This blog is the second of a three-part series where we’ll delve into the concept of evolving beyond being simply data-driven to analytics-driven, and onto analytics mastery. Read Part 1 at Evolving beyond data-driven – why being Analytics-Driven is the next evolutionary step.
In today’s very competitive economy, organizations need to identify and act on new business opportunities or unexpected events faster than their competition. Analytics-mastery provides the cutting edge to stay ahead of your competition. What’s analytics-mastery? It’s a state in which an organization has established the data systems and data culture that accelerate the data-to-decision process while increasing confidence in their analytics-informed decisions.
In my previous blog “Evolving beyond data-driven”, Part 1 of a 3-part blog series, I described the differences between being data-driven and analytics-driven and how AI/ML technologies are the critical factors that are paramount to becoming analytics-driven. But the analytics journey doesn’t end there. There’s another, more aspirational level that we call analytics-mastery. Boris Evelson, Vice President, Principal Analyst at Forrester, calls this next level of analytics “insights-driven” (The State of the Insights-driven Business, 2022). Regardless of the name (i.e., to-MAY-to, to-MAH-to), the potential business benefits of this elevated state of analytics is very clear. According to Forrester, companies that have achieved analytics-mastery are 8 times more likely to grow by 20% than those that haven’t. In this blog, we’ll explore what it means to achieve analytics-mastery and the steps you need to take to achieve this during your analytics journey.
What is analytics-mastery?
Analytics-mastery is a cultural shift toward analytics-literacy, data sharing, collaboration, and innovation with AI/ML services.
By achieving analytics-mastery, organizations can unlock significant business benefits by better monetizing their data assets. This includes improved decision-making, increased efficiency and productivity, reduced costs, and improved customer experiences. By maximizing the use of data assets, businesses can gain a competitive edge by rapidly adapting to market disruptions and seizing new opportunities ahead of their competitors.
As organizations become increasingly savvy with data analytics, achieving analytics-mastery is the natural next step in their analytics journey to unlock the full potential of their data assets. Analytics-mastery builds upon the foundation of being data-driven and analytics-driven, representing the top-tier for organizations that put data and analytics at the heart of their decision-making. By reaching this level, businesses can gain a significant competitive edge and reap significant business benefits.
How to achieve analytics-mastery
Achieving analytics-mastery isn’t as easy as buying the latest and greatest AI/ML or analytics tool(s) for your data science team. It’s a fundamental shift in the organization’s culture that places data and analytics at the center of the decision-making process for everyone. Achieving analytics-mastery requires a continuous focus on developing and refining analytics skills and capabilities, staying up to date with the latest technologies, and creating an analytics culture that encourages experimentation and learning.
There are several key steps that organizations can take to achieve analytics-mastery:
- Build a culture centered around data: This means establishing a culture where analytics is at the heart of all decision-making, and employees are empowered to use data and analytics to inform their decisions.
- Establish a strong analytics workflow: This involves creating a seamless data-to-decision process that includes data ingestion, enrichment, modeling, visualization, analysis, and collaboration.
- Hire the right talent: To achieve analytics-mastery, organizations need to have the right people in place, including data analysts, data engineers, data scientists, and business analysts.
- Use the right technology: It’s important to have the right capabilities and technologies in place that support the analytics workflow for everyone in the organization. This includes data ingestion, data modeling, data preparation and enrichment, machine learning (ML), and data visualization.
- Implement AnalyticsOps processes: This involves setting up a dedicated team to manage the analytics workflow, proactively monitoring the analytics platform, and working with business stakeholders to optimize queries and processes and grow adoption of the analytics platform.
- Strive for continuous improvement: Achieving analytics-mastery is an ongoing journey, and businesses need to continuously assess and improve their data culture, analytics workflow, talent, technology, and AnalyticsOps processes to stay ahead of the competition and achieve maximum business impact.
How AnalyticsOps contributes to analytics-mastery
AnalyticsOps is a relatively new term that some analysts refer to as “the last mile of DataOps”, which refers to the practice of applying continuous delivery principles of application development to the deployment of an analytics platform. This covers monitoring, managing, operationalizing, and improving analytics workflows within an organization. It involves establishing a set of best practices, processes, and tools to ensure that data pipelines, analytical models, and content (metrics, KPIs, dashboards, and similar) are properly developed, tested, and deployed in a timely and reliable manner. AnalyticsOps takes a proactive approach to involve non-IT teams in the deployment of analytics, to ensure that it meets their expectations and keeps up with the pace of business processes and decision-making.
In the context of achieving analytics-mastery, AnalyticsOps is essential, because it provides a framework for managing the end-to-end data and analytics lifecycle, from data ingestion to model deployment to data visualization and analysis. It helps organizations ensure that data is processed, analyzed, and delivered in a way that’s efficient, scalable, and accurate. For example, a proactive AnalyticsOps team continuously monitors the analytics platform for poor-performing queries sent to data sources, and works with business stakeholders to optimize those queries before issues arise. This approach is in stark contrast to the typical reactive process of waiting for complaints before acting. This proactive approach ensures positive user experience that in turn promotes analytics platform adoption – a key driver that measures the success of the analytics platform.
More about AnalyticsOps in Part 3 of this 3-part blog series – What is AnalyticsOps, and how could it improve your business decisions?
Summary
Many organizations are still coming to grips with the first step in their analytics journey – becoming data-driven – and need to wrap their heads around the idea of adopting AI/ML capabilities within their analytics to become analytics-driven. So, for these organizations, analytics-mastery may seem like climbing Mt Everest. For most organizations, achieving analytics-mastery is an aspirational goal. And that’s how it should be, because it’s a guiding light that helps define the next best steps and create a plan for the analytics journey.
Achieving analytics-mastery isn’t as simple as buying a new tool; instead it’s a fundamental, gradual shift in the culture of the organization. It’s a journey that places data and analytics at the center of the decision-making process for everyone in the organization. Moving toward analytics-mastery requires organizations to break down silos, foster collaboration, and create a culture that values analytics-driven decision-making. It involves developing an ecosystem that supports data collection, integration, storage, analysis, and visualization, and ensures the accuracy and integrity of the data. Achieving analytics-mastery is a long-term journey, but the benefits can be transformational and it’s a new strategic imperative for organizations to achieve significant competitive advantages.