Data and analytics have gained traction in organizations, driven by the promise of big data a few years ago and the potential of machine learning and other types of artificial intelligence more recently. Even as many enterprises seemed to be stalled in their production AI plans, they are still making those plans, and know they are crucial for success in the years to come.
That's because data and analytics are serving an expanded role in digital business, according to Gartner analyst and VP Rita Sallam. Data and analytics have become key parts of how you serve customers, hire people, optimize supply chains, optimize finance, and perform so many other key functions in the organization.
With that in mind, there are a number of trends and technologies laying the foundation for successful deployment in the years to come, designed to make you faster and more stable with your efforts.
"You are facing a faster pace of business change, a faster pace of technology change than ever before," said Sallam. "You need an agile data and analytics architecture that can support that constant change."
With an eye to that future, Sallam provided a look at "10 Data and Analytics Trends that will Change Your Business" during a session at the recent Gartner IT Symposium, in Orlando, Florida. These trends fit into three major themes. The first one is intelligence. It means that machine learning and AI techniques are being infused into workloads and activities, augmenting user roles, reducing the skills required and automating tasks to improve time-to-insight. The second one is about new data formats. AI and machine learning are supporting more agile and emergent data formats than they have in the past. Finally, there's scale.
All these trends are 3 to 5 years away, she said, so you won't see self-service on this list because that's everywhere now, and you won't see quantum computing here either because that's too far away. Cloud is also not on this list because it permeates everything. With those rules in mind, watch for the following 10 trends to change your business in the years to come:
1. Augmented analytics
Across analytics, business intelligence, data science, and machine learning, organizations will leverage augmented analytics to enable more people to gain insights from data. Sallam said that augmented analytics will become the dominant thing that organizations look at when they are assessing vendor selections over the next few years. Also, vendors of other technologies like Salesforce and Workday are incorporating augmented analytics into their products and services to improve the experience for users.
"It's really about democratizing analytics," Sallam said. "…It is really about getting insight in a fraction of the time with less skill than is possible today."
2. Augmented data management
This trend will improve organizations' ability to analyze data that is coming in more dynamically and with greater levels of automation in closer to real time. There are many different tasks that come with the data management side of the operation such as schema recognition, capacity, utilization, regulatory/compliance, and cost models, among others. Augmented data management will target those pieces.
Through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management, Sallam said.
3. NLP (natural language processing)/conversational analytics
NLP and conversational analytics are highly complementary with augmented analytics. They provide non-data experts with a new kind of interface into queries and insights.
"Most people don't know SQL, and they can't build their own queries themselves," said Sallam. "These tools have made it easier."
By 2020, 50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated, according to Gartner. Still, there is also plenty of room for improvement.
Today most analytics and BI platforms have implemented basic keyword search. For instance, you can ask "What were my sales by product?" Sallam said. But more complex questions are still a challenge. You probably won't be able to ask "What were my top 10 products or customers within a 50-mile radius of New York this year versus last year."
"That's more complex," Sallam said, and it involves ranking functions and synonyms and other functions that not every vendor can do today.
Another emerging feature in this area is conversational analytics, which will let you drill down with more specific questions.
"Until recently, it's all been about visualization," Sallam said. Conversational analytics will add another dimension to the insights.
Graph processing and graph databases enable data exploration in the way that most people think, revealing relationships between logical concepts and entities such as organizations, people, and transactions, Sallam said.
Gartner predicts that the application of graph processing and graph databases will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.
Graph enables emergent semantic graphs and knowledge networks, Sallam said. One example might be an emergent linking of diverse data such the data from exercise apps and diet apps with medical advice and health news feeds.
5. Commercial AI/ML will dominate the market over open source
Open source has been a big driver of big data and AI and machine learning, particularly at digital giant companies such as Google and Amazon. But most organizations don't fit into the digital giant category. These companies have run AI and ML pilots, but have been struggling to scale their projects to production. Gartner believes these companies will ultimately leverage commercial platforms to manage their AI programs.
Gartner predicts that by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercial, instead of open source, platforms.
6. Data fabric
This trend is tied closely to augmented data management, Sallam said, and it lets you support agile data at scale. It used to be the goal was to have all your data in a single data warehouse. But data has become more distributed. Data fabric by design is created for data in silos. It enables a logical data warehouse architecture that enables seamless access and integration of data across heterogeneous storage.
Gartner forecasts that through 2022, custom-made data fabric designs will be deployed as static infrastructure, forcing a new wave of cost to completely redesign for more dynamic approaches.
7. Explainable AI
"We believe this will be a critical lynchpin for you to be able to govern the increasing use of AI," Sallam said. That's because models are growing more complex and opaque. Organizations will need to be able to explain results for internal monitoring and also to comply with regulations. Organizations will need to know if there's a privacy risk in a model or if bias is detected. Sallam said vendors are working on this problem now and have plans to implement solutions.
Gartner predicts that by 2023, over 75% of large organizations will hire AI behavior forensic, privacy, and customer trust specialists to reduce brand and reputation risk.
This is a trend across many technology areas beyond data and analytics, Sallam said. But it's important in data and analytics particularly in the area of trust. "It is really about cryptographically supporting immutability across a network of trusted participants," Sallam said. It tracks if something has changed, so from a data perspective blockchain will be useful to track things like deep fakes or fake news.
Gartner predicts that by 2021, most private and permissioned blockchain uses will be replaced by ledger DBMS products.
9. Continuous intelligence
Continuous intelligence is about enabling smarter decisions through real-time data and advanced analytics. It incorporates situation awareness and prescribes the action to take. It is intelligent, automated, and outcome-focused, according to Sallam.
Gartner predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.
10. Persistent memory servers
These servers enable larger memory, affordable performance, and less complex availability, Sallam said. Some database vendors are rewriting their systems in order to support this type of server, which enables the analysis of more data, in-memory, and in real time.
Gartner predicts that by 2021, persistent memory will represent over 10% of in-memory computing memory GB consumption.
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