A typical tech stack is comprised of six areas which facilitate an organization’s ability to make decisions, conduct audits, document action items, and other outcomes (Figure 1):
To better understand the connection between technology and insights, think about your decision support segmented into three layers (figure 2):
1) a Semantic Layer,
2) a Data Layer,
3) a Technology layer (figure 1).
The Semantic Layer, or the “business layer,” maps complex data into familiar business terms such as revenue or customer. The Data Layer, commonly called a data pipeline, is the key to generating actionable insights from data. A healthy data pipeline, in-sync with a data strategy, maximizes an organization’s technology stack. It does so by expanding its use to improve business and mission outcomes while simultaneously streamlining the information gathering process.
Today, most organizations have very manual data gathering processes which impede their ability to make timely decisions. By leveraging modern technology solutions, many data collection processes can be automated–saving time, resources, and employee frustration. Ultimately, leveraging technology and data integration transforms a leader’s ability to make decisions, allowing them to become proactive and escape the endless reactive cycle many leaders find themselves in today.
Typically, data pipelines are hosted in the Cloud or a hybrid on-premises-cloud environment. The data pipeline starts by ingesting data from all or some business systems.
The Data Pipeline (Figure 3) is comprised of:
All of which are supported by
Utilizing data workflow automation platforms such as Databricks, or no/low-code solutions such as Apache AirFlow or NiFi, coupled with AI/ML solutions, greatly improves data pipelines by increasing speed-to-value. Increasing speed-to-value is only useful if an organization is creating actionable insights. Speeding up the consumption of poor insights does nothing but allow organizations to make wrong decisions faster. How do you prevent this?
Using an organization’s goals and objectives, you can accurately assess and predict mission outcomes that empower leaders to make decisions at the speed-of-relevance, meaning they have the right data at the right time. With the right data, ML models can derive actionable insights – or significant predictors of the target variable in question. (Figure 4).
From here, the art of turning data into actionable insights begins. To build actionable insights, ask yourself:
With this conversion, decision makers can easily assess progress at any point in the year. Leaders can compare today’s output against their overall goal; they can also compare today’s output to last year’s point in time. If a gap exists leaders may take time to reflect and ideate on potential actions they may take to close the gap. They can consider:
These insights create actionable possibilities, for instance, a leader may now be able to answer the following questions:
Converting data into actionable insights enables leaders to act based on insight and make more informed decisions. Learning to generate actionable insights is an organization-wide initiative. An organization must understand the relationship and interdependency between technology, data, and semantic layers related to organizational objectives and goals. Powered by automation, every organization can learn to create actionable insights to enable speed-of-relevancy and improve business and mission outcomes.