4 Ways Unstructured Data Is Costing Your Business
Executives need insight into their companies to better understand what they are working with and to:
- Make strategic business decisions quickly
- Mitigate risk and meet compliance regulations
- Provide rich customer experience
However, with 80% of data locked away in Unstructured Data, getting to and leveraging it has proven difficult for many companies. In the context of limited budgets and prioritizing immediate infrastructure needs, making sense of complex data may seem like a nice-to-have versus a need-to-have.
Automation and artificial intelligence (AI) solutions effectively decipher unstructured data and yield almost immediate results; there are consequences when waiting to implement them.
Read on to learn the four ways inaction towards unstructured data is costing your company.
1. Risk Exposure
The best way to avoid regulatory non-compliance is to proactively identify and mitigate risk within contracts.
However, historical contracts—often unstructured, unsearchable, and unusable—are a risky proposition when compliance is in question. Companies largely don’t know where their contracts are, or what is in them.
Relying on manual efforts to analyze thousands of records in time to meet compliance demands is an expensive and losing strategy.
It is estimated that the cost of non-compliance is
3 times higher than the cost of compliance.
Contract Lifecycle Management (CLM) became a trend in 2018, where more and more businesses see real operational benefits associated with digitizing and analyzing contractual agreements. Rightfully so!
- 60-80% of business operations are governed by contracts (COTTRILL Research)
- 9% in annual revenue is the cost of poor contract management practices (World Commerce and Contracting)
- Automating contract management can help reduce erroneous payments by 75-90% (Goldman Sachs)
A platform that can digitize company contracts into searchable digital content can help recover millions of dollars in business expenses and mismanaged revenue.
2. Challenges extracting business insight from unstructured data
Most companies make significant investments in technology platforms, like BI and analytics solutions, designed to help make businesses run more efficiently. The quality of business insight for these solutions depends on the quality of the data fed into them, and the statistics are rather daunting:
- 85% of big data projects fail (Gartner, 2017)
- 87% of data science projects never make it to production (VentureBeat, 2019)
- In 2022, only 20% of analytics insights will deliver business outcomes (Gartner, 2019)
Considering that 80% of company data is hidden away in unstructured format and is not accessible by analytics solutions, it almost seems obvious why these statistics exist. How can data insight projects succeed with only a fraction of data available to analyze?
Once a company implements a robust unstructured data processing methodology, formerly “dark data” becomes premium fuel for data analytics engines and automated workflows.
With intelligent data, companies realize the ROI of these systems and processes faster.
3. Rising Data Storage Costs
Every inch of data needs to be stored either in the cloud or on-premise, whether structured or unstructured. No matter where it is housed, data storage is costly and has become big business.
According to a survey conducted by AFCEA, top 3 data storage concerns were:
- 76% - Data Security
- 48% - Data Loss
- 41% - Insufficient Funds
This signifies that IT leaders understand the value of their company data by prioritizing its safety, but according to the Seagate survey of mid-large enterprises, only about 25% of company data is actively leveraged, while the rest remains largely unutilized.
As a result, enterprises keep amassing more data year over year, purchasing more storage space to accommodate it, while only recovering a quarter of its value.
When businesses tackle their unstructured data by implementing automation tools to remove duplicates, legacy, and useless information, they can cut data storage costs up to 50%.
4. Poor Customer Experience and Low Innovation
Agile digital competitors will win market share because of their ability to use data to deliver superior customer experiences and the services they demand. But legacy organizations find it hard to compete because so much of their content is tied up and not consumable by analytics engines or usable in automated workflows.
Data-driven organizations are 23 times more likely to acquire customers than their peers.
Only 15% of business leaders surveyed consider themselves very effective in delivering relevant and reliable customer experiences.
Enterprises that implement effective Unstructured Data Analysis methods to feed more and better content into their systems are the ones who will see significant competitive advantages.
How to Untangle Unstructured Data
Rather than assemble teams of subject matter experts to comb through mountains of unstructured data manually, businesses can implement intelligent automation to leverage the most relevant and valuable information.
Adlib’s Transform 2022 includes five essential steps to finding, transforming, and applying unstructured data to accelerate workflows, address risk, and bolster performance.
- Discover: Valuable information can be buried under hundreds of different file formats and siloed across various systems and departments. Automation makes it possible to discover unstructured data across an enterprise regardless of location or format. Adlib works with over 300+ file formats.
- Convert: Utilize OCR or create PDFs to generate content that can be searched for, read, and analyzed by downstream investments, such as Robotics Process Automat, Data Lakes, etc. Converting unstructured data enables companies to extract more significant ROI from these systems.
- Enrich: Build trainable AI models that categorize and meta-tag documents to extract critical data faster and more scalable than humans can perform with fewer errors.
- Review: Classified, metadata-rich content can be analyzed, sorted, and filtered to surface data-driven insights that fuel innovation and intelligent decisions.
- Empower: Clean, structured data is automatically delivered to empower the people, processes, and technology that rely on it to reduce risk, improve customer experiences, boost brand integrity, meet compliance, and increase profits.
Adlib customers are ahead of the curve
By getting the first step in the document lifecycle right (i.e. the Discover stage), Adlib customers are one step ahead of the game.
How are these organizations improving capture processes so that ALL of their information is gathered, standardized and stored appropriately for easy access and long-term preservation? In a few different ways, including:
- Email integration techniques - capturing not only the subjects and bodies of emails, but also the attachments and metadata contained within
- Automated classification of data – identifying and analyzing content to reveal the right path for that content to take to improve findability
- Enterprise-grade Optical Character Recognition (OCR) technology – to capture information contained in images, scans and faxes and to make that information searchable
Success Story 1: Decreasing risk with underwriting insights
A global leading insurance provider relies on Adlib to increase data quality for analytics resulting in improved decision-making and reduced underwriting risk.
Adlib Transform intakes all company contracts, agreements and policies from various sources such as Microsoft Exchange inboxes, SharePoint sites and other Enterprise Content Management systems, leverages the best-of-breed OCR to convert the documents into searchable PDFs and feeds clean key metadata into insight analytics tools.
- 99% reduced risk
- ~$4M/yr reduction in operational costs
- 25% reduction in manual effort
Success Story 2: Critical insight for strategic business decisions
A Fortune500 petroleum organization leveraged Adlib to digitize well log data to help the executive team better assess drilling opportunities.
- Millions of logs were digitized and standardized for increased searchability and data extraction.
- Data storage costs decreased due to 40% reduction in duplicate documentation
- Log data standardized for long-term archival
The reality is that not transforming dark data into intelligent data is a sure-fire way for enterprises to leave money and opportunity on the table. Organizations that use Adlib’s Document Transformation Platform to discover, enrich, and evolve complex content will experience a whole new level of performance and profitability.