Data Analytics – Thematic Intelligence
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Data Analytics Thematic Report Overview
Human activity generates a vast amount of data from databases containing information about citizens to user-generated content on social media platforms and sensor data generated by smartphones and industrial machinery. Data analytics tools can help us convert such raw data into valuable insights and actionable knowledge. It is a relatively mature market, yet significant innovation has recently emerged. Prescriptive analytics is the most advanced type, aiming to tell organizations what to do next rather than just describing what happened and why. Machine learning (ML) techniques can now provide data-driven recommendations by parsing large amounts of data and assessing probable scenarios.
The Data Analytics thematic intelligence report provides an overview of the current landscape, including technology, macroeconomic, and regulatory trends, as well as key players. The report provides an industry-specific analysis based on GlobalData databases and surveys.
| Report Pages | 84 |
| Regions Covered | Global |
| Market Size (2022) | $100.8 billion |
| Historical Period | 2010-2022 |
| Forecast Period | 2023-2027 |
| Value Chain | · Hardware
· Data Management · Applications · Delivery |
| Public Companies | · Alibaba
· Alphabet (parent company of Google) · Alteryx |
| Private Companies | · Databricks
· H2O.ai · Qlik |
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Data Analytics – Key Trends
The main trends shaping the data analytics industry theme over the next 12 to 24 months are technology trends, macroeconomic trends, and regulatory trends.
- Technology trends: Some of the key technology trends impacting the data analytics theme are IoT, Cloud, AI, edge analytics, and Low-code and no-code (LCNC).
- Macroeconomic trends: China considers data as the fourth factor of production. The other key macroeconomic trends explained in the report are talent and ESG.
- Regulatory trends: AI regulation, data privacy and technology protectionism are some of the regulatory trends impacting data analytics.
Data Analytics – Industry Analysis
The data and analytics market size was $100.8 billion in 2022. The market is expected to grow at a CAGR of more than 13% during 2022-2027. The data and content management category is currently the largest market segment, but business intelligence and data discovery tools are the fastest-growing. Moreover, a significant proportion of business intelligence and data discovery revenues are driven by the sale of big data platforms.
The Data Analytics industry analysis also covers:
- Timeline
Global Data and Analytics Revenue by Product, 2019-2027 (%)
Buy Full Report for More Industry Analysis Insights into the Data Analytics Market
Data Analytics - Value Chain Analysis
The data analytics value chain is split into four segments which are hardware, data management, applications, and delivery.
Hardware: The hardware segment is further divided into semiconductors, cameras, sensors and lasers, servers, storage devices, networking equipment, and edge equipment. The data analytics hardware stack is not too dissimilar from any IT system and so, the servers, storage, and networking equipment are standard and not designed specifically for data analytics use cases. However, as AI capabilities are embedded in various applications, using AI-optimized hardware components, such as semiconductors, for specific analytics workloads will become more frequent.
Data Analytics Value Chain Analysis
Buy Full Report for More Insights into the Data Analytics Value Chain
Public Companies
Some of the leading listed players associated with this theme are:
- Alibaba
- Alphabet (parent company of Google)
- Alteryx
Private Companies
Some of the interesting private companies associated with this theme are:
- Databricks
- ai
- Qlik
Buy Full Report to Know More About the Public and Private Companies in the Data Analytics Theme
Application Software Sector Scorecard
At GlobalData, we use a scorecard approach to predict tomorrow’s leading companies within each sector. Our sector scorecard has three screens: a thematic screen, a valuation screen, and a risk screen.
- The thematic screen ranks companies based on overall leadership in the 10 themes that matter most to their industry, generating a leading indicator of future performance.
- The valuation screen ranks our universe of companies within a sector based on selected valuation metrics.
- The risk screen ranks companies within a particular sector based on overall investment risk.
Application Software Sector Scorecard – Thematic Screen
Buy Full Report to Know More About the Sector Scorecard
The Data Analytics sector scorecard also covers:
- Cloud services sector scorecard
- IT infrastructure sector scorecard
Scope
This report provides:
- An overview of the data analytics theme.
