How to Make Your Data Analytics Strategy Work for Your Organization

data analytics strategy

Table Of Contents

Staying competitive in a data-driven world means that companies need to be able to utilize data analytics fully. Proper data analytic methods are essential to glean more informed decisions for your company’s success. With the right data analytics strategy, you can realize your data’s maximum potential to fully understand where your company stands in a target market, who your customers are, who your competitors are, how your products and/or services are performing, and much more. Utilizing data analytics properly is essential for effective monetization, sharing, and data exchange.

Data is one of the primary tools that you can use to boost your company’s overall performance. You must employ the right data analytics strategy roadmap to make better data-driven decisions for your company’s growth.

This guide goes over how to develop a data analytics strategy that will help your business see continued success and offer a competitive advantage. Additionally, it goes over what it takes to execute a successful data analysis strategy within your organization.

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What Is a Data Analysis Strategy?

A data analysis strategy is a roadmap of a company’s long-term plan for gathering, storing, using, and sharing its information and data sets. Data analysis involves three main aspects:

  1. Cleaning: Removing incorrect, corrupted, duplicate, or incomplete data within a dataset.
  2. Processing: Data is transformed into usable information.
  3. Changing: Identifying how data is captured and changed within a database and then delivering those changes to another process or system.

Utilizing these actionable aspects allows business leaders to create more informed decisions, especially about how they can sell, share, and exchange data.

A data analysis strategy prevents risky and poor decision-making through relevant statistics and insights presented in graphs, charts, tables, and images. For instance, thinking about what we did in the past to improve our future is a way of analyzing previous data to make better decisions for the future.

Employing the right data analysis strategy means understanding that data comes in various types, structures, and formats. The table below outlines a few of the most common data analysis strategies and their purposes.

Big Data Structured Data Metadata Real-data Machine Data
Definition Large datasets that constantly grow exponentially Predefined data model that comes with the standard column-row database Provides information you need about another data set Groups of data presented the moment they are acquired Information acquired by the Internet of Things, sensors, and other tech tools
Purpose Utilizes variety, volume, velocity, and variability of data Promotes easier management and analysis than unstructured data Provides more accessible organization of unstructured data into smaller categories Reliable data when companies must make decisions about real-time information, especially in the stock market Reduces the risk of human errors because data is solely produced by machines

Knowing the type of data you’re working with and analyzing helps you determine what method and technique to employ. It also becomes more valuable when looking for relevant data insights, including figures, metrics, and facts.

Apart from knowing various data types, it’s also imperative to know the two primary methods of analyzing data: quantitative and qualitative. Doing so enables you to ensure that you’re organizing your data in a way that makes sense.

Data analysis strategies require team leaders to consider your company’s objectives and the following considerations:

  1. How employees feel more empowered to use your data more effectively
  2. The proper analytical process ensures your data is high-quality and accessible
  3. Determining the right tech tools for properly sharing, storing, and analyzing data

With these considerations in mind, it becomes easier for organizations to develop a strong data analytics strategy tailored to their needs.

Purpose of a Data Analytics Strategy

The primary purpose of an analytical data strategy is to enable more informed decision-making. The right tactic can guide your company’s key decision-makers to evaluate data based adequately on available information. It totally scratches off decisions based on simple intuition.

A good example is better understanding your company’s growth opportunities, capital investment, and income predictions before they present challenges.

A data analysis strategy can also help target your customers better by specifying data types and sources for optimal collection and reporting. Because the strategy gives you a complete 360-degree view of your available data, you can better understand your clients based on the following:

  • Purchasing behaviors
  • Demographics
  • Interests
  • Habits and patterns

Your strategy’s success can also allow you to employ methods to attract potential customers to avoid wasting your available resources on the wrong people.

Additionally, because a data analysis strategy creates cycles for data-driven goal settings, it helps your company reduce costs through advanced technologies. For instance, predictive analytics can help businesses manage opportunities, patterns, and data trends they can leverage to improve their strategies.

And finally, an efficient data analytics strategy can provide a practical framework for how businesses can hone their capabilities in influencing operations, answering questions, and improving their reports.

Key Elements of a Data Analysis Strategy

Before looking into various ways to employ an effective data analysis strategy, you must look at the five key elements for its success. These five key elements generally consist of collecting data, conducting data analysis, reporting results, improving processes, and building a data-driven culture.

Let’s go over each of these key elements in greater detail.

