14 Essential Big Data Do’s & Don’ts for 2024

Big data is used across multiple business domains as data analytics, artificial intelligence (AI), and machine learning (ML) continue to become mainstream. Analytics can extract the real value out of large volumes of data, which can be structured, unstructured, or semi-structured.

Big data analytics has become a cornerstone for decision-making in industries ranging from healthcare to marketing. The insights gained from data analytics can help organizations in their decision-making process.

However, the real benefit of big data is achieved only if it is managed properly.

In this article, we explore how to use big data in business and provide you with 14 key do’s and don’ts of big data initiatives.

Table of Contents Table of Contents

1. Key Takeaways
2. Handling Big Data Effectively: Top 10 Do's
3. The Bottom Line
4. FAQs

Show Full Guide

Table of Contents

1. Key Takeaways
2. Handling Big Data Effectively: Top 10 Do's
3. The Bottom Line
Show Full Guide
4. FAQs

Handling Big Data Effectively: Top 10 Do’s

1. Do Know the Purpose & Starting Point

A clear understanding of the purpose of data collection and the right starting point is crucial for the success of any project implementing big data for business. Stakeholders should also identify what value they want to extract and how it will affect business decisions.

Before diving into collecting data that may not be useful or complete, clarify what you aim to achieve. Is the objective to improve customer retention, streamline business operations, or predict market trends? Clear objectives help you focus on collecting and analyzing relevant data and avoid wasting resources.

To start with, the objective should be to identify the most promising use cases of big data for the business and the elements that make them appealing.

After this, proper planning is required to apply data analytics techniques to these use cases and extract valuable insights that can drive business growth.

Organizations should always start with a simple and easy-to-implement application of big data as a pilot project.

Avoid gathering data “just in case,” as this can lead to unnecessary costs and overwhelming datasets. More data is not necessarily better; it is actionable data that matters.

2. Do Prioritize Data Quality & Hygiene

Data quality and hygiene are the foundation of any successful big data strategy. Ensure that the data you collect is of high quality, meaning it is relevant, accurate, complete, and up to date.

Clean and well-organized datasets are essential for reliable insights. Otherwise, even the most sophisticated analytics tools and algorithms can produce misleading results and misguided decisions.

It’s important to regularly audit your datasets to ensure they maintain integrity. This allows you to identify and rectify errors, such as duplicate entries, incorrect information, or incomplete records.

Standardize data entry processes so that data is entered, formatted, and updated consistently.

You should also identify and delete redundant or outdated data so that it remains timely.

3. Do Use Advanced Analytics Tools

Leveraging advanced analytics platforms is key to successful big data implementations. Machine learning and AI-based tools can efficiently compile and analyze large volumes of information to uncover patterns and big data trends to extract deeper insights.

Use data visualization methods such as charts, graphs and dashboards to make data easier to understand. Clear visuals that are not overloaded with information can effectively communicate insights and action points to technical and non-technical stakeholders alike.

However, it’s important not to rely solely on automated tools to deliver desired results without human expertise.

Even the best data tools require skilled professionals to interpret results. Blindly relying on algorithms without human oversight can result in errors or biased outcomes.

4. Do Evaluate Data Licenses Properly

Data is the fuel for any big data and analytics projects. So, it is very important to protect your data from misuse. Proper licensing terms and conditions for big data usage should be in place before granting data access to any vendor or third-party user.

There will also be other critical parameters in the license agreement. If there is any failure in licensing, the resulting data loss and misuse will have an undeniably negative impact on the business.

Don’t rely on data scientists to ensure legal compliance. Their role is collecting information and generating reports derived from the data—checking whether the relevant rights, licenses, and consent have been obtained is not.

5. Do Allow Data Democratization

Data democratization is a continuous process in which everyone in an organization has access to data. The people in an organization should be comfortable working with the data and expressing their opinions confidently.

Data democratization helps organizations become more agile and make data-informed business decisions.

6. Do Build a Collaborative Culture

In the game of big data in companies, mutual collaboration among different departments and groups in an organization is important.

A big data initiative can only be successful when a proper organizational culture is built across all the layers, irrespective of their roles and responsibilities. Promoting a culture of data sharing to maximize utility is key.

The management of an organization should have a clear vision for the future and must encourage new ideas.

Don’t silo data. Restricting access to data to one team or department limits its potential value. All employees and their departments should contribute relevant data and be allowed to find opportunities and build proof of concepts to validate it.

Integrating insights across teams—such as marketing, sales, and operations—encourages innovation and can lead to the creation of more comprehensive strategies.

