Turning Projects Into Programs That Deliver Measurable Business Value: A Holistic Focus on the Analytics Environment

Over the past several years, we have seen a significant increase in the volume of available data coupled with more sophisticated and powerful analytic-specific tools and technologies. As the availability and accessibility of this data has increased, so has the desire for easy-to-consume information that can be leveraged to make decisions at near the speed of thought. The result is a large volume of analytics initiatives across organizations of varying industry sector, size, and geographies with many initiatives encountering issues and failure. To mitigate potential issues in the post-pandemic world, changes in project focus and methods should be considered. Below, I explore how current challenges (new remote or hybrid work models, the need for more self-service capabilities, new or stricter evolving regulatory requirements, etc.) result in project challenges and how they can be mitigated.

The access to and desire for data has fueled a large volume of analytics specific initiatives across organizations of varying industry sector, size, and geographies.  However, in spite of increasing investment in analytic initiatives, the percentage of organizations identifying themselves as being data-driven has declined in each of the past three years — from 37.1% in 2017 to 32.4% in 2018 to 31.0% in 2021. Given the post-pandemic shift in how business is conducted, analytic projects and project management need to consider unique (current) environmental challenges — remote or hybrid work models that require more self-service capabilities, stricter and evolving regulatory requirements, and an ever-changing cultural dynamic — that will not only result in challenges acquiring, developing, and retaining top data and analytic talent, but can also lead to failure of the initiative underway.

The failure of an analytics project can be attributed to a multitude of reasons. In the paragraphs that follow, we will address this question of why some fail and provide thoughts on how the tide can be turned to realize near- and long-term business objectives. While we could list some of the various practices we have seen leveraged on analytics projects, we will focus instead on some core concepts for consideration when charting the path forward specific to analytics initiatives. Rather than focusing on how technology, singularly, might address challenges and opportunities, leadership should aspire to create a scalable and sustainable, trusted analytics environment that addresses all core strategic considerations (technical, cultural, political, and even regulatory). An environment defined is the overall structure within which a user, computer, or program operates. This “structure” is influenced by several key internal and external factors such as the organizational culture and departmental dynamics, resource readiness, aptitude and desire, budgetary constraints, regulatory considerations as well as agreed-upon business use cases. The focus should be on creating a trusted analytics environment that includes strong governance, adherence to compliance, the ability to adapt and offer support for change, and an eye toward the future with the ability to scale and properly leverage automation to take advantage of today and tomorrow’s technology enhancements.  

Organizational leaders need to ensure that the path to making informed business decisions is not an ill-defined business decision in and of itself. Too many organizations only consider the data asset to be exploited and the technology that will be used for that exploitation. They fail to consider the business as a whole and the steps necessary to ensure that not only will the undertaking be successful from a technology perspective, but from the business-as-usual viewpoint. To optimize the business value from a data or analytics-centric undertaking, one should ensure that proper attention is given to checking some of the critical, yet often overlooked or undervalued aspects. Some of these include the unification of siloed and often competing projects/initiatives that don’t have the proper business visibility, the creation of relevant and agreed-to business use cases, access to the proper data sets, and the proper program management and governance necessary to take a program across the enterprise.  For example, layering governance over the framework of a data analytics platform will result in a holistic understanding that ultimately improves the company’s ability to manage and measure return on investment. Not doing so may result in failure to extract proper results/insights from any given body of data. This differentiates a program from a project and helps to ensure that the current initiative will fit into the fabric of the organization. According to the Project Management Institute (PMI), the term “project” refers to any temporary endeavor with a definite beginning and end, and a program of a group of related projects managed in a coordinated way to obtain benefits and control not available from managing them individually.  With a broader lens, looking beyond the immediate and taking into consideration the various internal and external influences, challenges, and opportunities, organizations can maximize the benefits realized — even in an ever-changing environment.  

For those embarking upon or already in the throes of an analytics-centric project that may not align to the considerations highlighted in this article, it might be time to hit the pause button. Or, at least ask yourself, your team, and your business partners if you’ve gone beyond the standard boxes and looked at things from a more holistic perspective, allowing you to achieve your current objective(s) and offer future business value.

Scott Schlesinger is the U.S. Data and Analytics Lead at PA Consulting. Aaron Gavzy is a data and analytics expert at PA Consulting.