9 top trends that are driving AI and software investments
IT and data leaders are constantly challenged to keep up with new trends in emerging and disruptive technologies, and to determine how each can best aid the organization. In the midst of all the changes going on in 2019, it gets increasingly hard to know where to invest in all this new technology.
To help add clarity, here are my thoughts on some of the most important trends that will shape data management and software development for the next couple of years.
The business multi-verse expands through multi-cloud as data inefficiencies are solved: Multi-cloud promises tremendous reward if it can be used properly, but data inefficiencies and complicated compliance policies hinder progress for many.
Expect to see some of those data inefficiencies fade away as effective data strategies are implemented and new technologies unleash true multi-cloud functionality to the masses.
AI / machine learning / ML trust/ethics/bias
Questions around data morality will slow innovation in artificial intelligence and machine learning: Last year saw the hype around AI/ML explode, and data ethics, trust, bias and fairness have all surfaced to combat inequalities in the process to make everything intelligent.
There are many layers to data morality, and while ML advancements won’t cease -- they’ll slow down as researchers try to hash out a fair, balanced approach to machine-made decisions.
The black box of algorithms becomes less opaque: Part of the issue with data morality with AI and machine learning is that numbers and scenarios are crunched without insight into subsequent answers came to be. Even researchers can have a hard time sorting it out after the fact.
In the coming years, while it won’t lead to complete transparency with proprietary algorithms, the black box will still become less opaque as end users become increasingly educated about data and how it’s used.
GDPR / CA consumer privacy laws / Data privacy
The “G” in GDPR will soon stand for “Global”: Data privacy regulations are going to become more widespread. For example, California, Japan and China are already working on their own regulations to adopt rules similar to the EU’s GDPR.
Additionally, companies like Facebook, Google and Twitter have all severely mishandled consumer data, showing the need for increased and widespread data privacy regulations -- even prompting Apple CEO Tim Cook to call for global privacy regulations. With consumers now viewing data privacy as a human right, increased data governance policies are sure to follow.
As privacy regulations spread, organizations will mistake data governance for data harassment: Based on what consumers do online, companies are able to determine, through their data, their demographics, interests and even what’s going on in their personal lives. This results in marketing so hyper targeted, it could feel like harassment.
While organizations struggle to comply with privacy regulations and create more well rounded and informed views of each of their consumers, the lines between governance and harassment will blur and there will be rocky roads as best practices are formed.
Social media is officially too big to fail: Social media companies have become the biggest publishing media brands and they are finally coming under scrutiny this year. However, there were no real repercussions for advertising fiascos and data privacy controversies despite Congress’s involvement, and the reality is that social media brands have become too big to fail.
While there will still be fights to remedy it -- and there should be work done on this end -- 2019 will solidify how social media companies are now too big to fail (or become regulated).
Data skills / Data as a team sport
The data skills gap will increase – but so will data literacy: Data is both the problem and the answer for businesses. It’s a problem because businesses manage to collect more data than they know how to use, yet it’s the answer because it can predict forecasts and offer insight into how the business should run.
Expect to see the data skills gap continue to increase -- users need to be able to analyze properly where data comes from and how to use it, and it only gets more complicated as more data is made available and as algorithms enter the fray. But at the same time, business users will also grow more data literate as they seek to approach data as a team, and help one another get what they need from their data.
Serverless and open source
Serverless will move beyond the hype as developers take hold: Last year was all about understanding what serverless is, but as more developers learn the benefits and begin testing in serverless environments, more tools will be created to allow them to take full advantage of the architecture and to leverage functions-as-a-service.
Serverless will create new application ecosystems where startups can thrive off the low-cost architecture and creatively solve deployment challenges.
The market will double down on open source technologies: Last year saw $53 billion in deals involving open source following the Cloudera/Hortonworks merger and acquisitions of Red Hat, GitHub and others.
Expect to see businesses double down on open source technologies -- more investments and deals will get done, and open source communities will also pour more effort and energy into projects after having seen the opportunity for open source in the marketplace.
To date, open source has still functioned with a freemium model, but the coming years may see that shift as the enterprise finds value in conventional open source technologies.
This article originally appeared on Information Management.