Complex business environments of today generate an abundance of data that is disparate and disconnected. Organizations need the ability to orchestrate a variety of highly distributed data with speed and insights.
In response to the business needs, over the past several years companies have invested heavily in methods and technologies to automate data integration, preparation, and ingestion related tasks. However, the standard data integration methods have been failing in scenarios where there are a variety of new and expanding challenges: complex data sources (structured, unstructured, semi-structured, streaming, experience data, etc.), connectivity limitations, rigid transformation workflows, growing data volume, and distribution of data across multi- and hybrid cloud environments. While collecting data from traditional sources is often straightforward, enterprises struggle to integrate, process, curate, govern, transform, and augment data with other non-traditional data sources (including 3rd party data) in order to deliver a comprehensive, trusted view of the customer, partner, product, or employee data.
Additionally, in most organizations the business users are dependent on static data models prepared by their IT teams – upfront data modeling and schema assignment. Typically these predefined data models aren’t sufficient to address business use cases where data structures are unknown or where business requirements are not well defined - data and questions are not known ahead of time.
These complex environments require an ability to orchestrate a variety of highly distributed data, metadata, and processes, as well as deliver data quality and security, self-service options, and a high degree of automation, speed, and intelligence
As a result, most organizations often struggle and stumble at the very early stage of their data journey. They are unable to implement a strategy for the integration of data that is flexible and scalable. Due to this they end up confusing the data fabric with a tool/technology/platform and end up buying a single tool hoping that it would deliver the entire data management design for them.
Companies have invested heavily in methods and technologies to automate data integration, preparation, and ingestion related tasks. However, the standard data integration methods have been failing in scenarios where there are a variety of new and expanding challenges
Data fabric is an emerging data management design for attaining unified, flexible and reusable data integration pipelines. An intelligent enterprise needs to consume integrated and enriched data – at the right time, in the right format to support various data and analytics use cases, instrumental to thrive in this highly competitive environment. Data fabric delivers a unified and integrated end-to-end platform that stitches integrated data from various applications and sources to enable real-time analytics and insights for successful business outcomes.
An 'ideal' enterprise data fabric should enable capabilities to automate data integration, preparation, curation and orchestration to deliver analytics faster, reducing time to insight. The key goal should be to minimize the complexity by automating processes, workflows, pipelines to accelerate use cases. The key success factors for a robust data fabric are as follows:
- Acquire, Transform & Enrich Data from all the sources: The real value of the data can only be extracted when an organization can investigate data acquired from all the sources. Data warehouse, data lakes, data marts, transactional data storage, all the third-party participating systems – Data fabric architecture should be able to extract metadata from all the sources. Your data fabric design should help discover relationships, automate integration from diverse data sources and simplify data transformation.
- Ability to instill agility to your business use cases: End-to-end data management capability that includes ingestion, preparation, discovery, integration, and governance are crucial to accelerating ready-to-use data consumption. A lot of data management functions once automated provides agility to business users enabling them to draw insights faster.
- Self-service capability for the business users: Without much dependence on IT, business users should be able to curate their data view through a user-friendly no-code environment. Business users need to be empowered to acquire, transform, and enrich data from various sources.
- A modern data fabric design should enable re-placement of separately deployed and maintained data management technology and infrastructure by one integrated data architecture that can address the extreme levels of diversity, distribution, scale and complexity of enterprises' data assets.
- The architecture should ensure that the data fabric supports the combination of different data delivery styles dynamically (through metadata-driven design) as needed by existing and upcoming use cases. These styles could include a mix of batch integration with virtual data delivery or support for data replication.
We can design and implement a modern data fabric that can help overcome the traditional challenges that organizations have faced with respect to limited data access, lengthy time to value, lack of real-time data, and high costs of entry. Most importantly, we can help deliver true self-service analytics to the business by empowering users to curate data and consume insights from the data on a single user-friendly platform
"How do I stitch together a scalable and flexible data fabric for an enterprise, and what are the components needed to do so?”
- Evaluate existing data management processes and technologies.
- Outline the key components and technology parts needed to deliver a scalable and flexible data fabric for the enterprise. Identify the team structures and skills needed to make the data fabric more usable.
- Develop a roadmap and implementation plan for the enterprise data fabric
"How do I design a unified data and analytics in a multi-cloud solution that includes data integration, database, data warehouse, and analytics capabilities for a data-driven enterprise?"
- Evaluate on-premise data warehousing solutions and develop an approach for transitioning to a cloud based data warehousing platform.
- Implement data acquisition strategy to connect data across multi-cloud and on-premise repositories in real-time while preserving business context.
- Develop an approach to empower users to connect, model, visualize, and share data securely in an IT-governed environment.
- Outline a tactical plan for reusing your existing BW models, transformations, and customizations with the BW bridge option.
“How do I ensure that my data lake does not become a data swamp - dumping all data into data lake hoping to do something with it down the road. But then lose track of what’s there!"
- Design a centralized repository that allows to storing all structured and unstructured data at any scale.
- Enable key capabilities on the data lake/data lakehouse: Exploratory Analysis, Learn and Burn, Deploy, Agile Access to Data, Low Governance, Data Tiering, Platform for advanced analytics
- Develop processes and controls aligned with organizational culture and business priorities
- Analyze current state of data readiness and data gaps within a very short duration of time to curate a high-level roadmap plan
- Advisement, tools and enablement to establish robust business processes to maintain the quality and data governance model of master data
- Strategy & roadmap addressing the future state of data governance by designing processes, policies, business rules and data ownership
- Customized assessment and strategy workshops to help plan, deploy and optimize analytics investments
- Design and delivery of embedded insights for SAP business processes
S/4HANA Starter Pack
- Unified data and analytics solution in a multi-cloud SaaS environment
SAP Datasphere Migration
- Rapid deployment of focused analytics packaged solutions
- Visualization design, use case development and implementation
SAP Analytics Cloud
- Design of modern organization community and processes