Gartner Top 10 Trends in Data and Analytics - Reviewed

Gartner called out these 10 trends for Data and Analytics leaders to focus on, but where does the world currently stand? Here we briefly touch all ten trends and indicate their status at the end of 2020. To ensure fair observations we will use Google Trends as our datasource unless mentioned otherwise.

Trend 10: Relationships form the foundation of data and analytics value

Summary: This trend focuses on the rise of graph technology, a special sort of database and querying capability.

Observation: A quick trend search on Graph DB or the largest variant Neo4j shows no mentionable growth in the past 12 months or even 5 years. Adding in additional solutions, such as cloud native graph databases, does not change this picture. Also stepping away from databases towards a query solution such as GraphX does not have any impact.

Evaluation: This trend is not currently happening, perhaps graph solutions are in decline as there are so many use cases that can be solved well enough with the capabilities of a regular database. It seems unlikely that we will observe this trend at all, unless one of the current niche players changes the game.

Trend 9: Blockchain in data and analytics

Summary: This trend focuses on how Blockchain technology enables transparency and lineage.

Observation: In the past few months the blockchain is trending, though not nearly as much as in the years leading up to 2017. It follows a nearly identical pattern as Bitcoin, the most famous application of blockchain. Related terms such as the enterprise solution Hyperledger, Ledger Database or features such as Smart Contracts also do not show a noticable trend since 2017.

Evalution: The global blockchain awareness seems to be driven by Bitcoin rather than enterprise implementations. As this outshadows anything else it is difficult to make firm conclusions, but a quick glance at Hyperledger suggests a decline rather than an increase in the corporate world. It is worth noting that Gartner also calls out the underlying concepts of transparency and lineage, perhaps these will receive more attention than the actual blockchain technology itself.

Trend 8: Data marketplaces and exchanges

Summary: Companies will be buying data, or even selling it via marketplace technologies.

Observation: Many companies or institutions that want to economize their data created their own solutions for this. Even to the point that most data marketplaces appear to be outside of platforms built specifically for this purpose. A quick search of individual data suppliers suggests that some are up, while others are down, but a generic search for Data Marketplace shows a modest but steady upward trend across many years.

Evaluation: Data marketplaces have been trending, and are showing no sign of slowing down. Perhaps a dominant technology will rise, but even without this the sheer number of companies that want to market their data will drive the trend.

Trend 7: Data and analytics worlds collide

Summary: The worlds of Data (e.g. Data Engineering, Data Governance) and Analytics (e.g. Reporting, Data Science) are coming closer together.

Observation: Though it is hard to capture this objectively, this trend is certainly happening everywhere in our view. Analytics team work more closely with data teams, and terms like Agile and Devops which somewhat reflect this were firmly trending untill Covid became part of our lives.

Evaluation: The sheer scale of data operations, as well as the continuously stricter regulations have been and will likely remain driving this collision in the long term. However, since the arrival of Covid this appears to have less focus, possibly companies are first attempting to stabilize before further improving the way of working.

Trend 6: Cloud is a given

Summary: Public Cloud services will be used to drive much of the innovation, and the main challenges to overcome are selecting the right services, governance and integration.

Observation: Public Cloud in general, as well as individual public clouds (AWS, Azure, GCP) have been firmly trending over the past years, with a small dip since September. This is also in line with the revenue figures published by all major Cloud providers. Though Cloudera does not publish separate Cloud metrics at this time, it is safe to say that it is also seeing rapid growth of its Cloud Platform usage.

Evaluation: Cloud has been a strong trend, and will likely continue to grow at a similar rate in the coming years. More and more sectors across countries keep opening up policies to allow cloud, and though there will certainly be many use cases that prove to be cheaper on-premises, the trend is currently unstoppable and the time to grow into Cloud and Hybrid is certainly now.

Trend 5: Augmented data management

Summary: The power of AI and ML will be used to augment Data Management and improve operations.

