Accelerating Insights to Action – Part II

Organizations need to build cultures that nurture good practices and support data-informed decision making. They also need to raise data literacy so personnel are confident in their abilities to work with and share data and analytics. The result is faster insights from faster data.

In this second in a series I take a look at the organizational and cultural changes required to speed up data insights.

Through AI-driven augmentation, BI and analytics solutions are evolving to offer recommendations about data sets users might explore, visualizations to use, and, ultimately, decisions or actions to take. Such recommendations can result in faster insights, which can translate into more responsive and proactive engagement with customers and partners as well as strategies for success in the marketplace, supply chains, and other business contexts. With the appropriate stack of technologies to support it, users can experience right-time or actual real-time dashboards in contexts that focus attention on particular key performance indicators and other metrics. These can be supplemented with prescriptive, AI-driven recommendations based on the data. 

AI and advanced analytics on top of streamed, real-time data feeds enable organizations to spot trends and patterns, apply predictive insights, and potentially automate responses to situations, particularly those in fraud detection, securities trading, e-commerce, emergency healthcare, and population health where instantaneous response is critical. Solutions that employ AI techniques such as machine learning can help organizations profile, transform, and enrich real-time data as it flows through pipelines so these steps are faster and more efficient than with traditional extract, transform, and load (ETL) technologies. 

AI techniques are being combined with other technologies to accelerate data processing, access, and interaction. AI is becoming useful for governance by enabling organizations to learn more, faster about new big data and improving tracking to ensure that data use adheres to governance rules and policies. AI’s contribution to data lineage tracking can help with other data management requirements such as performance and concurrency that demand monitoring of what data is being used and shared in preparation and pipeline processes.

Data Literacy and the Analytics Culture

Faster data flows and speedier data processing and transformation are critical, but organizations also need to develop a supportive and innovative culture for the data riches to have a meaningful effect. This involves establishing an analytics culture, which is about fostering leadership, communication, and collaboration to overcome resistance and build trust in the process of creating insights from data and applying them to decisions and actions. If the culture does not thrive, successes in analytics will remain isolated and not lift the entire organization’s data- driven intelligence. 

An analytics culture nurtures good practices in using data and analytics to support decisions throughout the organization. It supports challenging assumptions, learning from the data, and owning the outcome by objectively measuring the impact of decisions based on the analytics. It is not something that can be created overnight. Organizations need to work on developing an analytics culture that matures in a healthy direction over time. Analytics cultures depend on raising the data literacy of individuals in the organization. Users may have great BI and analytics tools, but if they struggle to understand data, visualizations, and analytics and cannot effectively interact with and share data and insights, the tools will not be enough.

Growth in data literacy can increase an organization’s overall speed to insight and accelerate innovation with data. Personnel will be better prepared to contribute to projects for development of analytics, data-driven applications, and AI. Along with data literacy, these issues can either propel or thwart an organization’s progress toward building value from data.

Leadership, Organizational, and Project Challenges

It’s not just the technology, it’s the people and organizations that often fall short of realizing the value of technology investments due to organizational and project management difficulties. Key issues in these areas include strategy, culture, leadership, skills, and funding. Because most analytics and data management projects impact a range of stakeholders, from executive leadership and business managers to IT developers, data scientists, business and data analysts, and frontline users, developing good collaboration and communication among them is vital. 

This suggests a relationship between the two: that is, stakeholders believe that if they can access and analyze certain data sources properly, it will lead to better business outcomes. 

Next, I’ll look at faster data. 

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