The world of data architecture has witnessed a paradigm shift over the last few years, with many organizations having to make use of new technologies to drive market-driven innovations. These include the deployment of, personalized offer, real-time alerts, and predictive maintenance. Moreover, it has been found that increase in the use of these new technical additions, be it data lakes, customer analytics platforms or even stream processing has completed changed the complexity of data architecture. Such has been the impact of innovation development that organizations are struggling to deliver new capabilities while maintaining existing infrastructure and at the same time ensuring the integrity of artificial intelligence (AI) models.Now, it is a fact that under the current market dynamics slowdowns are not a viable option. As a result of which, many companies such as Amazon and Google have begun using innovations in AI to update traditional business models to stay relevant. Furthermore, the sudden arrival of the COVID-19 pandemic around the world has also meant that companies seeking out more flexibility and speed in doing business. And so, it has been discovered that companies have begun making six foundational shifts to their data architecture blueprint so that they can deliver results at a more rapid rate. Here is then, presenting those ‘6’ game-changing shifts in data architecture that companies now have started to use.
1.Flicking the switch to go from On-Premise to Cloud-Based Data Platforms:
In the highly digital world that we live in, Cloud certainly seems to have become the most innovative and disruptive technology available with regards to a new approach in the field of data architecture. This is mainly because it allows several companies to scale up the use of AI tools and their capabilities to gain an edge over the competition. Many global cloud providers such as Amazon, Google, and Microsoft have by revolutionizing this technology ensured that organizations of any size can now source, deploy, and run data infrastructure along, with platforms and architecture on a large scale. For example, one utility services company has combined cloud-based data platform with container technology, which comprises of microservices such as searching bill data or adding new properties to the account. The intention of increasing use of cloud-based technology by companies is to deliver large amounts of real-time inventory and transaction data to end-users for analytic purposes. More importantly, the cloud is also being used on a large scale to reduce costs on ‘buffering’ transactions, and avoid the use of the more expensive on-premise legacy systems.
2.The low expenditure and convenience involved in using real-time data processing:
The increased use of real-time data messaging and streaming capabilities has been mainly due to the drastic reduction of the cost involved. Additionally, these cloud-centric technologies also conveniently help host a few new business applications. For example, transportation companies through the use of these apps can accurately help the customers with arrival predictions of the transport. On the other hand, insurance companies can make use of real-time behavioral data obtained from smart devices to analyze and individualize rates. In the meanwhile, manufacturers can also make use of real-time data to predict infrastructural issues. Also, data consumers such as data marts and data-driven employees can make use of real-time streaming functions such as subscription mechanisms, to subscribe to topics, so that they can get constant feedback about transactions they need.
3.Availability of pre-integrated commercial solutions to the modular, considered to be best-of-breed platforms:
Due to technological advancement that the world is witnessing today, many companies are now beginning to use a more modular flexible data architecture, that uses best-of-the-breed and frequently open-source components, which can be replaced with new technologies without affecting other parts in it. And so, many utility service companies are making use of this approach to connect cloud-based applications and thereby deliver new data-heavy digital services to millions of customers across the globe. For example, this innovative technology provides companies with accurate daily information about customer energy consumption. It also provides real-time analytical insight that compares individual consumption with peer groups. In this method, data is also synched with back end systems via proprietary enterprise service bus, and micro services hosted in containers run business logic on the data.
4.Free access to point-to-point as well as decoupled data:
Exposure of data through the use of API’s is one way that can ensure that direct access to viewing and modifying data is limited and secure. On the other hand, it also simultaneously offers much more rapid up-to-date access to common data sets. This then allows organizations to reuse data very quickly. It also enables collaboration within the analytics team to occur seamlessly, which in turn helps in developing a more efficient use of Artificial Intelligence or AI. For example, one pharmaceutical industry has begun setting up an internal ‘data marketplace’ to make available to all employees via API. This is being done mainly to simplify and standardize access to core data assets, and to avoid complete reliance on proprietary interfaces.
5.Conversion of an enterprise warehouse to a domain-based structure:
A paradigm shift has been observed in the world of ‘Data Architecture’ with many leaders belonging to this field pivoting from central enterprise Data Lake to a more domain-driven architecture. This has designed basically to improve time to market new products and services. The advantage of using this approach is that it gives ‘Product Owners’ in each business domain an opportunity in organizing their data sets in a manner which is very consumable for the users within their domain as well as downstream data consumers in other business domains. However, for this approach to prove to be efficient and thereby successful, careful balancing is required, so that fragmenting does not occur. One European telecommunications provider used a distributed domain-based architecture, to expose customer order and billing data to data scientists for use in AI models or even share it directly to customers via digital platforms.
6.Move from rigid data models to a more flexible and extensible data sharing schemes:
Predefined and proprietary rigid data models obtained from software vendors are generally used to serve specific business-intelligence needs. Now, this approach when used requires the organization using it to undergo lengthy development cycles and also possess a strong system knowledge, especially when they want to incorporate new data elements or data sources. This is because any changes to it (approach) can alter data integrity. And so, to gain greater flexibility and acquire a powerful competitive edge while exploring data or supporting advanced analytics, companies have begun using ‘schema-light’ approaches. This includes increased use of denormalized data models, which comprises of few physical tables required to organize data for maximum performance. The biggest benefit of using such a unique approach is that provides for agile data exploration, greater flexibility in storing structured and unstructured data as well as reducing complexity, which means that data leaders no longer need to add additional abstraction layers such as ‘multiple joints’ to query for relational data.