If an enterprise data architecture does not differentiate itself, it will not get adopted by the business.
A data architecture can truly become enterprise data architecture, if it gets adopted by majority of the business functions within the organization. But for that to happen, it needs to have elements that will solve existing business problem, improve their experience and bring substantial benefits in terms of functionality, convenience and cost.
Some of the elements that truly differentiate an enterprise data architect are listed below:
Big Data platforms
As more and more data is available to companies, many new opportunities to leverage data are getting created. Many of these new opportunities cannot be realised in a cost effective manner using traditional data management techniques. Big data platforms provide new cost effective ways to leverage large and complex data. A futuristic enterprise data architecture must include big data platform as part of the overall enterprise architecture strategy.
Massively parallel processing databases
Massively parallel processing databases like Teradata can be expensive, but they eventually reduce a lot of overheads by making it possible for business users to access data directly from the central data hub in efficient ways.
If innovation is central to your data strategy, an enterprise data architecture should include a data lab to enable data analysts and data scientists to explore new data and build new use cases to leverage data for business outcomes.
Data Science and Machine Learning capabilities
Even when your analytical models are working just fine, it may be worthwhile to replace them with newer self learning algorithms that leverage past data to improve decisions over time. This transition can be gradual, but it will give huge benefits in the long run by reducing maintenance overheads and improving predictive capability overtime.
Artificial Intelligence capabilities
An enterprise data architecture can greatly benefit from the recent progress made within the artificial intelligence sector. Image and speech recognition capabilities can lead to creating data for deriving new insights. Natural language processing capabilities can help in ingesting unstructured data for structured analysis.
A framework that enables quick data cataloging can create huge efficiencies in the process. Enabling data ownership, traceability and protection capabilities can save a lot of regulatory hassles and improve quality and integrity of enterprise data. Managing master data and reference data effectively can help to get a consistent view of enterprise data.
A good data platform uses accelerators to build quality data solutions quickly. Using industry standard data models, ETL tools, reporting tools and data quality tools can speed up the process of building data management solutions, and reduce maintenance overheads. Building competencies, frameworks and design patterns can speed up the process and help in creating consistent solution.