Successful digital transformations are not possible without successful data transformation. After all, it’s data—and the ability to analyze that data—about an organization’s customers, employees, infrastructure, market dynamics, supply chain, business-technology systems and more that make digital transformation possible.

However, last year Gartner said that “information as an asset” is still in its infancy. “Data and analytics are the key accelerant of an organization’s digitization and transformation efforts. Yet today, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value,” the analyst firm wrote in its report, “Why Data and Analytics Are Key to Digital Transformation.”

Further, Gartner predicts change is coming and quickly: Gartner says that 90% of corporate strategies will unequivocally list information as a critical enterprise asset and analytics as an essential competency within two years.

Getting there and being successful with data and data analytics won’t come without significant challenges.

According to a survey released by IDG (sponsored by Matillion, a cloud data transformation provider for cloud data warehouses), the challenges are steep. The survey polled more than 200 IT, data science and data engineering professionals at North American organizations with at least 1,000 employees.

What are the challenges enterprises are facing? According to the survey results, 47% said data control issues are the biggest challenge to data analytics projects. In contrast, other top challenges indicated are lack of a scalable, reliable technology platform to process large data sets (45%), having too many manual processes (38%) and challenges cleansing and preparing data (36%). Additionally, 40% cited a lack of visibility and control of data silos as a challenge when collaborating with business users.

With that, it’s not surprising to learn that it takes on average one week to pull together and prepare data for useful analysis, and 45% of all time spent on data analytics initiatives is consumed by data preparation. About 60% said they lack the scalability and flexibility they need when preparing data for analysis.

It was almost unanimous when it came to the number of companies that sought ways to accelerate their approaches to data analysis improvement.

So how are enterprises accelerating their efforts to improve data analysis to better power their digital transformations? Respondents said data portability (57%), ease of onboarding (57%) and cost-effectiveness (52%) are the top features of an analytics platform that can help them move past current obstacles. The IT professionals surveyed favored user-friendly (50%) and easier connections to data sources (50%) as top features; however, data engineers, data scientists, data architects and others cited time-to-value (57%) and ability to provide self-service (51%) as essential capabilities.

A sizeable portion of those surveyed indicated they are already on their way, adopting the technologies and processes they need to improve their ability to analyze their data for better decision-making rapidly. According to the survey results, 38% are already using cloud data warehouses. And in the future, 43% said they expect to have all of their data in the cloud, with 57% planning to use both cloud and on-premises data strategies.

Those that master their data analytics more rapidly may enjoy an early mover advantage. “A company’s ability to compete in the emerging digital economy will require faster-paced, forward-looking decisions,” said Douglas Laney, distinguished VP analyst at Gartner. “Data and analytics leaders need to assert themselves into corporate strategic planning to ensure that data and analytics competencies are incorporated within the highest-level public-facing enterprise plans.”

Source: devops