1. Collecting Data

Data collection is the first step when dealing with the data analytics process. It involves gathering external and internal data from various sources, including the following:

Data Source Examples
Operational systems System data, including CRM, ERP, and HRMS
Transaction data Point-of-sale systems, financial databases, e-commerce platforms
Machine data Sensors, connected devices, RFID tags
Social media and web data Social media platforms, online surveys, web analytics tool

Depending on your data type and source, you can employ various methods of data collection. Operational data, for instance, involves manual collection by extracting data from your systems. But you can also use ETL (Extract, Transform, and Load) tools.

On the other hand, data collection for transaction data can be done by downloading from your financial databases. There’s also automated data collection through integration with your chosen payment processing provider.

Data from the web and social media promotes collection by leveraging social media monitoring tools or setting up your tracking scripts. And finally, machine data involves using sensors for activity-tracking and IoT platform integrations.

After data collection, cleansing and standardizing your data are also critical because they help prepare it for analysis. They involve correcting errors, removing duplicate records, and providing the most reliable data format for easier analysis.

2. Data Analysis

Data analysis involves statistical techniques for optimum extraction and examination of data and valuable information. This step involves various goals, depending on your business objectives:

Goal Purpose
Identify current industry trends and patterns You can use this information to make informed decisions about promotions, products, and pricing
Predict future outcomes Establish predictive models to forecast demands and future events for marketing, staffing, and inventory
Detect anomalies Detect unusual patterns that can be signs of a data breach, fraud, and other concerns to prevent losses

3. Reporting Results

After data analysis, efficient reporting of results must be accomplished. The main purpose of this step is to share your data insights with concerned members for better decision-making. You can also report the results of your analysis in several ways.

First, you can create a data dashboard where all the findings are presented. It features a graphical representation of all your data findings for easier viewing and access. Moreover, you can also use your dashboards to monitor your company’s performance and compare different data sets.

You can also report the results through a summary of your key findings. The summaries you did can be used by others not skilled enough to interpret the data.

You can also create infographics and visual representations of your data to make everything easier to understand. Most companies use infographics to share their data analytics with wider audience groups.

Regardless of your chosen method, your ultimate goal in generating reports is to allow your team members to utilize your data to create better decisions. This drives a more positive impact on your company.

4. Improving Processes

You must remember that data analytics is not a single event. It’s an ongoing process that requires continuous focus to improve your analysis process. In this step, you constantly seek ways to improve on collection, processing, and analysis of your data.

An important element of a data analytics strategy is using data to improve processes continuously. This ensures that the analysis you conduct continues to be important well into the future and supports a strong and successful business.

5. Building A Data-driven Culture

You must think of the bigger picture with data analytics. More than the data, it’s all about your customers and other individuals who need your data to decide on essential matters better and more effectively.

This is why building a data-driven culture is important. You can train your employees on properly using data analytics and provide them with the necessary tech and data discovery tools. Ultimately, data analytics should create a culture of accountability, where everyone can access the data they need anytime and anywhere.

Data Analytic Strategy for First-Party Data

A first-party data analytic strategy involves a framework that supports the application and collection of first-party data. Generally, first-party data refers to the information obtained and collected from your customers on your owned channels.

A first-party data analytic strategy allows the following:

  • Future-proof your team’s marketing efforts in case of cookie changes
  • Establish more direct and deeper connections with your customers to retain them and unlock more relevant customer lifetime values
  • Boost your company’s ROI by at least 2.9 times
  • Improve customer experiences and make them more personalized to deliver better values

What is First-Party Data?

First-party data involves information about your company’s customers owned and collected by your business. These datasets are compiled using your company’s reliable system for future purposes.

First-party data includes the following:

  • Basic customer information, including email address and name
  • Purchasing information, including payments and invoices
  • Demographic information, including employment, age, income, and gender
  • Tracked information, including viewed websites, comments, and content downloads

One of the most common ways of utilizing first-party data is to create content, ads, and experiences that will cater better to your consumers’ interests. For instance, you can use your first-party data to understand your customers’ mobile app and web behavior, purchase history, interactions with your support, and loyalty status.

The ultimate goal of leveraging first-party data is to generate better-targeted ads to catch your customers’ interest.

How it Helps Data Analysts Activate, Collect, and Leverage Data

how data analysts leverage data


Companies, especially B2B, must understand and find relevance in the value and significance of first-party data. Moreover, a survey revealed that 85% of B2B companies believe utilizing this data helps their overall success.

