Breaking Down Data Analytics Transformation: Benefits, Blockers, and Tools

We’ve reached a new crossroads in digital transformation and it’s all about data analytics.

Data analytics transformation enables modern enterprises to operate more efficiently, make better decisions, and gain a competitive advantage in their markets. While it may look like a clear path, it’s not a simple one to tread as a business.

Analytics transformation requires overcoming big blockers like data silos, lack of skilled resources, data quality and accuracy, data security and privacy, legacy systems, and lack of executive support. None of which are quick fixes.

We’ve identified some important guideposts along the way and want to share them with you. Let’s start with an overview of the territory, the benefits for your business, who to involve, the challenges you’ll face, and the tools you can use to transform data analytics into a strategic advantage.

What is data analytics transformation?

Data analytics transformation is the ongoing process of applying advanced data analytics techniques, tools, and technologies to improve the way your business uses and analyzes data to drive business decisions and outcomes.

It involves modernizing your company’s data operations and modern data stack—including everything from data collection to storage, processing, and analysis. The goal is to make all of these areas more agile, efficient, and scalable to enable data-driven decision-making across your organization.

This transformation typically involves adopting new technologies like cloud computing, artificial intelligence, and machine learning while establishing data governance policies and processes to ensure the accuracy, security, and privacy of your data.

Easier said than done, we know. But we also know the business benefits are worth the effort to get data analytics transformation right.

What are the benefits of data analytics transformation?

There’s a long list of benefits that every business unit in your org can realize that, when combined, add directly to your bottom line. Here are some of the key benefits of data analytics transformation that are industry-agnostic.

Improve decision-making: Advanced analytics techniques and tools enable you to process and analyze large volumes of data in real time. This makes it easier to reach data-driven decisions faster and with more confidence. Over time the ability to make better decisions reduces your operational errors and improves the accuracy of decisions.

• Identify trends and patterns that may not be obvious in traditional analysis.
• Make more informed decisions based on accurate data.
• Take advantage of opportunities and mitigate risks faster.

Increase operational efficiency: Data analytics transformation can help enterprises streamline their data operations, reducing the time and resources needed to collect, process, and analyze data.

• Automate data collection, cleaning, and processing tasks.
• Reduce the risk of data quality errors and data duplication.
• Accelerate time-to-answer and speed up data-driven decision-making.

Improve customer experience: When you understand customer data more clearly, you get new insights into customer behavior, preferences, and needs. This gives you the intelligence to tailor your products, services, and marketing efforts accordingly.

• Personalize marketing and advertising messages to improve relevance and conversion rates.
• Improve customer engagement and loyalty by addressing their needs more accurately.
• Deliver better customer experiences and drive higher customer satisfaction.

Enhance competitive advantage: Use data analytics to better understand your markets, competitors, and industry trends. With an advanced understanding of your big data, your teams can develop new products and services, optimize operations and workflows, and gain a competitive advantage.

• Identify new market opportunities and niche areas.
• Monitor and analyze competitor activities to stay ahead of the competition.
• Improve product and service offerings to meet changing customer demands.

Increase revenue and profitability: By leveraging data analytics to optimize their operations and improve their products and services, enterprises can increase revenue and profitability.

• Identify areas where costs can be reduced or efficiencies improved.
• Optimize pricing strategies to increase margins.
• Improve product and service offerings to drive revenue growth.

Sounds like a list that every business wants and needs, right? Many companies are trying and failing because there’s also a significant list of blockers to data analytics transformation.

What are the main blockers to transforming your data analytics?

Implementing data analytics transformation can be challenging, and there are several blockers that can prevent enterprises from achieving the full benefits of this transformation. Some main blockers include the following:

Data silos: Many enterprises have data stored in multiple systems and departments, making it difficult to access and analyze the data holistically. This can lead to incomplete or inaccurate insights and may prevent the enterprise from achieving the full benefits of data analytics transformation.

Lack of skilled resources: Data analytics requires a range of specialized skills, including data science, data engineering, data and BI analysis, and data visualization. Finding and hiring skilled employees can be difficult, especially in today's competitive job market.

Data quality and accuracy: Data quality and accuracy are critical to the success of data analytics transformation. Enterprises need to ensure that their data is complete, accurate, and up-to-date, but this can be challenging, especially if the data is collected from multiple sources.

Data security and privacy: With the increasing importance of data analytics, enterprises need to ensure that their data is secure and protected from cyber threats. Data privacy regulations also add complexity to data analytics transformation, requiring enterprises to establish policies and processes for data handling and protection.

Legacy systems: Many enterprises have legacy systems that are not designed for modern data analytics. These systems can be difficult to integrate with modern data analytics tools and technologies, preventing enterprises from achieving the full benefits of data analytics transformation.

Lack of executive support: Data analytics transformation requires a significant investment of time, resources, and budget. Without strong executive support, enterprises may not be able to secure the necessary funding or overcome internal resistance to change.

These blockers can make it difficult for any startup or enterprise to implement data analytics transformation effectively. Overcoming these challenges requires a strategic approach and a clear plan for addressing each blocker in turn. That means you need to know who to talk to and recruit for your team.

