What is a data silo?
A data silo is a collection of information isolated from an organization and inaccessible to all parts of a company hierarchy. Data silos create expensive and time consuming problems for businesses, but they are relatively simple to resolve.
By getting rid of data silos, you can access the right information at the appropriate time, helping you make smart decisions for the business. Removing data silos also reduces information storage costs and duplicate information.
How do data silos occur?
There are three common reasons for the occurrence of data silos:
Working culture of the organization
In most organizations, departments and teams tend to work in isolation. This is particularly seen in larger companies. This isolation can lead to internal competition as teams view themselves as separate from the company. Information sharing doesn’t happen, which creates silos.
Structure of an organization
Organizations need to integrate all departments to avoid information silos.
Different technologies for different departments
In an organization, it is common practice for different departments to use a wide range of applications. For example, the sales team may use Salesforce, while the marketing department uses HootSuite, and the media team uses SproutSocial. Each of these apps contain large amounts of information that each team could benefit from, if it was shared. Surveys have shown that organizations can use up to 1200 apps across departments. This results in multiple sources of information, which can be difficult to share.
Why are data silos a problem?
Data silos can be problematic for organizations for several reasons:
No holistic view of data
When data remains in silos, organizations cannot have a comprehensive 360-degree enterprise-wide view. When this happens, any relevant data connections are missed. Take for example a marketing campaign and the interest generated from it. If this data is combined with the sales team’s information on current sales figures in the same geography, it will make campaign insights more informed and effective. But, with silos, information sharing cannot happen.
Waste of resources
Every team has a database of customer information and different formats for saving this information. The chances of duplicate information is high. Organizations end up taking on the costs of storing information of both teams, despite the high levels of duplication. Such data silos cost money to store and increase pressure on financial resources.
Inconsistency in data
When data is duplicated and stored together, data inconsistencies can be introduced into a company’s information flow. A field in the information set, like a customer address, may be stored in multiple formats,resulting in inconsistencies. Add to this the probability of human error with inputting addresses, and there are a number of inconsistencies in stored data.
How data silos affect organizations
Departments may operate separately, but they are interdependent on many levels. For example, data that comes in from the finance department can be analyzed by the marketing and sales departments. The desire to gain an edge over the competition, improve operational efficiencies, and open up new business opportunities while cutting down on costs pushes organizations to achieve more with their data. For this, access to enterprise-wide information is key. Data silos can hamper this way forward.
Limiting the view of data
Since silos prevent the sharing of information, every department’s analysis remains contained within itself. Any inefficiencies that may be widespread in an enterprise will not come to light, if data is not shared across all stakeholders. All opportunities to find ways to reduce operational costs are thus lost.
Threatening data integrity
Data silos cause inconsistencies in departmental data. With time, each occurrence leads to inaccurate and useless data. This is often seen in the medical field when patient information is stored in multiple silos such as doctor summaries, nursing protocols, medicine intake, and procedural notes. When data silos are not connected, they tend to go out of sync and result in widespread discrepancies.
Multiple sets of data (often duplicate) burdens the financial resources of the company allotted to storage space. When various departments download this information, the resource quality suffers.
Discouraging collaborative work
A company’s working culture drives the creation of silos, which in turn reinforces a non-collaborative culture. Data that is difficult to access reduces collaborative efforts.
How to break down data silos
The simplest way for an organization to remove data silos is to consolidate it into a data warehouse.
Some companies utilize scripts written in SQL or Python to write code to extract data and move it to a central location. The only drawback is that it is time-consuming and needs a lot of expertise.
On-premise ETL tools
Extract, Transform, Load (ETL) tools can help remove the hassle of moving data by automating the whole process. This extracts data from source, performs necessary transformations, and then loads the data into the recipient data warehouse. These tools are usually hosted on the organization’s site.
Cloud-based ETL tools
Hosted in the Cloud, these ETL tools leverage the expertise and infrastructure offered by the vendor.
Most organizations recognize the fact that data silos are a challenge. When you have an ingrained culture of data separation, changing the mind-sets of employees can be difficult. Additionally, undoing silos can be difficult. There are a range of permissions and hierarchies that are difficult to untangle. The easiest way to start modifying this is to move data from the varied systems into a data warehouse, which acts as the repository for all collected data. Data warehouses are optimized for easy access and analysis—not for transactional processing. This ensures a 360-view of company data.
Avoid getting swayed by new technological trends that constantly enter the market. Rather, look for high-value opportunities that your business can take advantage of; analyze business needs, and narrow down on data solutions. Draw in the data from every department of the organization, and invest in analyzing use cases.
From here, a company can move forwards with the goal of integration. Each step should work towards building an integrated platform for enterprise data.
To do all of this, cross-organizational support is of utmost importance. Executive leadership needs to come in with their full support as well. With the gradual use of data in operational and strategic applications, the changes you need to see in an organizational set up will take place naturally.
The task of getting rid of data silos is not easy. In data analysis, a majority of the work lies in ensuring that data preparation is done accurately. The same principle applies to silos—to be more data driven, organizations need to integrate data and make it available to the whole organization.