Data Optimization: Why it Matters & How to Optimize Your Data

Explore the crucial world of data optimization: enhance accuracy, boost efficiency, and unlock robust strategies for refined marketing and decision-making.

February 14, 2024
Written By
Rand Owens
Founding team member at Motive (Formerly KeepTruckin) and passionate about all things Marketing, RevOps, and Go-To-Market. VP of Marketing @ Polymer Search.

Data Optimization: Why it Matters & How to Optimize Your Data

You learned the fundamentals of data analysis.

You also have a better grasp of how the data collection process works because we covered it in the previous chapter.

Now, it’s time we take a deep dive into data optimization.

We discuss the importance of data optimization, techniques, tips, etc.

Table of Contents

What is data optimization?

Why data optimization is important

7 Data optimization techniques

Data cleaning techniques

Automated data processing tips

Data optimization FAQs

Leveraging Polymer for enhanced data optimization & views

What is data optimization?

One of the fundamentals of data analysis is data optimization—the process of improving or refining data for better accuracy, efficiency, relevance, and usefulness. 

The data optimization process involves data sorting and cleaning to remove redundancies, inconsistencies, and errors, improving its relevance and quality. 

Data optimization is designed to make data work better for an intended purpose, including refining existing marketing strategies, enhancing operational efficiency, and improving decision-making.

Why data optimization is important

Survey data shows that marketers spend an average of 31% of their budgets on poor data optimization, costing them millions. 

The solution?

Use a reliable data optimization framework.

Effective data optimization simplifies accessing, extracting, assessing, and using critical information efficiently. It also includes the following benefits:

Address consumer demands

Modern consumers expect accurate, fast, and extensive information from businesses. 

Big data optimization helps you address this need by maximizing your data collection efforts and giving you the relevant, usable information to promptly respond to customer queries and demands. 

Optimizing your data equips you with the information your business needs to provide real-time services that fulfill and even exceed customer expectations.   

Reduce potential risks in decision-making

The survival and growth of your company may hinge on its ability to swiftly react to the emerging opportunities and threats within your current business landscape.

Your company’s best chance of surviving is timely access to accurate information, which is possible through effective data optimization.  

Data optimization gives you organized, filtered, and ready-to-use data that your company decision-makers can easily access and leverage to make informed decisions and minimize potential risks. 

Strengthen business reputation 

Poor data quality can lead to inconsistencies and mistakes.

It can lead to uncertainties that create potential conflict with business partners, customer interaction and transaction delays, and employee and workflow inefficiencies.

Data optimization is key in maximizing data quality and reducing your company’s vulnerability to the above challenges while boosting your business’s overall reputation.

Maximize staff and infrastructure returns

According to the latest data, only 39.7% of businesses manage big data as a commercial asset. 

Data optimization tools and technologies can give you visibility into your server, network, system software component, and storage performance. 

Access to this information helps simplify your planning, forecasting, and troubleshooting, resulting in more efficient use of your software and hardware resources.  

7 Data optimization techniques

Below are tried and tested data optimization techniques that seasoned data analysts use.

1. Prioritize your first-party data

Make first-party data (information directly from customers) your main data source. 

Prioritizing first-party data instead of third-party information, such as those acquired from data vendors or competitors, gives you more reliable data that applies to your company’s business model. 

2. Standardize your data

Set a standard for inputting your data or use a system or software that can identify data entered in various ways. 

For instance, customers can input their legal names in one field and use another to enter their nicknames. 

Without a standard system, the software can accept the data as coming from two separate people, leading to discrepancies and duplicates. 

It can ruin the customer experience if your sales reps contact the same person twice with the same sales proposition. 

Standardizing your data eliminates this problem. 

3. Use data deduplication

Data deduplication identifies and eliminates duplicate copies or entries of unstructured data. 

Applying data deduplication through your data optimization process can improve data quality, lower storage costs, and make data management more efficient. 

4.  Leverage metadata

Metadata gives information about other data, such as the data’s structure, context, and format. 

Using metadata for optimization helps organize and classify your unstructured data for easy retrieval, searching, accessing, and management. 

5. Set data retention policies

By implementing a data retention policy, you can reduce storage costs and adhere to data privacy regulations. 

A data retention policy includes guidelines for how long your organization should retain unstructured data based on regulatory, business, and legal requirements. 

6. Visualize your data

Visualizing your data turns it into graphical representations that communicate trends and insights within your unstructured data. 

The data optimization technique helps you spot anomalies, opportunities, and patterns to shape and inform decision-making. 

7. Opt for a hybrid cloud

Tiering your unstructured data in the cloud helps you manage your data more efficiently at a more affordable cost. 

