Expersight Intelligence
Abstract
You cannot have information without data. The information populating at the application layer is an exact replica of how the backing data is sourced, processed, and archived at the database layer. In the past decade, big data has been the nucleus of all technological innovations that promise more, better and speedier information to the end consumer. The IoT, blockchain, and AI could not have been explosively successful case studies if there was no big data analytics supporting them.
In 2021, the priorities are the same and it’s the degree of expertise that’s driving all organizations. If 2011 was about managing big data then 2021 is all about managing ‘bigger data’ through automation. The COVID-19 ridden restrictions that pushed millions of users indoors further intensified the volume of data and its impact on the analytics infrastructure.
Historical data of the pre-COVID era may no longer be viable as everything from consumer preferences; behavior to the business landscape has changed. Inevitably, organizations have to up their game and automate their road to data automation.
In the following analysis article, we walk you through the key challenges of big data and introduce data fabric technology. Furthermore, we explain the key opportunities in this sector, the market standings in the upcoming years and the leading sectors, service providers and countries.
The Problem – Challenges in Big Data Management
In the post-pandemic era, the consumer’s inclination for on-demand digital services will continue to produce more data. As the data sources and the volume increase, achieving smarter & faster analytics will only get tougher. As most organizations embrace digitization, their big data challenges span across developing skill-sets and overhauling the utility of data science professionals.
The 80-20 Rule
Data Scientists have to spend more than 80% of their time in searching, cleansing, and governing data while less than 20% is left for analyzing.
Source: IDC
Add to it, they have a continuous responsibility to comply with the 4V principle of big data – Volume, Velocity, Variety, Veracity, and Value. Key challenges include:
- Inability to manage the rise in data: More source points & higher production rates.
- Poor Quality of Data Analytics: Existing data management techniques do not deliver anymore.
- Delayed System Response time: Sluggish data processing and delay times result in dissatisfied customers.
- Expensive Maintenance: Less automation means a rise in infrastructure cost & management.
- Cope Up with AI: The maturity of AI-enabled processes is outpacing organization readiness.
The Solution – The Data Fabric Approach
One of the major reasons for a messed-up data management vertical is multiple and disconnected teams. Data fabric resolves this process inefficiency by providing a unified architecture to perform automated data management throughout the lifecycle – sourcing, cleaning, orchestrating, preparing, archiving, & analyzing.
With a focus on accelerating digital transformation, fabrics perform tasks in record timelines such as connecting to sources through pre-packaged connectors.
As per Gartner, the data fabrics cut down on enterprise resource dependency across disciplines.
| Integration Design | 30% |
| Deployment | 30% |
| Maintenance | 70% |
Additionally, the research publication cited an increase in these numbers due to the rise in the adoption of ‘XOps’ (DataOps, MLOps, and DevOps) across organizations.
The Purpose of Data Fabric
- Use MLOps & AI to improve data quality by providing built-in sourcing, preparing, and orchestrating & governance capabilities.
- Fasten data accessing and collecting regardless of source location & storage type.
- Eliminate dependency on multiple tools, resources and fasten access to qualitative & trustworthy data. Thus, enable simpler data lifecycle implementation.
Businesses that exercise insights to execute their decision-making will grow at a rate of 30% and earn USD 1.8 trillion by the end of 2021.
Source: Forrester
As per the findings, data fabric will be one of the key technologies to drive these businesses to master the ever-growing volume.
Innovation: Use of Micro-DB
Apart from automating data management, fabrics have been put to innovative applications to upscale data analytics. For example, the use of micro-DB to store data has always been an attractive yet partially successful use case. K2View’s approach at representing data in the form of digital entities has pitched its signature product K2View Data Fabric amongst the best in the business. The K2View Data Fabric provides a linearly scalable architecture and in-parallel processing for computation.
The fabric supports storing data of a particular business entity in an exclusive micro-DB and managing millions of such databases.
The following representation explains the difference caused by a micro-DB.
