For data to be useful, it first has to be usable. Dirty data denies your organization the facts it needs to build business strategies. Improving your data quality is the first step in improving your decision-making process, operational efficiency, customer satisfaction, competitive edge, and more. Below, we’ll discuss the definition of data optimization, its benefits, and existing techniques.
Highlights
- Data optimization is the process of improving the usability of data for analysis, insights, and other business decisions
- Types of dirty data that require optimization include duplicate data entries, incomplete data, inaccurate data, outdated data, and inconsistent data
- Improving business decisions through data optimization also enhances customer satisfaction, operational efficiency, and competitiveness
- The most crucial part of data optimization is data cleansing, but other techniques include data partitioning, data standardization, first-party data prioritization, metadata, data retention policy implementation, and data visualization
What is Data Optimization?
Data optimization refers to a set of techniques aimed at improving the quality of an organization’s data. These techniques streamline data collection, storage, processing, and utilization to ensure that data is uniform, meaningful, and updated — and thus usable for analysis, insights, and other business decisions.
Benefits of Data Optimization
Data optimization aims to make data easier to interpret. Achieving this goal yields multiple business benefits.
Improved Decision-Making
As mentioned in our section on when to optimize data, eliminating poor-quality data gives your organization the clarity it needs to make sound business decisions. Keeping information accurate, organized, and up-to-date provides factual evidence for comprehensive strategy-building.
Enhanced Operational Efficiency
Cleaning and organizing your data makes it easier to assess. Teams can readily access the necessary information without wasting time sifting through noise. Reduced downtime then leads to enhanced productivity and speedier workflows
Increased Savings.
Making decisions based on poor-quality data costs you time, resources, and labor. Optimizing your data reduces mistakes and speeds up workflow efficiency, thus increasing savings.
Increased Customer Satisfaction
Data optimization adds clarity to your customer data. With organized customer information readily accessible, you can customize products, services, and recommendations according to customer preferences. This increases their satisfaction and loyalty.
Heightened Competitive Edge
The above benefits (improving strategic business decisions, speeding up workflows, and increasing the loyalty of your target market) give you a greater market edge. Having the right information also makes it quicker for you to adapt to any changes in market trends.
Increased Scalability
As your business grows, the volume of your data will inevitably increase. Adopting the correct data optimization techniques will make it easier for your systems to accommodate growth.
Increased Security
A significant part of data optimization is ensuring compliance with data protection laws and regulations. It provides increased privacy and security, protecting sensitive organizational data from cyber-attacks.
Enhanced Business Reputation
Satisfying your customers through relevant data-driven insights leads to increased loyalty. Additionally, improving operational efficiency allows you to work more smoothly, leading to better relationships with clients, vendors, stakeholders, and other business partners.
When Does Data Need to Be Optimized?
Data must be optimized when quality is too poor to yield useful analysis or insights. This poor-quality data is called typically dirty data. Below, we’ve listed a few examples of dirty data and explain why they require optimization.
When There Are Duplicate Entries
Duplication can occur when data is improperly collected through incorrect manual data entry, batch imports, or mismanaged data migration. Duplicate entries clutter your database and paint an inaccurate big picture, leading to confusion and misguided decision-making.
To illustrate, let’s say you’re running a survey to assess the market fit for a future product. Duplicating respondent entries can create an inflated perception of demand. Should your business strategy rely on this information, you’ll end up creating more products than people actually want to buy.
When Data Is Incomplete
Improper data collection sometimes fails to capture all relevant fields. With critical information missing, it becomes difficult to formulate effective decisions.
An example would be when your website tracking tool does not collect data on your visitors’ location. If your products are location-bound, you won’t have the data to assess whether your visitors can become customers.
When Data Is Inaccurate
As with any type of dirty data, incorrectly inputted data will give you an inaccurate big picture or force you to base decisions on incorrect information.
Let’s say a customer signs up for your newsletter with the wrong email address. You waste resources sending newsletters to an unattended inbox.
Another possibility is that multiple customers may confuse one type of entry with another with a similar name. For example, customers input their location as Portland, Oregon, when they mean Portland, Maine. Should this occur too many times, you’ll have an inaccurate picture of where your customers are from.
When Data Is Outdated
Should you fail to update your records, the analyses, insights, and decisions you generate will be irrelevant to your organization’s current state. One example is when you fail to update phone numbers or email addresses. Because you lack updated data, you miss opportunities to contact them properly.
When Data Is Inconsistent
Data that fails to follow a standardized format, such as discrepancies in format or unit, will create confusion. For example, you build a survey that asks customers to input their annual salary in CAD. They instead input their salary in USD. This causes you to miscalculate their buying power significantly and offer products that are out of their price range.
Characteristics of Optimized Data
You can measure the usefulness of data by paying attention to the following attributes:
- Validity: Is the information relevant to the problem you want to address?
- Integrity: Has the data been tampered with by outside sources?
- Accuracy: Is all information correct?
- Completeness: Is all information available?
- Time-relevance: Is the data up-to-date?
- Uniformity: Do entries follow the same format across sources, databases, and applications?
Data with these attributes yield more valuable analysis and interpretations.
Let’s say you want to restock inventory with the goal of ordering the exact right amount of items that align with demand. To forecast demand, you first need to understand which factors impact it. This might include data on what products customers ordered before, the volume of previous orders when they ordered, and responses to any ad campaigns you might have launched.
If data is inaccurate or tampered with, you will inevitably get an inaccurate analysis. Painting the clearest possible picture with completeness will get you closest to an accurate demand forecast. Meanwhile, time-relevance helps you deliver what your customers currently want.