- Key trends impacting the growth of the theme over the next 12 to 24 months, split into three categories: technology trends, macroeconomic trends, and regulatory trends.
- A comprehensive industry analysis, including market size and growth forecasts for the global data and analytics market, alongside analysis of trends in GlobalData’s proprietary signals data, including M&As, venture financing, patents, company filings, hiring, and social media.
- The detailed value chain which is split into four segments: hardware, data management, applications, and delivery.
- Profiles of leading players in the data analytics theme, including Alphabet, Amazon, IBM, Microsoft, Oracle, Salesforce, and Snowflake.
Key Highlights
Data analytics is a relatively mature market, yet significant innovation has recently emerged. Prescriptive analytics is the most advanced type, aiming to tell organizations what to do next rather than just describing what happened and why. Machine learning (ML) techniques can now provide data-driven recommendations by parsing large amounts of data and assessing “what if” scenarios. The traditional data analytics vendors such as SAS, IBM, Oracle, and SAP, which evolved from descriptive analytics roots, are being disrupted by AI-native vendors, such as C3.ai, CognitiveScale, and H2O.ai, which aim to help companies automate operational decision-making using ML. Furthermore, the recent emergence of generative AI, especially after the release of OpenAI’s ChatGPT, has led data analytics vendors to embed those AI capabilities in their platforms to democratize access to data science capabilities. For instance, Microsoft has launched Microsoft 365 Copilot, embedding ChatGPT into analytics products such as Excel and PowerBI.
Data privacy regulations have long impacted the data analytics sector, raising the importance of data governance and security. However, the adoption of AI technologies in the most advanced data analytics platforms, and the complex geopolitical situation, pose a mid-term risk to the sector.
Reasons to Buy
- Humans increasingly generate more and more data, largely due to the digitalization of our society, from databases containing information about citizens in a standardized format to user-created content on social media platforms and sensor data generated by smartphones and industrial machinery. Many industry forecasts expect over 175 zettabytes of data to be generated by 2025. We are drowning in data, and making sense of so much information is becoming difficult.
- Data analytics help us go from raw data to useful insights and actionable knowledge. This report is an invaluable guide to a theme that is relevant to every company in every industry.
Ab Initio
AccelData
Acer
Actifio
Adeptia
Adetiq
Adimec
Advantech
Aera Technology
AeroVironment
AEye
Airbyte
Alation
Alibaba
Alkira
Alphabet
Altair Engineering
Alteryx
Altibase
Amazon
Ambarella
AMD
American Software (Logility)
AMS
Amundsen
Anaplan
Anthropic
Anyscale
Apache Foundation
Appier
Apple
Applied Aeronautics
Aptiv
Aquant
Arista
Astronomer
Ataccama
Atlan
Attivio
Aviatrix
Ayasdi
Baidu
Basler
Bigeye
Black Crow AI
Blaize
Blue River
BMC Software
Boomi
Bosch
Broadcom
C3.ai
Cambricon
Cato Networks
CData
CEDQ
Celigo
Celona
Census
Cerebras
ChartBlocks
Check Point Software
Circonus
Cisco
ClickHouse
Cloud Software Group
Cloudera
Cobalt Iron
Cognex
CognitiveScale
Cohere AI
Cohesity
Colibra
Commnet
Commvault
Confluent
Continental
Couchbase
CrowdStrike