Opportunities in Data Fabric Technology Market
1) The explosive increase in consumer services on mobile
Today, not every service needs a data fabric. Only those who have huge volumes of structured, semi-structured, and unstructured data sets to integrate and analyze in real-time benefit from a fabric approach. However, this will not be the case by the end of this decade. The growth in the variety and volume of big data will require fabrics to align communication from scattered data sources.
The rapid rise of the mobile economy has populated millions of apps across services such as social media & weblogs, device-sensor data, geo-location data, machine data etc. Now storing and using such a mammoth of data is a concern outpacing the technology preparedness.
The total mobile traffic is expected to grow at a CAGR of 55% by 2030. By 2025, it’ll be 607 Exabytes.
Source: Researchgate
The following analysis from CISCO is a testimony of the dominance of mobile traffic today and the near-decade.
2) DataOps & MLOps are the frontiers
Apart from the evolving shape & size of data, it is the way organizations perceive and evaluate data that matters. They want a smarter approach towards cleansing data and that is what DataOps deliver. It is the practice of automating data lifecycle processing from capturing to populating with finesse. Until now, data fabrics did not grow because of manual restrictions. With DataOps, they are better, faster, and more valuable. They reduce the cycle of analytics and enable organizations to do more with their fabric infrastructure. Unlike perceiving data lifecycle as disconnected teams handling exclusive areas, DataOps combine them into a unified practice – from data generation to analytics.
MLOps are AI’s greatest creation to data science and organizations that have adopted it now will gain an edge over others.
72% of surveyed respondents confirmed pursuing data science initiatives to achieve automation.
Source: 451 Research
3) Increasing adoption of cloud
Cloud means faster and better access to services and thus more data. 2020 is a testimony of the fact that businesses that embraced cloud-backed digitization survived the harsh effects of the pandemic. For 2021 and beyond, this is not going to change. Most organizations are now looking forward to a pre-packaged cloud suite that supports data management through fabrics. This will enable organizations to save time & money and thus focus on their core business operations.
By the end of 2021, 95% of data center traffic will come from cloud services.
Source: Cisco.
Henceforth, the increasing adoption of the cloud will directly influence the industrial dependency on data fabric solutions for better analytics.
4) Increase in adoption of IoT
IoT has been big data’s greatest driver over the past few years. It provides the needed physical infrastructure to capture, store and stream data at the consumer’s end in real-time. Thus, the sizable impact is understandable. While IoT devices and appliances such as wearables, smart speakers, sensors, IIoT devices etc. grow in demand, organizations will experience a greater deal of handling big data through contemporary technologies such as the fabric. More IoT means more data and more data means more emphasis on automating data management.
As far as adoption is concerned, the following findings from Statista suffice.
By 2023, the total IoT spending is estimated to touch USD 1.1 Trillion.
Source: Statista
5) Edge Computing
While we are discussing IoT, Edge Computing deserves a mention too. Edge computing is an architectural solution for performing computations near the source of data. It has effective issues of limited latency tolerance, the risks of networking failures, regulatory requirements etc. However, the edge requires faster processing of data and that’s where data fabric fits in.
The Edge Computing Market Will Value USD 26 Bn by 2024.
Source: Statista
Data Fabric Market Predictions
Taking into account the discussed opportunities and the consumer’s increasing appetite for digital apps, the data fabric market has a promising future. As per Allied Market Research, the technology is gaining widespread acclaim across sectors.
During the forecast period of 2020-2026, the global data fabric market size will grow from USD 1.0 billion to USD 4.2 billion in 2026. At a Compound Annual Growth Rate (CAGR) of 26.3%, this is expected to be the game-changer for the data economy.
Factors such as enhanced volume and variety of personal as well as business data will push the need for streaming analytics in real-time and thus increase the adoption of data fabric services.
Likewise, The Next Move Strategy Consulting has a slightly different prediction. This report takes into account the effects of COVID-19 on the markets and the scope of data fabric adoption.
