To create the maximum value, urgency, and leverage in a data partnership, you must present the data available for sale or partnership in a clear and comprehensive way. Partnerships are based upon the concept that you are offering value for value, whether paid or traded. Friendship might need no reasons, but partnerships require some understanding of the exchange of value.

The most common way to demonstrate the value of data is to share three different files or documents, each with a slightly different view of your data assets. The first file, called the data brief, is a presentation or document describing your data assets highlighting their best qualities and potential uses, or case studies of actual use. Your best strategy is to present this in person or, at if that’s not possible, in a conversation. The second file is a comprehensive document called a data catalog that outlines all of the facts about your data assets. The last file is a sample data file to assist would-be partners to test your data. The brief, catalog, and sample files need to represent accurately and clearly the value of your data.

Companies that fail to present data assets effectively will also fail to attract partnerships and receive fair compensation for their data. The value of your data is not obvious — just ask anyone in your company who doesn’t directly work with a given dataset to evaluate its purpose and worth to see what we mean.  And those who do work directly with the data often can’t give a solid business case, either.  This means, unfortunately, that those closest to the data, meaning you and your team, are also those most likely to become frustrated with a potential partner’s misunderstanding the value of your data. To prevent that frustration, invest the time to create the brief, the catalog, and the sample file.

The Three Files That Prove the Value of your Data

The brief, the initial presentation file to highlight your data assets, need not be very long. To get potential customers or data partners to value the data appropriately, you should present it in simple terms, with appealing visuals that highlight the data assets clearly. Show the data at its most interesting, intriguing, exciting.

The brief needs to start with a simple definition of your dataset that describes it clearly, for example:

• The full semantic breakdown of the United States Patent library.
• The only user-generated, real-time gas price tracking platform.
• The deepest collection of opt-in consumer profiles for luxury sales.
• The highest resolution, hourly drone footage of US retail store locations.

Each of these sentences describe real datasets in their most appealing value terms. Focusing on the most important attributes like “user-generated,” “real-time,” or “highest resolution” are meant to highlight how they are both unique and valuable.

From here, your presentation should draw out each major section of your data catalog, which is the second file you must create. Use striking graphics or “hero” numbers that clearly demonstrate the highlights in your data, such as the total number of records, how often the data is updated, quality metrics, and geographic or sector breakdowns. Each dataset has its own unique highlights.

In the second file, the data catalog, key sections include the Initial Data Definition, the Method of Data Collection, the Refinement Process, the Commitment of Quality, and the Coverage, Fill Rates, and Refresh Rates per major data field. Your partner will appreciate it if you include a Field Definitions Library at the end of the document, showing each field available, its data format (number, Boolean, date, currency, or text string, for example), its fill quantity (the percentage of records in which the field is filled), and a brief definition of what it conveys.

Your business and data teams should outline the initial brief as well as the data catalog, and your marketing department or agency should design them.  Of course, for many companies, that means a single person doing the work, and so be prepared to allocate the resources necessary to give that person the support needed. This investment in clearly articulating what data assets you have will drastically shorten the partnership or sales cycle, immediately showcasing your data assets at a higher level than most.

Many companies shortchange this effort and as result, never receive full value for their data. When you jump right to “let us send you a sample file” you miss the opportunity to build up your data value and control the dialogue.  You also likely keep the decisionmaking at a lower level of authority with your prospective partner – a poorly explained proposal will get handed off to someone who isn’t a key decisionmaker, adding a level of delay and uncertainty to the process.

You should create the final file, the sample, dynamically based upon the circumstance. While it’s not usually necessary to customize the brief or the catalog, a custom sample data file will give each potential partnership its best chance at success. For example, for the drone footage company mentioned above, if they are meeting with a major electronics retailer, may want to specifically and only show similar footage or population density visuals for similarly situated electronics retailers. If the sample dataset has restaurants and laundromats in it, this will reveal shortcomings to the potential buyer or partner. They may show that the depth or coverage of the data file is inadequate, or maybe signal that the data owners are unable to deliver a custom data set quickly and in a format that is usable.

Most companies that are considering a data partnership or the purchase of data assets will not have any patience for file delivery or formatting issues. This means that you should be prepared with a file prior to your first meeting that is likely to be appropriate to your audience, but also have a plan to quickly generate a different file if the meeting indicates a different need.

Relationship Mapping Values

The process of mapping relationships between your data values and those of the potential partner can cause countless misunderstandings and unnecessary delays. This is typically because each party has very unique ways of looking at a business or person or product in their data; they need a Rosetta Stone of sorts to understand your dataset in their own context. This is central to the entire data partnership strategy for a business because there always needs to be a way to “crosswalk” from one dataset to another.  Here, we’ll show how to highlight your data catalog to streamline this process in each partnership discussion.

