Using Data Analytics to Improve Project Outcomes: Tips & Advice
Der Einsatz von Analysen in der Wirtschaft hat sich mit dem Wachstum des Internets und der digitalen Technologien explosionsartig entwickelt. Heute gibt es Software, die alles nachverfolgen kann, von Verkäufen und Kundenbindung über die Wege, die Verbraucher zu Unternehmen führen, bis hin zu den Reaktionen der Öffentlichkeit auf verschiedene Formen des Marketings und sogar auf bestimmte Elemente von Inhalten.
Data analytics can be used on both a small and a large scale to provide all kinds of extremely useful information to organizations. However, big data is about more than just analyzing what your company has accomplished – it can also be used in a predictive capacity to plan for future growth and success.
When used appropriately, analytics can provide the i
| The use of analytics in business has blossomed with the growth of the internet and digital technologies.Data analytics can be used on both small and large scale to provide all kinds of extremely useful information to organizations. |
nformation needed to improve project outcomes and reduce risk factors, not only at the outset, but during any stage of the life cycle. The ability to revisit big data use cases can provide insight into the problems that lead to failure and, ultimately, help to make efforts more productive. Here are a few tips for using data analytics to improve project outcomes.
Risikobewertung
There are any number of areas where a project may face the risk of failure. For example, realizing too late that a budget is inadequate could cause delays or even completely derail the project, as could underestimating the human resources and time required for completion, or the level of expertise needed for certain tasks. Check out our blog on the matter to see what we mean.
Eines der größten Risiken ist laut einer Studie des Beratungsunternehmens Deloitte die Komplexität der Projekte. Während ein Projekt mit einem Umfang von etwa $1-3 Millionen nach ihrer Schätzung eine Erfolgschance von etwa 34% hat, haben Projekte mit einem Wert von $10 Millionen oder mehr nur eine Erfolgsquote von etwa 7%. Und warum? Wegen der Probleme, die sich aus der Budgetierung, den Ressourcen und dem allgemeinen Projektmanagement ergeben.
However, with the right database of information, including big data use cases, companies can compare current projects to past projects to see how certain areas overlap and pinpoint potential obstacles and setbacks before they occur. In other words, businesses can better assess risks and avoid pitfalls that plagued them in the past, improving outcomes for upcoming projects.
Measurable Variables
In the past, the ability to measure a variety of variables was shaky at best. How could companies see the impact of advertising campaigns, for example, other than through subsequent sales or voluntary customer data (surveys, reviews, etc.)? How could businesses plan to meet client or customer expectations? How could complex projects be efficiently managed and progress monitored?
These days, big data provides the needed solutions, while project management software like cloud-based Planview AdaptiveWork is paving the way with automation, real-time visibility and dynamic reporting, as well as customizable interfaces and workflows. With proper tracking capabilities and software designed for project planning, communications and management, businesses are not only able to measure performance, but also implement effective improvements.
Utilizing big data requires ample tracking, but also the ability to pinpoint specific variables in order to set goals and measure successes. This, in turn, can significantly improve project outcomes and increase overall success.
