Several mid and large-scale organizations continue to rely on on-premises legacy data systems for data storage and management. Through legacy systems like relational database management systems and proprietary mainframe portals, businesses gather and store structured/unstructured data from software applications and websites. However, the batch-processing nature of legacy data systems largely hinders real-time processing and analysis of accumulated data. This lack of real-time processing capability in legacy data resources makes it difficult for business leaders and stakeholders to initiate and manage any advanced analytical initiatives. 

Harnessing the power of real-time data processing and analytics becomes easier when enterprises invest in the development of intelligent data lake repositories. Such repositories leverage artificial intelligence (AI) and machine learning (ML) algorithms to automate and manage various data operations, like ingestion, processing, transformation, analysis, etc. This enables leaders to gain valuable and quick insights into their operational processes or customer behavior and personalize services at scale.  

Relevance of Cognos in AI-Powered Data Lake Development 

IBM Cognos Analytics is one of the most reliable business intelligence platforms that offers an intuitive AI framework for configuring and designing exclusive data lake repositories. Repositories built using this framework support real-time data processing and analytics. By utilizing pre-built data pipelines in the Cognos AI framework, newly built data lakes are easily integrated with applications, websites, IoT devices, and other digital sources. This integration enables data lakes to extract and process structured, semi-structured, and unstructured data entities, and generate insights with ease. The bi-directional communication nature of data pipelines allows data lakes to route processed data insights to the connected applications and platform, making insights visible to app users.  

For the development and deployment of feature-packed data lakes, collaborating with a reputable Cognos consulting services provider is essential. Cognos consultants take into account key factors during data lake development, such as: 

·       Architecture Scalability – Cognos consultants design the architecture of data lakes by embedding scalable storage and computing mechanisms. These mechanisms enable data lakes to process and analyze large datasets without latency or performance hindrances. 

·       AI/ML Model Customization – Consultants configure pre-built AI and ML models (supervised, unsupervised, and reinforcement learning models) in the framework and integrate them with the newly designed lake repository. Custom model integration enables data lakes to perform analytics with greater precision and deliver insights relevant to business needs. 

·       Data Security and Governance – By implementing role-based access controls and other encryption mechanisms, consultants ensure that the data lakes protect sensitive application/web platform data against unauthorized access and breaches. Similarly, consultants integrate data provenance tools with the data lake repository to perform consistent compliance checks and maintain better data traceability and transparency. 

Key Data Operations Automated by Cognos-Built Data Lakes 

1.     Data Ingestion and Storage 

AI-powered data lakes developed using Cognos use data pipelines to connect to various applications and data sources, thereby establishing a secure passage for data ingestion. Once pipelines are integrated with external sources, data lakes autonomously collect and import data files to a real-time staging environment. During this ingestion process, pipelines in data lakes evaluate the attributes of data files and tag them as structured or unstructured data content. This tagging enables business analysts/leaders to categorize and organize files within data lakes, simplifying the search, query, and retrieval process. 

1.     Data Cleansing and Transformation

Raw structured/unstructured data files ingested and stored in the pipeline’s staging environment might contain duplicates, missing values, format inconsistencies, or errors. The staging environment programmed with appropriate cleansing and transformation algorithms effectively preprocesses the data files and transfers them to the Cognos data lakes environment. Cleansing algorithms, such as deduplication and outlier detection algorithms, identify and resolve redundant or unusual patterns in data files. 

Similarly, data transformation algorithms standardize the format of unstructured/structured data files and ensure consistency. Cleansing and transforming data files before analysis ensures that the datasets are usable and optimal for interpretation. These steps prevent data lakes from being misled by distorted data files, which leads to inappropriate insights generation.  

1.     Data Analysis and Insights Generation

After the pre-processing stage, the staging environment transfers cleansed and standardized data files to the data lakes. By leveraging the pre-trained or custom AI and ML models, data lakes analyze huge volumes of data files in real time and generate insights. For instance, data lakes ingest and analyze customer behavior data from ecommerce websites and deliver insights into purchasing patterns, user preferences, demographics, etc. With these insights, optimizing the ecommerce site and delivering personalized experience becomes more manageable for brands.  

To obtain accurate insights from data lakes, experts from a IBM Cognos consulting company design custom AI/ML models and train them by providing a range of historical or benchmark data inputs. With access to reliable analytical insights, business leaders and analysts make strategic decisions related to product/process optimization and drive business growth. 

1.     Report Generation 

AI-powered Cognos data lakes with its intelligent analytics capability autonomously prepare and generate query data reports. After processing and analyzing data files, AI models embedded in data lakes compile the generated insights into visually engaging and easy-to-read reports. These reports can be archived and utilized for in-depth querying. Cognos data lakes are programmed to generate reports at regular time intervals, enabling stakeholders to instantly act upon insights related to key performance indications (KPIs), forecasts, or emerging trends. In simpler terms, the automated query data report generation capability of data lakes minimizes the scope for manual report preparation and facilitates on-time decision-making for data leaders and stakeholders. 

Closing Thoughts 

To sum up, AI-based data lakes developed using the IBM Cognos Analytics platform hold immense potential for data processing and analytics modernization. Cognos-built data lakes significantly reduce human involvement in tasks like data cleaning, filtering, and transformation, enabling business leaders to query and obtain insights faster. However, to build such sophisticated data lake repositories, partnering with a trustworthy IBM Cognos consulting services provider is essential. Skilled Cognos developers design the architecture of data lakes in line with an organization’s data processing and querying requirements. 

After the development stage, experts meticulously connect the newly built data lake repositories with existing applications/websites in a phased approach. By adopting a phased integration approach, developers minimize the risk of analytical overload on data lakes and technical disruptions in applications and websites. Besides, the staged approach enables Cognos specialists to test the performance of data lakes across different applications/platforms and integrate them with genuine sources. This allows data lake repositories to perform effective data analytics and generate worthwhile business insights.