What is Unstructured Data?
Almost 80% of the data that enterprises and organizations collect is unstructured - data without a set record format or structure. Unstructured data includes data such as emails, web pages, PDFs, documents, customer feedback, in-app reviews, social media, video files, audio files, and images.
This data makes up a large amount of business-critical content. An IDC report puts the growth of unstructured data from 33 zettabytes in 2018 to 175 zettabytes by 2025.
Unstructured data is important for analysis, content, and reporting - key to invaluable business insights and is poised to become an increasingly important part of an organization’s enterprise data strategy. As technology expands and matures, a strategy for managing unstructured data will be necessary for new data types not yet considered.
Unstructured data can yield benefits like:
Seamless internal operations
Insights into the launch of new products and existing offerings
Customer service
Sales
Auditing
But to leverage these benefits, the unstructured data must be extracted, organized, and managed correctly. This process can be difficult to navigate and integrate with an organization’s existing strategy. For years, organizations have relied on traditional relational databases and data warehouses and are mostly unfamiliar with the process of handling and managing unstructured data.
On top of that, there are several challenges associated with unstructured data:
Data quality: Unstructured data can be incomplete, out of date or unreliable. It must be processed to be deduplicated, completed, and vetted for accuracy. Poor data quality can lead to errors. Master Data Management is an element of an enterprise’s data strategy that ensures data consistency throughout the organization using vetting and careful data stewardship.
Data consistency: Unstructured data can exist in differing formats; image files can be .gif, jpeg, or others. For this data to be of maximum use, it must be analyzed and designed to classify and store the data files correctly.
Data silos: Data silos are teams that keep data stores independent where the data is not shared with others in the organization. This leads to redundant storage and maintenance and causes issues because of dangerous inconsistencies between teams and the data that they use.
Data Expertise: To be fully leveraged, unstructured data requires the expertise of data professionals who can analyze a company’s needs, design a process for data ingestion and storage, and implement it for the organization’s maximum benefit. Data must be reliable and easily retrievable to be useful and it can be difficult to find people with the relevant experience.
Data costs: There is a cost associated with any data point, which mostly relates to data storage and its management. The cloud does offer organizations cheap storage, but with increased data volumes and velocity, the expenditure adds up. Data files and documents can be much larger that a row of data in a database and unstructured data might need you to buy storage in terabytes.
Enterprise Data Strategy is the Key to Unlocking Data’s Potential
Challenges aside, data is an organization’s most valuable asset. An effective enterprise data strategy is the key to turning the volumes of data pouring into your business ecosystem into real profit. A holistic enterprise data strategy leverages multi-dimensional capabilities and prioritizes the maintenance and improvement of data quality, integrity, and accessibility.
An effective enterprise data strategy consists of these key elements:
Data ingestion and storage
Offensive and defensive use of datasets
Master Data Management
Reliable retrieval and reporting
Unstructured data must be incorporated into an enterprise’s data strategy so data to be fully utilized. The key elements listed above must be applied to unstructured data as well as traditional databases and data warehouses. Companies have gigabytes of information that, if not incorporated into the master strategy, do not just fail to produce value but can also become a liability.
Components of Enterprise Data Strategy Development
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How Trianz Can Help?
Trianz has years of experience in creating enterprise data strategies. The influx of new data types, new technologies, and the sheer volume of data can be a monumental undertaking for any business to handle. A partnership with us will help organizations to:
Analyze their business data needs
Create enterprise data strategies and roadmaps
Build pipelines for ingesting and vetting data from several sources
Create a Master Data Management solution to ensure top data quality and consistency
Leverage BI and reporting tools to create valuable dashboards and reports for critical data insights
We pursue an IP-driven approach to enterprise data strategy. Based on our multi-year research on digital transformations worldwide, we have designed frameworks that create an effective data strategy for organizations to address key business needs. Our frameworks consist of pre-configured key performance indicators (KPIs) tailored to an organization’s business strategy, and performance.
Unlock the Potential of Your Unstructured Data
Unstructured data provides a wealth of new data to add to your enterprise data strategy. Increased data leads to more information, better decision making, and offers new opportunities. It’s vital to include unstructured data in an enterprise data strategy as it leaves companies open to the risks of data non-compliance and litigation.
With the right set of tools and help from Trianz, you can easily manage this data. The key is to align your data management closely with business objectives to drive an effective data strategy execution for deriving maximum value.
