2024年 データと分析のトップトレンド(生成AI以外のトレンドとは?)

テクノロジー   |   Shane Remer   |   2023年11月8日

Generative AI took the world by storm in 2023 and for good reason. While it’s a trendy topic, it’s just one of many data and analytics topics on leaders’ minds.

We used data from Google Trends to understand which other topics are growing in interest. Then, we compared them to data and recaps from our past Alteryx Analytics Maturity Assessment snapshots to provide possible explanations for the trends as we head into 2024.

1. Data Management and Governance

If you haven’t heard the term data lakehouse before, you will in 2024. Traffic for the term took off in the latter part of 2021 and has only increased since then.

A data lakehouse is a data storage structure that combines the strengths and benefits of a data lake and a data warehouse, and the reasons why people would be interested in them are reasonable.

As the amount of data needed to fuel analytics, user applications, and, of course, generative AI grows, the ability to quickly use it and govern it will also rise. A data lakehouse is one way to manage that.

The terms data as a service (DaaS), data democratization, and data governance have likely grown in search traffic alongside data lakehouse for those reasons, too.

As leaders increase their focus on managing and governing their data, they should also consider how to safely put it in the hands of people who need it.

In our February Analytics Maturity Snapshot, companies reported having plenty of data but needed help putting it into the hands of their decision-makers.

Percent of decision-makers with access to data

As we discussed in February, cloud analytics might be one way to increase data access, but data access is only part of the analytics problems business face with the increase in the amount of data companies collect.

Explore More: Best practices for deploying analytics with AWS and  Snowflake

2. Data Privacy and Security

It’s hard to say that data privacy and data security are trending topics since they’ve always been popular, but both are seeing increased search traffic.

However, with the sudden popularity of generative AI and increased data collection, companies are questioning how to use their data, if they should use it, and, if so, how to use it safely.

In our September Analytics Maturity Assessment data, we noticed a dip in the percentage of people who reported using predictive analytics as their dominant analytical technique.

Companies decreased their use of predictive analytics as their dominant analytics technique from Q2 to Q3 of 2023.

This dip was accompanied by a slight increase in the percentage of people who reported using advanced prescriptive analytics for the same purpose.

We provided three theories about this dip in our September snapshot, but now we have a fourth: Companies are placing more scrutiny on the use of data due to the privacy concerns that come with AI.

Leaders have a lot to focus on when it comes to data privacy and security, including:

  • Increasing data privacy safeguards to prevent breaches
  • Eliminating data security vulnerabilities in remote and hybrid work environments
  • Protecting and encrypting sensitive data

Most businesses will take the safe route, opting to be more restrictive with data rather than risk fines. But being too restrictive will hinder their ability to move fast and generate revenue from their data.

Explore More: Trust at Alteryx

3. Artificial Intelligence and Machine Learning Applications

Although 2023 put generative AI in the hands of the masses, companies have been using AI for a while. Two of its applications are also two of the top trending topics going into 2024: natural language processing (NLP) and automated machine learning (AutoML).

For years, AI has been helping companies create bots to interact with people and develop models to surface trends and patterns in their data.

Interest in NLP could be rising because it has immediate ties to generative AI. However, AutoML doesn’t. What it does have the ability to do is look for patterns and correlations in vast amounts of data.

As the amount of data companies have grows, so does their need to understand the connection between it and desired outcomes. NLP is great for transforming some of that data into usable formats, and AutoML can find correlations.

Not all auto ML tools are built the same, though. Some can only take care of specific tasks. To get the most from their investments, leaders should consider the following:

  • The types of models each AutoML tool uses
  • The way each AutoML software builds models
  • The data format AutoML needs to perform well

Leaders wanting to invest in NLP and AutoML should ensure that people at their companies now — and new hires later — can use any software investments to get the answers they need, though.

Explore More:How an insurance company used NLP to provide summaries of data | Take a tour of Alteryx AutoML

4. Operational Efficiency through Automation

The fear of a recession, climbing interest rates, and reduced spending that accompanied 2023 forced companies to tighten their budgets. It also meant they had fewer people and resources to handle the growing amount of data in their hands.

These factors might explain the rise in search traffic for the following three trending topics: data automation, cloud analytics, and decision intelligence.

There’s no question that data automation can help companies process the massive amounts of data they have. Meanwhile, cloud analytics and decision intelligence can help teams access and use their data to make good decisions.

In our decision intelligence survey, all leaders said that they could see analytics being fully or partially automated, even if it still required human input.

To what extent do you believe each of the following decisions could be automated in your organization?

But bringing automation, cloud analytics, and decision intelligence to a company is a tall task. One that requires leaders to take charge. (After all, they’re the secret sauce to analytics maturity.)

Leaders need to be aware of the roadblocks that might block their ability to onboard these solutions, such as:

  • Data silos hindering automation efforts
  • Overcomplicated cloud analytics setups
  • Poorly integrated data tech stacks

Leaders should also refrain from pursuing these initiatives on their own. As we saw in the first two sections of this post, data management, governance, privacy, and security are also top of mine.

Leaders from IT and other departments should work together to find an automation, cloud, and decision intelligence solution that gets the most from a company’s data sources while helping it remain compliant.

Conclusion

As we’ve seen from the past year of data from the Alteryx Analytics Maturity Assessment, data and analytics are still challenging for many companies.

However, if the current trends indicate what’s to come in 2024, many companies are keen on figuring out how to solve it.

All that’s left for them to do is work with their teams to find the best solution to meet their needs.

Explore More: Choose the best cloud analytics platform for your organization.

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