Much like grain in a farm silo are isolated from the outside, a data silo typically consists of stored data that is not available to the entire organisation, but only to some parts of it such as teams, departments or even individual employees. Thus, it is siloed within the organisation.
Data silos can have technical, organisational or cultural roots. They tend to arise naturally in large organisations, because separate business units often operate independently and have their own goals, ways of organising knowledge, priorities and IT budgets.
Siloed data typically is stored in a standalone system and often is incompatible with other data sets, usually either due to a format, lack of APIs or any other connectors. More often, though, information silos are considered to be a cultural problem caused by departments or individual workers who don’t want to share information or don’t have an easy way to do so. That makes it hard for users in other parts of the organisation to access and use the data. Worse, they may not even know that such data exist. Therefore, much of the today’s work is being done from scratch, while employees could be building on top on of already accumulated internal company information.
Why are data silos a problem?
Data silos hinder business operations and and any data-powered initiatives that support them. They limit the ability of executives and other employees to use data to manage business processes and make informed business decisions. They also prevent customer service, sales reps, HR and other operational knowledge workers from accessing relevant data about customers, possible solutions (that may be written in past support tickets for example), products, logistics and more.
You don’t know what you don’t know.
There are many ways that silos contribute to operational inefficiency and harm employees’ productivity. That includes:
- Less collaboration between end users – One of the biggest inefficiencies is manifested in reinventing the wheel. Isolated data sets in silos reduce the opportunities for solution sharing and collaboration between users in different departments. It’s harder to work together effectively when people don’t have visibility into what others may have done already in a relevant area that could be of use.
- A silo mentality – Sometimes, departments and business units that guard their data closely and are reluctant to share it with others. They often resist any data governance programs that aim to ensure that data is shared and consistent and correct across all of an organisation’s systems.
- Permissioning – Even when employees are happy to share their work, often, they operate within assigned permissioning levels. Data silos lock data away from users who can’t access them. As a result, business decisions aren’t based on all of the available data, which can lead to inaccurate decision-making.
- Inconsistent data – Due to a complex infrastructure that each larger company has, much of the same data may be stored in different formats. For example, a marketing team may format customer data differently than other departments. Humans are also prone into making data errors such as typos or storing data in inaccurate fields. Such inconsistencies create data quality, accuracy and integrity issues that affect end users in both operational and analytics applications.
- Duplicate data platforms and processes – Following on the data inconsistency, data silos also add to IT costs by increasing the number of systems and storage devices an organisation needs to purchase. In many cases, due to the nature of those, systems are also deployed and managed separately. That further increases spending and inefficient use of IT resources.
- Data security and regulatory compliance issues. Despite all the efforts of company governance strategy, data is stored by individual users in non-shared Excel spreadsheets or online business tools like Google Drive, often on mobile devices or (heaven forbid) personal devices. That increases data security and privacy risks for organisations if they don’t have suitable controls. Data silos complicate efforts to comply with data privacy and protection laws.
There is hope. It’s called AI.
In the age of Digital Transformation, data is constantly accumulating everywhere – often automatically. There is of course a lot of noise and not all data is important and relevant to what you’re trying to do. Yet, there is a clear value that can be extracted from data. Such data, if well connected and integrated enables a holistic look and gain new insights and information, utilising company’s know how more efficiently.
By utilising NLP / AI tools like Untrite‘s, company is able to connect otherwise siloed data without disrupting current working methods or putting enterprise architecture at risk. And because such tools can be integrated within company’s infrastructure on its cloud, no data leaves the company. Such AI system may sit on the site of company’s systems and connect via API or custom built connectors (legacy systems) and provide a better way to accessing information from one place, in real time. This helps company gaining a holistic view of the situation and all relevant knowledge that’s already in the company but has been omitted due to infrastructure complexity and lack of resources. This also helps in localising and identifying duplicated or inconsistent data, which can lead to better data governance practices.
Because NLP-powered tools analyse unstructured data like a human would do – they understand information contained on the contextual level – it is possible to show all relevant data file content for a particular workflow. If the user doesn’t have permissions to see the files containing relevant information, he can see who the owner of the file is, and therefore – request sharing permissions.
Truth is, technology is only useful if people are willing to use it. The key is to remove the resistance that holds departments and employees in a company and offer a better way of accessing and sharing information. One that doesn’t disrupt current workflow but augments it. One that allows people to be more efficient with their time, eliminate noise and shows only relevant information to a person’s goal. AI systems enabled with understanding human language (Natural Language Processing, or NLP for short) have proven to solve such problems and should be considered if a company is serious about digitalising its efforts.
