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April 15, 2024
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 min read

What is Data Silo? Challenges and Solutions

What is Data Silo? Challenges and Solutions
Fig.1: A data silo refers to a collection of information accessible exclusively to specific groups within an organization. Describing data as "siloed" implies that it is segregated and inaccessible to others.

Data silos refer to information repositories where a certain group of people in an organization controls it, making it hard for other departments to effectively access or use the data. Different departments like finance, administration, HR, and marketing require particular sets of data that facilitate the implementation of their duties. Therefore, these departments often store their data independently, just like silos utilized by farmers to separate grains. Due to the increase in the number of data assets in terms of volume and variety, we also expect a prevalence of data silos. However, what are the challenges that occur with data silos? If you are seeking the answer, you are at the right place. In today’s article, we are going to focus on the data silos, challenges and solutions.


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Let’s Define It First

Data silos can seem unthreatening, yet they significantly hinder the flow of information and cooperation among units. Frequently, splitting data into silos gives rise to discrepancies and duplications that reduce the quality of data. Moreover, isolation of data contributes to the complications faced by senior organizational leaders in accessing holistic information about company-level data.

Data silos, in simple words, are not healthy. Ideal data requires that all levels of an organization should be able to access and understand data easily. Failure to find, use, or trust the data efficiently will render it useless in contributing towards analyses and decision-making procedures. Any business undertaking digital transformation without deconstructing the barriers of data isolation will fail to tap into the full benefits offered by this transition. For organizations to be truly effective in driving data-driven decisions, they need to ensure that decision-makers have a holistic view of pertinent data for their analysis.

One key application of enterprise-wide data is that its use allows making informed decisions and brings to light concealed or unforeseen opportunities as well as risks. In addition to that, isolated data poses a danger. Database data separation among various silos is found to obstruct effective organization-level data governance, which in turn undermines regulatory compliance and increases chances for misuse of sensitive data. In order to analyze the extent to which data silos hinder holistic data analysis, we need to investigate where these silos come from, figure out how they undermine full data usage, and finally find ways of eliminating them through integration.

The Challenges With The Data Silos

Organizational data is often held back by the strict boundaries imposed by data silos. Once the relevant information has been separated into various units, such departments become a barrier to sharing important details with one another. In turn, analysis for every division ends up with a narrow focus only as there is no exposure to an enterprise-wide view of the data. Consequently, it is difficult to establish inefficiencies that extend throughout the whole organization; thus, unveiling possible areas for operational cost reductions requires integrating datasets related to both operations and costs.

An act of data silos is considered to be an important threat that may have an adverse impact on the accuracy and reliability of the information. This happens when each separate bit of information is in its own compartment, so that copies of the same data live in separate places, which leads to irregularities between datasets by department. Moreover, over time, as data ages, it gradually loses its accuracy which means less value for decision-making purposes. In this case, different medical records from distinct systems for a single patient may no longer be synchronized on a step-by-step basis thus diminishing the trustworthiness of the information provided by them.

Resource inefficiency often arises from data silos. When information is stored in multiple places or individual users download it to their personal or group storage facilities, resources are strained due to redundancy and the system is very hard to manage. Reducing this burden is done through the consolidation of all data into a single source, meaning that once data has been downloaded and processed, it will be deleted from central storage and thus free up valuable space; it is also less burden on the IT department who have already cumbersome work in getting unnecessary storage infrastructure or else two birds with one stone as they say – solving one problem solves two. For example, when employees frequently download copies of datasets for analysis purposes, it results in many redundant files that take up unnecessary storage space.

Data silos block cooperative operations by reflecting a disposition towards departments; thus, perpetuating the culture of compartmentalization within organizations. By using data to drive activities in a company, different data-driven companies understand collaboration as an efficient approach for finding and exploiting new knowledge. In creating channels for departments to share their data, it means that there is an environment which enables them to do it effortlessly. On the other hand, where data cannot be accessed or shared easily, the potential for collaborative work is frustrated hence hindering the ability of the organization to exploit collective intelligence in strategic decision-making. Let’s check out the solutions to these challenges now.

Fig.2: Logo

Solutions For Data Silos

For a company to eliminate data silos, it would be essential to make changes on the technological front and at the same time bring about organizational changes. One of the important revolutionized advances in the recent past is cloud technology, which has enabled centralization and analysis of data, making these processes much faster than before. By using cloud-based tools, it becomes easier to streamline mechanisms that facilitate aggregating data into one pool for analysis purposes, which hitherto would have taken weeks, months, or even years to fully analyze.

Data silos are eliminated by change management. Since the company’s culture can contribute to silo formation, it is necessary to inform employees about the benefits of data sharing and integrity. By mentioning adverse effects of silos, namely data inconsistency and the need to keep ahead of competitors, organizations will be able to develop a tendency to engage in cooperation. In order for such a cultural shift to occur, top management must not only initiate but also demonstrate personal involvement in this process.

To break the barriers of silos, you can achieve through data centralization. Creating a cloud-based data warehouse or data lake acts as an effective measure whereby all corporate data is stored in a single place. By doing so, this repository streamlines information and brings together details from different sources, ensuring ease of retrieval without disregarding business demands and addressing privacy issues.

Efficient data integration is a priority in avoiding future silos. There are a number of ways in which organizations employ for data integration, scripting, on-premises ETL tools, and cloud-based ETL solutions. For example, scripting involves IT professionals who write scripts in languages like SQL or Python to extract data from siloed sources and load it into the warehouse. However, scripting might turn out to be too complex and cumbersome when the sources of data change and so do the needs for that data over time. On-premises ETL tools help to automate data movement by extracting data from sources, transforming it into a standard form, then loading it into the organizations’ warehouses.

The companies that provide the cloud platform as a service can offer help to the development of the ETL tools and allow us the use of their servers, which results in cost savings. The whole process can be simplified by using this approach, and then integration will take place through a unified system, i.e., data warehouse. Such systems deal with issues associated with the integrity of data quality  and collect information from different sources into one place for analysis so that everyone works on fresh data.

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