Who’s responsible for data analytics transformation?

Data analytics transformation demands cooperation from a wide range of roles across your organization. The specific titles will vary depending on the size and structure, but here are some of the key roles involved in data analytics transformation:

Chief Data Officer (CDO): The CDO is responsible for developing and executing the enterprise's data strategy, including data analytics transformation. They are responsible for establishing data governance policies and processes, ensuring data quality and accuracy, and driving data-driven decision-making across the organization.

Data analysts and data scientists: Data analysts and data scientists are responsible for analyzing and interpreting data to generate insights that inform business decisions. They use statistical and machine learning techniques to identify trends, patterns, and relationships in data.

Data engineers: Data engineers build and maintain the enterprise's data infrastructure. They design and implement data pipelines, data warehousing, and other systems to support data analytics.

Business analysts: Business analysts identify business problems and opportunities that data analytics can address. They work closely with data analysts and data scientists to define data requirements and ensure that insights are aligned with business objectives.

IT and technology teams: IT and technology teams select and implement data analytics tools and technologies like cloud computing, data visualization, and data management platforms.

Operations and business leaders: Operations and business leaders drive the implementation of data analytics transformation. They set the vision and priorities for data analytics, communicate the value of data analytics to the organization, and ensure that data analytics is integrated into key business processes.

These roles work together to ensure the integration of data analytics into the organization's culture, processes, and systems. For example, without data engineers and all of your data producers in alignment (e.g., IT and tech teams), your data quality won’t reach an optimal level. Everyone needs the right set of data tools to transform analytics in your business.

Which tools kickstart your data analytics transformation journey?

There’s a wide range of tools you can use to build a modern data stack that supports this kind of analytics transformation. Many of your data tools and processes can move to the cloud, while others will stay on-site.

Some of today’s most popular data tools include the following:

1. Cloud computing platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide scalable, secure, and cost-effective data storage and processing services. These platforms also offer a range of data analytics tools and services like machine learning, data warehousing, and data visualization.
2. Data warehouse tools: Snowflake, Google BigQuery, and Microsoft Azure Synapse Analytics enable enterprises to store and process large volumes of data in a scalable and cost-effective way. These tools also provide advanced data analytics capabilities, such as real-time data processing, machine learning, and predictive analytics.
3. Data integration tools: Talend, Informatica, and Mulesoft enable enterprises to integrate data from multiple sources and transform it into a common format for analysis. These tools also provide data quality and governance features to ensure that data is accurate, complete, and consistent.
4. Data visualization tools: Tableau, Power BI, and QlikView enable enterprises to create interactive and insightful dashboards, reports, and visualizations from data. These tools make it easier for users to explore data and identify trends and insights.
5. Machine learning tools: Python's Scikit-learn, TensorFlow, and Keras enable enterprises to build and train machine learning models to automate and optimize their data analytics workflows. These tools make it easier to extract insights from data and build predictive models for forecasting and decision-making.
6. Data orchestration platforms: Tools like Shipyard enable enterprises to easily automate and manage their data workflows—from collection to transformation to analysis. They provide a unified platform for managing data from multiple sources, including databases, applications, and cloud services.

If you want to reap the full benefits of data analytics transformation, you need to develop (and follow) a strategic approach.

How to think about data analytics transformation strategy?

In order to spark meaningful data analytics transformation, your company needs to establish a clear data strategy, select the right tools and technologies, and ensure that the necessary roles and responsibilities are in place.

Overall, data analytics transformation is not a one-time event, but a continuous process that requires ongoing investment, adaptation, and improvement. If you prioritize data analytics transformation and incorporate it into your organizational culture, your business can unlock the full potential of its data and drive better business outcomes.

Here’s a basic (but effective) four-step approach to data analytics transformation:

1. Define your data analytics objectives. Define the business objectives that your company wants to achieve through data analytics. Identify the key areas where data can add value, then set specific, measurable goals to guide the transformation.
2. Assess your data capabilities. Assess your current data capabilities—including data sources, quality, storage, and processing. Identify the gaps and areas of improvement to determine the necessary data analytics tools and technologies.
3. Design and implement a plan. Develop a plan to implement the necessary data analytics tools and technologies, establish data governance policies and processes, and train the necessary people to support data analytics. Monitor the progress of those goals and make necessary adjustments to ensure the transformation is successful.
4. Repeat steps 1, 2, and 3. While each of these steps will vary in complexity at any company, they’ll need to be repeated over time. As you gradually build, measure, and learn, each step can become more sophisticated and keep you moving toward data analytics that bring value to your business and speed up time-to-decision.

Now you know more than enough to get started, and the best place to begin is with your data technology.

What can I do right now?

Whether you’ve started to build a modern data stack, have an enterprise-grade set of tools that already power your analytics, or have no idea where to begin, you can benefit from a data orchestration platform like Shipyard. With it, you can connect your various data sources, move data to a central cloud warehouse, and make your datasets useful for analytics.

You can try it right now and see how it fits your data tools. Our Developer plan is free (and always will be). Sign up today to transform and refine your datasets into workflows in 10 minutes or less, reducing downtime, and speeding up business processes—all without needing a credit card to sign up.