The software solutions landscape can be difficult to navigate, especially with all the buzzwords that have become associated with digital transformation. To help those on their digital transformation journey navigate the waters of procurement more pragmatically, outlined below are some of those buzzwords, what they refer to, and some things to keep and mind to further question claims.
Reviewing these commonly used phrases may just save you some time in the future and make your life easier as you move forward with digital transformation.
5 Digital Transformation Buzzwords to Know
Does the solution actually manage a workload, or is it just surfacing data and tasks? Solutions that just give information and make your teams do all the follow-up work may actually increase the workload, although they may claim and appear to do otherwise.
Some solutions may even provide an interface as an alternate way to perform a task, but ask these questions:
- Does this capability actually reduce the workload of my teams and make us more efficient and secure?
- Is the tool just reporting?
- Is it simply assigning tasks and surfacing information, or does it actually automate a process?
- If so, how much value is it actually bringing compared to its cost?
Any level of automation can bring ROI and scalability that enhances your operational capacity, but it’s good to make sure those capabilities are what a solution is actually bringing to the table.
Related Article: Is It Unethical to Secretly Automate Your Job?
AI and Machine Learning
Artificial intelligence and machine learning are terms that we hear more and more often in the software world. Typically, these imply a solution can find commonalities in a data set and further extend and point out correlations and similarities to other data sets based on learned parameters.
In the collaboration world this looks like speech recognition, searching images by their content, making decisions based on data or even logically learning how to respond to or even imitate human interactions.
If a solution is claiming to utilize AI, ask yourself:
- Is it actually replacing human-based activities, or is it just an interaction with data or processes based on parameters?
- If the AI or machine learning is actually replacing a task a human would normally do, is it making predictions based on analytics, or is it simply performing a pre-configured advanced or scheduled task?
Machine learning and AI are very powerful. While tools are available which use these capabilities, the terms are definitely used loosely nowadays.
Related Article: Confused By AI Hype and Fear? You're Not Alone
Software-as-a-Service is a phrase used by many vendors. The terminology is supposed to refer to a solution that requires no overhead cost or maintenance, and brings all the functionality it offers out-of-the-box.
Typically, these take the form of a solution you can essentially visit via a website, sign in and begin using its features. Quite a few solutions out there however claim to be a SaaS product, but still may require you to maintain your own cloud server or pay for your own storage to utilize.
- Is the solution actually SaaS?
- If you are being forces to pay for some overhead, is there a solution that brings more to the table?
Just because it's in the cloud or designed to be in the cloud, does not make a solution SaaS.
Related Article: SaaS Support Best Practices: Passive, Proactive and Predictive
There are two elements to good governance — policies and enforcement. Governance without enforcement is simply a suggestion. You are basically just communicating policies to users and hoping it works out.
If you are looking at governance tools, really great questions to ask are:
- Is the tool really bringing enforcement to the table, or is it simply reporting or offering integration with an existing tool, without adding much value of its own?
- If a tool claims to be governing data, can it be proven trustworthy and reliable for the tasks at hand?
- Does it have a method of enforcement to actually prevent out-of-policy actions or errors?
These are all important things to consider as you look at bolstering compliance and operational controls.
Many tools list lifecycle management among their capabilities or offerings. The definition of lifecycle management is a tool that can manage the disposition and other aspects (settings, security) of content, from creation to the time it is deleted.
A wide variety of solutions, touching on many use cases make this claim. As you’re shopping ask yourself:
- How much additional content management capability is the solution bringing to the table?
- Is it really capable of reducing a workload in a way that brings value to the organization?
- Does it actually manage the complete lifecycle of content n your environment?
- Does it archive or delete content in a way that is efficient for your organization and is scalable?
- What kind of controls does it enable around this process and are those controls scalable for your level of use?
Lifecycle management tools can help your organization reduce content clutter and help you meet retention compliance, but some do this in much more scalable ways than others.
Which Solution Will Deliver for You?
These terms are commonly used in a variety of ways across industries, across platforms and by many vendors and organizations. Though they mean different things to different people, they all have a base definition — and some solutions implement them in ways that incorporate their capabilities to bring a greater operational ROI.
No matter what problem you’re looking at solving with products, you should ask yourself a few pragmatic questions aside from the standard requirement checkboxes to make sure you are looking past the buzz and getting the most bang for your buck.