Moving From Enrichment To Data Processing

What Is Data Processing

Data processing involves the systematic collection, manipulation, and transformation of raw data into meaningful information. This process is crucial for SaaS brands as it enables them to derive insights and make informed decisions based on unified data.

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6 Stages Of The Data Processing Cycle

When we work with SaaS brands to process their data for actionable and smart decision-making, we follow these six steps to ensure you get the most out of your collected raw data.

Step 1. Data Collection

The initial step involves gathering raw data from various sources such as websites, databases, or surveys. It is crucial to ensure that the collected data is accurate, complete, and relevant to the objectives of the analysis to avoid selection bias.

Step 2. Data Preparation

Once the data is collected, it undergoes cleaning and organization. This stage, often referred to as data cleaning, involves checking for errors, removing duplicates, and enriching the dataset with additional information. The goal is to create high-quality data that can be reliably used in subsequent processing steps.

Best Deduplication Software For HubSpot

Two of our favorite software platforms to use for deduplication in HubSpot are ZoomInfo, formerly RingLead, Koalify, and Insycle.

ZoomInfo Deduplication Software

Begin your data cleansing process with custom, hands-off record matching and deduplication. Set up automated matching criteria to connect leads, contacts, and accounts to their correct parent accounts. Maintain ongoing data integrity with customizable deduplication software and record merging.

Visit ZoomInfo

Koalify Deduplication Software

Built for HubSpot and HubSpot only. Use directly inside HubSpot, no extra login needed. Users can get instant duplicate suggestions for contacts and companies via the CRM cards. The Koalify cards facilitate merges that are over 3 times faster than the built-in merging process.

Visit Koalify

Insycle Deduplication Software

Identify duplicate contacts, companies, and deals by any field and merge in bulk. CSV reports, flexible rules for picking master, 'preview mode', and automation. Identify incomplete, improperly formatted, inaccurate data and fix it. Remove redundant data, fake contact emails and phone numbers, and other bad data.

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Step 3. Data Input

The cleaned data is then input into a processing system. This can be done manually or through automated methods, such as importing data from external sources. The data must be in a format that the processing system can understand.

Step 4. Data Processing

At this stage, the input data is transformed and analyzed using various techniques such as filtering, sorting, and aggregation. The choice of methods depends on the desired outcomes and insights sought from the data. This step is where raw data is converted into usable information that can be used to make smart decisions.

Step 5. Data Output & Interpretation

The processed data is presented in a readable format, such as reports, graphs, or dashboard visualizations. This stage also involves interpreting the data to extract valuable insights that can inform decision-making.

Data Mapping

Real data output and interpretation also includes data mapping. SaaS brands each have unique software tech stacks, mapping how data connects and flows from one platform to another can ensure that interpretations are apples to apples to avoid one department sharing one insight and another department sharing something conflicting, mapping and centralizing your data within your HubSpot CRM ensures that the truth is in the pudding.

Step 6. Data Storage

Finally, the processed information is securely stored in databases or data warehouses for future retrieval and analysis. Proper storage ensures data longevity and accessibility while maintaining privacy and security.

Bring Your Data To The Modern Age

In summary, data processing is a foundational element in the modern digital landscape, enabling SaaS brands to harness the power of data for improved decision-making and operational efficiency. Unprocessed data is useless, it's just a bunch of numbers in a database, once organized and unified, you're able to make smart decisions as to what your SaaS brand should be doing next.

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Moving From Data Processing To Analytics

Data analytics uses data to extract meaningful insights, which can inform decision-making and drive business strategies. This involves identifying patterns, trends, and correlations within data sets, ultimately transforming data into actionable insights.

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