5 Critical Steps for Identifying the Value in Your Unstructured Information
I have spent many years helping organizations gain control and management over their unstructured data or information. The days where we looked for one content management solution to capture and manage our valued unstructured data (information) are long gone.
Organizations are now faced with the challenge of implementing multiple content management solutions focused on many tiers within the organization:
- Local business unit applications – these applications manage data and content localized to a single business unit
- Cross business unit applications – these applications manage data and content that is used by multiple business units
- Enterprise applications – these applications manage data and content that is use by the enterprise
Implementing a diverse set of content management solutions requires a consistent approach that reuses a proven methodology. All successful content management implementations address these basic five steps:
- Discover– how can we discover and categorize the valuable data contained in the file shares and many repositories?
- Organize– how can we organize information so the business users can identify, manage, and control it?
- Govern– what are the policies required to make the data available, accessible and reliable?
- Manage– how do we manage the data lifecycle to meet compliance/ retention requirements?
- Analyze – how can users access the data and turn it into assets that can be used to make business decisions?
Unstructured data or information is routinely found in many repositories, including email, file shares, Google drive, Dropbox and SharePoint, to name just a few. The ever-growing number of files has resulted in a problem I call digital hoarding. In many cases, digital hoarding has been caused by mismanaged information from the very beginning while in other cases the availability of cheap storage has quickly led to the out of control proliferation of various repositories.
Manually reviewing and organizing the vast quantity of information that we’ve stored is often a daunting and impossible task. It is critical in the cleanup process that we identify and separate the files that require management from those that can be either deleted or just left alone. Utilizing an automated tool that scans and groups your large amount information is the best approach to accomplishing this time-consuming task. A good example is their ability to differentiate files that contain PII information from those that are related to contracts. Understanding the universe of files and their associated groupings is a critical task when designing and implementing a content management solution.
Once we have discovered and sorted the digital information into stacks that I call file groupings, we now need to add metadata that will make the information more valuable to the organization. This step in the process involves the organization of the information into document types that align with the organization’s business structure or taxonomy. It is critical that the end-users understand how to access the information. End-users want to work in a familiar environment and should not be forced to think differently when trying to access information.
Developing the document types and metadata framework for each document type is often a long and tedious effort. Associating information to document types in a predefined taxonomy simplifies, if needed, the assignment of metadata. The maturity of automated tools, e.g. AI and Machine Learning, has led to better capabilities in identifying and assigning metadata resulting in higher accuracy and quality of the information that describes the content.
Once you have your unstructured data organized, the next step will be to develop a governance program that defines and enforces security, consistency and retention policies. Information governance ensures that the unstructured data is available, accessible and reliable when needed for analysis.
Timely access to the right information is critical when making strategic decisions. Too many times the wrong information is used in making decisions or communicating information to interested parties. Applying consistent policies to information provides uses with the assurance that they can the access and trust of the information.
An effective information governance program not only will ensure that you have reliable information but will also ensure that you have the implemented the right policies to keep your information protected and your executives out of jail.
The management step in the methodology refers to the information lifecycle that spans the creation to final deletion of information. The lifecycle phases cover creation, revision, approval, promotion, retention, and destruction.
Management will establish the information, security, and retention architecture. Defining an effective information architecture will ensure the unstructured data is secure and meets compliance and records management requirements.
Effective organization and management of your information creates an environment that provides your users better access and controls over their information. Applying retention policies to information will help users follow the rules for retaining and ultimately deleting information within a defined timeframe.
A good rule of thumb is that if there is no compliance/ regulatory need to manage the information or you never need to go back to the content for analysis/ reporting, there is no need to keep the information.
The final step in the methodology is the ability to access and use the information for analysis and reporting. Having a well-defined information architecture enables fast reliable access to the content. New content analytics tools have emerged that help uncover value buried in the information.
About the Author: Alan Weintraub is a senior information management leader and evangelist at DocAuthority. As an AIIM Fellow, he is focused on helping organizations maximize the value of their information. A former industry analyst at Forrester and Gartner, Alan is a recognized expert on multiple aspects of enterprise information management (EIM) including information governance (both data and content governance), enterprise content management, data management, digital rights management, and digital asset management. Get in touch with Alan on LinkedIn and Twitter.
