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As a leader, you’ve probably witnessed missed opportunities or inefficiencies from inconsistent or disorganized data which is why implementing data analytics has the potential to be a transformative process for your org. It goes beyond just setting up systems; it involves aligning people, technology, and processes with strategic goals. A successful implementation requires a clear roadmap, beginning with defining business objectives and progressing through stages of data preparation, model development, and visualization. This article provides a step-by-step guide to help organizations deploy analytics solutions that are aligned with their goals, ensuring that insights are accessible, meaningful, and actionable for users across all levels of expertise.
Defining business goals
The first step in an analytics implementation is to establish clear goals and objectives which are understood by all.
- What’s within scope for this implementation and what should be a focus for a future iteration?
- What does success look like for us?
- What processes are we aiming to improve?
- What strategic goals are we hoping to help achieve?
It is imperative to have organizational agreement on the expected implementation outcomes and what will not be delivered. C-Level and executive understanding is crucial to help drive strategic business objectives, priorities, budget, and expectations. With this understanding established, it is then possible to develop a framework for delivering clear, measurable objectives.
Data preparation and exploration
Bad data leads to bad decisions and a messy data analytics framework which is why it is so important to standardize and cleanse data. In the early stages of an analytics implementation, it is critical to explore and prepare data.
- Assess the quality: are there duplicates, inconsistencies or errors in the data?
- How complete is the data?
- Are you missing information?
Data profiling techniques will elicit an understanding of how the data is used, the completeness of the data, the clarity of the data, and the cleanliness of the data. From this understanding, it becomes clear how much effort will be spent on cleansing the data and making it useful for an analytics system. Handling things such as missing values, outliers, and inconsistencies in the data is a key component. This work leads to a more complete and robust data set for the business to use.
Model development and deployment
First, the team should select algorithms based on the problem type (e.g., regression, classification, clustering). After the model is selected, build and train the model. Evaluate the model’s performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score). Validate the model against a known dataset to ensure accuracy. Monitor the performance of the model over time, looking for outliers, anomalies, data drift. Keep in mind that the needs of the business will change over time, and likewise the model should iteratively change over time.
Access and visualization
This step is all about making the data understandable to your users.
- Who will need to access and absorb this data?
- What’s the best way to present the data to different users?
- Do you need to tailor access in any way?
Given the needs and requirements from a data perspective, decide on a toolset that matches those requirements. Some organizations are heavily visual and will require a visualization platform that offers broader or deeper capabilities in that regard, while other organizations are more focused on data science and statistics and may not require as robust a visualization layer.
Depending on the capabilities of each constituent group, data will be exposed to users in different ways. Non-technical users will require reports and dashboards which are designed, developed, tested and deployed for them. Once access is provided it is imperative to provide documentation and user training in order to facilitate proper usage.
Analysts and data scientists will require tools and platforms which allow for ad-hoc querying, data exploration, and advanced statistical capability. The requirements for this group will vary greatly from non-technical users. Understanding each audience and their level of desire to interact with the data leads to different tools and access methods.
Step into your future with data analytics that evolves along with your firm
Implementing data analytics is a significant step forward for organizations seeking to leverage data for strategic advantage. However, this journey is not without its obstacles, from aligning stakeholders to ensuring data quality and managing technical complexities. As you progress, be prepared to address these common challenges effectively to ensure a smooth implementation.
Curious to know more? Explore strategies for overcoming the hurdles that often stand between organizations and successful impactful analytics outcomes in our blog or, reach out to us if you’re looking for a partner to in your pursuit of data analytics.