Crafting a Successful Analytics Strategy: Essential Steps and Top Practices

The surge in popularity of analytics in today's market is largely attributable to the rapid growth of digital technologies and the ever-increasing availability of data. As organizations become increasingly data-driven, they collect data from various sources such as social media, e-commerce, and the Internet of Things (IoT). With the ongoing development and accessibility of analytics tools and techniques, organizations across industries and sizes are harnessing data to make informed decisions. Consequently, a structured approach comprising guidelines, tools, and processes for data collection, storage, and analysis is essential for businesses to unlock the full potential of analytics.

An analytics strategy is a plan that delineates how an organization will utilize its data assets to gain insights, make data-driven decisions, and achieve its business goals. A successful analytics strategy should align with the overall business strategy, consider the organization's data maturity level, and focus on developing the necessary capabilities to achieve its objectives.

There is often confusion between the terms Data Strategy and Analytics Strategy, as they are sometimes used interchangeably. A data strategy is a high-level framework that sets out the principles, policies, and standards for collecting, storing, securing, and sharing data across an organization. It concentrates on managing data assets, including data governance, data quality, and data integration. In contrast, an analytics strategy revolves around using data to gain insights and create business value. Thus, a data strategy serves as a critical foundation for an analytics strategy, ensuring that an organization has the necessary infrastructure and processes in place to effectively collect and manage data, which can then be employed for analytics initiatives.

Importance of an Analytics Strategy

An analytics strategy is vital for an organization for several reasons:

• Improved decision-making and business outcomes: By making data-driven decisions, organizations can gain insights, identify trends and patterns that might not be immediately evident, and make better-informed decisions that lead to enhanced business outcomes.
• Increased operational efficiency: Analytics can help organizations optimize their operations by providing insights into process optimization and identifying areas for improvement, which can help reduce waste, automate processes, and uncover opportunities for cost savings, leading to increased profitability.
• Competitive advantage: With analytics, organizations can act on opportunities more quickly than their competitors, anticipate market changes, adapt to customer needs, and develop new products and services, giving them a competitive edge in the marketplace.
• Enhanced customer experiences: Analytics can help organizations better understand their customers' needs and preferences, allowing them to deliver personalized experiences that meet their customers' expectations, leading to improved customer loyalty, satisfaction, and increased revenue and customer lifetime value.
• Risk prevention: By analyzing data patterns and trends, organizations can identify potential risks, such as fraud or compliance violations, before they become major issues. This can help organizations take corrective actions to mitigate risks and prevent future issues.

Steps to Create an Analytics Strategy

Developing an effective analytics strategy for an organization involves the following steps:

1. ?People (Identify Key Stakeholders)

The first step in formulating an analytics strategy is identifying the key players. These individuals should have a vested interest of making the organization more data-driven. The group should ideally be cross-functional to ensure that different interests of the organization are taken into consideration. The primary stakeholders could include:

• ?Senior leadership – Chief data officer or other C-level executive to oversee the entire strategy operation
• Project Managers – to coordinate cross-functional efforts and ensure deliverable and timelines are met
• Data users – to provide insights on how the data will be used to provide insights and what insights might help the business
• Centralized analytics team to help define the requirements and oversee the data strategy OR Decentralized analytics team consisting of subject matter experts and analytics professionals to handle department specific needs

2.???Process (Identify business objectives)

The identified should perform initial analysis to cover the current state of data assets and technologies. This step involves understanding the organization goals, identifying the relevant KPI’s and determining how analytics might help to achieve those objectives.

Some questions that should be answered in this step are:

• How do you access/use your data today?
• What tools are being used?
• What are the challenges that have not been addressed yet?
• What is the impact on business?
• What resources will be required to resolve the current issues?
• What does the timeline look like?

Prioritize use cases that hit the sweet spot between value and complexity, and continuously educate executives and stakeholders about what is possible and what to expect from the analytics initiatives.

3.??????Model (Determine analytics operating model)

The analytics model (delivery model) will determine the strategies around data. Your analytics strategy should align with both your current and desired models. There are 3 common models:

• Centralized - The central team takes full responsibility for decision-making at the enterprise level, while Business Units and Functional Areas have limited or no involvement in the process.
• Decentralized - Business units and functional areas operate independently while striving to adhere to global standards to fulfill specific enterprise needs
• Hybrid - The central team is responsible for decision-making at the enterprise level and also provides structures for decision-making at the Business Unit level.

