Top 5 Data Management Challenges for Digital Transformation
With digital transformation of businesses in full swing, data has now become the life line of businesses. Most businesses work with a number of entities and exchange business data with them. For some businesses, data exchange is happening with the organization across various team. But managing data is easier said than done. Here are some challenges that businesses routinely face:
- Tracking Data Update, Accuracy and Completeness: One of the biggest challenge is tracking if the data has been received from 3rd party system. If there’s a delay, then downstream systems that depend on that data need to be notified so that they can take the appropriate corrective actions. Businesses also need to know if the data received is accurate and complete. Any malformations or incompleteness leads to garbage in – garbage out situation and the entire process has to be repeated again. This may lead to delay in downstream data processing resulting in delayed / missed business decision making.
- New Data Sources and Destinations: As businesses evolve, they need the flexibility to receive data from new data partners and push that data to other destinations. Every data source may have slightly different method of pushing and pulling data. Being able to work with these various technologies can pose a big challenge to businesses. Data can come in any of these formats, JSON, CSV, Xlsx etc.
- Scaling with Data: Data received and pushed from 3rd party system can vary based on various parameters. Sometimes it can grow rapidly and at times it can drop. Businesses should have systems that scale as the data grows and becomes larger and complicated to handle. Inability to do so can result in data truncation leading to data incompleteness.
- Predicting Data Collection Timings: As businesses depend more and more on data driven decision making, they need to know when and how they can have access to dependable and the most recent data. If that’s an unknown then Businesses will most likely make decisions on older data set or incomplete data, which may lead to incorrect decisions. Hence, it’s critical that a dependable and consistent process be set up so that Business owners know when to expect updated data and what actions to take in case data is not updated.
- Alerts and Notifications: This is perhaps the most critical thing needed for any Data Ops / Data Analyst / Data Engineering person or team who relies on data updates as part of their daily work. They need to know if the data is delayed, malformed, incomplete and any other scenarios. They should be able to set their own custom alerts so that they can re-request data or restart processing in case of data issues. This is a Big-time saver and increases productivity of teams.
In part two of this blog series will we will discuss specific features that Businesses look for when comparing various data exchanges.
Do check our blog post about YuktaOne Data Marketplace.
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