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.
February 19, 2020
- Sisense Announces New Tailored Packages to Empower Builders of Analytics
- Okera Enhances Automatic Discovery of Sensitive Data Using Machine Learning
- Syncsort Recognizes Excellence in Maximizing the Value of Data and Customer Engagement
- erwin Introduces Enterprise Architecture, Business Process Modeling Software Platform
- Exasol Named a Leader in Dresner ADI Report for Third Consecutive Year
- Grid4C Collaborates with Itron to Embed AI-Powered Analytics into Smart Meters
- Cloudian Announces Collaborative Solution with Veeam for Ransomware Protection
- Dell EMC Unveils Streaming Data Platform
February 18, 2020
- ScyllaDB Extends Support for Streaming with High-Performance Apache Kafka Connector
- Snowflake Announces General Availability on Google Cloud
- Qlik Tapped by Cheshire Healthcare Leaders to Help Solve Health Challenges
- DataRobot Unveils Snowflake Integration to Streamline and Simplify AI
- ASG Technologies Partners with 4th-IR to Enrich ASG Data Intelligence with AI Models and Data Expertise
- Prisma Analytics, Decisive Group Partner on UAE’s Big Data, AI Situation Room
- Dataiku Selected as Platform for NATO’s Allied Command Transformation Focusing on AI Projects
February 14, 2020
- DataRobot Unveils Beneficiaries of AI for Good: Powered by DataRobot Program
- Reltio Introduces Reltio Connected Customer 360
February 13, 2020
- data.world Joins Snowflake Partner Connect
- Oracle Adds Advanced Analytics Capabilities to its FCCM Products
- Study: Just 32% of Data Teams Able to Extract the Insights Their Organizations Need for Better Decision-Making
Most Read Features
- An Open Source Alternative to AWS SageMaker
- Big Data File Formats Demystified
- ML and BI Are Coming Together, Gartner Says
- How the Coronavirus Response Is Aided by Analytics
- Predictive Maintenance Drives Big Gains in Real World
- Room for Improvement in Data Quality, Report Says
- How to Build a Better Machine Learning Pipeline
- Rob Bearden Returns to Lead Cloudera’s Second Act
- Optimizing Utilization Forecasting with Artificial Intelligence and Machine Learning
- Big Data Predictions: What 2020 Will Bring
- More Features…
Most Read News In Brief
- Hitachi Vantara Buys Cataloger Waterline Data
- DoD Looks to Scale Predictive Maintenance
- War Unfolding for Control of Elasticsearch
- MongoDB Embraces GraphQL with Document Database
- Global DataSphere to Hit 175 Zettabytes by 2025, IDC Says
- Google Advances Data Set Search Tool
- Inside Fortnite’s Massive Data Analytics Pipeline
- HPE Acquires MapR
- Defenses Emerge to Combat Adversarial AI
- Tick Data Comes to BigQuery
- More News In Brief…
Most Read This Just In
- Qlik Acquires RoxAI to Extend Qlik Sense’s AI Capabilities with Advanced Alerting and Intelligent Automation
- Research: Data Skills Gap is Costing Organizations Billions in Lost Productivity
- Okera Delivers Real-Time Actionable Insights into Data Lakes
- Iguazio Deployed by Payoneer to Prevent Fraud with Real-time Machine Learning
- Luminoso Announces AI Application for Better Search Engine Results
- Collibra Launches Data Lineage, an Automated Data Lifecycle Mapping Capability
- Spark + AI Summit Reveals 2020 Keynote Speakers and Expanded Training
- Pepperdata Introduces Query Spotlight
- Elastic Announces the General Availability of Elastic App Search on Elasticsearch Service
- Sisense Expands Presence in Australia to Support Growing Demand for Business Intelligence, Data Analytics
- More This Just In…