The Technical Side of Data vs. the Business Side of Data

Huge investments have been made in recent years into data architectures, analytics, artificial intelligence, machine learning. There's also been more significant investment into the most skilled data scientists, engineers, and analysts in order to produce more actionable insight for employees, translating to increased business value. Despite this, returns aren't being felt on business side of data as intensely as executives anticipated.

In reality, measurably falling short in delivering actionable insight. The business side of data is failing relative to what organizations currently need, and around what is currently possible to do with data today. Quite simply, businesses are struggling to keep pace with the pace of innovation that they’ve created.

How bad is the imbalance? Well, it’s measurable, and it’s costing organizations already by way of lost time and wasted resources. With data volumes also expected to expand in coming years, organizations have to move quickly and take more intelligent steps around their data strategy. Otherwise, they’ll be left in the dust by their competitors, who will heed such predictions and get their data strategy right.

Organizations need to restore balance to their people, process, and technologies in order to become truly data-driven. But how can they, when the barriers to becoming data-driven largely stem from human and organizational challenges? We’re continually seeing a reluctance to change, frustrations, annoyances, and more around the current data approach.

To alleviate some of these difficulties, we can start by addressing some technical prerequisites. Addressing these prerequisites will help build a more solid foundation so that data-driven cultures can have a better chance of taking hold.

Pre-requisites:

• Ensure self-service initiatives are backed by a degree of centralized management in the form of unified data models and agreed-upon definitions and measures. In this way, we can reduce the profusion of reports and dashboards that often lead to conflicting and overlapping versions of the truth, and stop generating entirely new sources of frustration around BI, reports, and dashboards for employees.
• Apply more concise, to-the-point insight, along with increased use of natural language querying, low-code/no-code development options, and improved workflows and automation to lessen workloads and speed up progress.
• Develop values of trust and accountability, and have leadership empower employees with transparent access to governed, accurate data to ensure greater employee responsibility and accountability around the data used.

To learn what these new standards can look like in 2022, be sure to join us for our upcoming webinar, “Actionable Analytics: Reengineering Decision Making & Adapting for Change” on May 24th. Register here >

Process Tempo is a Decision Intelligence Data Platform built on industry-leading graph technology. The no-code, collaborative data science, data engineering, and data analytics platform simplifies complex data environments, empowering people, processes, and technologies to work together harmoniously. The secure, governed, high-performance environment delivers actionable data and insight rapidly to all stakeholders, helping to accelerate the delivery of quality, data-driven decision-making and improve business outcomes at scale. Schedule a discovery session