Streamline Enterprise Data Management & Analytics practices with Data Integration

by Sachin Rane, on Jan 15, 2021 11:03:18 PM

Estimated reading time: 2 mins

Analytics and Artificial Intelligence-based processes are direct functions of your data quality. That is to say, ‘Garbage-in; Garbage-out’ is the ultimate truth for any data story. Hence a curated, prepared, and integrated, single source of truth of data is quite essential in paving the way for success of an Enterprise Data Management or EDM practice. Today, as many enterprises tread the way of mergers and acquisitions (M&As), priming and integrating the data of the source and destination in order to build a common enterprise database becomes one of the prime points of action post an M&A.

EDM strategy – Get the whole data picture for 360 degree analysis

Streamline Enterprise Data Management (EDM) and Analytics practices through Data Integration

As businesses vie for implementing the ‘Intelligence First’ principle in their day-to-day operations, building a strong EDM practice for a 360-degree view of the business operations becomes essential. With both the merging entities accumulating high volume data on almost a daily basis, the new merged entity needs to quickly validate the data, take stock of the situation, and integrate the data on a priority basis. Here, Data Management solutions on the Cloud make the post-M&A data integration a lot simpler and less time-consuming.

Essentials of building a successful EDM strategy for the merged entity

For making the data integration a success, the merging entities need to devise a workable strategy. The important elements include –

  • Data experts from both the entities: Onboard the experts, who were involved in creating the data blueprint and building the data bases on the either side, on the strategy panel to devise the strategy of the new entity.
  • Template for data transformation: Design a template in which the data extraction, data transformation, and data loading will take place. This step will help in the integration and unification exercise.
  • Business rules on the two data sets: Enlist, decode, and understand the rules that are defined on top of the data sets to facilitate the integration.
  • Data sampling for quality checks: Conducting quality analysis and sampling of the data that is being considered from the acquired entity side. Perform the check for various cross sections to cover the entire data set.
  • ETL tool selection: Select the industry leading ETL tools, such as IBM Datastage, Informatica, etc., for the extraction, transformation, and load cycle. This is very important as the ETL cycle will happen in an ongoing basis till the separate entities cease to exist and the new entity assumes the only legal existence.
  • Data chunking and prioritization: Plan the creation of data chunks for the migration and subsequent merger. Prioritize between the data sets according to the importance, sensitivity, and the overall process cycles planned to be executed in the merged entity.
  • Volumetrics of the data: Consider the volumes of the data being transported in chunks and will be eventually integrated in the data sets of the merged entity.

Data Integration is the primary step in building the EDM systems. Sachin Rane offers some quintessential guidelines for Data Integration. Watch now >>

Streamline Enterprise Data Management (EDM) and Analytics practices through Data Integration

In conclusion

Data curation and integration seems to be simple as a bystander. However, it is a fairly complicated and elaborate process requiring months and sometimes years to follow a systematically planned blueprint, governance, and elaborate ETL cycles till the new entity commences full and seamless operations.

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Topics:CloudEnterprise Data Management

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