Must-Have Elements of a Modern Data Approach

The current global situation has highlighted the importance of digitalization for organizations of every kind—from businesses to hospitals and schools. But data-driven organizations must be able to access all the relevant data, store it cost efficiently, ensure it is of the highest quality, and make its insights available in real time to all users. Now more than ever, a strong data strategy is essential to every enterprise’s success.

According to the "2017 Gartner Chief Data Officer" survey, 86% of data and analytics leaders said defining such a strategy was a top responsibility, up 64% from 2016. As many leaders have realized, a large part of this responsibility requires them to implement new strategies that empower citizen and specialist users with self-service capabilities. To make this possible and accelerate digital transformation, enterprises need to adopt a modern data platform and approach.

A democratized data analysis strategy  

For enterprises to keep up with the rapid proliferation of data, and more importantly, leverage this data for valuable business insights, they need to take a broader approach to data science. According to Eckerson Group’s recent report, "The Rise of Unified Data and Analytics Platforms," this is also why many companies are starting to utilize unified data and analytics platforms (UDAP). By implementing these types of platforms, enterprises can accelerate their time to insight, consolidate data and tools, and see a positive—and quick—return on their data investments. As businesses embark on this journey, it’s important for them to invite users of all levels into the process in order to truly democratize the modern data platform. With this expansion to more users and the empowerment of self-service, businesses are more likely to experience significant results of data-driven analyses.

For example, with no-code technologies that can successfully automate advanced analytics processes, data science tools can be an asset for less technical users, allowing them to do everything from modeling a scenario, run experiments, and select the right algorithms. By adopting these tools and practices, businesses can spend less time with the mechanics of analytics and more time on its outcomes in the form of insights and predictions—driving enhanced creativity and better results. Furthermore, according to the report, a growing number of products for modern data and analytics platforms support the full machine learning lifecycle—all the way from data exploration and preparation to model building, deployment and management. Leveraging a modern platform makes it simple for organizations to experiment with data science and build internal expertise without making an additional investment in tools and people.

A holistic value chain with data management, quality and storage

The modern data platform that has risen to the business forefront interprets data as part of a larger value chain that comprises data integration, management, storage, and analytics. This model places increased emphasis on the interconnectivity of these functions rather than treating each one as a separate silo with isolated applications that address only specific points of the data journey.

For enterprises to achieve success, it is critical that each element of the chain—from the initial integration of the data to deriving its insights—is optimized to yield the maximum possible value of their data.

A capability to embed analytics, artificial intelligence, and machine learning

Beyond this, the ability to embed analytics within every element of the modern data platform provides business leaders with the capabilities to understand information in context and achieve situational awareness to act in the moment. For example, high-performance predictive and machine learning algorithms can reveal meaningful patterns in data and build applications that automate manual business processes. With search capability analysis that helps extracts insights from unstructured data, business leaders can generate conclusions and more importantly, apply them to the business, faster than ever before. 

As the needs for data management grow more complex, it’s no surprise that companies are turning to AI and machine learning to expand upon the capabilities of traditional platforms and increase efficiency. With capabilities such as conversational AI and robotic process automation integrated directly into business processes, companies can empower users of all backgrounds to modify processes and become more efficient. With this, business users will benefit from simplified workflows and experience an overall increase in productivity.

For many enterprises, the reality of a highly distributed data landscape and the mixture of cloud and on-prem solutions means that they have to radically rethink their approach to data management and analytics. A data strategy that follows the principles of the interconnected data value chain can help such enterprises understand—and, more importantly, use—the full potential of their data.