Organizations implementing well-executed analytics strategies see better financial, customer, and employee outcomes. They’re also able to make better, data-driven decisions that lead to improved ROI. Despite this, only 8% of leaders are actively engaged in analytics and treat it as a strategic imperative. With the increased use of advanced analytics, including machine learning and AI, analytics strategies are only growing in importance.
A good analytics strategy helps put analytics at the center of every decision. It outlines how an organization uses data and analytics to achieve business objectives, how analytics are created and managed, the tools used for analysis, and how analytics align with its overall goals. An analytics strategy differs from a data strategy, which focuses on storing, governing, and managing data across an organization.
Creating a Data and Analytics Strategy Framework
A data strategy framework should support all the initiatives within an organization. You can create a strategy that empowers the entire organization by focusing on business goals, governance, security, compliance, data integration, training, and skill development.
Business Goals
Clear business goals can make or break an analytics strategy. The strategy needs to include measurable objectives that align with the company’s vision and mission. Well-defined goals might be to increase customer satisfaction, boost sales, or reduce operational costs. By identifying what success looks like, analytics can be tailored to monitor progress and inform decision-making, ensuring efforts are directly contributing to the company’s goals.
Data Governance
Establishing policies and standards for data collection, storage, and usage that align with business objectives ensures that data across the organization is managed properly to maintain quality, consistency, and accessibility. Effective governance requires a strong framework that guides the production of analytics pipelines from concepts and prototypes to fully vetted and functioning models. Collaboration is needed to create a unified framework that controls data access, integrates disparate data sources, and maintains a single source of truth.
Security
All strategies must include measures to protect data from unauthorized access, breaches, and other cyber threats. Good security involves data encryption, access controls, and regular security audits to safeguard sensitive information. A strong security posture not only protects the organization’s data assets but also builds trust with customers and complies with regulatory requirements.
Compliance
Laws, regulations, and ethical standards often depend on an organization’s industry. Because of this, your analytics strategy needs to include enough rigidity to maintain compliance, but also have flexibility to adapt as new laws are passed. Ensuring compliance requires regular review of data policies, procedures, and systems to align with evolving legal and ethical standards, minimizing legal risks and fostering a culture of accountability.
Data Integration
Most companies need to consolidate data from various sources into a cohesive, accessible format that provides a comprehensive view of the business. This allows for more accurate analysis, reporting, and decision-making. Using technologies and methodologies that can seamlessly combine data from internal systems, cloud applications, and external databases, ensure data is consistent, reliable, and timely.
Training
Analytics infrastructures need to cater to the employees in your organization. Training equips employees with the knowledge and skills needed to effectively utilize analytics tools and interpret data.
This process involves providing ongoing education and resources on data analytics concepts, software, and best practices. By fostering a data-literate workforce, organizations can enhance decision-making capabilities and encourage a culture of continuous improvement and innovation.
Skill Development
Skill development in analytics goes beyond initial training to include advanced learning opportunities and career progression paths for employees interested in deepening their expertise in data analysis. This process might involve workshops, certification programs, or mentorship schemes focused on areas such as data science, machine learning, or data visualization. Investing in skill development not only enhances the organization’s analytics capabilities but also boosts employee engagement and retention by providing clear avenues for professional growth.
Building an Analytics Strategy
Although building an analytics strategy is a nuanced process, it can be simplified into a ten-step process.
1. Establish Your Business Goals. Tie your objective(s) to business goals, such as delivering better products, increasing sales or customer satisfaction, or reducing costs.
2. Establish an Analytics Vision. Determine what your organization can learn from your analytics. This exercise helps focus the strategy on producing a measurable outcome, such as understanding your customers or anticipating trends.
3. Determine Stakeholders. Determine the stakeholders who will benefit the most from your analytics output. Stakeholders should include the people who will use the data to make business decisions.
4. Tie Your Strategic Initiative to Business Goals. Consider what answer the data should provide and how to share that information with others. If a leader were to propose a new action plan, would they have the data they need to support that decision?
5. Establish Metrics. Define the KPIs everyone will need to determine whether your analytics strategy delivered on goals. These metrics will help the organization assess and refine as it moves forward.
