What is a Data Silo? Learn about problems and solutions
“Judge based on facts. Make the most effective decision based on facts. Decisions should be made based on accurate data, not ideology nor instinct.” This is what Anna Rosling, the author of the must-read CEO literature “Factfulness,” once said. This once foreign statement is now a standard many would agree upon. Today, data-driven management has become the norm. So what is the most important issue to be wary of in this data-driven era? The answer is data silos. Today, we’re going to summarize the concept of data silos, their problems, and their solutions.
Data silo refers to a state in which data generated within a company is fragmented. This means that the data generated and collected by different systems is being used only by certain departments or tasks, and isn’t seeing proper use elsewhere. As a result, the data values of the indicators that should be identical start to vary by department. Data silos are a critical issue for businesses that handle data. Because data is most effective when utilized towards a single goal, fragmented and mismatched data can affect the outcome of an enterprise’s operations adversely. Let’s take a closer look at the bigger problems data silos impose.
Adverse customer experiences from misguided insights
For example, let’s say that a company called A produces various data such as sales, personnel, customers, and so on. While this data might be put to use in some departments, they may not be utilized to their full potential in others. There are even cases where the data is not shared and thus departments store differing data. In this case, this interferes with drawing meaningful conclusions from data analysis. Making decisions based on distorted insights, in particular, can lead to wrong results. For example, if the criteria for leads defined by the sales team and the criteria for leads defined by the marketing team are different, you might come to the wrong conclusion when you try to consolidate the collected data for insights.
Data analysis is an important task for deriving insights from a comprehensive range of data. However, in a state of data silos, it is difficult to ensure data quality, making comprehensive data analysis challenging and leading to inconsistencies and reduced reliability in the results. Eventually, data silos can end up ruining the customer experience. Furthermore, it can not only have a negative impact on the customer experience but also lead to cost losses from a corporate perspective.
Duplication costs and increased learning costs
Operating separate data storage for different purposes typically incurs an additional 40% in costs compared to establishing an integrated data environment. Overlapping costs invested into solutions will also result in unnecessary losses. In addition, more time and money may be lost in determining the cause of a failure due to the difficulty of pinpointing the location of raw data. This is also a major obstacle in training new members. Each change in management increases the time and cost of learning.
Hindrances in collaboration between departments
There are several departments within an enterprise. While each department does operate separately, they are bound to be dependent on each other for projects that benefit from collaboration. For example, the data generated by the finance department could be used by departments such as marketing, sales, and business development. What any organization wants is to leverage data to achieve good results. Data sharing and access to information between organizations are key to achieving this, and data silos act to hinder this symbiotic relationship. Inaccessible data hinders collaboration efforts between organizations and reduces resource quality.
As such, data silos not only hinder enterprise data utilization but also lead to unnecessary expenditure. Therefore, if there are issues arising from data silos within the enterprise, it is necessary to identify the cause and status of the problem and actively resolve it. How do you address the significant issue of data silos?
The first step must be data integration and data sharing. Data integration involves aggregating data generated from multiple departments or operations into a single database or data warehouse, whereas data sharing entails making the integrated data accessible and usable by various departments or operations. Companies will need to build infrastructure for data integration and data sharing. Recently, several platforms have been developed for data integration. You will need to actively utilize these platforms.
Extract, Transform, Load (ETL) processes, cloud, and integration tools (ex: CDP) can be leveraged for data integration. Data integration improves data consistency and reliability, and provides comprehensive data for analysis. The data stored on the platform is then shared to different departments.
Addressing data silos also requires a change in corporate culture. We must establish a new corporate culture that is conducive to data sharing and appliances. Efforts will be needed to encourage data sharing and its infrastructure within the enterprise.
The best way to go about this is to form a data analysis team. A data analysis team comprehensively analyzes the various data generated within the enterprise to derive insights. It is also responsible for data quality management. This helps companies not only solve data silos, but also make data-driven decisions. And so companies will need to actively train data scientists to retain the aforementioned skills.
Data integration analysis is challenging if the data that comes from within the enterprise exists in a variety of forms. As a result, shared data will need to be standardized to improve data consistency and reliability. To do this, you can provide data schema design standards, or you can use data quality checking tools and the like. Data quality is also important for data silo resolution. This is because if the data is inconsistent or if there are many errors, the accuracy of the data analysis results will suffer. Data quality inspection tools and data quality metrics can aid in the management of these errors.
Finally, tighten security by setting up data access rights. Companies have a lot of data that includes sensitive information. Additional data masking technology can be utilized to protect this data. For reference, data masking is a way of hiding sensitive data or putting real data behind fake data.
So far, we’ve summarized the definition of data silos and how to solve them. Data silos can become a big, urgent problem for companies. However, they are often left unattended because companies are occupied focusing on the immediate interests of departments and their relations. It is no exaggeration to say that the growth and success of a company depends on how well they can use data. Data training has become a necessity for businesses to survive in this era.
Elice, the No. 1 digital transformation education provider, provides both basic programming training for data analysis as well as project training using actual data. If you want to analyze customer patterns and connect them to sales, or if you want to develop your employees’ data analysis skills to directly increase productivity, you can look to Elice training. Elice offers a cloud-based education platform that gives you the freedom to learn anytime, anywhere. Join Elice for a successful digital transformation!
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