We recently reported that analyst and research firm TDWI had released its latest report on IT modernization: Maximizing the Business Value of Data: Platforms, Integration, and Management. The report surveyed more than 300 IT executives, data analysts, data scientists, developers, and enterprise architects to find out what their priorities, objectives, and experiences have been in terms of IT modernization. In many ways, organizations have made great progress.
From new data management and data integration capabilities to smarter processes for higher business efficiency and innovations, IT departments have helped organizations get more value from the data they generate. In other cases, organizations are still stuck in data silos and struggling with improving data quality as data distribution increases due to the proliferation of multi-cloud environments.
In this article, we'll summarize the top three areas where organizations are winning and the top three ways that organizations are left wanting when it comes to digital transformation and IT modernization.
Download the complete report, Maximizing the Business Value of Data: Platforms, Integration, and Management, and find out the latest strategies, trends, and challenges for businesses seeking to modernize.
1. Cloud migration
Moving legacy applications to the cloud is essential for organizations seeking to increase operational efficiency and effectiveness, generate new business models through analytics, and support automated decision-making — the three biggest drivers of modernization efforts. And, most organizations are succeeding.
Seventy-two percent of respondents in the TDWI survey reported being very or somewhat successful moving legacy applications to cloud services. Migrating to the cloud is one thing, but getting data to the right people and systems at the right time is another. For organizations to get full value of their data in the cloud, they also need to ensure the flow of data into business intelligence (BI) reports, data warehouses, and embedded analytics in applications.
2. 24/7 operations
The ability to run continuous operations is a widely shared objective when organizations take on a transformation effort. Increasingly global supply chains, smaller and more dispersed office locations, and growing international customer bases are major drivers of 24/7 ops. And, according to the TDWI survey, more than two-thirds of organizations say they've successfully transitioned to continuous operations.
3. User satisfaction
Organizations are also winning the race to match users' needs when provisioning data for BI, analytics, data integration, and the data management stack. Eighty percent of respondents said their users were satisfied with these capabilities. Additionally, 72% trusted in the quality of data and how it's governed, and 68% were satisfied that role-based access controls were doing a good job of ensuring that only authorized users had access to sensitive data.
1. Artificial intelligence, machine learning, and predictive intelligence
Machine learning (ML) and artificial intelligence (AI) comprise a key area where organizations are left wanting. While 51% of respondents were somewhat or very satisfied with their use of AI and ML data, almost the same number (49%) said they were neither satisfied nor dissatisfied, somewhat dissatisfied, or very dissatisfied. Similar results were also reported for data-driven predictive modeling.
The report notes that provisioning data for AI/ML is more complex and varied than for BI reporting and dashboards, but that cloud-based data integration and management platforms for analytics and AI/ML could increase satisfaction for these use cases.
2. More value from data
Perhaps related to the AI/ML point, the desire to get more value out of their data was cited as the biggest challenge organizations face by almost 50% of respondents. Organizations today capture more raw, unstructured, and streaming data than ever, and they're still generating and storing structured enterprise data from a range of sources.
One of the big challenges organizations reported is running analytics on so many different data types. According to TDWI, organizations need to overcome this challenge to inform data science and capitalize modern, analytics-infused applications.
3. Easier search
A big part of extracting more value from data is making it easy to search. Traditional search functionality, however, depends on technically challenging SQL queries. According to the TDWI report, 19% of users were dissatisfied with their ability to search for data, reports, and dashboards using natural language. Unsurprisingly, frustration with legacy technologies was cited as the third biggest challenge facing organizations, according to the survey.
The way forward
"In most cases, data becomes more valuable when data owners share data," the TDWI report concludes. Additionally, the key to making data more shareable is moving toward a cloud data platform, one that makes data more available while simultaneously governing access when there's a need to protect the confidentiality of sensitive data. Not only does a cloud data platform make data more accessible and shareable for users, it also creates a pipeline for delivering data to applications that can use it for analytics, AI, and ML.
Read the full TDWI report: Maximizing the Business Value of Data: Platforms, Integration, and Management.