IDC recently released IDC PeerScape: Best Practice Cases of Data Management Analysis Services in the industrial field (Doc#CHC51598524, June 2024) report, the report summarizes the main challenges and practice paths faced by industry users in the application process, and selects best practice cases to provide relevant guidance and suggestions for industry users for market reference.

In 2023, the big data market is facing greater challenges, and the growth of enterprises is generally less than expected, and companies with a heavier proportion of consulting and customized services are more affected, but entering 2024, longer POC and long-term after-sales support are still more important services for customers. Since the beginning of this year, the national central enterprises digital transformation still has a stable cash flow, in addition to some manufacturers in finance, operators have more stable business, most of the big data manufacturers will manufacturing, energy, electricity and other users as this year’s key market development goals, and customer demand is more obvious.

GDD471A001-2UBA002322R0001 For example, the demand of manufacturing, energy, power and other industries includes digital industrial/energy middle desk, production monitoring, data assets, production data analysis, disaster warning, scheduling forecast, production express, supply chain optimization, equipment monitoring alarm, application marketing and other scenarios, and the time demand is gradually shortened, from monthly report summary to hour-level or minut-level. Shorter data processing efficiency is used to analyze production and safety data.

Bottleneck of enterprise development:

Low data quality: The internal data quality and integrity of the enterprise are low, and the data storage architecture is chaotic, resulting in inaccurate upper-layer data analysis results, affecting the final business decision and management view.

GDD471A001-2UBA002322R0001

High technical threshold: Compared with the financial and Internet industries, industrial enterprises lack professional senior talents, data association analysis often involves a variety of technologies, such as big data processing, machine learning, data visualization, etc., and it is difficult for enterprises to recruit professional technical talents.

Enterprise focus:

GDD471A001-2UBA002322R0001 Stability and reliability: the richness of existing cases and the matching degree of users themselves are more important, and the popularity of general software + external ISV service forms is lower;

Data analytics performance and platform scalability: It is sufficient to support the company’s massive business data access and carrying capacity for data processing, and at the same time, the linear expansion of the subsequent platform should be smooth enough. The big data services of large state-owned enterprises, energy and manufacturing enterprises are more long-term, and the data is distributed in many places, unable to fully realize physical centralized and unified storage. The completion of 50% of the data migration is already a good achievement.

Data governance: Enterprise data asset governance, multi-source data fusion, unified data modeling, and data compliance are the upgrading directions emphasized by users, especially in terms of metadata management. The collection, processing, flow interface and ID of multi-source devices in industrial scenarios are inconsistent, resulting in the failure to fully capture data flow actions and achieve full lifecycle management. In addition, some manufacturers have built a data catalog and data asset primary platform, but due to the uncertainty of national policies and markets, they do not clearly know the use of data assets.