If you are like me, you juggle an increasing number of apps on your mobile devices. I tend to have a love-hate relationship with apps: On the one hand, I love playing with new apps, and particularly those that are related to helping one organize tasks. I’m one of those hyper-organized people who loves having lists, calendar reminders, and so forth. Ironically, I probably don’t need apps to help keep me organized, but I can’t help but want to download them. On the other hand, my love of organization apps means that I have too much information scattered in different places. I download a number of apps because I often become dissatisfied with the quality and functions of individual apps, which means I load them with content, then realize that they don’t work too well, which results in deletion of the app, and starting over with another one. Plus, of course, I’m always on the lookout for the new and improved app, which creates another vicious circle. I feel compelled, also, to keep abreast on new apps as part of my role as an instructor in the field of records and information manager. This post discusses common pitfalls to avoid in the design of apps: I recognize most of them.
Information is everywhere and it is poorly (if at all) managed. Amidst all of this opportunity, organizations are drowning in a sea of content and information. File servers are overflowing and multiplying, making it difficult for anyone to find anything. Information is leaking out of the organization at every turn. If information silos in our existing solutions weren’t bad enough, we now have our content popping up in new silos in cloud applications that are beyond the reach of our conventional information governance frameworks (and that’s even assuming our employees are using a company approved cloud provider and not their own).
The report provides 34 strategies that organizations can implement to make better use of their information assets. The Slideshare presentation below provides a summary. Information Managers are essential to turning our information chaos into information opportunities.
Cloud computing is often heralded as providing a greener and more environmentally-friendly way to store large amounts of data, although this has not always been supported by hard evidence, especially as the number of mobile and computing devices that use cloud storage continues to grow. This post discusses eight companies that have created successful green data centres:
- Verne Global
- Equinix AM3
- Green House Data WY2
The distinction between data governance and data management may not always be very distinct. There are various definitions of both concepts, for example:
- Data management is the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner. TechTarget
- Administrative process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is [sic] ensured to satisfy the needs of the data users. Business Dictionary
- Data management refers to an organization’s management of information and data for secure and structured access and storage. Data management tasks include the creation of data governance policies, analysis and architecture; database management system (DMS) integration; data security and data source identification, segregation and storage. Techopedia
- Data governance (DG) refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures. TechTarget
- Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods. Data Governance Institute
- A data governance program can shape the corporate philosophy of data acquisition, management and archiving. It’s a cultural shift that requires both business and IT sides of the organization to come together to define data elements and the rules that govern this data across applications. SAS
In this post, Charles Betz discusses the difference between data governance and management, two concepts that are often considered synonymous but which, argues Betz, are different. Betz suggests that governance is a broader concept, and governance is concerned with the overall context of the organization, and the influences affecting it, as outlined below:
Management is concerned with creating policies and procedures to implement the information lifecycle within the framework of data governance.
This post examines how the knowledge-sharing practices of millennials can affect the organizations for which they work. The article discusses the importance of collaboration in the workplace, and notes that while older workers prefer face-to-face interactions, for millennials, this is anathema. They would rather communicate using online meetings, chat apps or online tools to get things done. A coffee and a face-to-face meeting is too outdated for them. I’m not a millennial, but I have to agree with them on this one, but this might be related more to my introversion than it does to my age. This is the danger of generalization, of course.
The author notes further that it is the millennials who want tools to help them work through a problem the fastest. When looked at by age groups, a large number of millennials (71%) said they face challenges with their collaboration tools, compared with Generation Xers (62%) and baby boomers (45%). The always-on generation need [sic] to fix their cravings for information instantly.
The author points to the importance of having collaborative tools that function efficiently, but that have robust features to maintain the integrity and security of information.
In this post, Bernie Palowitch, President of Iknow LLC, discusses the challenges facing knowledge management (KM):
- Relevance. Ensuring that KM programs have direct relevance or impact on the organization.
- Grappling with the explosion of available content. Dealing with the increasing amounts of content in varied forms, including social media and public websites.
- Finding the right technology solution for a specific use case. Finding the KM software that best meets your needs is becoming increasingly challenging with the growth in the number of this software.
- Funding. Ensuring executive support and KM program funding.
- Tailoring your KM program to meet your organization’s “big picture” demographics. Do you have a good understanding of the demographic composition of your employees and clients?
In this post, Robert Steiner expands the notion of a Data Steward (someone who has formal accountability for data in an organization) to extend to everyone in the organization:
My premise is based on the fact that everybody that comes in contact with data should have formal accountability for that contact. In other words, people that define, produce, and use data must be held accountable for how they define, produce, and use the data. This may be common sense, but the truth is that this is not taking place. Formalizing accountability to execute and enforce authority over data is the essence of using stewardship to govern data.
Steiner argues that everyone who comes in contact with sensitive data is subject to all regulations that govern its use. Steiner advocates for a “Non-Invasive Data Governance” program that formalizes that level of data usage accountability. In this program:
Organizations should identify people who have a level of accountability for the data they are defining, producing and using to complete their job or function.
Organizations should identify existing escalation paths and decision making capabilities from both a positive (how and why is it working) and negative (why doesn’t it always work) perspective.
Organizations should recognize people for what they do with data, and help them formalize their behavior to the benefit of others potentially impacted by that behavior.
This program uses the following Data Governance Operating Model of Roles & Responsibilities: