Acme Data, San Ramon, CA (2026)

06/24/2026

𝗖𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗻𝗴 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 𝗔𝗳𝘁𝗲𝗿 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻𝘀

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟭𝟮 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

A successful software company in the financial space pursued a strategy of acquiring companies that sold complimentary products. Instead of acquiring competitors to reduce competition and gain market share, they wanted to expand their product offering and widen their market.

The acquirer did purchase several companies with complimentary products and treated them as separate entities for years. Each used different support applications and had different call centers. Eventually the acquirer decided to consolidate three separate CRM applications into one. This provided several benefits but also presented a challenge. The customer bases of the acquiring company had significant overlap with the companies they were purchasing. Consolidating three CRM applications, each with millions of records, would create a high volume of duplicate records.

𝗧𝗵𝗮𝘁’𝘀 𝗪𝗵𝗲𝗻 𝗧𝗵𝗲𝘆 𝗖𝗮𝗹𝗹𝗲𝗱 𝗨𝘀

Acme Data provided the enterprise expertise and software needed to address this issue. The solution involved large scale data migration, combined with match and merge of records that originated from different brand CRM applications, all while managing complex survivorship and parent child record reconciliation.

𝗪𝗵𝗮𝘁 𝗪𝗲 𝗗𝗼

Our primary focus is the implementation of our data quality platform, Data Studio, but we’ve frequently worked with Global 2000 companies to solve complex, large scale data problems.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

Facebook

06/17/2026

𝗪𝗵𝗲𝗻 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱𝗻’𝘁 𝗠𝗲𝗿𝗴𝗲 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: 𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟭𝟭 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

A large software company had a large duplicate record problem with a twist. The number of duplicate records in their CRM system was in the millions. For the bulk of these duplicates, once found, they could simply merge them. But for a minority of these records, when found, they could not merge them or they had to be very careful about how the records were merged.

𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺

This company sold several products, many of which managed financial records. The financial component of one of their products required the company to capture legal documentation about their customers. Once captured, the company was legally bound to not change the legal record or the record’s lineage.

𝗧𝗵𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁

Because the legal records and their lineage had to remain intact, they had two options for duplicate customer records that had this legal documentation.

1) If only one of the duplicate customer records had the associated legal document, the merge could take place as long as the customer record with the legal document was the survivor. In this case, the legal document was preserved as was its lineage.
2) If both duplicate customer records had the associated legal document, the merge had to be aborted.

𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻

They needed a data quality platform that supported sophisticated, powerful survivorship and merge disqualification rules.

𝗧𝗵𝗮𝘁’𝘀 𝗪𝗵𝗲𝗻 𝗧𝗵𝗲𝘆 𝗖𝗮𝗹𝗹𝗲𝗱 𝗨𝘀

Data Studio from Acme Data allows a business user to define rules using point and click functionality to achieve the following:

• Identify when duplicate records meet criteria which should prevent a merge
• Select which record, among two or more duplicate records, will survive a merge (and therefore, which one will not)
• Once a survivor is selected, examine the non-survivors to cherry pick data values that should be copied to the survivor

And to optimize the ROI, do all of the above automatically.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

06/10/2026

𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗚𝗿𝗼𝘂𝗽 𝗼𝗳 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 𝗪𝗲 𝗘𝘃𝗲𝗿 𝗦𝗮𝘄

𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀: #𝟭𝟬 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

A large software company—one of the "Big Two" battling for dominance in their industry—faced a unique challenge: they had as many as 1,600 duplicate records for a single customer. This issue stemmed from an integration between their accounting software and CRM system. For every invoice, the accounting system created a new customer record in their CRM system. As undesirable as this was, it went on for a long time.

𝗧𝗵𝗮𝘁’𝘀 𝗪𝗵𝗲𝗻 𝗧𝗵𝗲𝘆 𝗖𝗮𝗹𝗹𝗲𝗱 𝗨𝘀

They had tried to address this manually, but the record volume was high and after doing a little math, they knew they needed automation.

When we arrived, we did a couple of tests and surfaced a particular challenge. It was known that each non-surviving record that merged into a survivor, brought child records with it. With each merge, the number of child records under the survivor grew. What we learned was that we were creating several extremely short and wide pyramids with one record at the top and hundreds or even thousands of records under them.

This was a problem. Each merge process examined every child record to see how it needed to be reconciled. As the child records grew, each merge process took longer. The processes would slow down and take weeks to finish (this was an on-premise CRM system).

𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻

The math was pretty straight forward, but still, it was an interesting case. We wrote a small program which broke up the large duplicate groups into lots of smaller groups. First, pairs of records that had never been merged were merged. Then records that had been merged once were merged with other records that were merged once. And so on. This ensured that records with the smallest number of child records would always merge first. We ran 800 individual merges for the biggest group, then 400 for the remaining, then 200, etc. The last merge was quite slow but the whole database finished in a few days.

𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

We are data experts. It’s all we do. Our primary focus is the implementation of our data quality platform, Data Studio, but we’ve frequently worked with Global 2000 companies to solve complex, large-scale data problems.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

06/03/2026

𝗛𝗶𝗴𝗵-𝗩𝗼𝗹𝘂𝗺𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟵 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

High-volume enterprise data quality solutions require exceptional processing speeds to prevent operational bottlenecks. A major North American big-box retailer recently faced a critical data challenge. They needed to implement a real-time "search before create" workflow to manage millions of customer profiles across hundreds of retail stores, digital kiosks, point-of-sale (POS) registers, and high-traffic e-commerce platforms.

During peak shopping events like Black Friday, their website experiences massive traffic spikes. The retailer needed to query a database of tens of millions of customer records simultaneously during peak hours.

𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗲𝘁𝗮𝗶𝗹 𝗗𝗮𝘁𝗮 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀

The retailer defined three primary technical requirements:

• 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻: Eliminate duplicate customer profiles by instantly identifying existing master data records.
• 𝗙𝘂𝘇𝘇𝘆 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴: Locate customer files using minimal, incomplete, or inaccurate search data.
• 𝗛𝗶𝗴𝗵-𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Maintain sub-second search latency while executing thousands of search queries per second during sustained two-minute traffic bursts.

𝗧𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗕𝗮𝗸𝗲-𝗢𝗳𝗳

To find the best data quality tool, the company engineered a sophisticated, automated performance test bed. This testing environment simulated real-world traffic by rapidly scaling search request volumes up and down. The engineering team established a strict performance benchmark based on their maximum projected Black Friday peak volume.

𝗧𝗵𝗲 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: 𝗘𝘅𝗰𝗲𝗲𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗯𝘆 𝟯𝘅

Data Studio by Acme Data (acmedata.net) was the only enterprise data quality software that successfully achieved the retailer's baseline performance criteria. Furthermore, Data Studio shattered expectations by exceeding the required throughput benchmark by a factor of 3x.

𝗪𝗵𝘆 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗠𝗮𝘁𝘁𝗲𝗿𝘀

𝟭. 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗻𝗴 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀
Slow data validation delays business-critical operations like real-time fraud detection, instant customer profiling, and automated shipping updates. High-performance software prevents these costly system delays.

𝟮. 𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗗𝗼𝘄𝗻𝘀𝘁𝗿𝗲𝗮𝗺 𝗗𝗮𝘁𝗮 𝗘𝗿𝗿𝗼𝗿𝘀
Rapid data scrubbing catches data entry errors at the ingestion point. This prevents bad data from corrupting downstream analytics pipelines, enterprise resource planning (ERP) systems, and customer relationship management (CRM) platforms.

𝟯. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆

High-volume data streams—such as IoT sensor data and streaming financial transactions—require millisecond-level verification. Fast data validation ensures business intelligence systems act on fresh, accurate information.

𝟰. 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆

Advanced AI models and large language models (LLMs) demand massive, pristine datasets. High-performance data profiling ensures training data remains accurate, directly maximizing machine learning ROI and model efficiency.

𝟱. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁

As corporate big data grows, automated data quality solutions eliminate the need for manual, expensive data remediation. Catching errors early prevents structural technical debt from compounding over time.

𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 𝗳𝗿𝗼𝗺 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

Data Studio from Acme Data is an enterprise class solution that was designed to meet the highest volume data requirements.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗮𝗱𝗱𝗿𝗲𝘀𝘀 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

05/27/2026

𝟴 𝗪𝗮𝘆𝘀 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘆𝘀 𝗢𝗳𝗳

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟴 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

When discussing data quality challenges with clients, they frequently identify address verification as a top priority. Below is a list of some of the many reasons they give.

• 𝗖𝗼𝘀𝘁 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻

Returned, lost, or redirected shipments come with a high cost. DPV (Delivery Point Validation), that comes with Data Studio’s address verification, ensures that an address is valid and has a mailbox that will receive mail.

• 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲

Ensuring that materials reach the targeted recipients, higher response rates are achieved as well as higher engagement.

• 𝗠𝗮𝗶𝗹 𝗗𝗶𝘀𝗰𝗼𝘂𝗻𝘁𝘀

CASS Certified, that comes with Data Studio’s address verification, is required to qualify for mail automation and bulk mail discounts.

• 𝗦𝗮𝗹𝗲𝘀 𝗧𝗮𝘅 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲

Address verification is crucial for accurate sales tax calculation and compliance. It ensures tax is calculated based on the precise, validated shipping address—required for destination-based taxing—preventing overcharging or undercharging customers and reducing audit risks.

• 𝗧𝗲𝗿𝗿𝗶𝘁𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁

Verified addresses allow for better, more detailed territory analysis and segmentation. It enables CRM systems to automatically route leads and accounts to the correct sales representative, ensuring fair distribution and optimal coverage while preventing ownership disputes between sales reps.

• 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 (𝗞𝗬𝗖) 𝗮𝗻𝗱 𝗔𝗻𝘁𝗶-𝗠𝗼𝗻𝗲𝘆 𝗟𝗮𝘂𝗻𝗱𝗲𝗿𝗶𝗻𝗴 (𝗔𝗠𝗟)

KYC and AML are mandatory verification process used by financial institutions and other regulated businesses to prevent crimes like money laundering and terrorist financing. Identity verification, including address verification, is a key step in KYC and AML processes.

• 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗥𝗲𝗰𝗼𝗿𝗱 𝗠𝗮𝘁𝗰𝗵 𝗮𝗻𝗱 𝗠𝗲𝗿𝗴𝗲 𝗥𝗮𝘁𝗲𝘀

Verified addresses have complete address data and follow a common format. By improving the completeness and consistency of address data, match processes that rely significantly on address data points have much higher match and subsequent merge rates.

• 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 (𝗖𝗫)

CX is improved by eliminating frustration caused by failed deliveries. It creates a smoother, faster purchasing process that increases customer satisfaction and loyalty. It builds brand trust.

𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 𝗯𝘆 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

Data Studio from Acme Data provides global address verification including DPV and NCOA, that integrates seamlessly with your enterprise applications and databases.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗮𝗱𝗱𝗿𝗲𝘀𝘀 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

05/20/2026

𝗖𝗮𝗻’𝘁 𝗙𝗶𝗻𝗱 𝗥𝗲𝘄𝗮𝗿𝗱𝘀 𝗜𝗗 𝗮𝘁 𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟳 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

Several times I have been to a retail store where I shop frequently, and when making a purchase, they can’t find my Rewards ID. They ask for a phone number, which I give them, but it doesn’t find my record. They ask for other phone numbers, and we go through a “try again” ritual. There are a number of options for solving this problem, but many do not create the optimal customer experience.

• I can put yet another app on my phone
• I can carry a rewards card in my wallet
• I can always use the same phone number for rewards accounts

You may be able to think of others.

𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝗔𝗽𝗽

I am getting app fatigue. Every business I work with tells me to download an app. It takes up storage and memory on my phone. I assume they might collect information that I don’t want them to collect. And there is no way I will read the fine print of the contract. It feels intrusive.

𝗥𝗲𝘄𝗮𝗿𝗱𝘀 𝗖𝗮𝗿𝗱

This actually works pretty well. It doesn’t have the privacy problem mentioned above. And it eliminates the “try again” ritual. But in the same way my phone is getting clogged with Apps, my wallet is filling up with rewards cards. And when a card cracks, I need to go through a process to get a replacement.

𝗦𝗮𝗺𝗲 𝗣𝗵𝗼𝗻𝗲 𝗡𝘂𝗺𝗯𝗲𝗿

Sometimes I might want a retailer to have my phone number, like the ones that deliver. Sometimes I give my rarely used landline as a throw away. When that phone rings I know it is someone who wants to sell me something. Now I am managing my rewards accounts, which is work that I definitely don’t want.

𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲

• What if a customer could simply tell you some basic information like “My last name is Jones and my area code is 415”, or just tell you that their last name is McGillicuddy?
• What if the customer could choose what pieces of information to give you, and the pieces could be different at any time?
• What if the person entering the information typed it in wrong but still found the correct record?

Those are questions our clients have put to us. The customer doesn’t have to download anything, carry a card or manage their rewards accounts.

𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗠𝗮𝘁𝘁𝗲𝗿𝘀

Small items like this add up. Customers remember positive experiences. Customers remember better experiences. Customers are more likely to come back to businesses that provide a better customer experience.

𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 𝗯𝘆 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

So this is where I tell you that Data Studio from Acme Data provides this better experience. Finding records is easy in Data Studio, in the user interface or with the API. It works at the cash register, on your website, and in the applications that run your business.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

05/13/2026

𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗹𝗲 𝘃𝘀. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜: 𝗪𝗵𝗼 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗧𝗿𝘂𝘀𝘁 𝗳𝗼𝗿 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴?

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟲 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

Many of our clients are large enterprises that deal with significant volumes of customer records, often millions. If you have a million customer records and a duplicate rate of 20%, that means 200,000 of your customer records are duplicates. Some simple math quickly informs you that it is cost prohibitive to match and merge that volume using only manual efforts.

What is needed, and what our customers require, is a system that can positively identify and automatically merge the largest number of duplicate records without user intervention. That is where AI comes in. We can use AI to positively identify the largest number of duplicate records that can then be automatically merged.

That takes a lot of trust. If the AI positively identifies 3/4 of the duplicates, and you trust the AI, that means that it will merge 3/4 of your duplicate records without any human supervision. In the case where the customer has 1,000,000 records and a 20% duplicate rate, and assuming they are all pairs, 150,000 of the 200,000 duplicate records will automatically be merged as non-survivors. 150,000 records will no longer exist. Get that wrong and there will be trouble.

𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗹𝗲 𝗔𝗜

In the world of AI, the concept of interpretability is a big deal. It means that users inherently understand a model's internal mechanics, that they comprehend the logic beforehand. Interpretable AI is built to be transparent from the start.

𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜

Explainable AI involves post-hoc techniques to explain black box model decisions after decisions have been made, after it merged your records. Black box is the opposite of transparent.

𝗟𝗮𝗯𝗼𝗿𝗶𝗻𝗴 𝗧𝗵𝗲 𝗣𝗼𝗶𝗻𝘁

• Interpretable = Transparency
• Explainable = Justification

Our customers do not want to have to justify their actions.

𝗖𝗮𝘀𝗲 𝗜𝗻 𝗣𝗼𝗶𝗻𝘁

A very large retailer built a duplicate detection engine using an AI algorithm provided by one of the “Big Three” cloud providers. This algorithm was purpose built for duplicate detection. They trained the model and ran the model against their database and found that the results were unusable. The algorithm matched many, many records that were not duplicates.

There were discussions about how the model would probably do better given higher volumes of data. Those discussions didn’t sway anyone and the company abandoned the model.

That’s when they called us.

𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 𝗯𝘆 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

Our customers want to know why records are merged and they want to know before it happens. They want clarity around what the AI is doing, what decisions it is making and how the AI is making those decisions. Only then can they trust.

Data Studio’s AI is interpretable. It puts you in control.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

05/06/2026

𝗛𝗼𝘄 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲 𝗼𝗳 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟱 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

Our customers want to know exactly what value they are getting out of Data Studio, Acme Data’s data quality solution. They want specific metrics, nothing vague.

𝗢𝗻𝗲 𝗠𝗲𝘁𝗿𝗶𝗰 𝗧𝗵𝗲𝘆 𝗪𝗲𝗿𝗲𝗻’𝘁 𝗦𝘂𝗿𝗲 𝗢𝗳

• 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 – While they could do surveys or measure NPS, or hear anecdotal information from employees, they found it difficult to directly attribute higher customer satisfaction to higher quality data. While that might seem inherently true, it might have limited value when trying to justify a purchase.

𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗲𝘆 𝗗𝗶𝗱 𝗟𝗶𝗸𝗲

o 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗥𝗮𝘁𝗲𝘀: While they found it difficult to attribute more sales to higher quality data, they could definitively measure response rates from campaigns before and after data cleansing.
o 𝗣𝗼𝘀𝘁𝗮𝗹 𝗖𝗼𝘀𝘁𝘀: They were able to measure the money saved on postage and physical mail by not mailing to undeliverable or duplicate records
o 𝗖𝗮𝗹𝗹 𝗛𝗮𝗻𝗱𝗹𝗲 𝗧𝗶𝗺𝗲: One customer measured that our accurate and reliable “search before create” reduced their average customer support call handle time by 45 seconds (at 3 million calls per year)
o 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱 𝗦𝘂𝗽𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻: The number of “search before create” calls that found an existing record that was selected as opposed to creating a new one
o 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 𝗠𝗲𝗿𝗴𝗲𝗱: The number of duplicate records merged
o 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝘀 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱: The number of addresses validated against the USPS, Canada Post and global address databases
o 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗣𝗼𝗶𝗻𝘁 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 (𝗗𝗣𝗩) 𝗥𝗮𝘁𝗲: The number of addresses positively identified as having a mailbox that would accept mail
o 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗙𝗶𝘅 𝗥𝗮𝘁𝗲: The percentage of records where missing data (e.g., apartment numbers, ZIP+4) was appended
o 𝗜𝗻𝘃𝗮𝗹𝗶𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 %: The number of addresses deemed invalid by postal data
o 𝗨𝗽𝗱𝗮𝘁𝗲𝗱 𝗠𝗼𝘃𝗲𝗿 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 %: The percentage of records that matched against the 48-month NCOA database of moves

That’s a lot of metrics and there are more.

