Data Analytics projects to improve decision-making, bringing a competitive advantage in this data-driven landscape. But when these projects do not serve the expectations of CXOs, it gets frustrating. This blog analyzes why the projects fail and suggests remediation based on FMCG Global Company's experience with embarking on its own analytics journey.
FMCG Global Company, based in Techville, embarked on an ambitious analytics project to transform its decision-making processes. The CEO, Ms. Smith, aimed to use analytics to unlock new opportunities and simplify operations. However, the journey to this vision was marked by challenges that many organizations face today.
Poor execution is one of the main issues in analytics projects. Even with well-laid plans, execution often falters. The integration of new tools and methodologies can be complicated, leading to delays and frustration among stakeholders. FMCG Global Company faced such execution issues, particularly due to a lack of a clear strategy for integrating the various tools they planned to use. This resulted in missed deadlines and inefficiencies. To overcome this, Ms. Smith hired a seasoned project manager with expertise in agile methodologies. By breaking the project into smaller sprints, they created an environment for continuous improvement, where feedback could be gathered and bottlenecks addressed early. This approach also encouraged transparency and accountability, ensuring the team stayed aligned with the project's goals.
Another critical issue in many analytics projects is inadequate planning. Organizations often set overly ambitious timelines and do not allocate resources effectively, which can derail the entire project. At FMCG Global Company, the initial planning was not very detailed, and it was hard to navigate the complexities of the project. To address this, Ms. Smith facilitated strategic planning sessions to map out every phase, milestone, and deliverable. They introduced a project management tool to track resources and ensure the team stayed aligned with the project's objectives, with buffer times for unexpected delays.
Misalignment between customer requirements and project deliverables is another challenge in analytics initiatives. Too often, teams work according to assumptions rather than genuine customer feedback, and subsequently, products are developed that the organization does not need. In FMCG Global Company, the team began working with vague assumptions, which resulted in mismatched expectations. Thus, the company started holding regular meetings with key stakeholders to gather feedback so that the scope of the project could be adjusted correspondingly. The team also used design thinking workshops to understand user needs better so that the final product met organizational requirements. The iterative approach allowed for flexibility and responsiveness to changing demands.
Many organizations still use outdated methodologies like the waterfall approach, which is slow to adapt to changes and often results in delays. FMCG Global Company's systems integrators were initially using this outdated method, which hindered innovation and caused setbacks. This will call for the Ms. Smith to transition the team into an agile approach, hence allowing flexibility and faster adaptation to changing requirements. In this case, the approach not only improved cross-functional collaboration but also led to quicker iterations and faster delivery of value.
Another obstacle is starting an analytics project without reliable data. When analytics projects run in parallel with large-scale transformations, such as S/4 Hana, the companies have to rely on imagination or incomplete data, resulting in poor insights. In FMCG Global Company's case, their analytics effort was based on projections instead of real data. To solve this, Ms. Smith readjusted the project timeline so that the stabilization of S/4 Hana would precede analytics so that this team can work with precise, real-time data. A phased approach to this helps ensure the credibility and actionability of the insights derived from analytics.
Another factor is that without advanced analytics tools, the success of a project might be limited by the availability of these resources. Without generative AI or prompt-based reporting, analytics projects may not do justice in offering real-time insights. FMCG Global Company knew their tools were outdated and could not address the modern requirements. Ms. Smith invested in advanced platforms that integrated AI and machine learning to enable her team to present real-time data visualization, thus enabling better predictions. This upgraded their ability to carry actionable insights and be ahead in a competitive market.
The third cause is a simplified, one-size-fits-all approach to analytics, which leads to bad results. Every industry has different requirements, and general solutions are not always up to the challenge. FMCG Global Company had a learning curve when they tried to implement the same analytics solutions for every industry with suboptimal results. To rectify this, Ms. Smith collaborated with industry experts in tailoring analytics solutions that are more suited to each specific industry.
One common issue is choosing the right tool. There are many analytics tools, and choosing the appropriate one can be a challenging task. FMCG Global Company was faced with such a situation when they had to decide on between SAP Analytics Cloud, SAP BI/BW, SAP Datasphere, and SAP BusinessObjects. After assessing each tool's ability, the company ran pilot projects in order to test them in actual use cases. This careful, strategic evaluation helped them choose the right tools, minimizing risk and ensuring the project's success.
Several significant takeaways for other organizations that wish to embark on their analytics journeys could be learned from FMCG Global Company's experience. Effective execution, proper planning, alignment with customer needs, adoption of modern methodologies, and selection of tools represent some of the critical elements on which the success of any analytics project is dependent. Hence, by learning from those issues and implementing solutions, organisations improve their chances of delivering analytics projects successful enough to grow the business.