Meanwhile, non-uniform entries will slow down the process of data analysis. For example, you would need to identify an estimate for the number of customers who are likely to purchase based on ad responses before you aggregate the data with the number of customers who are likely to purchase again based on historical data.
Data Optimization Techniques
There are multiple techniques for optimizing data. Which techniques you employ depend on your business needs. Generally, the process starts with the elimination of dirty data through data cleansing and then continues with the implementation of standards that keep collected data organized and accessible. We name common data optimization techniques below.
Clean Data
The first step in data optimization is data cleansing. Data cleansing, also known as data cleaning or scrubbing, is eliminating all dirty data.
It involves the following steps:
- Inspect data: Audit your data sets to identify lingering issues. Check whether data is inputted and formatted correctly, then track relationships between elements.
- Deduplicate data: Once you’ve inspected your data, scan for redundancies and eliminate duplicates.
- Remove inaccurate or irrelevant data: Address all erroneous entries, such as typos and incorrect information. It also helps to remove data that is irrelevant to the problem you intend to solve. For example, if your product exclusively ships to Canada, you should remove records of US-based customers.
- Verify data: Once you’ve cleaned your data, run another inspection to ensure everything is accounted for.
- Validate relevance: Check your data and validate its relevance to your goals. Ask yourself if the available evidence is usable for affirming theories or insights.
Partition Data
Data processing speed hinges on the capacity of your hardware. The more data you need to run, the more energy you demand from your hardware. To improve speed, you must break large amounts of data into smaller datasets.
Data partitioning, as the process is known, allows your hardware to process one set of data at a time. With fewer data sets competing for your hardware’s resources, data processing speeds up.
Standardize Data
Data standardization is the process of creating a uniform data format that applies to all databases, applications, and sources. Converting your data into this format makes it easier for computers to read.
Below are a few examples of data standardization:
- Categorical consistency: This process assigns a uniform label for multiple versions of the same piece of information. For example, “U.S.A.” reads the same as “US” and “USA.”
- Scale adjustment: This sets a standard for differing units of measurement. For example, all entries written in feet would be converted to meters.
- Date formatting: This process converts dates into a uniform format, such as YYYY-MM
Prioritize First-Party Data
While data from third-party services is useful, the data you collect directly from your customers will always be most relevant to your organization. Because you have more control over the information you collect, first-party data provides a more accurate picture of your customer needs and business performance.
Use Metadata
Metadata, such as author, date created, date modified, and file size, provides basic descriptions of unstructured data. Assigning metadata tags helps data teams search, sort, classify, process, and retrieve unstructured data faster.
For instance, algorithms will have difficulty sorting through the raw images. If you add metadata tags, such as date created, file size, color, and origin, you and your computer will have an easier time sorting and making sense of your files.
Set Data Retention Policies
Data retention policies dictate how an organization should hold onto data. An effective data retention policy would outline the following:
- What data to retain
- The standard format for retained data
- Data retention periods
- Standardizing whether to archive or delete data that has exceeded the retention period
- Authorization for disposing of data
- Standard procedures for policy violations
Implementing data retention policies keeps outdated data off your systems. It also frees up storage, reducing costs and increasing operational speed.
Leverage Data Visualization
Data visualization refers to the process of designing intuitive visual representations of quantitative or qualitative data. It aims to make trends easier to identify, analyze, and interpret.
Some data visualization formats you can take advantage of include:
- Bar graphs: Useful for visualizing comparisons between categories
- Line graphs: Useful for visualizing continuous data over a defined period of time
- Pie charts: Useful for comparing parts to a whole
- Maps: Useful for comparing trends per location
- Infographics: Useful for communicating complex messages efficiently
Begin Your Data Optimization Journey With Bronson.AI
Bronson.AI’s audit services use AI solutions to identify lingering data issues, such as unmanaged volume, poor quality, and inconsistency. The same AI tools clean up and standardize poor-quality data across all relevant sources.
Benefits of Bronson.AI audits include optimized data management, enhanced data quality and consistency, and strengthened data security and privacy. Bronson.AI will use advanced storage solutions to handle large datasets then provide AI-powered data quality audits on a regular basis. It also uses encryption, access control measures, customized data security strategies, and robust cybersecurity frameworks to safeguard sensitive information.
Leverage data optimization to empower your business’ data-driven decisions. Read the Bronson.AI audit page for more information.
Frequently Asked Questions
What is the purpose of data optimization?
The purpose of data optimization is to maximize the quality of your data for interpretation, analysis, and other business decisions. Successful data optimization leads to multiple benefits, including improved decision-making, operational efficiency, customer satisfaction, and scalability.
What is the best data optimization method?
There is no “best” data optimization method. All techniques will help you enhance the quality of your data for faster operational efficiency. However, data cleansing is the most crucial data optimization technique. Ignoring data cleansing will leave data dirty in your system, leading to slower operations, inaccurate analyses, and miscalculations in business decisions.
What is an example of data optimization?
A Canadian telecommunications company required Bronson.AI’s assistance in translating quarterly survey data into a Tableau-compatible format.
First, Bronson.AI cleansed the data by eliminating all undesired and superfluous records and fields and then conversions to standardize units of measurement across the dataset. It also created new data classifications that simplified data categorization and presentation within a Tableau environment. Finally, Bronson.AI used dashboards to visualize results. Read more about Bronson.AI’s telecommunications agency data transformation project.
What is the difference between data optimization and data modernization?
The primary purpose of data optimization is to improve data quality and useability. It uses multiple techniques to make data cleaner and more accessible. Meanwhile, data modernization refers to processes that upgrade the systems, infrastructures, and technologies used to support business data. It aims to adapt data systems to the evolving demands of the modern business landscape.
