Analytic projects can come in various sizes, big or small, as well as in different complexities in connections and the statistics used. No matter the details, successful analytics projects should be approached in a strategic way. Strategic thought on how the analytics will be applied and how the project will be prioritize are critical for all projects, but when scaling up analytics projects, an organization needs to integrate analytics into all of their strategic planning for individual projects to successfully create a data-driven decision making culture that leads to the most optimal results. Analytics can allow an organization to benchmark whether investments have met targets or confirm a project’s success. Analytics can also provide new insights into areas that could generate new revenue or reduce operating costs (Covington 2016, 63). The impacts that analytics can make when applied to a broader company strategy can be huge but must be implemented properly in order to succeed.
As mentioned, a necessary component for a project to be successful is to think of the project strategically. Approaching a project strategically involves building out a proper use case and understanding the overall goal that is to be achieved from the project. Determining what the benefits of a solution would be, and then comparing it to the cost and impact to implement it operationally, is needed to ensure a project is worth the effort (Covington 2016, 61). Also, incorrect analysis can lead to decision-level errors that can negatively impact a business, so it is important to understand the weaknesses of the data and should be viewed as a critical step that should be part of any strategic planning process (Covington 2016, 53). Developing a project within this strategic framework will help lead to a successful implementation.
Larger, or more numerous projects, require additional aspects to ensure success. A necessary component of analytics within an organization is to integrate them into the broader company strategy. Many large projects begin to require data from different departments and users. This can present a variety of challenges in capturing, storing, and analyzing different data sources. To overcome these challenges, it is important to have executive level buy-in for large scale projects to be effective. With executive level buy-in, cross-departmental roadblocks can be overcome quickly instead of leading to time consuming bottlenecks. Executives can enable resource mobilization and ultimately will be part of the decision-making group utilizing the results of a project. Getting user feedback from other decision-makers that would be utilizing the analyses is also important in custom tailoring the project process (Covington 2016, 30). A successful project would also include discussions with analysts and operational employees to ensure any weaknesses of the data, or other data hurdles, are known upfront to avoid incorrect action from the analytics.
Running strategic analytics initiatives requires someone to be able to manage these complexities. An analytics translator would be an excellent choice as the project manager, allowing them to communicate to any audience as needed. Analysts know the numbers but can be weak in the knowledge of business direction. An analytics translator could provide insight to the analysts by utilizing probing questions that can lead to a better understanding of the data, its weaknesses, and potentially new insights. Executives and other business leaders typically know the business direction but aren’t necessarily aware of what can and can’t be accomplished with the data. An analytics translator could help this steering committee take more effective action by providing them with different scenarios of what can be done with the data and let them choose the goals they are hoping to achieve. An analytics translator in this kind of role can help projects get buy-in from leadership and ensure that they can be achieved operationally in addition to providing solutions to the overall goals of the project by utilizing effective communication.
Organizations approaching analytics from a strategic perspective need to do so both at a high-level but also at the granular project level. Ensuring each project is developed strategically and that each of those pieces fit into the overall organizational plans is critical. Developing a culture that is focused on data-driven decision making requires strategic planning but ultimately results in a stronger and sustainable organization.
Author: Logan Callen
Covington, Daniel. 2016. Analytics: Data Science, Data Analysis and Predictive Analytics for Business. 5th Edition. South Carolina: CreateSpace.
Harvard Business Review Press. 2018. HBR Guide to Data Analytics Basics for Managers. Boston: Harvard Business Press.