Data silos form when data specific to a business unit/department is closely controlled and isolated from the organization. The silos may include critical technical or cultural data pertinent to the organization bereaving them an opportunity to obtain critical insight that could contribute to the increased efficiency or substantial structural change or the discovery of a new revenue stream. Data silos have been discussed in almost every organization and are recognized as a challenge. But why are they considered a challenge and what are the consequences? How do you deal with them?
Why are data silos a major challenge?
According to the Findings of the Treasure Data survey, 54 percent of organizations agree that data silos are a major challenge and it is daunting to access and analyze such data since they do not have a connected system. Additionally, disparate operations result in a lack of inter-departmental coordination and collaboration. As a result, data exchange and visibility of operations are hindered reducing the possibility of their detection and correction.
The isolation of data also makes it tough to assign the policy and handle governance. Monitoring each storage unit and the exchange between them leads to security and compliance issues when efforts to monitor data integrity are diluted. Above all, multiple duplicates of data come to the forefront making it daunting to identify the point of origin making the process inefficient and increasing the storage costs for the organization.
Addressing challenges at the root level
Tackling data silos after identifying the challenges is often unproductive. Instead, the organization must make the necessary changes to how it structures and manages the data as a whole. IT operations play an important role in this. The best opportunity to do as is provided when the organization restructures its operations as part of annual maintenance or complete system overhaul to improve them. They can select an integrated system that connects end-to-end operations and offers a chance to customize the arrangement to suit individual requirements that business units may have.
Moreover, some industries are not so quick to adapt to evolution in the technical landscape and usually end up with legacy systems that are not only disconnected but are highly difficult to manage or integrate with a different core system. So, the onus lies on IT policymakers and purchasing stakeholders to realize the significance of connecting the organizations and actualizing a technological ecosystem that is fully connected, and exchanges and analyzes data in real-time for the benefit of the organization.
Value derivation for business
Organizations that choose to move away from the disparate system and become connected must select a platform that addresses the challenges at the root level and be capable of scaling up to tackle emerging roadblocks. So, what additional benefits should the organizations foresee apart from eliminating the challenges? What capabilities of the solution should users look out for?
The common ETL process – extract, transform, and load- has become intensive and redundant for computing and transferring the data. A single system can effectively eliminate data silos with ERP, especially by leveraging the cloud capabilities of advanced tools such as SAP S/4HANA. The HANA-based digital core system integrates data silos seamlessly eliminating data duplication and allowing organizations to run advanced analytics algorithms with access to a larger data set from which they can derive actionable business insights.
Data silos also result in multiple versions of truth where individual silo owner creates their own version backed by department-wise requirements. Having a structuring policy that allows free movement of data across silos using a powerful enterprise application such as SAP S/4HANA creates much-needed trust and transparency in the organization. All the time and resources saved by digitalizing and integrating data silos over a common source improves productivity and standardizes data access practices.
Finally, there are individual industry constraints and data regulations that limit data flow. Understanding these requirements and adjusting the solution architecture is crucial for realizing the best outcomes for the organization.
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