4.??????Technology (Select the tools)

It is imperative to identify the technical tools to be used for the analytics platform so that it is sustainable and robust enough to support existing as well as future business requirements. The below factors need to be taken into account for the same:

• Cost structure: understand the cost structure and pricing model of the solutions. Also consider the cost of upskilling team members to use the tool.
• UI/UX: The solution should be a combination of robust visuals and a clean, easy to use interface.
• Scalability: The tool should have the flexibility to scale up with growth while paying for what we are using today.
• Advanced features and capabilities: The tool should provide advanced analytical operations that may or may not be an immediate requirement.
• Security: The tool should provide security and privacy. The data security and governance standards need to be followed.

5.??????Culture (Establish a data literate culture)

One of the most important and most difficult aspects of an analytics strategy is the creation of a data-driven culture. It requires all employees to have a basic understanding of data and analytics. The cultural shift towards a sustainable data culture can be achieved by focusing on:

• Leading by example - To establish a data culture in your organization, it is essential to begin at the top. Leaders must model data-driven decision-making to lay the foundation for fostering a data culture.
• Making data accessible – Availability of data is one of the major pillars of any analytics initiative. Organizations may lose credibility if users are unable to access relevant data or unable to trust the data. The best approach here is to start with high-level, aggregate data and then delve deeper.
• Measuring the right KPIs – The measures and metrics chosen for decision making should be chosen after careful consideration. Integrating business or customer expectations with the questions necessary to determine success can aid in identifying the data sets required to measure that success.
• Internal trainings - A common mistake in developing a data culture is overwhelming users with formal or self-directed learning on tools or platforms without emphasizing the practical applications of analytics within the organization. It is crucial to invest in training that combines analytics tools with practical analytics challenges that align with your organization's needs. To achieve this, identify an evangelist or multiple evangelists who can mentor employees and serve as product owners for the technology.

6.??????Governance (Data quality control and maintenance)

The more mature and complex a data platform gets; the higher emphasis and detail is needed for data governance. The core areas of data governance should include:

• Authentication and authorization – to ensure that the right set of users have access to the right data
• Information architecture – effective structure, organization and labeling of data
• Data catalog – easy access and management of data via creation of metadata and development of a clear understanding of data assets
• Data lineage – It refers to the complete history of a particular data asset, starting from its source and tracking its journey through different systems and processes until its current location and usage. This comprehensive record of the movement of data enables users to understand the origin of the data, any transformations it underwent, and where it has been utilized. By providing such insights, data lineage helps in ensuring the data's accuracy, completeness, and traceability.
• Data mastering – It is a process of consolidating multiple versions of a data asset from various sources to create a single, authoritative version. This "master" version becomes the definitive source of truth for that data, thereby eliminating data inconsistencies, redundancies, and errors that may exist across different versions of the same data asset. The objective of data mastering is to ensure that there is a single reliable source of data for any given data asset within an organization.

As data platforms and analytics programs become more advanced, a data governance program can have a significant impact. However, going overboard with a data governance program can hinder progress and limit the potential benefits for the business. Achieve a balance by distinguishing between non-negotiable and flexible aspects of data governance that can be developed later in the data platform life cycle.

7.??????Optimize (Own, document, review)

The leadership team needs to monitor the performance of the analytics strategy and identify areas for improvement.

Own – The overall strategy process needs to be owned internally by an analytic strategy product owner(s). This individual ensures the principles of the strategy are well promoted, reviewed, and followed.

Document - Any strategy development process will produce key documentation around the procedures, protocols, and outputs of interrelated processes. However, an analytics strategy engagement should also track the procedures, protocols, and outputs of the strategic engagement itself.

Review - Continuously review and update the strategy to align with the organization's changing business objectives and priorities. The analytics maturity of the organization needs to be taken into account in the review process. As an organization becomes more analytically mature, there is a need to evaluate both the timeline of the strategy and the investment involved.

There are four stages of analytics maturity:

• Descriptive – Looks at past data points to determine “What happened”
• Diagnostic – More explanatory than descriptive. Tells us “why and under what circumstances” did something happen.
• Predictive – It needs a lot of data points and scenarios to tell us “What will happen if”
• Prescriptive – It is similar to looking into the future based on current data points and attempting to form a causal link between business activities and outcomes.

Different departments within an organization could be at different stages of analytics maturity.

An ideal analytics strategy should be dynamic and adaptable, guiding the organization's approach to data challenges rather than imposing rigid rules. It should prioritize people and collaboration over tools or technology and be flexible enough to respond to changes in priorities or data availability. By engaging key stakeholders and communicating regularly, the strategy can achieve success and satisfaction.

In conclusion, crafting a successful analytics strategy is crucial for organizations to unlock the full potential of their data and stay competitive in today's data-driven market. An effective analytics strategy aligns with the overall business strategy, takes into account the organization's data maturity level, and focuses on building the necessary capabilities to achieve its objectives. It also serves to improve decision-making, enhance operational efficiency, provide a competitive advantage, deliver better customer experiences, and mitigate risks.

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