Businesses racing to leverage generative AI must develop a data strategy that is integrated, accurate, and democratised as they strive for tangible business results.
Data is the essential building block of any AI strategy – without high quality inputs, any AI venture is unlikely to deliver outcomes that boardrooms demand.
Putting together an ambitious data strategy that encompasses increasingly fragmented technology ecosystems can make CIOs trepidatious. But the rewards could be AI-fuelled growth.
Some information, such as that held in web-based customer management or planning systems, will be relatively straightforward to access. But important data often resides in applications where extraction is a more complex challenge.
A prime example of this is data embedded in SAP systems, which support critical functions in many organisations. It might not be going too far to say they encode not just the history, but the very DNA of a business.
But while SAP systems usually “just work”, SAP data is complex, and not easily extracted for use in generative AI (genAI) systems. Moreover, it can feel like the teams responsible for SAP systems speak a different language to those responsible for other data sources.
Data strategy
It can become easy for senior leaders to overlook or bypass SAP installations when planning their AI transformation strategy.
But that can leave their AI strategy bereft of key data, generating insights on a partial picture of the business. Getting that data into AI systems could drive operational efficiencies that free up skilled staff time and improve customer experience.
Solving this challenge requires more than simply lifting and shifting on-prem SAP installations to the cloud.
A robust data migration strategy puts a company on course to integrate SAP data into a larger cloud-based-data ecosystem. Think of it as a safe space for connecting data and accessing the tooling and infrastructure needed to develop a transformational AI strategy.
Creating that strategy requires deep knowledge of both SAP, and other data sources, as well as the target cloud-based platforms.
This is where Snap Analytics can help. It has proven expertise in SAP, and deep experience developing data migration plans for FTSE 100 companies.
That means they can analyse data and map a migration path for an organisation to follow or scale up to manage and implement the migration and integration completely.
As Snap Analytics CEO David Rice explains: “Ensuring you can leverage and create connected AI models which include both SAP and non-SAP data will ensure you can maximise the potential of enterprise AI.”
It also removes the headache of devising and standing up a new on-prem data architecture, which Rice says, can account for upwards of 30% of a traditional, non-cloud project.
This was highlighted in its work with Premier Foods, where Snap Analytics created a data platform to deliver new insights across the business and deliver efficiencies and reduce risk by eliminating manual data work.
This included integrating SAP data and ensuring it could be exploited in modern tools such as AWS RedShift and SageMaker. This has enabled new solutions across the business spanning manufacturing and sales reporting, but also in customer service and product development.
Business leaders know they can’t afford to fall behind as genAI establishes itself. It’s essential that SAP data is part of the strategy to unlock the full benefit of AI. Unlocking this opportunity will be much easier with help from experienced partners like Snap Analytics.
Learn more about Snap Analytics.