Reduce Costs

You don’t need an intermediary when dealing with first-party data. This means there’s no need to purchase or shelter money from other parties. It’s less expensive than utilizing third-party data while ensuring accuracy because you collect it directly from your business’s customers.

Promotes Relevance, Accuracy, and Reliability

You can guarantee precision and relevance with first-party data. Using the right strategy to utilize this data maintains a close connection with your customers. This also helps ensure an accurate data source, reducing possible errors.

Moreover, first-party data is even more reliable because you can track customers’ purchasing patterns and behavior. This allows you to gain better insights to improve their overall experience.

Gives Control Over Your Data

The right data analytic strategy for first-party data gives you complete control and accuracy of the information. Moreover, you can use a CDP or a customer data platform to ensure your data is of high quality across all your channels.

Data Analytic Strategy for Third-Party Data

A third-party data analytic strategy involves leveraging external data sources to identify how your business can improve customer experience and your overall growth. The strategy helps you personalize marketing efforts and develop new data monetization opportunities to anticipate demands and shifts in the current market.

When you utilize third-party data most efficiently, your company becomes competent in evaluating, preparing, identifying, and procuring external data. There’s also the constant process of evaluating, engaging with, and determining potential new external data sources.

Ultimately, maximizing the third-party data’s full potential allows more insightful data analyses.

What is Third-Party Data?

Third-party data is not directly sourced from the relationship created between your company and a customer. Instead, it involves an outside or external source responsible for collecting data. Several third-party data sources are segmented and aggregated before they are made available to companies in a data marketplace.

How it Helps Data Analysts Activate, Collect, and Leverage Data

Third-party data analytic strategies help data analysis manage large information volumes easier through digital services and online apps. These tactics also promote an extensive scope to cater to various types of businesses, ensuring convenience and reliability.

Better Engagement

Strategies for utilizing third-party data are ideal for developing better engagement with your current and future customers. These methods help data analysts determine efficient omnichannel marketing strategies in the digital world.

Moreover, third-party data promotes seamless integrations in your company’s tech stack, allowing analysts to manage and easily manipulate the data they need.

Establish Personalizations

Data analysts enable marketing professionals to leverage the correct third-party data to establish personalized customer interaction strategies. This helps build trust and reputation, increasing the chance of your visitors returning and staying loyal to your business.

What Does It Take To Execute an Organizational Data Analytics Strategy Roadmap?

Choosing your data analytic strategy is the first step to your company’s growth. But utilizing it efficiently to succeed is another path you must take seriously. Your plan must be actionable and well-documented so that everything is clear on how you can move forward with your strategy.

This is why an organizational data analytics strategy roadmap is important. It promotes easier management of the following:

  • Allocation of your available resources
  • Establish tangible processes and policies
  • Improve and create an efficient data infrastructure
  • Train your staff to promote a data-driven culture
  • Accomplish critical buy-in for other initiatives

An organizational data analytics strategy roadmap is an actionable plan outlining your entire implementation process. It zeroes in how your company manages, utilizes, and analyzes data to achieve your goals. The roadmap includes your business’s necessary resources, objectives, and timeline.

Define the Most Important Business Problems to Solve

The primary advantage of an analytics roadmap is it aligns your company’s data initiatives with your main vision and objectives. You can use the roadmap to track your current progress and identify potential loopholes where you can improve toward success.

The roadmap also helps develop collaboration between the team members across all your company’s departments. This leads to better business-driven decisions for your overall growth and improved performance.

Set Measurable Goals for the Data and Analytics Initiatives

Regardless of the industry, a company’s goals should always be measurable and attainable. With a roadmap in place, you can guarantee efficiency in achieving SMART goals for your data and analytic initiatives.

A roadmap also helps define your business objectives and goals more effectively. It enables you to identify key areas where your company will likely use data and analytics for better overall outcomes.

You can employ various approaches when defining and prioritizing measurable goals and objectives. These include the following:

  1. Aligning with your current company strategy to support long-term goals
  2. Considering the impact of your business goals to know what to prioritize
  3. Involve your company’s stakeholders so you can gather input from different perspectives

Establish An Analytical Capability

Always remember that data analytics is one of the main business outcome enablers. It connects your tech expertise and commercial management via four layers: business, analytics, technique, and technology.

Moreover, an organizational roadmap helps you adopt a methodology focusing on your business goals and priorities. This prevents you from concentrating on unwanted technological questions, leading to a more coherent approach for more effective results.

Let’s dive deeper into each layer’s tasks, roadmap creation, and main owner.