April 19, 2021
- Tencent Cloud Makes Metrics and Data Monitoring More Efficient Through Integration with Easy-to-Use Grafana
- Novel Use of 3D Geoinformation to Identify Urban Farming Sites
- Loft Labs Open-Sources Virtual Cluster Technology for Kubernetes
- GoodData Launches Cloud-Native Platform as First Step in New Data as a Service Category
- Druva Secures $147 Million Investment to Extend Market Leadership
- Unity Dramatically Reduces AI Training Time, Budgets with Launch of Synthetic Datasets
April 16, 2021
- Chain.io Secures $5M Funding Round for Cloud-Based Supply Chain Integration
- Rockset Enables Real-Time Analytics for MySQL and PostgreSQL
- Alluxio Improves Interface Support to Simplify Onboarding of Data Driven Applications
April 15, 2021
- Tecton Unveils Major New Release of Feast Open Source Feature Store
- AWS Launches Free Course on Machine Learning For Business Leaders
- New FORMULA 1 Insights Powered by AWS Will Help Fans Make Sense of Split-Second Decisions
- New MIT Technology Review Insights Report: Building a High-Performance Data and AI Organization
- AI.Reverie Appoints Former NVIDIA Deep Learning Guru Aayush Prakash as Head of Machine Learning
- Gigamon Partners with Riverbed to Simplify Hybrid Cloud Deployment, Monitoring and Management
- Confluent Opens Operations in Japan with Masaki Katsumata as Area Vice President, Japan Country Manager
- AWS Announces General Availability of AQUA for Amazon Redshift
- Carleton University and IBM Partner in AI, ML and Data Science for a Future-Ready Workforce
- Crate.io Expands CrateDB Cloud with the Launch of CrateDB Edge
April 14, 2021
Most Read Features
- Big Data File Formats Demystified
- A ‘Glut’ of Innovation Spotted in Data Science and ML Platforms
- Synthetic Data: Sometimes Better Than the Real Thing
- Who’s Winning In the $17B AIOps and Observability Market
- He Couldn’t Beat Teradata. Now He’s Its CEO
- Why Data Science Is Still a Top Job
- Cloud Data Warehousing: Understanding Your Options
- Is Python Strangling R to Death?
- Big Data Predictions: What 2020 Will Bring
- A Nutrition Label for AI
- More Features…
Most Read News In Brief
- Data Prep Still Dominates Data Scientists’ Time, Survey Finds
- AWS Adds Explainability to SageMaker
- Global DataSphere to Hit 175 Zettabytes by 2025, IDC Says
- The Union of Salesforce, Tableau Yields Hybrid ‘Business Science’
- Databricks Edges Closer to IPO with $1B Round
- Esri Simplifies Developer Access to Location Data with ArcGIS Platform
- Domo Gets the Lead Out with a ‘Palooza
- CDOs Must Shift to Offense, Survey Finds
- Fiverr Adds Data Science Recruiting Category
- The AI Inside NASA’s Latest Mars Rover, Perseverance
- More News In Brief…
Most Read This Just In
- Moody’s Analytics Wins Award for Best Use of AI in Banking or FinTech
- Aiven Raises $100M Series C to Expand Global Open Source Innovation
- Alluxio Advances Analytics and AI with NVIDIA Accelerated Computing
- AWS Announced Strategic Partnership with Hugging Face NLP Startup
- GrafanaCONline Returns June 7-17, CFP Is Open Now
- y42 Raises $2.9M to Provide a Scalable and Affordable Data Stack to Companies of All Sizes
- Domino Data Lab Debuts New Solutions with NVIDIA to Enhance the Productivity of Data Scientists
- ThoughtSpot Acquires SeekWell to Operationalize Analytics, Push Cloud Data Insights to Business Apps
- Alteryx Global Inspire 2021 Conference to Showcase New Products in Analytics and Data Science
- Trifacta Announces Industry’s First Data Engineering Cloud
- More This Just In…
Sponsored Partner Content
May 4 - May 5
May 13 @ 11:00 am - 12:30 pm