Predictive analytics helps organizations dive deeply into past data and combine it with current patterns to forecast future results. It also adjusts to market changes and stays competitive.
Although it is not a new concept, more businesses from all industries are only now beginning to appreciate the benefits of using data to forecast future results for all facets of their operations.
An organization can use predictive analytics to improve decision-making, streamline corporate processes, and better comprehend set objectives.
It’s a clear investment for most organizations, but how can you assess if your company is ready for predictive analytics? In this article, we will learn how you can prepare for a successful business ready predictive analytics implementation.
1. Does your company have a developed data culture and suitable data architecture?
Any predictive analytics project should begin by considering the organization’s analytics maturity.
You must start by evaluating your organization’s internal structure and capabilities, the current data architecture, team, and available data. Then, you can engage with internal or external data scientists to create the predictive model to help revolutionize the organization.
Effective data use depends on the correct data architecture, not on hastily acquiring the latest technology. The business should instead think about making sustainable data architecture in the long run.
Ineffective legacy systems must be abandoned, redundant or poorly thought-out data storage must be eliminated, and a culture of sustainability must be fostered. One example would be consolidating customer data from many data warehouses into a single storage location.
2. Can your business collect performance information from ongoing operations?
A data project will never advance past the initial query stage without the proper tools. To that aim, organizations must examine their organizational setup and determine whether they can collect performance data. Will the data be accurate, making it usable for a predictive project?
3. Does your business have the funds to invest in cloud computing solutions for your company?
Big Data, cloud computing, business theory, and business intelligence are just a few of the topics that data science touches on. Predictive modeling fits well into the overlapping subsets, but if a corporation is unwilling to engage in cloud solutions, it may not achieve its ultimate objective.
The ultimate benefit of predictive modeling (and data science in general) is the ability to analyze data and developments in real-time from anywhere. The business must ascertain whether there is sufficient funding for an investment in a cloud solution or a hybrid solution (one of the rising trends of 2018).
4. Do you have a project lead in place?
A digital transformation strategy involves the use of data analytics, but without a project manager, it’s like owning a ship without a rudder. The vessel may be ready, but you cannot direct it toward the goal.
Business-ready predictive analytics operates under the same assumption. A successful data science project requires top-down support from senior management all the way down to the operators who will be in charge of implementing the model’s changes.
In order to properly usher in the sea change that is a predictive model, leadership must be on board with analytics. In the end, someone will have to assume responsibility and take charge of the data science project.
5. Does your business have enough resources for data analytics, or do you have access to outside providers?
The company should be able to analyze and visualize data so that it can tell the story of the data. The data model should result in a framework for efficiently analyzing (clean) data.
The data and the personnel that will analyze and use it ultimately determine whether an organization is prepared for predictive analysis.
The organization may decide to develop its own in-house team using its resources, or it may choose to work with a third-party data science service. In any case, the result should be a predictive model integrated into routine operations and enhanced company operations and decision-making.
It’s not necessary to use predictive analytics blindly. To test a predictive analytics model’s effectiveness, start with a tiny area of your organization. Build on what you learn and scale it to other parts of your organization. Predictive analytics will bring you closer to understanding future outcomes and how you can prepare to meet them.