Data Analytics Strategy: What it Is and How to Build One

For most companies, becoming data-driven doesn’t happen overnight. No CEO or CTO wakes up one day and decides that more or better data will help them crush their business goals. In reality, data can do a lot for your decision-making processes. But you have to put in the hard work first.

You need to create a data analytics strategy before you set metrics for every imaginable process and role. This shapes how you collect data, why you collect and analyze it, and what your end goal is.

What is a data analytics strategy?

A data analytics strategy is a plan that outlines how an organization collects, analyzes, and uses data to empower data-driven decision-making to achieve business goals. 

A data analytics strategy is the key difference between an organization that collects data for the sake of collecting data and one that uses it for strategic insights and ways to improve day-to-day operations.

For example, a data analytics strategy for your business could be to collect customer feedback (in the form of in-app NPS surveys) to improve customer satisfaction and increase retention.

What does a data analytics strategy include?

Before we give you a full blueprint of a data analytics strategy, let’s discuss what it should include to provide insights for your business.

Business objectives: the main goals the organization wants to achieve through data analytics.

Data collection: methods, tools, stakeholders, and other details on data collection.

Technology and tools: analytics, visualization, data integration sources, etc.

Data governance: policies for data security, privacy, and data control.

Analytics approach: determining the type of data analytics to use: descriptive, diagnostic, predictive, or prescriptive.

Talent and skills: who you have on your team to help you build out a strategy.

Execution and scaling: how the strategy will be implemented and scaled over time, with tangible KPIs.

How to build a data analytics strategy, step by step

You don’t need a verifiable degree in data science or previous experience with analytics tools to build a data analytics strategy from scratch. Here’s how you can go from raw data to better decisions.

1. Define your business objectives

Your analytics initiatives should align with your overarching business goals. Before deciding what to do with your organization’s data, talk to key decision-makers in your business and ask them about their biggest challenges.

Are you trying to increase customer retention, lower churn, optimize and automate operations or something else? Based on these objectives, you can set goals and key performance indicators for success.

2. Assess your existing data capabilities

In other words, take a good look at your processes around data. What data do you collect and how? Who has data access and what happens once data is collected?

Doing an audit of your processes before starting can help you prepare a better data analytics roadmap.

3. Identify the data you need

If you took care of the previous steps, you know exactly what type of data you need to power up your data analytics strategy.

For example, if you want to increase sales, these are some of the metrics you’ll need to make better business decisions:

• Total revenue
• Percentage of revenue from new business
• Percentage of revenue from existing customers
• Conversion rate
• Cost of selling
• The average length of the sales cycle
• Market penetration
• Win rate

And others. Listing out the data you need makes the data collection process easier because you’ll know which tools (or stakeholders) to reach out to when you need to collect this data.

4. Choose the right tools and technology

If you don’t already have a BI tool you’re using for data analysis and visualization, now is the time to do the painstaking task of choosing the right one. Power BI, Tableau, Domo, and Qlik, are some of the most common tools in the business.

Evaluate your business needs and choose a tool that will be easy to implement and that can scale with your business. For example, Tableau and Power BI are known as enterprise data analytics tools that can cost a pretty penny and take a long time to implement fully.

5. Define data governance policies

Determine how you manage data assets, especially if you collect classified information from your customers. Establish guidelines for data privacy, such as GDPR and CCPA. Define who is in charge of data security, quality assurance, and control, as well as ownership.

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As part of your data management initiative, define roles and responsibilities in your team.

6. Choose the type of analytics

Depending on your potential use cases, you can opt for one of the many types of data analytics:

• Descriptive (describes what happened)
• Diagnostic (explains why something happened)
• Predictive (gives predictions on what will happen in the future)
• Prescriptive (gives actionable insights on what to improve in your business strategy to achieve your desired results)

7. Develop a skilled team

With all the steps so far, you can determine what skills and competencies you need to make this data analytics strategy come to life. For example, you may need a team of data scientists, engineers, and analysts if you want to tackle complex processes and business intelligence tools like Power BI.

If on the other hand, you have ready data sources and need something like real-time data visualizations, you can get by with upskilling your team and getting a tool such as Luzmo.

8. Implement data-driven processes

If your entire organization is collecting data, they need to see practical value from it. Unfortunately, just data points are not enough for actionable insights.

This is why you need to create dashboards, reports, and automated workflows to show your team the value of business analytics. This way, they can understand the numbers they’re seeing and make data-driven decisions quickly.

9. Set clear KPIs

To find out if your data analytics strategy is working, you need to set clear KPIs for success. For example, if your aim is to increase revenue, you can tie success to metrics such as sales volume, MOM growth, average customer lifetime value, and others.

You’ll get a clear overview of progress and you can use real-time data for more data-driven decision-making.

10. Scale and optimize

Over time, you can see if your new data analytics strategy has business value or if it’s collecting data for its own sake. Simply collecting data may not be enough and your data analytics strategy may not be enough to provide real value, but this is no sign to give up. Instead, you should find roadblocks and scale and optimize your strategy.

Some ways you can do this include:

• Improving the data quality
• Introducing generative AI and machine learning for easier forecasting
• Trying out new data sources
• And others

11. Foster a data-driven culture

Throughout your organization, encourage stakeholders to use data in their business processes. From the CEO to the sales rep, using the data sets from your analytics strategy gives you a competitive advantage and insights on what to improve to achieve better results and business outcomes.

Provide your end-users with a data analytics strategy powered by Luzmo

At Luzmo, we specialize in helping software companies give actionable insights to their end-users. With Luzmo, you can visualize the data from your product so that the business users can see the value your software provides.

Luzmo helps you visualize various types of data from your software in beautiful, functional dashboards for your customers. It’s easy to integrate, has a robust API, and is customizable so it fits into your product’s design and user experience.

Want to learn more? Get a free demo of Luzmo and we’ll answer any questions you may have.