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Your frequently asked questions; Answered

FAQs

Many businesses want to unlock the value in their data but don’t know where to start.

Enterprise Resource Planning (ERP) is a software system that helps you run your entire business, supporting automation and processes in finance, human resources, manufacturing, supply chain, services, procurement, and more. Different vendors sell different ERP solutions, such as Oracle (ERP Cloud / NetSuite, IFS, Microsoft (Dynamics). SAP provides the world's most widely used ERP solutions, SAP ECC (which is due to go out of support in 2027) and SAP S/4HANA. SAP's ERP systems can run on premise, in the cloud and be used as a Software as a Service (SaaS) solution.

SAP ERP has been around since 1972. The data structures in the SAP ERP system still reflect the limitations of technology from that time: Table names and field names are very short (as there was limited memory available) and cryptic. The origin of SAP is in Germany, and many abbreviations used for field names and table names are based on the German words. Over the years, SAP have developed many proprietary technologies for extraction and communication as open standards were simply not available yet. Building an analytics solution for SAP ERP systems requires a good understanding of the SAP table structures and interfaces. Most other ERP systems have a data model based which is more descriptive, using English words instead of German abbreviations, and use open protocols for interfacing (JDBC, Database CDC and REST APIs).

Traditionally, data platforms for SAP data were developed using SAP data & analytics products. These platforms were optimized for onboarding SAP ERP data, but very difficult to use for integrating non-SAP systems, such as SalesForce, ServiceNow and Shopify. This resulted in silo'd solutions: Enterprises would often have one data warehouse for SAP data, and another data warehouse for non-SAP systems. As business users typically want to combine SAP with non-SAP data, this then led to shadow-IT where teams download reports from both data warehouses into Excel and manually combine the data there. In this blog post there are some more considerations about specific challenges for analytics on SAP data
There are many options and factors like scale, complexity of SAP landscape, (near) real-time requirements, available tools and skills in the organisation and of course the available budget should all be taken into consideration. At Snap Analytics, we work with many 3rd party products which all have their specific strengths and weaknesses. We can help you decide which tool is best for you. Amongst the products we like using, and in no particular order, are: Fivetran, Matillion, SNP Glue, Theobald, Informatica, Talend and Qlik Replicate. There are other products on the market, and you can even build your own interface with using ODate and SAP REST APIs - although that solution is not very scalable. 

Your SAP license is an agreement between the customer (you) and SAP. There is no generic answer to this question. In doubt, check with your legal team and check with your SAP representative. 
There are usually license constraints that apply. You can find an article on the Snap blog which explains the most common considerations with regards to licensing

At Snap Analytics, we want to ensure our customers find the solution that is best for them. Sometimes an SAP data & analytics platform is best for the customer, sometimes we think a customer is better of using non-SAP technology for data & analytics.
SAP have made great progress with modernising their data & analytics solutions, with SAP DataSphere and SAP Analytics Cloud. We are proud to have certified SAP consultants who can help you with your SAP data & analytics projects.

AI systems consist of algorithms, data sets, computing power, and human-machine interfaces for interaction and learning. The main types of AI are narrow AI (focused on specific tasks), general AI (capable of human-like intelligence), and superintelligent AI (exceeding human intelligence).

Trends include AI democratisation, AI-powered edge computing, AI ethics frameworks, and advances in quantum computing for AI. Future directions include AI-human collaboration, AI systems with emotional intelligence, AI for social good initiatives, and interdisciplinary AI research.

Using AI solutions can helps businesses analyse data, predict trends, optimise processes, and make data-driven decisions for growth and efficiency. Our AI workshop demystifies AI to align your strategic goals with high impact AI solutions. We match the right AI approach for your business, helping you to chart a course to success with increased profitability, improved processes and empowered people.

A data solution is a set of technologies, processes, and tools designed to collect, store, process, and analyse data to provide meaningful insights and support decision-making.

Cloud data solutions involve use cloud services to store, manage, and process data, offering scalability, flexibility, and cost-effectiveness.

You need to consider factors like data volume, data types, business goals, budget, existing infrastructure, and scalability requirements. We can help you to choose the correct solution for your business.

Managed data services involve outsourcing the management of your data-related tasks and processes to a third-party provider such as Snap Analytics. These services include data storage, processing, security, integration, and analytics.

Services include data storage and backup, database management, data integration, data security, data analytics, data governance, and cloud data services.

Cloud-managed data services involve managing your organisation’s data infrastructure and processes in the cloud, providing scalability, flexibility, and cost-efficiency.

Do you have more questions?

Contact one of our experts today.

FAQs | snap team@2x