The rise in popularity of data, artificial intelligence, and machine learning has dramatically increased the volume of conversations around all of the above. That can make it hard to separate the hype from what’s really happening.
This much opportunity inevitably brings lots of players into the space. Organizations looking for the right data transformation partner deserve a stronger signal-to-noise ratio, where credible information and advice stand out from a noisy crowd of sellers cashing in on the latest trends.
Don’t listen to folks who tell you that they need time and money to assemble big data sets to get the project started. There’s mostly likely value waiting in the information you already have — it just needs to be unlocked.
Sometimes, even small amounts of the right data can be enough to get a project started. Additionally, depending on the problem being solved, third-party or synthetic data sets can be used to build and train models, so you can get started with very little first-party data.
Consultants love timelines and presentations. And while they both are exceptionally useful in context, be wary of partners pitching long discovery phases that don’t yield much beyond presentations and meetings.
But as with software, a more agile approach is possible. The right team can move quickly towards a prototype or working model, which gives everybody a starting point for iteration and improvement. Make sure long timelines are being driven by real technical constraints.
It’s true that most advanced technology solutions need significant verticalization to succeed in some industries. Precise last-inch optimization is often the difference between a successful implementation and time lost to deploy solutions that aren’t ready.
At the same time, deep domain expertise shouldn’t be a requirement for your data partners. Good specialists will map your world in ways that inform model creation and curation. This is good news —it gives you more experts to choose from.
Many of the most compelling data use cases end with complete automation of manual, human-intensive processes. Reality, on the other hand, shows us that total automation is a tall order, meaning setting that as a goal almost guarantees a that project fails. Same with reinvention—continuous improvement has as much, if not more, value.
The best approach is to break big challenges into smaller pieces. The ability to automate pieces and parts of a larger whole is still immensely valuable. It reduces the time and budget required to get started and can unlock important and surprising insights early on in the process.
If you’re considering investing in applied data solutions for your business, there’s no time like the present, and StandardData is always up for a conversation.