The data fabric growth will directly benefit from the explosive rise of AI start-ups in 2019 until just before the pandemic spread. The collective funding in the start-ups that valued USD 29.33 Bn then will grow further. In the post-pandemic era, organizations have realized the importance of new-age digital transformation with AI & ML-enabled processes.
Thus, the growth of artificial intelligence and machine learning algorithms is expected to drive the growth of the data fabric market in the coming years. However, high capital investment and lack of skilled labor will continue to pose challenges in the road to adoption.
Data Fabric Market by Sector
No one owns data more than the BFSI and that’s exactly why the sector has led the adoption of contemporary technologies to manage big data.
The volume of API calls increased from 1 million in May 2018 to 66.7 million in June 2019.
Source: Open Banking Implementation Entity (OBIE)
This will directly influence the adoption of data fabric technology in the sector followed by other industrial sectors such as the healthcare, retail & supply chain.
Key Players
IBM Corporation, NetApp, Inc. K2View, Neptune.AI, Oracle Corporation, SAP SE, Hewlett Packard Enterprise, and VMWare, Inc. are some of the major companies operating in the space of data fabric market.
1) IBM
IBM’s Cloud Pak solution offers access to the right data at the right time across any cloud and on premise platform. This solution of IBM provides intelligent data fabrication for faster and trusted data outcomes. Cloud Pak is an integrated and end-to-end data and AI platform that can potentially decrease the infrastructure management by around 65% to 85%. It can function effectively on hybrid cloud environments using AutoSQL to gather and deliver trusted data to businesses. The Cloud Pak also offers comprehensive data and AI capabilities and provides an automated metadata and governance layer to increase transparency and collaboration while working on cloud and to reduce compliance risks.
2) NetApp, Inc.
NetApp’s data fabric solutions are mainly focused on providing simplicity and agility. NetApp’s Fabric Orchestrator increases operational efficiency seamlessly in on-premises as well as cloud environments by providing the necessary features to build and manage the data fabric. The AI-powered infrastructure helps in reducing the chances for financial risks. The Fabric Orchestrator can be deployed on Azure, Google Cloud Services and Amazon Web Services for agility and scalability. On-premise infrastructure such as VM and containerized environments can also be automated.
3) K2View
K2view has a single data fabric solution that can integrate, transform, enrich, prepare and deliver data in a single platform. K2View’s Data Fabric organizes data from various sources based on data such as customer, location, device, etc. Every entity has its own unique micro-database. The digital entity unifies every data that it collects from a business entity including the transactions, interactions and master data. The data fabric system can transfer the data to the source systems. It is also scalable to support numerous micro-databases simultaneously in a distributed, high-performance architecture. K2View’s Data Fabric can also be deployed on cloud and on-premise environments.
4) Oracle Corporation
Oracle is well known for its data management systems. Oracle’s data fabric solution includes industry-leading ELT, data preparation, replication and can effectively work together. This data fabric solution can transform data without having an impact on the systems, cleanse and repair data to make it trustworthy and reliable, provide data with 0 downtime for operational purposes, replicate and recover data in case of any failures and finally, use algorithms to streamline data pipelines. Oracle’s data fabric solution helps companies gain actionable insights and arrive at better business decisions.
5) SAP SE
SAP’s data fabric solution consists of the combined capabilities of SAP data intelligence and SAP HANA. SAP Data Intelligence transforms the collected data into valuable information which can be accessed at the right time using the right context. Whilst, SAP HANA provides unique capabilities to the data fabric solution by bringing in built-in functionalities to access the data within the enterprise. With smart data access, queries can be sent to external data sources such as external databases, web services, files, etc in a cost-effective manner.
6) Hewlett Packard Enterprise
HPE’s Ezmeral data fabric is mainly developed to simplify data management on a global scale with enterprise-garde reliability, flexibility and performance. This data fabric platform is built using MapR technology to deliver a unified platform where the data are collected, stored, managed and applied in various formats from various sources. HPE’s data fabric platform also offers mutli-protocol data access, multi-cloud data management, reliance and data protection at any level. HPE’s Data Fabric solution can increase the efficiency of the data analytics team by around 33%.
