Capitalizing on artificial intelligence (AI) is key to remaining competitive today. While many business leaders recognize that, fewer are able to deploy AI to its full potential. Data silos are some of the most common and significant barriers.
Some silos are intentional. Others arise from teams splitting into various groups, or the company implementing new tools. Whatever their causes, they impede AI progress by limiting the technology in three main areas.
1. Limited Data Scope
The first way silos hinder AI is by limiting the scope of the data it analyzes. Organizations have over 2,000 information silos on average, making it near-impossible to get the full picture of large trends. This fragmentation is particularly harmful in AI applications, as machine learning models need context to produce reliable results.
Incomplete records or out-of-context information can be just as misleading as factually incorrect data. As a result, when an AI algorithm can only work within a few segmented databases, it’s unlikely to produce the most accurate predictions possible. Its outputs may be relevant and true to the siloed data it analyzed, but without context, those takeaways may not apply to more complex, real-world problems.
2. Limited Data Quality
Similarly, data silos limit AI by introducing quality issues. When teams need to gather information between independent databases, they must take on a considerable amount of manual data transfers and entry. Moving all these data points between places introduces many opportunities for errors to occur.
A higher chance of mistakes leads to less reliable datasets for AI to analyze, and as the saying goes, “garbage in, garbage out.”
Unreliable data costs companies $12.9 million annually on average. While silos are certainly not the only cause of informational errors, they increase their likelihood, so removing them is crucial.
3. Limited Data Velocity
A silo’s impact on the speed of data collection and analysis is also worth considering. Real-time analytics is important to many workflows today. It can help institutions reduce processing times by 80% and supply chains respond to incoming disruptions, preventing stock-outs. However, such achievements are only possible when AI can access all the data it needs quickly.
Data silos are the enemy of efficient analysis. Even if a model has access to many separate databases, it will take time to pull information from them and organize this data before learning from it. Any delays in this process limit AI’s ability to act quickly, which cuts off some of the technology’s most valuable use cases.
How to Break Down Data Silos
Given how detrimental silos are to AI applications, teams must do all they can to remove or work around them. The first step is to recognize where these barriers exist.
Silos often arise between separate departments, as teams that don’t traditionally collaborate have implemented their own tools and databases. Consequently, most compartmentalization happens here, so it’s a good area for businesses to focus on. Once leaders identify a silo, they can compare each side’s software and needs to see if there’s any common ground for a single platform to take the place of or connect several individual apps.
As IT admins look for silos, they should also question why they exist. While most barriers are likely unnecessary, some serve an important purpose. For example, the privacy laws that cover 75% of the world’s population sometimes require specific protections for some information, but not all. In such cases, it’s best to leave highly sensitive databases siloed, as it’s a matter of regulatory compliance.
Switching from on-premise to cloud-based solutions is another critical step in de-compartmentalizing data. Moving to the cloud ensures AI tools have room to grow and provides a single point of access for all the information they need. Automated data discovery and network mapping tools may be necessary. These resources can uncover silos, create a single source of truth for all relevant records and reveal duplicates, which teams can then consolidate to ensure accurate AI results.
Once the organization has dismantled data silos, it must employ proper cybersecurity protections. Free-flowing information may make a database or AI model a larger target. Thankfully, AI itself can be a solution here. AI incident detection and response tools save $2.22 million on average by containing suspicious behavior as soon as it occurs.
Effective AI Needs Unsiloed Data
AI relies on data, and that data must be complete, reliable and quickly available. Firms that want to make the most of their AI applications must remove silos wherever they can. Breaking down these barriers will make any AI-driven results more reliable and effective.
The post How Data Silos Limit AI Progress appeared first on Datafloq.
