How To Apply This Year’s Top Data & Analytics Trends to Your Business

Blog: The Tibco Blog

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As we know, with the start of a new year comes a slew of fresh data and analytics trends that businesses can apply to boost customer intimacy, operational excellence, and business reinvention. The data and analytics trends predicted for 2020 will have significant disruptive potential over the next three to five years, so it’s key that your business takes advantage of them this year for maximum digital transformation potential.

Without further ado, here are Gartner’s top 10 data and analytics trends for 2020 and how you can apply them to your business.

Augmented analytics

Augmented analytics automates finding and surfacing the most important insights or changes in the business to optimize decision making, in a fraction of the time. It makes insights available to all business roles; however, it does require increased data literacy across the organization. This year, augmented analytics will be the driver of analytics and business intelligence purchases in addition to data science and machine learning platforms.

Read this eBook to learn about the different types of augmented analytics and when to apply each.

Augmented data management

Organizations are continually on the hunt to automate data management tasks by adding machine learning (ML) and artificial intelligence (AI) capabilities to make the process self-configuring and self-tuning. This enables highly-skilled technical staff to focus on higher-value tasks. All enterprise data management categories are affected, including data quality, metadata, master data, and more.

Tune into this webinar to learn how you can unify all of your different data types.

Natural language processing (NLP) and conversational analytics

NLP gives businesses an easier way to ask questions about data and receive an explanation of the insights. Conversational analytics takes the concept of NLP a step further by enabling questions to be asked and answered verbally. By next year, NLP and conversational analytics will boost analytics adoption by employees to over 50 percent.

Empower business users to self-serve insights though simple, real language requests.

Graph analytics

With the growth of structured and unstructured data, analyzing it at scale is not possible using traditional query tools. Graph analytics is a set of analytic techniques that show how entities such as people, places, and things are related to one another. The application of graph processing and graph databases are predicted to grow at 100 percent annually over the next few years to accelerate data preparation and enable more complex and adaptive data science.

Leverage the power of relationships to solve connectivity problems at scale.

Commercial AI and ML

Open-source platforms for AI and ML have been the primary source of innovation in algorithms and development environments. While commercial vendors were slow to respond, they now provide connectors, offering enterprise features necessary to scale AI and ML, such as project and model management, reuse, transparency, and integration.

Watch this webinar to learn how you can drive digital transformation with AI and ML.

Data fabric

In order to derive the most value from your analytics, you need to have an agile and trusted data fabric, a custom-made design that provides reusable data services, pipelines, semantic tiers, or APIs via a combination of data integration. This enables frictionless access and sharing of data in a distributed environment.

Learn how you can provide an agile data infrastructure that unifies your data with a data fabric solution.

Explainable AI

“Black box” AI and algorithmic bias can pose risks to an organization. With explainable AI, you can increase the transparency and trustworthiness of AI solutions and outcomes, and reduce regulatory and reputational risk. It is the set of capabilities that describe a model while highlighting its strengths and weaknesses, predicts its likely behavior, and identifies any potential bias.

Accelerate your analytics journey with the application of explainable AI.

Blockchain in data and analytics

Blockchain technologies help address two challenges: lineage of assets and transactions, and transparency for complex networks of participants. However, since blockchain hasn’t yet matured to real-world, production-level scalability beyond cryptocurrency, it needs to work with additional systems to act as a system of record.

Untap the potential of blockchain technology to make your business truly digital.

Continuous intelligence

Organizations have sought out real-time intelligence. Now, they are finally able to implement these systems using continuous intelligence. Cloud, advances in streaming software, and the explosive growth of data from Internet of Things (IoT) sensors makes this possible. It is projected that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

Watch this webinar to learn how you can implement continuous intelligence in your organization.

Persistent memory servers

Persistent memory technology will help businesses extract more actionable insights from data. Right now, most database management systems (DBMS) make use of in-memory database structures. However, with data volumes growing rapidly, memory size can be restrictive. To keep up, they need faster performance, massive memory, and faster storage. Many DBMS providers are experimenting with persistent memory, though it may take several years to perfect.

Avoid exhausting available memory in your servers with these best practices.

So what are you waiting for? Apply these 2020 trends to your business today to continue to tap the power of your data to innovate, collaborate, and grow.