Data silos are an issue for a lot of companies, and sometimes create boundaries to info sharing and collaboration throughout departments inside a corporation. Though AI and related applied sciences are usually not a panacea for siloed information, they will present manufacturers with methods to reduce the in any other case tedious efforts to manually eradicate information silos.

Let’s take a l have a look at how synthetic intelligence (AI), machine studying (ML) and pure language processing (NLP) can be utilized to tame the information silo beast.

Data Silos: How AI Can Assist Companies Unlock Hidden Insights

Merely put, a knowledge silo is a repository of knowledge managed by a single division and remoted from different departments inside a enterprise. Siloed information is an issue for a lot of companies, notably for these manufacturers which can be or will likely be utilizing AI purposes. In response to a McKinsey World Survey about AI capabilities, solely 8% of these polled throughout industries indicated that their AI-relevant information is accessible by methods throughout the group.

There are various causes that information leads to a silo, starting from vendor lock-in, to legacy methods, to information lakes. Listed below are a few of the commonest causes of siloed information:

  • Vendor Lock-in: Many companies rely on software program platforms as a part of their martech stack. Very often, these platforms use information that’s in a proprietary format, or they depend upon databases that aren’t interchangeable with different platforms. This “vendor lock-in” happens when distributors don’t incorporate the capacity for information to be exported to different purposes. 
  • Data Limitations: Typically, information siloes are usually not attributable to software program limitations, however reasonably they’re attributable to staff who don’t really feel snug sharing information with different departments, or they merely don’t perceive how their information could possibly be helpful to different groups. Though permissions and limitations are sometimes put in place by default to scale back the misuse of knowledge, such “data ownership” can cut back the general usefulness of the information inside a enterprise. 
  • Data Lakes: It’s been mentioned that companies as we speak are drowning in information, and with a lot information coming from myriad sources, it’s usually difficult for manufacturers to design an efficient information infrastructure with out disrupting enterprise.
  • Legacy Programs: Particularly for well-established manufacturers, older legacy software program methods may be extraordinarily restricted of their capacity to work with different, extra present platforms. With out a big funds (and incentive) to interchange legacy methods which can be presently working simply high quality, sharing information is commonly not a precedence.

Simon Tanné, head of knowledge science at Echobox, a publishing automation platform supplier, informed CMSWire most corporations as we speak try to base their selections on information, however very often, their information is siloed in several groups or inside completely different instruments, rendering this information arduous to entry or virtually invisible.

“Because AI and ML draw and learn from multiple data sources across a business, AI technology can consequently break these silos by automatically generating insights or recommendations that are visible, accessible and actionable across various business units,” mentioned Tanné. “This enables for extra cross-team collaborations and deeper insights that may finally affect the firm tradition and innovation throughout the board.”

Related Article: Your Silos Are Showing in Your Customer Experience

How AI ad NLP Can Break Down Data Silos

Often, there is information buried within unstructured data that lives in a silo, so the challenge is not only obtaining access to the data, but structuring and formatting it into a usable form. Through the use of AI and natural language processing, brands can overcome this challenge by extracting structured facts from unstructured documents and textual data.

NLP is a computational methodology that can process natural human language. Recently, NLP has been used as a text-mining solution with unstructured data. NLP is able to decipher unstructured data such as social media posts, pre-processing the data to create structured data which can then be used for analysis. NLP is able to quickly standardize mass amounts of unstructured data into actionable information. 

Bob Rogers, former chief data scientist at Intel, and CEO at, a data science business specializing in supply chain modeling, told CMSWire that using AI and ML effectively to manage siloed data will depend on which industry the algorithms are tackling. 

Rogers gave the example of work he did at University of California, San Francisco, leading a data science team that was trying to solve a particular healthcare problem: 1.4 million yearly faxes that resulted in three separate data silos. Without AI, the process of eliminating the data silo was very tedious for Rogers and his team. “First, the raw data from faxes were dumped into a processing queue. Next, patient appointments were added to an electronic health record. And finally, various diagnostic reports were scanned and uploaded to the patient’s chart,” said Rogers.

Using AI, Rogers was able to greatly simplify the process. “Use AI feature extraction (a combination of computer vision and natural language understanding) to pull key information from each fax,” said Rogers. “Connect the fax directly to the charts of existing patients and create a new record for new patients. Now, schedule the patient electronically. Use AI feature extraction to index key contents of additional uploaded diagnostic files.”

This process allowed the silos to be connected together through shared identifiers in the electronic health record, and the data was then digitally actionable. “This is a game-changer for hospitals and health systems.”

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Related Article: NLP and Text Analytics Enhance VoC Programs, Boost CX Engagement

Managing Complex Data Challenges With AI and ML

The challenges of data silos are multifaceted and are largely related to the reasons that data ends up in silos to begin with. Along with those causes, data overload continues to be a problem for many brands — and not all data is useful.

Kevin Gordon, vice president of AI technologies at NexOptic, an AI imaging solutions company, told CMSWire many brands took the axiom “information is the new oil” literally and implemented their own version of “information hoarding,” with the result of massive stores of data with different levels of management (organized, consistent, up to date, etc.). 

At that point, these brands had to ask themselves the question “is that this an untapped useful resource?” “The primary problem of siloed information is managing it nicely so it may be used,” said Gordon. “Primarily doing one thing helpful with it. A whole lot of information is sufficiently complicated that each managing and using it’s a arduous drawback.” Gordon believes that machine studying (ML) has modified this considerably, because it allows complicated patterns and usages of siloed information. “There’s still the issue of managing the data well so that it can be fed into these machine learning algorithms.”

“Depending on where a company is in the data/ML cycle, they’ll need different advice to overcome challenges,” said Gordon. “If they’re just starting the data collection process, they’ll need advice on what and how to do it. If they’re collecting data, they’ll need advice on scaling and connecting their data to machine learning workflows, and if they’re using ML on collected data, they’ll need advice for optimizing and deploying,” defined Gordon, who recommended that after deployment, organizations could wish to revisit the cycle to see in the event that they’re lacking something related to the firm’s “massive image”objectives.

Sarcastically, whereas AI and ML may be efficient instruments in the battle to eradicate information silos, to ensure that AI and ML purposes to be simplest, information silos have to be handled to take away conflicting variations of the fact. This requires each staff inside a corporation to be on board with the aim of eliminating information silos. Communication and collaboration between groups have to be inspired and supported. Lastly, a enterprise should embrace a knowledge warehouse resolution that has the scale and efficiency to facilitate each division’s information wants.

Closing Ideas on Eliminating Data Silos

Many companies wrestle with the drawback of siloed information, an impediment that successfully strips away the worth of that information for different departments and groups inside the enterprise. By utilizing AI and ML, the usually tedious and mind-numbing work of manually extracting helpful information from information silos may be simplified and vastly improved.

Paradoxically, the elimination of knowledge silos will improve and enhance the effectivity and accuracy of AI and ML purposes, offering manufacturers with a win-win resolution to making a single supply of fact for information of their group.

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The Obsessed Guy
Hi, I'm The Obsessed Guy and I am passionate about artificial intelligence. I have spent years studying and working in the field, and I am fascinated by the potential of machine learning, deep learning, and natural language processing. I love exploring how these technologies are being used to solve real-world problems and am always eager to learn more. In my spare time, you can find me tinkering with neural networks and reading about the latest AI research.


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