Why data optimization matters (and tips for how to optimize your data)

In the digital age, data is king. However, not all data is created equal. In fact, much of the data resting in your network may be of poor quality. For accurate and reliable metrics, you have to “clean” the data—this is known as data optimization. Here’s why it’s so crucial for business success across all industries.

What is data optimization?

Data optimization refers to a set of practices aimed at improving data quality. The goal is to ensure the information is relevant, meaningful, and up to date. This way, when you extract data sets and analyze them as metrics and key performance indicators, the information is accurate and reflective of consumer actions, desires, and pain points.

Why does data optimization matter?

Acting on poor data quality steers your decision-making process in the wrong direction. This is a major problem for a growing number of companies. In a 2019 Experian report, 69% of Fortune 500 companies cited poor data as having a negative impact on their business. Furthermore, 30% of companies also named poor data as a significant roadblock to creating a positive customer experience.

Data optimization benefits

Data optimization yields accurate data. With high-quality information, your company benefits in the following ways:

• Provide customers with custom solutions based on their behavioral profiles
• Develop more precise A/B testing models based on the latest incoming data sets
• Predict customer churn with greater accuracy
• Accurately define your audience segments based on browsing and purchasing history

Examples of poor data quality

Poor data goes by many names, such as data pollution, or simply bad data. Whatever you call it, it’s a serious problem for any company collecting customer information. Examples of poor data include:

• Duplicate data: Companies unwittingly end up with duplicate data for a number of reasons. This may happen through incorrect manual entry, data migration, or batch imports.
• Incomplete data: Data is incomplete if not all of the fields are completed. For example, if a user profile is missing the birth date, this keeps the company from categorizing the customer into the appropriate audience segment.
• Inaccurate data: This is self-explanatory and includes improperly inputted emails or mobile numbers.
• Incorrect data: Not to be confused with inaccurate data, incorrect data refers to data input in the wrong location. An example is entering a number in a text-only field.

Big data optimization techniques

Data optimization isn’t just a singular technique. It comprises multiple methods, each enhancing data quality in increments. Here are some of the methodologies to integrate into your data optimization strategy.

1. Standardize the data

Create a standard for inputting data. Alternatively, you can set the system to recognize data entered in different ways. For example, a customer may enter his full legal name in one field, but then use a nickname in another field. The system may identify the data as coming from two separate individuals, leading to duplicate data and discrepancies. This can negatively impact customer experience. For instance, you may contact the same customer twice with identical sales propositions.

2. Use a cloud network

Siloed data is a major concern, especially when relying on legacy storage systems that have no way of integrating varying data sets. A more effective solution is to store the data in a secure cloud network. This keeps all data points within a singular location and readily accessible to authorized individuals. This significantly reduces latency in data processing.

3. Put the data to use in real time

In the modern age, data can become outdated real fast, sometimes in a matter of days or even hours, depending on the industry. Use data optimization tools with real-time data-processing capabilities. Automate and manage the data as it comes in and compile the information into a metrics report. For the user, the resulting graph or charts are from fresh data that came in minutes or even seconds ago.

4. Prioritize first-party data

Make first-party data your main data source. This is data that comes straight to you from the consumers, as opposed to third-party data that’s acquired from competitors or purchased from data vendors. Third-party data, even if sourced from consumers in your industry, may not provide data fully applicable to your company-specific model.

5. Transition away from manual data entry

Manually inputting numbers into an Excel sheet is not only time-consuming but also invites human error. The manual data entry error rate is roughly 1%. This may not seem like a lot, but when you have data coming in daily and non-stop, the errors do add up. Adopt a data optimization tool with automated data input features.

Make data optimization intuitive with Lytics

Your data holds a wealth of information. However, the extent it can assist your decision-making process is highly dependent on how well the raw data is optimized. Lytics provides a wealth of data analysis tools and solutions for maximizing data quality.