They like being able to see these statistical totals and percentages, the month to month trends (going up or down), as well as the effects on new data being entered.

This information can help you justify an initial data quality purchase or maintain an existing one.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

04/30/2026

𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗮𝗻𝗴𝗲 𝗼𝗳 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 (𝗡𝗖𝗢𝗔) 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝗲𝘀𝘁 𝗗𝗮𝘁𝗮

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟰 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

A large and very well known manufacturer of consumer durables ( #1 is their space) has to deal with recalls from time to time. Product issues can have a number of unwanted outcomes:

• 𝗛𝗮𝗿𝗺 𝘁𝗼 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿𝘀
• 𝗟𝗮𝘄𝘀𝘂𝗶𝘁𝘀
• 𝗟𝗲𝗴𝗮𝗹 𝗻𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀

When they called us, and due to a potential fire hazard, they had a court order to notify anyone who had purchased a specific product. They were also required to show the court the specific actions they took to demonstrate “best efforts”.

They used Data Studio’s 𝗡𝗖𝗢𝗔 product to update stale addresses in their database with mover data provided by the USPS. The tight integration between Data Studio and their CRM system made this process seamless and easy. Taking things one step further, Data Studio provides a full audit trail of changes. They could see what addresses were updated and they could provide this audit data to the court to demonstrate compliance.

𝗕𝗲𝘆𝗼𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲

Having the most current information about their customers also improved the yield of their marketing efforts. Some of their products have consumables that are highly profitable. By having the correct addresses, the company knew that marketing offers were sure to make it to their customers.

𝗙𝗶𝘅𝗶𝗻𝗴 𝗕𝗮𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝘀 𝗜𝘀 𝗦𝗼 𝗘𝗮𝘀𝘆

With 𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼, 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮’𝘀 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻, address verification is implemented in less than 5 minutes. If that sounds hard to believe, make us prove it. It’s a good investment of one phone call.

If you could use some help cleansing and enriching your data, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. We’re easy.

04/28/2026

𝗪𝗵𝗲𝗻 𝗚𝗼𝗼𝗱 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗜𝘀𝗻’𝘁 𝗚𝗼𝗼𝗱 𝗘𝗻𝗼𝘂𝗴𝗵

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟯 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

One of our clients, a well known software company, had a CRM system that contained more than 12 million customer records. They had purchased a popular, mid-tier deduping solution. The fact that the mid-tier solution was widely adopted by other companies gave the client confidence that they were making a good choice.

Post implementation, they experienced some undesirable outcomes.

𝗟𝗼𝘄 𝗬𝗶𝗲𝗹𝗱, 𝗕𝗶𝗴 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀

With 2,000 support reps incentivized to close calls quickly, speed was everything. When customers called for support, reps used the "search before create" feature to locate existing records. They found that too often this search feature did not find matching records and was slow to respond. The support reps learned that creating new customer records was faster than searching for existing ones. The result was that the client ended up with an army of support reps creating duplicate records.

Compounding the problem was the fact that the product registration web page used the same matching logic. If the system couldn't positively identify a returning customer during registration, it simply created a new customer record. Between the call center and the registration page, the company was generating duplicate records at scale.

And then they called us.

𝗧𝗵𝗲 𝗞𝗲𝘆 𝘁𝗼 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀

When the client first engaged us, we found that roughly 25% of the customer records in their CRM system were duplicates (~3 million). For this volume for records, manual intervention was not practical. For any significant volume of records, it is essential that matching processes positively identify the largest number of duplicate records. Positive ID is required for the automated merging of duplicate records.

Precision positive identification also maximizes the accuracy of the search before create function. Only when search before create works very well do users then adopt and rely on it.

𝗕𝗲𝘁𝘁𝗲𝗿 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝗛𝗶𝗴𝗵𝗲𝗿 𝗥𝗢𝗜

Data Studio, Acme Data’s data quality solution, identifies more duplicate records with greater precision than competing products—and implements in minutes.

If the end users of your CRM, Analytics and AI systems rave about the quality of your data, you should stick with it. If not, you should put us to the test.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

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