How to Win New Business with External Data

Increasingly, external data (alternative data, public data, open data – call it what you want) is being called the “secret sauce” of driving advanced analytics, developing machine learning and AI capabilities, enriching existing models, and delivering unrealized insights to every part of your organization.

The difficulty in connecting to this data is top of mind for many businesses, and the issue of governing its use is crystallizing into a core strategy among organizations that have seen the GDPR’s writing on the wall.

For the moment, put aside the slightly fevered predictions about delivering artificial intelligence across the enterprise with a few datasets and a new CDO (setting up an external data strategy relies as much on culture as it does on data). It’s important to acknowledge that for many organizations, external data should be approached not as a replacement for what’s already working, but as an enhancement to what you’ve got today. 

Take Data from Good to Great

Most organizations use data. Many use it effectively. Regardless of where an organization is on their journey towards being data-driven, the chances are good that data, in some form or another, is already hardwired into their architecture. For a lot of these organizations, the excitement about external data is not that it’s a brand-new resource, but that it has the potential to enrich existing processes to make them better.

For example: as a business owner, I wouldn’t consider replacing census data as a core data element, but if a solution came along that let me layer customer footfall data over that core demographic layer, I’d jump at the opportunity to enrich my existing data. 

What you do with the data is up to you. But increased access to better information has never been a bad thing.

The “Blue Sky” Problem in Data

For some organizations, however, there’s almost too much potential. The idea that there’s a virtually unlimited supply of valuable data out there that can be applied anywhere to achieve any result leads to a lack of direction and focus. “I want more data” is a great sentiment, but it’s not a great strategy. We call this the Blue Sky Problem: given complete freedom to start using an unlimited resource, it can be difficult to know where to start. 

As a rule, start with what you know. Net new data products can take a while to test and build into a production environment. Starting from scratch can mean that you must wait for setup, testing, deployment, and iteration before you’re seeing real results. Long before that happens, the data will be flagged as an unnecessary expense and the project will be abandoned. It takes either a very confident or a very foolish organization to pour money into a project where no one’s thought through how it will be productionalized.

This doesn’t mean you shouldn’t allocate time and resources towards pure data science where teams are encouraged to experiment without a deterministic outcome, but these skunkworks projects should account for maybe 20% of your external data budget and resource allocation. The remaining 80% of your resources should go towards projects where you know that additional or better data can improve an existing process.

For some organizations, this can be a difficult concept to grasp. The idea of external data is so wound up in pure innovation, it might not be obvious that one of its most immediate applications will be in processes that are already running smoothly. This is where savvy businesses are spending their time, and it’s paying off. 

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Data Revitalization

By looking at some of their most fundamental data assets, forward-thinking businesses are slapping a fresh coat of paint on some processes that have been running for decades. Take Dun & Bradstreet – for almost 200 years D&B has provided central access to one of the most comprehensive and valuable data sources in the world. Their data is often elemental, written into processes that span divisions and sectors from sales and marketing to supply chain management. For companies that use this data, replacing D&B is a nonstarter. Love or hate them, the data is baked into the system and can’t just be lifted out. 

But the data can be improved. External data doesn’t aim primarily at replacing the established data vendors, but neutralizing blind spots in this data, adding records, enriching elements. New sources of data can’t necessarily match D&B’s directory for breadth, but what they lack in size they more than make up for with insight into previously opaque areas.

DaaM (Data as a Microscope)

Small and medium businesses (SMBs) account for the vast majority of businesses in a given market, but they remain a big blind spot for most organizations. These are the mom-and-pop convenience store, Jim’s Plumbing, and pre-seed tech startup that traditional business directories don’t have in their database. They’re too new, or too small, to show up, but they’re valuable data points.

By using new sources of data from upstart data providers like Soleadify, businesses can breathe new life into their traditional business directory data to juice their overall hit rate, leading to a jump in value of the overall system.

The upshot here is that since businesses have already created pipelines to and from D&B data, any additional records they flow into this model can be applied immediately across the enterprise. They can understand, in a very real way, how much value an extra 100 or 100,000 records will create for their business, which makes it a lot easier to benchmark success. 

Something Old, Something New

Data provides value when it’s applied correctly. External data is no different. Some organizations have established a pure data science division that doesn’t have a mandate to generate revenue for the business. If that’s you, good on you – giving your team complete creative control shows a lot of trust. If your business is a bit more practical, it might be a good idea to look at the processes you wouldn’t dream of replacing; a better database is a well-placed dataset away.

The Payout

Introducing alternative, external data with rich attribution to your existing, internal datasets allow you to improve the depth and effectiveness of your client and prospect profiling and segmentation work.

That same alternative data source may also allow you to expand the number of records available for all your acquisition and retention activities. Together, with richer attribution, better external data can help improve the accuracy of your targeting activities, drive higher sales conversion rates, and generate incremental revenue. And for those organizations who rely heavily on due diligence to validate data received by prospective business clients through, for example, online channels, alternative data can also help to improve your validation activities.  

What could be better? More business with more customers that meet your criteria…

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Drew Doherty

Drew Doherty

Drew is the Director of Partnerships at ThinkData Works. ThinkData is one of Canada's Top Growing Companies, Canada's Most Admired Corporate Cultures, and was awarded Best Overall Data Science Solution.

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