Two tools I think are great that a lot of people don't know about are PDQ Deploy/Inventory for software deployment too big to do manually but too small to justify buying SCCM, and PRTG is a pretty robust modern network monitoring system.
As I said, a lot of people don't know about them. ;) PDQ Deploy is a remarkably affordable software deployment tool, they charge by the admin using it, not by the PCs covered by it, and the yearly cost gets them automatically building for you... most of the deployment packages for common Windows software IT environments deploy. I've never used PDQ Inventory, but it ties in pretty closely with Deploy if you use it as your auditing and asset management tool.
PRTG is in the vein of Nagios and WhatsUpGold, but far more delightful to use.
I wouldn't call either specialized, except perhaps in terms of organization size. If you're in Fortune 500s, probably not going to end up with these tools.
Yes, 100% this. It doesn't matter how good the tool is (and so many knowledge-management tools are not good), you need the processes and organizational habits in place to make knowledge management a success.
Effective knowledge management practices are something we think about a lot at Spoke (https://www.askspoke.com). We've studied other companies' practices and have come up with some organizing practices/principles:
1. Adopt the right tools:
- Adopt cloud-based knowledge management tools that are simple to use. If new tools are overly complex, no one will use them.
- Find solutions integrate with the applications employees are already using. (Be where employees already are, like Slack.)
- A central source of information is best. If knowledge is spread across multiple tools, it will still be difficult for people to find.
2. Take advantage of new technologies:
- Adopt tools and technologies that use AI to process and catalog resources. Ideal solutions may automate the processes of updating knowledge and/or automatically categorize and tag new content to make it easier to find.
- Look for tools that use machine learning to improve as data is collected. Machine-learning technologies learn how people search for certain types of information, getting better over time at helping users find the exact information they’re looking for.
3. Document important processes:
- Set aside time once a month for employees to create documentation on the tasks they’re responsible for.
- Save all documentation on the cloud or some other shared server so everyone has access to it and to prevent document loss.
4. Find creative ways for employees to share tacit knowledge:
- Establish a mentor program that pairs new hires with long-time employees.
- Make sure managers know how to perform the most critical tasks that their teams are responsible for.
- Set aside time for employees in related roles to cross-train. This will expand institutional knowledge, provide a source of backup when employees take time off, and reduce the likelihood of total knowledge loss caused by unexpected turnover.
Spoke | Software Engineer - AI and Learning, Frontend, Backend | Onsite | Fulltime | San Francisco | http://askspoke.com Spoke is a young, San Francisco-based startup that is reinventing workplace request management. A few weeks ago, we announced $28M in funding from Greylock, Accel and others.
We’re a small team of designers, engineers and machine-learning experts who are repeat entrepreneurs and most recently worked at Google and Twitter. We are looking for exceptional engineers to join our team in San Francisco.
At Spoke we are using ML and NLP technologies to make workplace ticketing systems smarter. The work spans many disciplines: Information Retrieval, NLP, ML, and deep learning.
Spoke | Software Engineer - AI and Learning, Frontend, Backend | Onsite | Fulltime | San Francisco | http://askspoke.com
Spoke is a young, San Francisco-based startup that is reinventing workplace request management. A few weeks ago, we announced $28M in funding from Greylock, Accel and others.
We’re a small team of designers, engineers and machine-learning experts who are repeat entrepreneurs and most recently worked at Google and Twitter. We are looking for exceptional engineers to join our team in San Francisco.
At Spoke we are using ML and NLP technologies to make workplace ticketing systems smarter. The work spans many disciplines: Information Retrieval, NLP, ML, and deep learning.
Spoke | Software Engineer - AI and Learning, Frontend, Backend | Onsite | Fulltime | San Francisco | http://askspoke.com
Spoke is a young, San Francisco-based startup that is reinventing workplace request management. A few weeks ago, we announced $28M in funding from Greylock, Accel and others.
We’re a small team of designers, engineers and machine-learning experts who are repeat entrepreneurs and most recently worked at Google and Twitter. We are looking for exceptional engineers to join our team in San Francisco.
At Spoke we are using ML and NLP technologies to make workplace ticketing systems smarter. The work spans many disciplines: Information Retrieval, NLP, ML, and deep learning.
️ Spoke | Software Engineer - AI and Learning, Frontend, Backend | Onsite | Fulltime | San Francisco | http://askspoke.com
Spoke is a young, San Francisco-based startup that is reinventing workplace request management. A few weeks ago, we announced $28M in funding from Greylock, Accel and others.
We’re a small team of designers, engineers and machine-learning experts who are repeat entrepreneurs and most recently worked at Google and Twitter.
We are looking for exceptional engineers to join our team in San Francisco.
At Spoke we are using ML and NLP technologies to make workplace ticketing systems smarter. The work spans many disciplines: Information Retrieval, NLP, ML, and deep learning.
Spoke | Software Engineer - AI and Learning | San Francisco | http://askspoke.com
Spoke is a young, well funded, San Francisco-based startup that is reinventing workplace ticketing systems. Our goal is to make Spoke the primary business application that companies use for all of their knowledge and service requests. We’re a small team of designers, engineers and machine-learning experts who are repeat entrepreneurs and most recently worked at Google and Twitter.
We are looking for exceptional engineers to join our team in San Francisco. At Spoke we are using ML and NLP technologies to make workplace ticketing systems smarter. The work spans many disciplines: Information Retrieval, NLP, ML, and deep learning. You can learn more and apply here: https://jobs.lever.co/askspoke/135f082c-de82-4875-bbd1-35f6a.... jobs@askspoke.com
Spoke | Software Engineer - AI and Learning | San Francisco | http://askspoke.com
Spoke is a young, well funded, San Francisco-based startup that is reinventing workplace ticketing systems. Our goal is to make Spoke the primary business application that companies use for all of their knowledge and service requests. We’re a small team of designers, engineers and machine-learning experts who are repeat entrepreneurs and most recently worked at Google and Twitter.
We are looking for exceptional engineers to join our team in San Francisco. At Spoke we are using ML and NLP technologies to make workplace ticketing systems smarter. The work spans many disciplines: Information Retrieval, NLP, ML, and deep learning.
You can learn more and apply here: https://jobs.lever.co/askspoke/135f082c-de82-4875-bbd1-35f6a....
jobs@askspoke.com
http://people.itkit.io http://jobs.itkit.io