Your mission is to build an information asset register Designing an information asset register (IAR) sounds easy. But as soon as you start, a number of questions emerge. What are the business drivers? Compliance requirements? Findability problems? Privacy and Data...
Taxonomy - what's in the name? The term Taxonomy is used widely used across a range of contexts and depending on which discipline you follow you will encounter multiple definitions. According to Wikipedia, the word taxonomy finds its roots in the Greek language:...
"Data is the new oil" Over the last decade, the constant refrain from the digital media is that data is an asset to be leveraged, in order to generate value for the business.Data is an asset. What is its value? | by Adam Votava | Towards Data Science As a product or...
While the rest of the world were stiffening their cybersecurity systems and privacy legislation, the previous Australia government refused to address the growing problem of identity theft and cybersecurity fraud.
Data security threats aren’t just coming from outside the organisation. A large portion of data breaches are directly caused by human error.
It can be tempting to use a spreadsheet to build information governance tools, but purpose-built software like a.k.a. offers big advantages.
It’s hard to build a classification scheme that stands the test of time. Those that persist do so because they meet the collective needs of the organisation.
Businesses that develop fit-for-purpose standardised metadata can find the right information, in the right place, at the right time.
The rampant spread of Microsoft Teams sites happened so quickly that access and security controls couldn’t keep pace.
Almost anywhere there’s evidence of trade, there’s evidence of someone keeping tabs on who owns what and who owes who.
As well as minimising data security risks, timely disposal protects the sensitive information entrusted to organisations.
Information asset registers have an often overlooked application: a self-serve means of navigating the organisation.
At Synercon, we managed a speedy re-platform because we had all the knowledge needed to build a new information architecture in SharePoint.
Most organisations fail to execute their digital transformation strategy because they don’t know where to start or how to clean up the current mess.
We combined existing knowledge and toolsets build an intelligent automation platform for records and information management.
To enable automation we need to transform our information governance controls into machine readable formats that underpin autoclassification.
Autoclassification depends on machine readable data models, such as ontologies, that convey the requisite knowledge.
We autoclassified and appraised our legacy client records with help from our partner, Pingar. These were the results.
Few people outside of the records management sector are familiar with the DIRKS methodology. So what is it?
A short but useful guide, defining some of the common terminology used when describing classification schemes.
There is both art and science in creating classification schemes. The starting point is understanding the needs of your organisation.
The starting point for data governance is to build a framework representing the business environment – we call this Enterprise DNA.
The canonical data model is the accepted standard for logical data models and it provides software developers with a standard template to build to.
Managing metadata and developing taxonomies is easy! And so it is – when you are working within a confined context.
When major systems fail, poor governance is often cited as the reason. Dig in and you’ll find taxonomy and data/metadata quality is a factor.
Read about our research with metadata and data models to enable automation in this August 2014 issue of Information and Data Manager.
a.k.a. software unites several key information governance requirements within a single application.
With so many standards to choose from, it’s essential to understand the types of metadata standards and their purpose.
Without defined rules, descriptive metadata becomes subjective and results in ambiguous language.
Our challenge is harnessing language as a tool for describing information resources with consistency and precision.
It’s not surprising that users opt out of records management systems that don’t deliver functionality for adding, connecting and leveraging metadata.
The challenge is building information governance tools that fit into a variety of information architectures. And this is where entity relationship modelling is useful.
By studying successful systems, we can understand which design and functionality features will allow us to automate recordkeeping processes.
Shortcomings of digital recordkeeping aren’t due to implementation, training or change management – it’s the design of recordkeeping systems.
Little is said about the theory and discipline of building classification schemes beyond their search and retrieval function.
The info genome is a documented map of your entire organisation that grows as knowledge about information assets is gathered.
There’s no silver bullet software solution. Before investing in new information governance technology, try these five enablers.
Aggregation allows for greater context when managing records, but there are challenges to achieving it in a digital environment.
Digital transition can support child protection efforts by overcoming the issues of access to, incomplete and inaccurate records.
The records continuum: what is is, how it works, the benefits, and innovations.
Today’s records managers need to be taxonomists, metadata managers, business analysts, system designers, systems integrators, and trainers.
Five good reasons to invest in specialist software for building taxonomies or metadata, instead of using a spreadsheet.
This article by Barclay Blair, which appeared in CIO Update, provides definitions and examples of information governance.
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