Syniti, a global leader in enterprise data management, announced the availability of a new AI-driven operational data matching offering focused on ERP and supply chain data. Improving this type of business-critical operational data drives process improvements while helping deliver meaningful cost savings and unlocking new business value via data quality with higher data integrity. This functionality will be available as a module of the company’s unified data platform, the Syniti Knowledge Platform, or as a standalone offering, Syniti Match (previously 360Science).
Data matching is the key first step to deliver enterprise-wide quality data needed to help drive superior business outcomes. Most current industry solutions focus on party data – including contact and account information, names, addresses and locations – to help ensure data is accurate and not duplicative to improve the overall customer experience as well as streamline marketing and sales efforts. With this new offering, Syniti expands its best-in-class matching technology from party to operational data, which includes ERP or supply chain data such as items, equipment, parts, assets, products and SKUs.
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Industry standard annual carrying costs are 20-30% of inventory value. This new offering helps enterprises quickly build trust in their data and free up working capital tied up in excess inventory by working to eliminate error ridden and duplicate data. It will provide customers with:
- Find & Fix Partial Data: Identify materials or parts where key information is missing, making it easier to find replacements. Limit overbuying, save money on storage and have the critical parts you need that expose similar replacement parts or materials.
- Standardize Inconsistent Data: Catch parts or materials that were setup outside of your organization’s standard operating procedures. Match parts and align them with vendors enterprise-wide, helping you to identify millions in potential procurement savings by recognizing bulk purchasing opportunities across countries or regions.
- Classifying Identical Data: Detect spelling errors or abbreviated ERP duplicative data with human-like perception at super-human scale, reducing the reliance on critical line of business users to make sense of the tribal operational data.
Recent Gartner research on the state of data quality solutions states the lack of smart and augmented data quality capabilities, “Current data quality practices using traditional tools require significant manual efforts and rely on SMEs to assess and remediate data quality problems.” Further the research states, “Within the data quality field, Natural Language Processing (NLP) is especially helpful to profile, parse, match, standardize and cleanse unstructured data such as comment or free-text fields, social media and documents.”
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Syniti’s match solution uses contextual scoring, NLP, and a purpose-built, proprietary phonetic algorithm to deliver more accurate and quicker matching than conventional solutions – an organization can even parse and standardize free text entries going back years. This is particularly useful when integrating product information into online stores where product descriptions need to be standardized for potential buyers.
Emily Williams, Vice President, Product Alliances, Syniti, said: “We’ve worked with clients who initially implemented their ERP system 10, 15, 20 years ago. The amount of errors or discrepancies in the input data such as abbreviations, spelling mistakes and duplicative information has compounded over the years resulting in messy data. We’re excited to expand our matching technology to what’s historically an incredibly labor-intensive, manual cleanup process, building trust in your data.”
volumes and with very little tuning – allowing customers to realize the true business value associated with deduplication and data quality, including greater productivity, better decision making and perhaps most importantly, cost savings. For a Global 2000 company, a reduction in duplicate spare parts alone could save millions – freeing up that capital for more value-add initiatives.”
John Price, Manager, Data Management Services, Rio Tinto, said: “As long-time Syniti customers dedicated to consistently improving our data quality to drive business value, we’re excited to participate in the beta program for the new Operational Data Matching product. We’ve been impressed with the agility the solution provided in quickly returning initial matching results based on key materials and data objects and its ability to rapidly refine, iterate and improve those results. This has allowed us to consider multiple data-driven use cases and define business opportunities without a massive effort or the need for expensive, dedicated coders or data scientists.”
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