Large enterprises must be able to accurately identify and verify potential small business customers but cannot because of incorrect, outdated data.
This means small businesses miss out on services because they cannot be identified, so they cannot grow, and large enterprises are unable to onboard new customers. Let us look at how Markaaz addresses this with David Clarke, Chief Data Officer in Residence, Markaaz
Forty years ago, bad data did not hurt companies, either large or small. Decisions were made in person or by people at the end of a phone. For example, small businesses interacted directly with their banks by going to a branch. A human teller would speak to the small business owner. Spelling issues or misinterpretations were easily corrected by humans on either side of the transaction.
In the digital age, most decisions are made by computers, or better said, by computer programs. The spoken or written word, or series of numbers, have been transformed into data. Data strings are compared to acceptable norms and approval thresholds, and automated decisions are made in milliseconds.
Data automation revolutionized business but also created a new problem: Incorrect data.
Only 3% of business data is accurate
Finding and verifying a small business for onboarding is extremely difficult for large enterprises looking to serve those businesses because of the inconsistent data held by most traditional business intelligence providers. According to Harvard Business Review research, only 3% of business data meets basic quality standards.
A single small business might have several different records in a business intelligence database because of data field variants. A single spelling mistake or variation means the enterprise cannot automatically onboard a valid, legitimate applicant and the small business cannot access the services that the enterprise is offering.
“If you own a small business, you exist on LinkedIn and probably in the Better Business Bureau. If you had a small business during COVID and applied for a PPP loan, you will exist in that database. When you register your business, that registration remains in the Secretary of State’s business records. The problem is that you may have entered something that was not the same as already existed, or the system might force you to enter different information, leading to inconsistent records,” said David Clarke, Chief Data Officer in Residence, Markaaz.
When an enterprise is trying to onboard a small business, its onboarding systems often cannot match the small business to a single data record because of these inconsistencies. The software they use cannot match the data variants. If the data entered into any given transaction does not match exactly the data the software is using as its reference data, the transaction is rejected.
“Jonathan Smith living at 1 Main St, Smalltown, NJ, applies for a small business card. However, the small business is registered at 27. First Avenue, Bigtown, PA [which is where it operates], and when Mr. Smith registered his company with the Secretary of State of PA some years ago, he registered the Managing Member of the small business as Mr. J. Smith. His credit card application, albeit 100% legitimate, could be rejected,” explained Clarke.
Business data is collected in many ways and stored in many places, so the problem is difficult to resolve. Every time a small business enters into any transaction, data about the small business is collected and stored. It might be online, with the Secretary of State, with banks or insurance companies, Yellow Pages, tax authorities, payroll data, and even printed material is used and stored. Unless the data used in every transaction is 100% consistent with every other transaction, there will be differences leading to all the problems which we call ‘Bad Data.’
What Markaaz does
Markaaz collates multiple sets of data from multiple sources and merges them into a single Directory, within which is a ‘Golden Record’ for every business entity. The Markaaz Directory has tied all of the variants of each small business data field into a single, searchable Golden Record. It will connect the registered address with the place of business with all the various addresses linked to the small business, plus all the name variants. Simply said, we match against all name and address variants, together with website and phone and all legal entity numbering systems, to effortlessly and uniquely identify all small businesses. No small business is left behind.
“We don’t solve the data problem because we don’t need to. Traditional data companies have been trying to solve an unsolvable problem for many years. They have tired, legacy technology that is difficult to maintain, let alone change. The smaller, web mining, AI-focused companies who simply scan the web will find hundreds of disparate entries for each small business,” stated Clarke.
The Markaaz Directory connects all inconsistent business records and matches a business across multiple data attributes to reach a final confident match. We are alone in being able to do that in the market today, and a recently completed customer data study demonstrated how much better we are than anyone else. An incremental twelve percent of previously rejected business credit card applications were able to be approved.
“We store more versions of the business data attributes and match against more versions of them. That’s why we find more small businesses for our large clients. It’s as simple as that,” noted Clarke.
- We merge the best versions of all data attributes from our data company partners to create the Markaaz Golden Record.
- We match all possible data attributes and multiple variations of data attributes, using data from our Directory while considering both the current and past versions.
- We match against Business Address, Mailing Address, Registered Address, and Agent Address using the current and former versions.
- We can do this cost-effectively because we use 2020’s technology, data, and techniques and are 100% cloud-based.
- The Markaaz approach produces high-quality match rates of 90%-95%.
David is an independent Advisor/Consultant specializing in Data Strategy and the business information industry. His clients have included John Wiley, TiVo, CreditSafe, and Mastercard. From 2015 to 2017, David was Chief Data Officer at Avention Inc., subsequently acquired by Dun & Bradstreet. Previously David spent 30 years at Dun & Bradstreet in various executive positions, including President, Sales & Marketing Solutions, US CIO, and Chief Data Officer. David was born in the United Kingdom but has lived in New Jersey for 20 years. He loves to travel and relaxes by skiing in the Alps and collecting Burgundy wine.