7. Do Evaluate Big Data Infrastructure

The infrastructure part of any big data project is equally important. The volume of data is measured in petabytes, which are processed to extract insight. Because of this, both the storage and the processing infrastructure have to be evaluated properly.

Data centers are used for storage purposes, so they must be evaluated in terms of cost components, management, backup, reliability, security, scalability, and many other factors.

Similarly, the processing of big data and the related technology infrastructure have to be checked carefully before finalizing the deal.

Cloud services are generally flexible in terms of usage and cost. Established cloud vendors include heavy hitters like AWS, Azure, and GCP, but many more are also on the market.

8. Do Monitor Performance Metrics

Tracking key performance indicators (KPIs) to measure the impact of big data strategies allows you to evaluate their effectiveness and adjust initiatives in real time to ensure they meet objectives.

You can measure progress, identify bottlenecks and areas for improvement, enhance accountability, and demonstrate the value of your data initiatives.

A considered approach to monitoring clear metrics ensures that your efforts align with your business goals and adapt to changing conditions.

9. Do Ensure Ethical Data Use

Transparency about how you collect, store, and use customers’, clients’, and partners’ data builds trust.

Ethical practices can strengthen these relationships and contribute to the growth of your business.

Misrepresenting data to fit a narrative can damage an organization’s credibility and lead to severe repercussions. You should always maintain honesty in your data analyses and reporting.

10. Do Invest in Employee Training

Big data applications are only as strong as the people who manage, analyze, and use the data.

Equip your team with the skills needed to work effectively with complex datasets. Continuous learning bridges skills gaps and helps employees stay updated on the latest tools and techniques. In turn, they ensure your organization remains ahead of the curve.

Training empowers employees to integrate data into their work, fostering a data-driven culture in which insights and evidence guide actions.

Employees who understand data are better equipped to identify opportunities, solve problems, and innovate within their roles.

1. Don’t Go All In

It is risky to start all your big data projects at the same time. This approach will likely only lead to partial success or total failure. Organizations should plan properly before starting big data initiatives rather than going all in or taking a leap of faith. It is always recommended to start with a simple, small, and measurable application.

You should evaluate the usefulness of the tech you are considering based on the size of the project and the organizational budget. Lots of open-source platforms are available for free to run pilot projects. Small and mid-size organizations can explore open-source solutions to start their big data journey.

Once the pilot is successful, then it can be implemented on a larger scale and in multiple big data applications. It is key to take the time to develop a plan and to select the pilot project carefully.

2. Don’t Get Lost in a Sea of Data

Good data governance is central to the success of big data projects. Organizations should plan a proper data collection strategy before implementation. There is a common tendency to collect every piece of a business’s legacy data, but all this data may not be relevant for efficient and actionable analytics.

Don’t depend solely on technology to deliver the desired results from irrelevant or disorganized data. It is important to identify the business use cases first and determine where and how the data will be applied to understand what data is required.

Once the data strategy is well-defined and directly connects to the target business application, you can plan the next step of implementation. After this, new data can be augmented to improve the model and its efficiency.

3. Don’t Neglect Security

Data security is a critical aspect of big data projects. In any big data scenario, petabytes of data are pulled from different sources and processed to provide the input to the analytical model.

The output of the analytics tools provides valuable insight to the business. Once raw data has been refined and meaningful information has been mined from that raw data, then the confidentiality, integrity, and availability (CIA) of that information becomes critical.

When data contains critical business information, it must be secured from external threats. Data security must be planned as part of the big data implementation life cycle.

Protect sensitive data with robust security protocols, encryption, and access controls to avoid the risks of big data losses.

Cybersecurity breaches can harm your reputation and lead to legal penalties, and failing to comply with regulations like GDPR or CCPA can incur hefty fines.

You should always obtain user consent and follow regulations for data usage.

4. Don’t Assume Immediate ROI

Big data projects often require time to deliver tangible results. Be patient and set realistic expectations for return on investment (ROI).

As your business grows, your data needs will expand. Implementing systems that can scale up to handle increasing data volumes and complexity will ensure your data infrastructure remains effective and continues to deliver tangible results.

Opting for quick fixes might save time initially but can cause bottlenecks as data demands increase.

The Bottom Line

There is no specific path to success for big data implementation. It is a combination of planning, strategy, approach, and various other factors that leads to success.

Each organization has a specific goal to achieve, so the strategy should be planned accordingly, pilot projects must be chosen with care, and the resulting information must be protected and treated properly.

FAQs