Data Silos: Part 1 – Decoding it For Clarity and Business Agility

Data silos, in their simplest form, are isolated repositories of data that typically exist within an organization. They generally are used to store and securely manage data, providing an effective way to keep track of large amounts of information. However, they also have their drawbacks, so it’s essential to understand when they can be helpful and when they can become detrimental to an organization’s success. 

In this three-part blog series, we will explore the following topics centered around data silos:

• This post: What is it, Why do we care, and are there benefits/drawbacks of having them?
• Part 2: What leads to data Silos and real-life scenarios that can lead or are necessary to manage them
• Part 3: Breaking the barriers by solidifying strategy to create a framework for managing data silos

Who started this dumpster fire and why care now?

The concept of data silos can be traced back to the early days of computing when databases and applications were designed to serve specific, isolated purposes within organizations. The term “data silo” was likely coined in the 1990s or early 2000s as a metaphor for the isolation and separation of data within organizations.

A data silo is a problem that can plague any organization that relies on data. Essentially, a data silo occurs when data is segmented and stored in separate places, making it difficult to get a complete picture of what is happening. This blog post will discuss various facets of data silos and how businesses can be ready for their next digital transformation.

Data investment will increase and accelerate over the next two years at a faster rate than overall IT investment in global enterprises:

• Eighty percent (80%) of executives agree that by 2022, data will be a top 1, 2, or 3 priority investment area due to its strategic value and potential
• The top-ranked pursued data outcomes include (1) accelerating digital transformation, (2) innovating new products and services, (3) reducing downtime and interruptions, (4) optimizing returns from analytics, predictive modeling, artificial intelligence (AI), and machine learning (ML), and (5) increasing overall valuation of the company

Source: SAPInsiders’ excellent benchmark report on the topic of RISE with SAP

Silos are not always a bad thing

Data silos can offer a variety of benefits for businesses. They provide a secure way to store and manage sensitive information without worrying about unauthorized access or manipulation. Additionally, data silos allow organizations to separate different types of data into individual repositories; this makes it easier to organize information according to its purpose or source. This is especially useful for businesses that rely heavily on customer data; different customer segments can be separated into distinct silos that should be addressed individually. By keeping data in isolated silos, organizations can ensure that sensitive information is secure and protected from unauthorized access.

Data silos also help organizations keep track of large amounts of data more effectively. By separating different data types into individual repositories, businesses can more easily search for specific pieces of information as needed instead of having to sift through all their data simultaneously. This makes it easier for companies to find the information they need quickly and efficiently.

When it comes to historical preservation or freezing data from a specific timeline due to compliance or other factors, classifying the data can provide a way to preserve historical data and maintain an accurate record of business activities over time.

Privacy Policies are get assist from data silos

Privacy policies play an important role in the management of data silos. When data is stored in silos, it may be subject to privacy and security risks, as well as regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). To ensure compliance with privacy laws and protect sensitive information, organizations should establish clear privacy policies that outline how data is collected, stored, used, and shared.

Some key elements of privacy policies for data silos may include:

1. Purpose: Specifying the reasons for collecting, storing, and using personal data, as well as the types of data that will be collected.
2. Ownership: Defining who owns the data and who is responsible for ensuring its privacy and security.
3. Access: Outlining who can access the data, how they can access it, and the conditions under which they can access it.
4. Sharing: Specifying when and how data may be shared with third parties, and ensuring that adequate privacy and security measures are in place.
5. Retention: Establishing how long data will be stored, and how it will be securely deleted once it is no longer needed.
6. Rights: Explaining the rights of individuals with regards to their personal data, such as the right to access, correct, or delete their data.

But be careful when they have potential to become barriers

Data silos tend to behave unfavorably; where they can result in a lack of visibility across an organization’s entire dataset. When different segments are stored in separate repositories, it becomes difficult (or even impossible) for business leaders to get a comprehensive view of the entire dataset at any given time. This lack of visibility makes it difficult for business leaders to make informed decisions about their operations or plan for future growth.

Additionally, if the same piece of information is stored multiple times in different silos, there is an increased risk that some versions may become outdated while others remain current; this could lead to inconsistencies between datasets which could confuse decision-makers or lead them astray when attempting to analyze their organization’s performance.

1. Data Duplication: Data silos result in multiple copies of the same data, leading to data duplication and inconsistency.
2. Inefficient Data Sharing: Data silos make it difficult to share data across departments, leading to inefficiencies and delays in decision-making.
3. Increased Costs: Maintaining and managing multiple data silos can be expensive, adding unnecessary costs to an organization.
4. Data Integrability: Silos can prevent data integration from different sources, making it difficult to gain a comprehensive view of the data.
5. Decreased Data Quality: Data silos can result in the loss of data quality, as the same data may be transformed or modified differently.

Holistically, it can prevent an organization’s inability to Leap Ahead in innovation spaces. Data is the foundation for building and leveraging AI/ML capabilities and for businesses to leap ahead with innovation and unorganized data can be detrimental to organizational goals.

Conclusion

In conclusion, data silos are a common challenge faced by many organizations. They can limit the full potential of data by making it difficult to integrate data and access a single source of truth. With this knowledge under our belt, we now know where silos can be helpful and where they are barriers for success. With increased compliance and privacy policies forged cross-border, they are required to be managed and isolated. However, we can maintain other areas of business and that is where intelligent decisions must be made.