Companies today face serious problems in managing their data and delivering strategic value to them. The structure and business logic of data is fragmented into different solutions, architectures and technology platforms. In addition, different project teams, one for each solution, impose their views on the business, limiting the business to which each of these solutions are able to do. The database becomes a simple repository of processed data under the partial view delivered by each application, process, analysis, message and integration.
The value of the data for the company strategy is not consolidated and corporate and then the database, as well as everything around it becomes anemic, devoid of logic or structure that transforms given into information and corporate knowledge.
The technology department suffers to deliver structuring and strategic solutions to the business. The technology department suffers to deliver structuring and strategic solutions to the business as it needs to assemble complex architectures, composed of several products from different providers.
Example: If a company needs to improve its logistics process, it will need to change its ERP and SCM applications, change the orchestrations in the ESB and manipulate messages and documents in MQ, and finally build new ETL maps and analytical visions to deliver what the user needs. These changes will also generate a strong impact on other processes, such as the purchasing process, requiring a change in the corresponding process. In addition, the entire data structure and business logic of the logistics theme will continue to reside in different systems and technology platforms, with different teams, because in the anemic model, structure and logic are separated and fragmented.
The correct scenario would be from a single, interdisciplinary team changing the logic of the architectural block of logistics theme within a unified data platform, evolving the structure and behavior of the logistics business theme uniformly under analytical, transactional and SOA perspectives.
This eliminates complexity, redundancy and cost and expands reuse, productivity, agility in delivery. It also increases performance and scalability and reduces network latency and consumption of processing, memory, and storage.
The ADP architecture also promotes the use of small, agile, multidisciplinary and full stack teams, because in a single technological environment all the transactional, analytical and business behavior structure and behavior is delivered.
The architecture of ADP also enables what will be the future of technology, the data lakes. In the data lake architecture, the ingestion and processing of data for the generation of strategic value is performed in real time and the data cannot arrive anemic. This would pollute the lake. Since most data lake implementations use anemic architecture, it is necessary to create noise reduction algorithms, but the lake remains polluted. This kills 5V Big Data (velocity, variety, variability, volume and value).
So the solution is to adopt a data platform (Intersystems IRIS is an excellent option) and retire the bag of legacy software responsible for fragmenting, separating and making it impossible to have data behavior and logic together in a single architectural block, to have for each business theme an architectural block with data structure and business logic together, from the transactional, analytical and integration perspectives.