find, access, and leverage data from outside their stack.

Companies building advanced analytics capabilities, often in the cloud, must plan to understand and share their ERP metadata (which are typically complex) to exploit this for reporting. Tools, like Safyr, that make ERP and CRM metadata easy to find help to manage this risk.

Master data strategies must cater for data shared between the ERP and other specialist applications – especially for customer, vendor, and employee data sets. And finally, as business processes cause data to flow between applications it will become more important to understand key roles and responsibilities and implement cross-platform data integrity processes to ensure data is fit for purpose. The opportunities to drive business agility and advanced analytics via a composite architecture will depend heavily on data integrity.

Establishing a data-driven culture

Moving beyond technology, BARCs research on top BI trends shows the drive to become data-driven is increasing. Data-driven organisations are investing in data management foundations including data quality, data governance, and master data management.

The drive for better insights will continue with ongoing investments in self-service BI, data discovery and modernising the data analytics stack leading the trends. According to research by McKinsey, companies that make data accessible to their entire workforce see a substantial increase in the impact of analytics on revenue. Data leaders that invest in single dashboards enabling data discovery, data context, and access to data for all knowledge workers will see productivity gains and improvements in data integrity.

Data leaders will also look to link data products to business outcomes. Investments in data management and analytics products will increasingly be prioritised according to the measurable impact on achieving business goals.

In conclusion, data governance remains critical in order to ensure that the correct stakeholders are involved, at the right time, in decision-making – whether to agree on the priorities for investment, to increase agility through improved impact analysis, or to manage access and ensure data privacy while enabling analytics.