Data and Analytics Trends Report 2023

SOONER OR LATER, YOU WILL BE IN THE DATA BUSINESS!

Nine Data Trends for 2023

In this report, we explore nine data use cases for emerging technologies that can improve on capabilities needed to compete in the data-driven economy. Use cases combine emerging data trends and modernization of existing capabilities.

1. VOLUME
   • Data Gravity
2. VELOCITY
• Democratizing Real-Time Data
3. VARIETY
• Augmented Data Management
4. VERACITY
• Identity Authenticity
5. VALUE
• Data Monetization
6. VIRTUE
• Adaptive Data Governance
7. VISUALIZATION
• AI-Driven Storytelling & Augmented Analytics
8. VIRALITY
• Data Marketplace
9. VISCOSITY
• DevOps – DataOps – XOps

VOLUME

Data Gravity

Trend 01 Demand for storage and bandwidth continues to grow

When organizations begin to prioritize data, they first consider the sheer volume of data, which will influence data system design. Your data systems must consider the existing and growing volume of data by assessing industry initiatives such as digital transformation, Industry 4.0, IoT, consumer digital footprint, etc.

VOLUME

Data Gravity

Data attracts more data and an ecosystem of applications and services

SharePoint, OneDrive, Google Drive, and Dropbox offer APIs and integration opportunities for developers to enhance their products.

Social media platforms thought about this early by allowing for an ecosystem of filters, apps, games, and effects that engage their users with little to no additional effort from internal resources.

VOLUME

Data Gravity

Focus on data gravity and avoid cloud repatriation

Data gravity is the tendency of data to attract applications, services, and other data. A growing number of cloud migration decisions will be made based on the data gravity concept. It will become increasingly important in data strategies, with failure potentially resulting in costly cloud repatriations.

Emerging technologies and capabilities:

Data Lakehouse, Data Mesh, Data Fabric, Hybrid Data, Cloud Data, Edge Computing

Source: CIO, 2022

VOLUME

Data Gravity

What worked for terabytes is ineffective for petabytes

When compared to on-premises infrastructure, cloud computing is less expensive and easier to implement. However, poor data replication and data gravity can significantly increase cloud costs to the point of failure. Data gravity will help organizations make better cloud migration decisions.

It is also critical to recognize changes in the industry landscape. The goal of data processing and analytics is to generate the right data for users to act on. In most cases, the user is a human being, but in the case of autonomous driving (AD), the car takes on the role of the user (DXC Technology).

To avoid cloud repatriation, it will become prudent for all organizations to consider data gravity and the timing of cloud migration.

VELOCITY

Democratizing Real-Time Data

Trend 02 Real-time analytics presents an important differentiator

The velocity element of data can be assessed from two standpoints: the speed at which data is being generated and how fast the organization needs to respond to the incoming information through capture, analysis, and use. Traditionally data was processed in a batch format (all at once or in incremental nightly data loads). There is a growing demand to process data continuously using streaming data-processing techniques.

Emerging technologies and capabilities:

Edge Computing

VELOCITY

Democratizing Real-Time Data

Make data accessible to everyone in real time

• 90% of an organization’s data is replicated or redundant.
• Build API and web services that allow for live access to data.
• Most social media platforms, like Twitter and Facebook, have APIs that offer access to incredible amounts of data and insights.

VELOCITY

Democratizing Real-Time Data

Trend in Data Velocity

Data democratization means data is widely accessible to all stakeholders without bottlenecks or barriers. Success in data democratization comes with ubiquitous real-time analytics. Google highlights a need to address democratization in two different frames:

1. Democratizing stream analytics for all businesses to ensure real-time data at the company level.
2. Democratizing stream analytics for all personas and the ability of all users to generate real-time insights.

Emerging technologies and capabilities:

Data Lakehouse, Streaming API Ecosystem, Industry 4.0, Zero-Copy Cloning

Nearly 70% of all new vehicles globally will be connected to the internet by 2023.

Source: “Connected light-duty vehicles,” Statista, 2022

VELOCITY

Democratizing Real-Time Data

Enable real-time processing with API

In the past, data democratization has largely translated into a free data set and open data portals. This has allowed the government to freely share data with the public. Also, the data science community has embraced the availability of large data sets such as weather data, stock data, etc. In the future, more focus will be on the combination of IoT and steaming analytics, which will provide better responsiveness and agility.

