In SaaS, a carefully crafted data strategy is your business’s roadmap for getting the most out of data and charting a course for reaching your goals.
What’s even more, it helps streamline operations and run more efficient, cost-effective business processes.
Let’s take a closer look at what you’ll need to start building your own data analytics strategy.
What Is Data Strategy?
A data strategy is a visualization of how your business will collect, use, store, manage, and share data. It blends in a range of different tools, processes, and rules that help your company make informed decisions, as well as ensure your data is – and remains – safe and compliant.
The main purpose of a data strategy is to ensure all data resources are positioned in a way that they can easily and efficiently be used, moved, and shared.
The Importance of Data-Driven Strategy
In the past few years, the role of data has changed dramatically. What was once a supporting input is now an astounding strategic resource to gain better insights and enable data-based business models.
Considering the sheer volume of data that is available these days, having a comprehensive data strategy – with broad involvement and support – is crucial for ensuring that data is managed well and used productively.
In addition, a strong data analytics strategy can enhance your SaaS brand’s ability to:
- Improve internal operations
- Make data-driven business decisions
- Understand customers and identify trends
- Provide smarter products and services that meet each user’s unique needs
- Avoid siloed projects that use the same data
- Minimize resource-consuming data activities that don’t contribute to the overall business objectives
- Better understand – and leverage – the power of data science in marketing
The 6 Elements of a Strong Data Strategy
An actionable big data strategy helps businesses clarify the main purpose of their data and guides them in strategic data management.
Let’s take a look at the six key elements of a data strategy that work together to comprehensively support data management across an organization.
Building an effective data strategy is not a standalone activity. Rather, it must be driven by your business strategy (and the processes you use to operate and improve your business).
Consequently, your first step in creating a data strategy should be to focus on your business objectives and key questions. This will help make sure you are not wasting valuable time and resources collecting, analyzing, and storing the wrong types of data. Think about questions such as:
- What are my business objectives and strategic priorities?
- How can data help meet these objectives?
- What insights can help make progress?
Once you know how you will use your data, you can think through the processes needed to actually gather the data.
Start by thinking about your data sources and whether you will need access to internal and external assets. Additionally, you’ll need to decide how the data will be collected, as well as whether it’ll be done manually or whether you will need to set up exact scheduling.
Data architecture is a framework that is primarily concerned with building the IT infrastructure to create the business impacts that are identified in the data strategy. Simply put, it describes how data is collected, stored, transformed, distributed, and consumed.
Data architecture further includes the rules governing structured formats (e.g. databases) and the systems that connect data with the business processes that consume it.
A flexible and scalable data architecture is key for an efficient data-driven strategy. Building one, however, is not always an easy task.
Because it’s responsible for setting the data standards that define what kinds of data will pass through it, data architecture often involves an array of different components such as first-party data, APIs, data pipelines, cloud storage, and real-time analytics.
One way to achieve these data standards is to create a data schema. This will define the entities that should be collected, the type of data each piece should be, and the connection between entities in different databases.
There are several other considerations that often go into data architecture. Here are some important ones to keep in mind:
- In most cases, a central data repository is generally more beneficial for supporting analytical needs (compared to operational systems)
- Remember that you should decide on how access to data will be provided or provisioned (e.g. self-service or reports created by IT)
- Consider the data that doesn’t yet exist and think about whether it can be estimated, purchased from third-party market trend data, or whether you’ll need to implement a new source system to generate this data
Turning Data Into Insights
A strong data strategy should provide recommendations for how to best apply analytics to extract insights.
Here’s where data visualization can play a key role.
Leveraging data visualization tools in your data analytics strategy allows you to see analytics presented visually, making data easier to understand and interpret. These tools excel at translating data into visuals in the forms of pie charts, line and area charts, heatmaps, bar charts, table list records, scatterplots, and treemaps.
Data visualization can further help identify areas that need fine-tuning and shed light on factors that influence consumer behavior.
Governance plays an important role within the overall data strategy. It ensures that data is used correctly and consistently across the company. To achieve this, you need to carefully craft policies and procedures for proper data use. These may regard data security, data privacy, and data transparency.
It might also be worth noting that – with a data analytics strategy – decisions about how data is processed, manipulated, or shared are established by the rules and policies of data governance (rather than by an individual developer).
Data Strategy Roadmap
In a nutshell, a data strategy roadmap is a strategic plan to translate the data strategy’s intent into a plan of action. Typically, it maps out the phases and interactions of each step in the strategy and lists out the key performance indicators (KPIs) for measuring success.
In the roadmap, projects are charted on a schedule to showcase the chronological order of execution. Most data roadmaps have three main phases – planning, process, and review.
Moving Forward With Big Data
Today, data is central to any business, and managing it strategically is becoming increasingly important.
A well thought-through data strategy can be a strong foundation for consistent project approaches, effective integration, and overall business growth.