Data, in any form, is the reduction of the inputs from the world to values or ranges, so we can efficiently understand and analyze information. In other words, data is a description of, or the story of, our world. As such, every dataset can be related to any other dataset by identifying its key value when it comes to the classic questions of Who, What, When, Where, or Why. Just as in your first journalism class or writing class, these five factors are common ways to describe the world around us and to connect all of the elements of a story together.

By asking the following questions, both of your own data assets, and then of your potential data partner, you can quickly come to a common language to compare and analyze your data.

• Who is your data about?
• What is your data about?
• When did your data occur or change?
• Where (what location) is your data about?
• Why was your data created?

Is your data about people, products, or places? If so, each of those can be related to other databases with those as central themes. Consumer profiles, business locations, product codes, and medical reimbursement codes are all examples of common data anchors by which different datasets can be matched and compared. This means that Who, What, and Where are the most straightforward questions to answer, and you or your potential data partner can usually match corresponding data elements so that they can compare your data to theirs and analyze its value. Every data record needs to correspond to the same person, product, or place across datasets.

Time is another fantastic way to connect data assets. When two data sets don’t describe the same person, product, or place, time of occurrence or change is the next most likely area of correspondence. This is how drone footage and satellite imagery are tied to product sales at a retail store, for example. Both the photos of the cars or pedestrians in the parking lot of a retail store can be compared to transactional data from the store, because both have time stamps. While photos of parking lot density can’t directly be tied to a product SKU, they can be compared to the times a particular product or series of products is purchased. Time is a universal connector that powerfully connects seemingly disparate datasets.

The last and most complicated data relationship to map, in both data and in writing, is around the question of “why.” The best way to think about how this matches up with other datasets is to try to match up sentiment or indications of interest around an event, product, or service. For example, many firms like Yelp semantically extract the sentiment of reviews left by patrons at restaurants and hotels, sentiments that can help answer the question of “why.” When a reviewer of a restaurant leaves a comment like, “You have to try the 1-pound meatball appetizer, it’s amazing,” they can relate the object of the meatball to a positive consumer experience and the subsequent recommendation of that business to others. This is one of the hardest elements of data collection, but the increasingly easy access to user interest and sentiment through mobile phone apps has created a whole new world of “why” relationship mapping.

Data Through Time

When presenting data assets, reviewing changes over time is a powerful magnifier of value. Charts of daily, weekly, monthly, quarterly, or annual shifts put your data assets into a common perspective. Most data briefs and data catalogs show the growth of their data assets — records, fields, or fill rates – as time-based series. Since most data collection efforts are cumulative, they grow over time, which attractively demonstrates their value. For this reason, you should identify each dataset you have, how many, and when they were created to make this very clear. Charting growth over time can also be a great way to highlight that your lead over competing data suppliers, and how long it would take to recreate your data at the same scale. For this reason, your brief and data catalog should convey how your scale or head-start create dominance in your space.

Another important temporal view of your data will reveal changes or updates to your data and when they occur. To highlight just how dynamic your data assets are, create scatter plot diagrams that show fields, how often they update, and to what magnitude they change. This insight can visually help a potential partner to understand not just the need for your data, but the need for refreshing their feed of data from you on a timely basis. Some data suppliers will want to convey this early on in a discussion, because their potential data partners will have trouble ingesting data frequently. Partners’ legacy corporate data structures and slow compilation practices can create barriers where, even if your data is amazingly useful, a data partner may not be able to ingest it fast enough to use it properly. This is why a chart of the frequency of updates is very helpful early, enabling you to demonstrate your capabilities to a potential data partner. Frequency charts also help your partner understand just how large of a data inflow they may be purchasing — It is a sad discussion when a data partnership is abandoned after weeks of discovery only because the receiving party realizes they can’t ingest the fire hose of content you might be able to provide.

We mentioned the need for storing the date and time for each update in your data. This is where that effort really shines in the data partnership strategy. Work with your technology teams to ensure that creation, updates, changes, confirmations, and deletions of every data field are tracked and time-stamped. This will be a hallmark of your data value presentation.

Ultimately, the vale of a dataset is only apparent when explained, because anyone who has not directly worked with the data will only recognize the value that is surface-level.  It takes time, energy, and creativity to produce an explanation of data that will convince a prospective partner of value.  But the investment in making the data brief, data catalog, and sample data file will be well worth the resources required.  A weak presentation has been the downfall of many a potential partnership; a strong presentation goes a long way to showing why your data is worth your partner’s time, and yours.