Tasks Main Owner Roadmap Creation
Business Layer Business Partnership

  • Establish business priorities
  • Create partnership structure
  • Create support and urgency
  • Align organization
Business sponsors

Business owners


  • LOBs willing to invest
  • Identify their priorities
  • Estimate business case
Analytics Layer Analytics Value Generation

  • Understand business problems
  • Translate business problems into analytical problems
  • Assess and organize capabilities
  • Manage business processes and their quality
Analytics business partners Horizontal Capabilities

  • Maturity analysis
  • Capabilities needed
  • Investments and plans
  • Business case creation
Technique Layer Capability of Data Science Execution

  • Explore, generate, and transform data
  • Transform business knowledge into signals
  • Model, monitor, deploy, and disseminate
  • Provide business insights
Data science manager Building capabilities

  • In-house and partnership split
  • Resourcing and technical requirements
Technology Layer Technology Enablers

  • Data management
  • Development and deployment
  • Dissemination
  • Enterprise integration
IT sponsors and owners Platform building

  • Architectural design
  • Technology implementation plans
  • Technology selection

Steps to Developing a Data and Analytics Strategy

developing a data analytics strategy

The right data analytics strategy serves as your blueprint or master plan for leveraging more enhanced business decisions. It significantly boosts your performance for your business’s success.

Here are six critical steps to developing the best strategy for your company’s growth.

1. Developing the Data Culture

Your data strategy will only work when you adopt a data-driven culture within your company. Each team member should know the importance of data analysis because it can help you achieve your goals and your preferred business performance.

To develop a data-driven culture, there should be a proper setup of executive and low-level presentations and meetings. This helps demonstrate solid concept proofs. Moreover, a data-driven culture can develop better multimedia presentations where collaboration is enabled within various business unit networks.

2. Determining Key Performance Indicators (KPIs)

After adopting a data-driven culture, it’s time to choose the key performance indicators you intend to prioritize. This involves planning and collecting relevant data in all your business units to monitor better and track your performance.

It’s also imperative to store your data in a cloud or platform that promotes easy access and retrieval. If your collected data can integrate within other databases and systems faster, it could also contribute to more effective and efficient work ethics. These systems include ERP, CRM, or any marketing database.

KPIs also mean using external data channels to collect your competitor’s available customer data. And most importantly, don’t forget that your metrics must follow the SMART matrix for a more desirable outcome.

3. Defining Data Technology Infrastructure: Data Quality Objectives

Defining your tech infrastructure involves focusing on your internal processes to allow the collective conversion of raw data.

This occurs within various disparate systems, including:

  1. ERP
  2. CRM
  3. Cloud hosting
  4. On premise databases

These systems promote competitive market intelligence. That’s why using the right tech infrastructure is also essential to drive success to your current data analytics strategy. It should include data collection, preparation, storage, and integration techniques.

What makes this step critical is that it allows more seamless streamlining and reorganization of your customer data so that it’s aligned with your measurable goals.

4. Researching and Developing Target Markets

The next step involves synthesizing, collecting, organizing, and using information collected about specific markets and their customers to penetrate new markets.

5. Creating the Analytics Strategy Plan

When you have all you need, it’s time to create the blueprint for your strategy. But even when this serves as the master plan, it must be regularly updated and refined, depending on your future business goals or changing performance parameters.

Your data analytics strategy is also the starting point when encountering business problems. You must answer the following questions with the right strategy:

  1. What metric should you focus on?
  2. Are the metrics quantitative or qualitative?
  3. Can qualitative metrics be accurately measured?
  4. What are your expectations with the plan to be employed?

6. Implementing Data Visualization Techniques for Organizing Data

Apart from the right analytics strategy, your data visualization techniques also matter. They can help you employ data-driven decision-making, including increased cost savings, performance, and confidence.

Moreover, implementing proper visualization techniques is imperative for adding value to your company.


A data analytics strategy is the foundation for your company’s data and analytics needs. It’s a long-term plan that considers your customers, team members, current tech stack, and data processes.

Before developing an efficient strategy, remember the key elements you must include and utilize. But most importantly, you must utilize an organizational roadmap to help you meet your business goals and boost your company’s overall performance.

Revelate is an all-in-one tech platform that facilitates data discovery, cross-listing, internal sharing, and monetization to help develop a strong data analytics strategy. This tech solution ensures an enhanced customer experience through proper identification, packaging, and data distribution. Book a demo today to learn more about how it works and how it can help your organization be successful.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started