Observation: There do not appear to be any notable solutions in this space. Also the term Augmented Data Management does not yield enough information to speak of a trend. However, the terms AI and ML are clearly trending individually over the years, with an extra uptick in the past twelve months.

Evaluation: This is not a notable trend yet. However, unlike anything described so far, this is simply a brand new concept, rather than one that is showing a plateau. As such it might become a trend at any time. Conceptually the need makes sense, and as companies focus on Data Management, as well as AI and ML, they may well combine these sooner rather than later.

Trend 4: X analytics

Summary: Gartner coined this term to describe data analytics for structured and unstructured data.

Observation: Data analytics shows a modest but clear trend over the past 5 years. The more specific Unstructured Data concept shows an even stronger trend, a pattern also visible for Log Analytics. Surprisingly Text Analytics and Video Analytics have hardly shown a trend over the past years.

Evaluation: Analytics is already at the heart of any large company, and it is a safe bet that it will continue to grow as companies have more data to handle, and want to base their decisions on facts. Though modern use cases have become feasible, the rise in unstructured data analytics seems to be driven primarily by gaining more insights of traditional data sources such as Logs, rather than modern sources such as Video.

Trend 3: Decision intelligence

Summary: Decision intelligence is a gathering of disciplines, including Decision Management and Decision Support.

Observation: Each of the concepts Decision Intelligence, Decision Management and Decision support shows a barely perceptible positive trend in the past five years, with a similar pattern in the past twelve months. Individual components of this, such as Decision Automation do show a more firm trend across the years, and we may even mention Digital Transformation which showed the strongest trend of all across 5 years.

Evaluation: Decision intelligence as a term is fairly stable across the years, but underlying and related concepts such as Decision Automation and Digital Transformation are certainly hot and happening.

Trend 2: Decline of the dashboard

Summary: Users will spend less time looking at pre-defined dashboards and explore data in advanced forms, perhaps called Data Stories or Experiences. These may not be point and click solutions, but even solutions that listen to your typed or verbal commands.

Observation: When searching for Data Stories one soon lands at the leading BI solutions such as Tableau, Qlik, PowerBi. Though a quick inspection of other solutions such as Cloudera Data Visualization suggest that most BI providers now offer this kind of functionality. Though it is hard to express in numbers, it is very clear the capabilities now are much stronger than what they were a few years ago.

Evaluation: Decision makers need to see information, given that data becomes more critical to organizations this is only expected to increase. However, the way that data can be visualized has advanced a lot, and will likely continue to do so. Though most users will still find themselves clicking, a well spoken command to a modern tool is currently already enough to gain insights from data.

Trend 1: Smarter, faster, more responsible AI

Summary: AI and ML are already in use in several companies, but over the next years this will become more embedded in the Operations. As currently data must be used responsibly, transparency and trust become critical.

Observation: Enterprise AI and ML are strongly trending over the past five years, as well as in the past twelve months. Furthermore, Transparent ML is also growing upon us over the past year. Operational AI is starting to be discussed, but is still at a very low level. However to observer the rapidly growing power of AI and ML, no trend search is needed. We may simply open our phones, or start a video conference and see how well technology is already able to change or augment our faces in realtime, something that was definitely not possible on this level a few years ago.

Evaluation: AI and ML are growing strong, both in the corporate world and outside of that. The popularization will likely soon trigger further rules regarding permitted use and transparency or governance, so we will surely see this trend not only represented as a larger volume, but also in greater complexity. Fortunately the continuous strengthening of techniques and increased availability of computing power will also open up routes to value that have never been walked before.

Conclusion

Gartner has placed its bets in how the Corporate landscape will adopt Technology in Data and Analytics. Though some trends are not observable at this time, others are already undeniable. Think carefully on your strategy for the next years and decide on which trends to follow, or perhaps even which trends to set!

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Sources:

• Gartner Top 10 Trends in Data and Analytics
• Google Trends