The approach categorizes your data based on its value, importance, and how frequently it is accessed. 

The most critical and frequently accessed data gets stored in high-performance storage tiers, while less important and infrequently accessed data are placed in lower-cost storage tiers. 

It lets you optimize your storage costs while ensuring your crucial data is ready to access when necessary. 

Also, tiering your unstructured data in the cloud allows you to scale your storage resources up or down to meet fluctuating demands. 

It can save you costs and improve your data accessibility while optimizing your data. 

Data cleaning techniques

Below are the essential data-cleaning techniques to prepare and enhance your data's quality. 

Convert to the appropriate data types

Data types, such as numbers, strings or texts, and boolean (true or false) represent the information to access and process. 

Various data types come in when collecting data. 

For example, some numbers are numeric, while others are written as texts. This disparity can throw off your statistical calculations and analysis.

Take out unnecessary values

Irrelevant and unnecessary data is expected during the data collection stage. 

If you don’t remove unnecessary data, it can slow down your data analysis and lead to inaccuracies when presenting data insights. 

Remove irrelevant values like URLs, HTML tags, and boilerplate text to ensure clean and top-quality data. 

Remove any data that isn’t relevant to your data analysis. Doing so helps you ask the right data analysis questions and get accurate results. 

Additionally, remove outliers or unusual data values in your dataset. 

Outliers are often caused by natural variation and data entry and sampling errors. 

Remember to eliminate outliers likely impacting your data's validity and reliability. 

Set a clear format

You can use various formatting styles to structure your raw data. 

However, remember that data formats can impact how data analysis tools, such as automated data analysis software with sophisticated data quality tools, extract and process the information. 

Consider importing information into specific files when storing data in multiple formats. Doing so helps prevent errors and confusion during your data processing.  

Automated data processing tips

Here are some quick tips when adopting automated data processing into your data optimization process. 

  • Set clear objectives. Identify your data processing tasks’ goals by understanding what you want to achieve. Objectives such as data analysis, reporting, or cleansing will guide your automated data processing efforts. 
  • Implement seamless data integration. Automate data integration from various sources. Data should flow smoothly from spreadsheets, APIs, databases, and other sources into a single repository. 
  • Set up automated data processing schedules. Configure automated schedules for your data processing tasks. For instance, set your data processing tool to automatically process information daily, weekly, real-time, or on a custom schedule.
  • Aim for a scalable process. Ensure your automation process scales as your data volume grows. Optimize data processing performance through cloud-based resources, parallel processing, or distributed computing. 
  • Document and log your automation process. Keep detailed documentation of your automated data processing tasks and workflows. Maintaining data processing activity logs helps facilitate your auditing and troubleshooting.    

Data optimization FAQs

Below are the commonly asked questions about data optimization. 

What does business data optimization mean?

Business data optimization refers to the process of extracting, analyzing, and storing company data for maximum efficiency. 

There are various data optimization tools to optimize your data, such as modern automation solutions. 

What is process analysis and optimization?

Process analysis and the following process optimization are sustainable and continuous approaches to boost the efficiency of current business and production processes, including associated resources. 

How do you start optimizing data?

The main methods of optimizing data include tuning an optimization algorithm, standardizing format, fixing data errors, using the latest technology, and removing latency in data processing. 

Optimized data enhances the data processing rate and improves the accuracy of your results. 

Leveraging Polymer for enhanced data optimization & views

To support your data optimization efforts, create data visualizations.

Data visualization contributes significantly to your data optimization endeavors since it enhances your data comprehension.

Most data analysts use advanced data visualization tools to avoid the time-consuming tasks of creating reports and dashboards from scratch.

One such tool is Polymer, a modern Business Intelligence (BI) software that lets you build visualizations and dashboards in minutes without a complicated setup or a steep learning curve. 

Polymer offers easy integrations with multiple sources, including Google Analytics 4, Airtable, Google Ads, MS Excel, Linear, and more. 

Once connected, you can fetch data from your source instantly and filter and sort your data to prepare it for visualization. 

Then, add graphs, charts, trends, maps, and more insights. Polymer’s AI can auto-generate the visualizations based on your imported data.

Customize the generated visualizations with Polymer’s intuitive and easy-to-use Block Editor. 

You can build complete reports and dashboards with a few clicks. 

Polymer also offers data analysis examples with ready-to-use templates that you can easily customize with your data and preferred visualizations. 

Simplify and streamline your data optimization through data visualization with Polymer. 

Get started with Polymer for free for a first-hand experience of the software’s benefits. 

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