What is Data Strategy?: A Comprehensive Guide 101

Ratan Kumar • Last Modified: December 29th, 2022

Data Strategy | Hevo Data

Big Data has revolutionized the way organizations devise Products and Services for superior customer experience. However, most companies are still struggling to leverage Big Data for optimizing business operations. One of the primary reasons for such failures is the lack of an effective Data Strategy. While collecting a colossal amount of data has become more straightforward, the complexities related to Big Data have primarily increased due to Unstructured Data. Over the years, companies were only relying on Structured Data for insights generation. But with the rising Unstructured Data, organizations are having to revamp their Data Strategy for obtaining operational resilience.

Table of Contents


  • Basic understanding of Big Data.
  • Basic understanding of Structured Data.
  • Basic understanding of Unstructured Data.

What is Data Strategy?

Data Strategy is a set of choices and decisions that guides data-driven organizations to accomplish their business goals by leveraging Big Data. Just like Sales Strategies that help organizations in strengthening their Sales practices for successful outcomes, Data Strategies enable companies to improve every business decision. With Data Strategy, companies make a complete blueprint of every data workflow right from understanding business challenges to collecting data and generating insights for informed decision-making.

Why Data Strategy is Important?

Without a superior Data Strategy, companies often witness rising Data Silos, a collection of data that is rarely used. The rate at which organizations leverage data does not match up with the rate at which they collect Big Data. As a result, Data Silos have become common in companies that do not have a well-devised Data Strategy.

Unlike in the past, gathering a colossal amount of data does not provide companies with an edge in the Competitive Market until they know how to process information to garner insights for improving decision-making. To ensure companies make the most out of the data, it is crucial to have a robust Data Strategy that can support business decisions even in the rapidly changing landscape.

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Top 5 Data Strategy Goals

To craft an effective Data Strategy, you need to work towards several goals:

1. Decrease Operational Costs

With Data Strategy, the primary goal is to accomplish desired results while reducing Operational Costs as Data Strategy should be implemented to reduce the risk of failure with data-related initiatives. Companies target to succeed in achieving their goals in the very first attempt with proper Data Strategies.

2. Expedite Insights Delivery

Data Strategy should be used to avoid hindrances during the implementation of Tech Infrastructure. This is mainly related to assessing the needs and deploying suitable Data Warehouses and Data Analytics Tools. You can evaluate this goal by determining how Data Strategy improves your data culture. 

3. Boost Innovation

By incorporating Data Strategy, companies should be able to innovate as they can focus on building Machine Learning models rather than the overhead of Data Infrastructure and Data Requirements. With Data Strategy, companies should facilitate the foundation for further innovation.

4. Comply with Regulations

As Privacy Compliance Requirements have increased over the years, Data Strategy should assist companies in assessing the risk while processing Big Data. The goal should be to avoid penalties while working with sensitive data like financial information.

5. Improve Products and Services

Data Strategy should be devised in a way that can directly impact the bottom line of organizations. The target should be to ultimately enhance companies’ Products and Services.

What are the Key Elements & Best Practices for an Effective Data Strategy?

There are certain key elements of Data Strategy which are as follows:

1. Understanding Business needs

A Data Strategy should start with understanding business objectives by assessing the requirements of stakeholders and in-house teams. Until a problem is well understood, devising a Data Strategy will not be efficient enough to address all the challenges. Once all the business requirements are considered, companies need to set goals to measure their progress after implementing Data Strategies. With well-defined goals, it is easy to get feedback on the Performance of the Data Strategy with Periodic Reviews. 

Since business requirements, along with goals, are foundational for Data Strategy, companies should be critical of the needs put forth by various stakeholders. Some business challenges cannot be mitigated with data; trying to fix problems with data that could have been fixed otherwise might make Data Strategies ineffective. The best practice would be only to consider the issues that can be solved with data. For this, you would require data experts who can assimilate and segregate challenges that businesses should account for while devising a Data Strategy.

