Data silos are collections of information isolated from an organization that are not readily accessible by every level of the company. Solving the problem of data silos is expensive and time-consuming for enterprises, but the solution is relatively simple. Once you get rid of data silos, you can access the right information at the right time to help you make informed business decisions. Eliminating data silos also reduces information storage costs and duplication of information.
Data silos refer to the fact that information between different organizations is only privately owned, hidden from the outside world, and cannot be accessed by others. It’s like farmers keep the crops in their own barns, and do not exchange or trade with others, resulting in a waste of resources and reducing their potential benefits. In the era of big data, data is regarded as one of the competitive advantages of enterprises, so it is very difficult to convince everyone to share information, but we can still persuade everyone through the following points.
Why are Data Silos A Problem?
Data silos can be problematic for organizations for several reasons:
Lack of Full View
Organizations cannot have a full 360-degree enterprise-wide view when data is in silos. In this case, any relevant data links will be lost. Taking marketing activities and the profits they generate as an example, if this information can be combined with information about current sales data from the sales team for the same region, it will lead to more informed and effective decisions about marketing activities. But because of silos, such information sharing cannot happen. John Wanamaker, the father of American department stores, once said, "Half the money I spend on advertising is wasted; the trouble is, I don't know which half."
Waste of Resources
Each team has a database of customer information and saves this information in different formats, in which case there is a high chance of duplication of information. But despite the amount of duplication, organizations ultimately bear the cost of storing information from both teams. Such data silos generate higher storage costs and increase pressure on financial resources.
When data is copied and stored together, data inconsistencies are also introduced into a company's information flow. A field of an infoset, such as a customer's address, may be stored in multiple formats, resulting in inconsistencies. Coupled with the potential for human error when entering addresses, there can be many inconsistencies in the stored data.
How Data Silos Affect Organizations?
Although the departments operate separately, they are interdependent on many levels. For example, data from the finance department can be used for analysis by the marketing and sales departments. As businesses look to gain a competitive advantage, improve operational efficiency, and open up new business opportunities, all while reducing costs, these demands will drive organizations to accomplish more with data. To this end, access to enterprise-wide information is critical to success, and data silos can hinder this development.
Restrict Data View
Because silos prevent information sharing, each department's analytics are still only applied internally. Therefore, if data cannot be shared among all stakeholders, the inefficiencies that are common in the enterprise may not be exposed and all opportunities to reduce operating costs will be lost.
Data Integrity Threats
Data silos lead to inconsistencies in departmental data, and each inconsistency continues to lead to inaccurate and useless data over time. This is common in the medical field, where patient information is stored in multiple silos, such as physician summaries, care records, medication intake, and procedure descriptions, which are disconnected, often out of sync, and lead to widespread disparity.
Unfavorable Collaborative Culture
A company's work culture fosters silos, which in turn reinforce a culture that is not conducive to collaboration. Difficult-to-access data reduces opportunities for collaboration. In today's companies, there are often complex structures or subordinate organizations, but the information between them is not necessarily as fluid as we think. For example, the place where data is stored in each team is different, some may be on the department's mainframe, some may be on the company's server, and some may even be on its own cloud drive. At this time, each department may not know what the other party is doing, resulting in each team storing the same and repeated data, wasting the company's hardware resources, and making the amount of information between teams unable to flow.
Let's take another example, usually a company of a certain size will have some data engineers scattered in different departments. Usually, engineers do not have enough resources for development. At this time, if the company's demand for data continues to grow, requiring each department to build its own database, because the data between different departments cannot be connected, each engineer needs to start from scratch. Developing systems, building the same framework and working with the same underlying data, overworked engineers and kept them from reporting to the company on time.
How Data Silos Affect the Market?
Large Data Silos Monopolize the Market
In recent years, major e-commerce companies such as Google, Amazon, and Facebook have almost monopolized the digital advertising market. Since 2016, the growth of total advertising revenue in the United States has almost come from the two leading e-commerce companies, Google and Amazon. Such an exaggerated market share makes people have a deeper understanding of their ability to manipulate the market.
But there will always be people who think, "What's wrong with this?" Google and Facebook have also launched Google Ads and Facebook Ads Manager, respectively, so users can see how well the websites they post are performing.
It may not be a problem for the market in the short term, but in the long run, if we question Google's traffic calculation mechanism today, it cannot be confirmed by a third-party platform, even because large e-commerce companies have already formed a Data Walled Garden now.
Google Ads also uses this platform to allow users to deliver advertisements. The behavior of players and referees makes it impossible for users to break the restrictions of data silos, understand the conversion rate of advertisements between different platform channels, and then expand more marketing models. It can be self-sufficient in the garden of large-scale e-commerce.
I believe that everyone has a certain understanding of data silos, and also understand that whether it is an enterprise or an individual, we should break the data silos to maximize the overall benefits.