Many researchers, media companies, and organizations now have easy access to the Twitter/Facebook API platform to study various aspects of human behavior and sentiments. Large technology companies have already democratized their data using real-time APIs.

Thousands of sources for open data are available at your local municipalities alone.

6G will push Wi-Fi connectivity to 1 terabyte per second! This is expected to become commercially available by 2030.

VARIETY

Augmented Data Management

Trend 03 Need to manage unstructured data

The variety of data types is increasingly diverse. Structured data often comes from relational databases, while unstructured data comes from several sources such as photos, video, text documents, cell phones, etc. The variety of data is where technology can drive business value. However, unstructured data also poses a risk, especially for external data.

VARIETY

Augmented Data Management

Employ AI to automate data management

New tools will enhance many aspects of data management:

• Data preparation, integration, cataloging, and quality
• Metadata management
• Master data management

Enabling AI-assisted decision-making tools

VARIETY

Augmented Data Management

Trend in Data Variety

Augmented data management will enhance or automate data management capabilities by leveraging AI and related advanced techniques. It is quite possible to leverage existing data management tools and techniques, but most experts have recognized that more work and advanced patterns are needed to solve many complex data problems.

Emerging technologies and capabilities:

Data Factory, Data Mesh, Data Fabric, Artificial Intelligence, Machine Learning

VARIETY

Augmented Data Management

Data Fabric vs. Data Mesh: The Data Journey continues at an accelerated pace

More Unstructured Data

95% of businesses cite the need to manage unstructured data as a problem for their business.

VERACITY

Identity Authenticity

Trend 04 Veracity of data is a true test of your data capabilities

Data veracity is defined as the accuracy or truthfulness of a data set. More and more data is created in semi-structured and unstructured formats and originates from largely uncontrolled sources (e.g. social media platforms, external sources). The reliability and quality of the data being integrated should be a top concern. The veracity of data is imperative when looking to use data for predictive purposes. For example, energy companies rely heavily on weather patterns to optimize their service outputs, but weather patterns have an element of unpredictability.

VERACITY

Identity Authenticity

Veracity of data is a true test of your data capabilities

• Stop creating your own identity architectures and instead integrate a tried-and-true platform.
• Aim for a single source of truth for digital identity.
• Establish data governance that can withstand scrutiny.
• Imagine a day in the future where verified accounts on social media platforms are available.
• Zero-trust architecture should be used.

VERACITY

Identity Authenticity

Trend in Data Veracity

Veracity is a concept deeply linked to identity. As the value of the data increases, a greater degree of veracity is required: We must provide more proof to open a bank account than to make friends on Facebook. As a result, there is more trust in bank data than in Facebook data. There is also a growing need to protect marginalized communities.

Emerging technologies and capabilities:

Zero Trust, Blockchain, Data Governance, IoT, Cybersecurity

VERACITY

Identity Authenticity

The identity discussion is no longer limited to people or organizations. The development of new technologies, such as the IoT phenomenon, will lead to an explosion of objects, from refrigerators to shipping containers, coming online as well. If all these entities start communicating with each other, standards will be needed to establish who or what they are.

The IoT market is expected to grow 18% to 14.4 billion in 2022 and 27 billion by 2025.

Source: IoT Analytics, 2022

VALUE

Data Monetization

Trend 05 Not Many organization know the true value of their data

Data can be valuable if used effectively or dangerous if mishandled. The rise of the data economy has created significant opportunities but also has its challenges. It has become urgent to understand the value of data, which may vary for stakeholders based on their business model and strategy. Organizations first need to understand ownership of their data by establishing a data strategy, then they must improve data maturity by developing a deeper understanding of data value.

VALUE

Data Monetization

Start developing your data business

• Blockbuster ran its business well, but Netflix transformed the video rental industry overnight!
• Big players with data are catching up fast.
• You don’t have to be a giant to monetize data.
• Data monetization is probably closer than you think.
• You simply need to find it, catalog it, and deliver it.