7) VMWare, Inc.
VMWare, a global leader on Cloud infrastructure, has developed a solution to make accurate business decisions based on event-driven information. VMWare’s approach aims at reducing costs when compared to traditional database architectures. VMWare’s data fabric solution can offer dynamic scalability without increased costs, real-time data and event-driven communication, Zero-latency, transparency in transactions, faster and cost-effective processing and simplified database provisioning and governance.
Superpowers of the Big Data Economy
Expected Top countries to embrace Data Fabric technologies in Years to come
United Kingdom
United States
China
South Korea
India
Russia
Japan
Canada
Source: Analytics Insight
United Kingdom
Big data adoption in the UK is found to be growing at a rapid phase. At present, big data is widely adopted by telecom industries. Other business verticals are taking efforts towards adopting big data in their business operations as well. The adoption rate of big data in the financial sector will soon overtake the rate at which the telecom industries adopt this technology.
United States
The collection of massive amounts of data from various sources is the key factor that drives the adoption of big data in the US markets. As various industrial sectors depend on mobile networks, social media and other platforms to promote and develop their business, eventually companies have to deal with huge volumes of data. With the help of big data analytics, companies can gain valuable business insights and understand the market effectively.
China
In the Chinese market, policy support and technology integration are the two most important factors that contribute towards the growing adoption of big data. According to a report, it has been found that China’s domestic big data sector will reach up to $22.49 billion by the year 2023, with a CAGR of 23.5% between 2019 and 2023. It is also predicted that AI platforms will potentially take over the big data industry in the upcoming years.
South Korea
The South Korean government has decided to expand its big data market to 10 trillion won by 2022. The chairman of the fourth industrial revolution committee, Chang byeong-kyu, believes that improving the ways of data usage will strengthen their competitiveness in the big data industry.
India
The big data market in India has been gaining momentum in recent years with the involvement of many small and large big data and AI-based companies. This could potentially make India to be the largest big data market in the long run. This highly competitive big data market has well-established use-cases in the Indian market.
Russia
In Russia, the three major verticals to adopt big data are finance, telecom and retail. The major problem in adopting this technology is the lack of experienced professionals and the cost of implementation. According to a study, the Russian big data market will grow by 10 times. Based on this report, the total value of the Russian big data market will be 300 rubles by 2024. It has been predicted that big data will be adopted by public sectors at a significant rate in the near future.
Japan
The adoption of big data in the Japanese market is influenced by various factors. The increased usage of social media to understand the end-users/customers is one of the most important factors that contribute to the adoption of big data. Followed by this, the need to gain real-time information from various sources like the internet, smart devices, sensors, etc have created various opportunities for big data implementation.
Canada
Based on a study, the total value of the Canadian market for big data and analytics was found to be $1,866.6 million in 2017. By the end of 2022, this value is expected to grow with a CAGR of 9.4% between 2017 and 2022. The development of the software industry is believed to be the key factor that drives the adoption of big data in the Canadian market.
Going Forward – Expectations from Data Fabric Solutions
The following parameters should narrate the best practices strategies for small, medium and large scale data landscapes.
- Ability to analyze available metadata continuously and correctly.
- Compatibility with all delivery styles such as streaming, ETL, messaging, virtualization or replication.
- Support for users from all disciplines such as IT, QA, Business etc.
- Should support self-servicing data preparation.
Conclusion
The above report explained the stature of existing big data markets and their demands to cope up with the impatient consumers. Data fabric is relatively new yet fast picking up as a trending practice to automate data science. It also highlighted key trends in the industry followed by expected organizations and countries. For the world to get smarter & securer, automation is the key.
[…] the raw data; their performance in automating the industrial equipment improves too. According to data fabric analysis, the fabric also helps in the transition from manual monitoring to the self-governed evaluation of […]
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