Why being data-centric is the first step to success with artificial intelligence
REGARDLESS of industry, artificial intelligence (AI) is a disruptive technology that is greatly sought after.
Many organizations are looking to deploy AI projects at scale, in hopes of boosting performance and ultimately increasing revenues.
However, many fail to see returns on their AI investments. Often, this is because AI projects are not approached in the right manner.
To be AI-first, organizations need to adopt a data-first mindset. Here’s how and why:
# 1 | Use data-centric tools
Using the right methodologies and technologies is crucial for the successful deployment of AI solutions.
It is not enough to just rely on agile methods, as they focus heavily on functionality and application logic delivery. Instead, data-centric methodologies such as the Cross Industry Standard Process for Data Mining (CRISP-DM) should be used, as they concentrate on the steps needed for a successful data project.
Depending on organizational needs, a ‘hybrid’ methodology can also be deployed by merging the non-agile CRISP-DM with agile methodologies, making it more relevant.
Data-centric methodologies must be followed by the use of data-centric technologies. For any AI projects, organizations must always keep the end in mind, and have clarity on what the desired outcomes are.
# 2 | Foster data-centric talent
Methodology and technology will not be of use without a data-proficient team.
There must be a specialized AI-team in place that can effectively collect, compile, and extract key information from seemingly haphazard data sets.
Ideally, the team should have a good mix of data scientists, engineers, and specialists that possess the skills to put models into operation.
There is no room for guesswork in AI deployment — randomly changing data sets wastes unnecessary time and resources and is simply disastrous.
# 3 | Investing for the long run
For a successful AI project to materialize, organizations ought to continuously invest for the long term.
Staying complacent is not an option. They must seek to refine the methodologies in place. If the technologies used are no longer relevant, they should be replaced.
AI projects will not work if employees lack the skills and tools needed to deploy them. Thus, employees should be upskilled, and also made to understand the value of AI, and how it can augment the work that they do.
While the technology is still in its infancy for large scale projects, it is only a matter of time before AI is deployed at scale, across organizations and markets.
Ultimately, it all boils down to agility and resilience in the midst of change. Those that adopt the right mindset will succeed, those that resist the change will suffer.
- Meta’s new AI chatbot is built with Gen Z in mind
- Oracle CloudWorld 2023: Everything you need to know about the Oracle and Microsoft partnership
- Can DALL-E 3 fix the problem with image generators?
- Is India easing limitations on the imports of laptops and tablets?
- The largest deal made by Cisco is heating up the AI cybersecurity race. Here’s why