Cyclr
Dahua
Damco Group
Darktrace
Dask
Data Cirect Networks
Data Virtuality
data.world
Databricks
DataCaptive
DataHub
Dataiku
DataRobot
DataStax
DataTorrent
DataVirtuality
dbt Labs
Dell Technologies
Delphix
Delta Lake
Denodo Technologies
Denso
DJI
Domo
dotData
Dremio
Eastern Jin Tech
EdgeConneX
Elementl
EleutherAI (GPT-J)
EnterpriseDB
Ericsson
Esaote
Exasol
Experian
Explorium
Extreme Networks
Faraday
FICO
Findability.AI
Finisar
FireEye
Fivetran
flexiWAN
Flexpoint
Flink
Fluidly
Forcepoint
Fortinet
Fractal
Fujifilm
Fujitsu
FusionCharts
GE
Global Laser
Goertek
Grafana
Graphcore
Great Expectations
Groq
H2O.ai
HCL Technologies
Helpsystems
Highcharts
Hightouch
Hikvision
Hitachi
Hive
Hologic
Horizon Robotics
HPE
Huawei
Hugging Face
HyperSense
IBM
Immuta
Imply
Indie Semiconductor
Infineon
Infinidat
Infiot
Infogram
Informatica
Information Builders
Innoviz
Insource
Inspur
Intel
Intellicus
Intenda
InterSystems
Inzata
Jitterbit
JMP
Juniper Networks
Kaminario
Keboola
Keyence
KNIME
Knowi
Leaflet
Lenovo
LexisNexis
LookML
Lumentum
Luminar Technologies
Magna
Make
MariaDB
MarkLogic
Materialize
MathWorks
Matillion
Meta
Metabase
Microsoft
Microstrategy
Microvision
Minitab
Mobileye
MongoDB
Monte Carlo
Murata
MyDataModels
NEC
NetApp
Neurala
Nile
Nippon Ceramic
Nokia
NuoDB
Nutanix
Nvidia
NXP
Obviously AI
OEM Automatic
Okta
Omron
OpenAI
OpteamX
Oracle
Orby
Ovaledge
Oxide Computer
Palantir
Palo Alto Networks
Panasonic
Pandas
Panoply
Pavilion Data Systems
PayPal (Simility)
Pecan
Pensando
Percepto
Percona
Philips
Plotly
PostgreSQL
Precisely
Prefect
Privacera
Profisee
Progress Software
Pulsar
PureStorage
PyTorch
Qlik
Qualcomm
Quanergy
Quanta
Qubole
RapidMiner
Redis Labs
Relx Group (LexisNexis)
Renesas
Rockport Networks
Rockset
Rockwell Automation
Rohm
Salesforce
SambaNova
Samsung Electronics
SAP
SAS
Scality
Schneider (Aveva)
Securonix
Shield AI
Shimadzu
Siemens
SiLC
SingleStore
Sisense
Skydio
Small Robot
SnapLogici
Snowflake
Software AG
Solver
Sony
Sophos
SparkCognition
Splunk
Squark
Stability AI
Starburst
Stateless
Stemmer Imaging
Stibo Systems
Stitch
STMicroelectronics
Stone Bond Technologies
StorONE
Stratio
StreamSets
Sugon
Supergrain
SuperLearner
Supermetrics
Supermicro
Tabular
Talend
TDK
TE Connectivity
Teledyne
Teradata
Tessian
Texas Instruments
Thales
ThoughtSpot
Toshiba
Transwarp
Tray.io
Trellix
Trend Micro
Trustgrid
Tung Thih
TuSimple
Upsolver
Vdoo
Velodyne
Veoneer
Verint
Veritas
Violin Systems
Visteon
VMware
VoltDB
Voyant Photonics
Western Digital
Wolfram
Workato
Wrapper
Zeetta Networks
Zepl
ZF
Zoho
Table of Contents
Frequently asked questions
-
What was the size of the data analytics market in 2022?
The data and analytics market size was $100.8 billion in 2022.
-
What is the growth rate of the data analytics market?
The data analytics market is expected to grow at a CAGR of more than 13% during 2022-2027.
-
What are the value chains in the data analytics theme?
The data analytics value chain includes hardware, data management, applications, and delivery.
-
Who are the leading public companies making their mark within the data analytics theme?
Some of the leading public companies making their mark within the data analytics theme are Alibaba, Alphabet, and Alteryx.
-
Who are the leading private companies making their mark within the data analytics theme?
Some of the interesting private companies making their mark within the data analytics theme are Databricks, H2O.ai, and Qlik.
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