6. Determine Funding. Ensure any planned analytics initiatives have proper funding for implementation, to provide ongoing support, and allow for any delays or obstacles. Here are some questions that can help guide this discussion:
- How will the project be paid for at the start?
- How much will it cost to continue paying for it?
- How much will it cost to expand?
- How much should we allocate to unforeseen costs?
7. Ensure Adoption and Buy-In. Platforms that reduce learning curves and come with resources to help people learn how to use them will see greater adoption.
8. Prepare for Challenges. Reduce potential delays by meeting with everyone involved and creating a list of potential challenges. Common problems include installing and integrating technology, developing training, and rolling a solution out to too many people too quickly.
9. Review Progress of Projects, Programs, and Technology. Ensure everything is running smoothly by creating a group to oversee and review the progress of projects, programs, and technology. Often, companies focus on the results of projects in terms of sales or deduction in costs, which they should. But they should also focus on the performance of training programs and how well technology is performing.
10. Build Your Roadmap. Once you’ve checked everything off the list, it’s time to create your roadmap. A roadmap helps you determine everything you need to successfully implement your analytics strategy. It should outline the core steps and the expected dates for completion. Once you have that, share it out with everyone involved so they know what to expect. It will also help you track your progress. Although this will most likely be the last step in your analytics strategy, you can begin developing a roadmap early in the process and refine it as needed.
Leveraging AI
To gain a competitive advantage, incorporate generative AI into your analytics within a trusted environment. Aligning an analytics strategy with business use cases that drive value, such as gaining customer insights through a 360-degree view, will increase revenue. AI also provides helpful features that can guide inexperienced users through analytics processes — and automate repetitive tasks to streamline the work of more experienced users. Other endeavors top companies are pursuing as part of their analytics in 2024 include:
- Migrating analytics from on-prem to cloud
- Implementing end-to-end analytic solutions
- Integrating advanced analytics, machine learning, and data visualization
- Data storytelling
- Explainable AI and ethical considerations
Addressing Governance and Data Quality
The increased use of generative AI threatens security, governance, and data quality. However, a good strategy can mitigate these risks by carefully implementing and adopting generative AI use policies. Policies should clearly define how people within the organization should use AI, including ethical uses, boundaries and limitations, and regulatory standards.
Generative AI use developed within analytics platforms within trusted platforms can provide extra security. It can also help ensure the data used for AI models is high quality, translating into accurate and reliable generative outputs.
Most importantly, an analytics strategy should plan for continually monitoring and updating these systems and policies. They must include flexibility to adapt to the evolving landscape of data privacy laws and cybersecurity threats.
Analytics Strategies for Revenue Optimization
You can enable and empower your team to maximize revenue by streamlining workflows and improving cost management. Below are strategies and use cases to get started:
Automation: The potential cost of overlooking insights can be significant. By embracing automation, teams can harness the full potential of their data. Reporting can be automated to save time and resources, improve accuracy, facilitate better collaboration, and deliver actionable insights. See how automation helps the Office of Finance through its biggest challenges and empowers teams to deliver insights at every department in the Digital Transformation Playbook.
Price elasticity: Analytics can be leveraged to measure how much consumers respond to changes in pricing and the elasticity of demand for their products or services. This enables strategy teams to further analyze price elasticity for strategic decision-making and to determine optimal pricing strategies that consider consumer preferences while maximizing revenue.
Root cause analysis: Root cause analysis is a process used to determine the underlying causes of issues to enhance performance or quality. It can shed light on the reasons behind fluctuations in data, allowing teams to better understand performance compared to previous periods.
Demand forecasting: Demand forecasting is used to predict consumer demand for a product or service. It is critical to estimating sales, planning inventory, and ensuring customer satisfaction. By leveraging historical data and analyzing forecasts, your teams can make informed decisions.
Get Started Today
A solid analytics strategy can help an organization see ROI from its current analytic investments — and have contribute to bottom-line revenue impact. The right strategy requires an approach that focuses on everyone’s analytical needs. Adopting the right technology that makes analytics accessible for everyone can improve your analytics maturity and deliver business value on key goals.
To learn more, check out this resource on how enterprises are using technology to make decisions and explore the Alteryx product tour.