2. Data Requirements

After finalizing the business requirements, organizations have to collect the necessary data, which could potentially assist them in solving problems. Here, data can be both Structured and Unstructured data from in-house or external sources. Often in-house data do not cater to all the Data Analytics or Machine Learning requirements. Consequently, organizations have to either leverage APIs or scrape data from external sources. However, while scraping data, companies have to be mindful of the information they are collecting. With several data privacy regulations in place, organizations are mandated to only process information that users agree to provide.

3. Data Governance

In identifying and collecting data from data sources, organizations store raw data in Data Lakes. This allows companies to organize every data from a single source, providing better Data Governance. But, information in Data Lakes is not ready for Data Analytics tasks. Organizations must incorporate ETL (Extract, Transform, and Load) to make it analysis-ready across different departments. With ETL, companies can enhance data quality and store information in Data Warehouses to support mission-critical Data requirements.

Data Warehousing allows companies to effectively apply policies for Data Management and Data Governance for streamlining data delivery while maintaining data privacy. Without proper Data Governance in a Data Lake and Data Warehouse, organizations risk not harnessing data to its full potential. This results in boosting Data Silos, thereby hampering companies’ Data Strategy. Collecting data is essential for solving business problems, but if organizations do not have a plan to leverage data effectively, it will increase operational costs. While superior Data Governance can minimize costs, it also provides companies with better regulatory compliance. By controlling the access of data across enterprises, data administrators can preserve users’ privacy to abide by data-protection laws.

4. Technology

To handle data and leverage it for business needs, companies must embrace a wide range of tools and techniques. Deploying the right technology can be a differentiating factor for companies in the competitive market. Starting with Data Collection Tools to Data Storage and Data Analytics Platforms, companies have to ensure they deploy the best technology for superior performance. While Data Collection can be carried out with premium APIs of different applications, companies may have to rely on manual coding to collect data. 

After Data Collection or Data Profiling, the next step is to choose the right Data Lake service provider. At this stage, companies may embrace a hybrid storage approach to protect their sensitive data. On storing the raw data, the next ideal approach is to incorporate No-code ETL tools like Hevo Data for streamlining the most strenuous process of ETL processes. Along with transformation tools, companies have to implement Cloud Data Warehouses either from AWS, GCP, or Snowflake for storing the transformed information to support Analytics and Reporting tools. Data Analytics tools can be either Power BI or Tableau but could also be more than one due to varying needs.

5. Skills

Hiring talent is one of the essential tasks in accomplishing business goals with Data Strategy. While the data landscape has matured over the years to offer No-code tools, companies still need top talents who can perform tasks that require customized solutions for addressing data requirements. For instance, data is not often readily available to perform Machine Learning and Data Analytics. As a result, Data Engineers have to extract specific information from Unstructured Data and organize it into a Structured form for simplifying insights generated with Relational Data. 

While most of the Relational Data can be handled by No-code or low-code tools, Data Engineers and Data Scientists are essential in a company for working with Unstructured Data. Since more than 80 percent of data collected by organizations is Unstructured, Data Professionals are the most in demand in the data-driven world. With Data Scientists, companies can build state-of-the-art Machine Learning models to either build recommendation engines or AI chatbots, among others, for solving critical business problems. Machine learning and Deep Learning are crucial for companies that are harnessing the potential of Unstructured Data to gain a competitive advantage in the market.


Most data-related projects fail within organizations due to the absence of a Data Strategy that could orchestrate the entire team to obtain favorable results. Following a well-devised Data Strategy can offer operational resilience during changing requirements. Companies that are highly reliant on data must consider the approach discussed in this article to get desired results with their data initiatives. However, since business requirements can vary, the Data Strategy will always be a bespoke solution for different companies. Nevertheless, the elements to consider would be the same for almost every organization.

Most businesses today, however, have an extremely high volume of data with a dynamic structure. Creating a Data Pipeline from scratch and creating Data Strategies for such data is a complex process since businesses will have to utilize a high amount of resources to develop the ETL Pipeline and then ensure that it can keep up with the increased Data Volume and Schema variations. Businesses can instead use Automated Platforms like Hevo.

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