In today’s digital age, information is only a click away. Social media platforms like Facebook and Instagram entice individuals to increasingly feel the need to share everything. Whether it’s political views, or the need to share you’re heading to the gym, people are living their lives completely out in the open.

This expectation for transparency has extended beyond personal interactions and is now a reality in business. The topic of transparency has never been more important to the business environment.

Why?
The answer is simple: transparency conveys trust.

There’s a misconception out there about transparency. Too often, companies see transparency only as a tool to be used when owning up to a mistake or righting a wrong. This approach is shortsighted and isn’t an effective way to build trust. Clients will be far more forgiving of mistakes if a company has a history of being forthright with all interactions, not just the negative ones.

Transparency implies openness, and accountability. Operating in a transparent manner means operating in such a way that it is easy for others to see what actions are performed.

So, why has this industry accepted the notion of anything less than transparency? Why doesn’t a client know what their data looks like prior to onboarding and post onboarding? Why doesn’t the client understand what kind of data they are providing? And more importantly, why aren’t clients getting insights to their data after onboarding?

As this industry matures and grows, transparent results are a necessity in making proper strategic marketing decisions. Marketers want their customer data onboarded accurately and need to understand the following:

• What data was onboarded in a deterministic way vs. a probabilistic way
• What data is household-based vs. individual-based
• What is the recency and frequency of the results
• How many devices are associated per record

Does it make sense to include a consumer into a segment if you know that the cookie is probabilistic and has been seen once in 90 days? Is that a true targetable customer?

Digital marketing is becoming more and more sophisticated and data is driving decisions and results. The trusted firms connecting consumers to devices need to make sure that all data is of high quality and is accurate. They must also deliver transparent reports to verify the matches. Marketers also need to know the status of the data that they provided (quality, NCOA, duplications, errors).

Ideally, an onboarder should be able to process a client’s data so that it is of the highest quality prior to matching the data to any device: The nature of data is that it is constantly changing, the population changes millions of times daily, for instance:

There were 3.9 million babies born in 1999 – This year they are targetable 18 year olds.
There were 2.7 million deaths in 2015 alone – They need to be removed.
There were 124 million households in 2015 of which only 81 million were families – Which household should be targeted?
The average household size in 2015 was 2.54 – Wouldn’t you want to know more about that household?
The point is, that in order to develop an effective marketing strategy that targets individuals (not households) and produces accurate results, the data being utilized needs to be accurate and be shared openly with the client.

There are many ways to onboard a customer today (offline to online, online to online, and online to offline). Once the data has been processed for onboarding and successfully matched, the client should fully understand exactly what data was matched. It is the transparency of these results that will allow for better planning, strategy, and analysis. For instance, transparent reports should include:

Pre-Onboarding Data Results:
• Address standardization
• Email append rate
• Attribute append rate
• Record and device duplication removal
• Deceased removal
• Unusable email and postal records
• Net usable records

Post-Onboarding Report:
• Individual match rate
• Household match rate
• Probabilistic match rate
• Deterministic match rate
• Cache clearers
• Frequency
• 30-60-90 day recency
• Segments & quantities

Brands and data providers should not settle for a simple match percentage. Instead, they should demand to know the results so they can better market to their existing customers and strategize to attract new ones. No longer will a one line match rate do. TRANSPARENCY should now and always be the new normal in data onboarding.

Sets of numbers can be misunderstood if they lack integrity

The media landscape has realigned itself with lightning speed to the power of data. After a century of being limited by “brute force media,” marketers quickly glommed onto the vast potential of digital and addressable audiences.

The data “exhaust” from the digital experience has been a game changer for marketers, powered first by sites, then by ad networks and finally by programmatic. Money shifted from reach-based to “people-based” planning, augmented by powerful new data companies, monetizing the categories and groupings of people brands want to reach in ever finer granular detail.

But somewhere along the way, the proposition fractured. We discovered that data itself is not the key to addressable marketing and better business outcomes—quality data is. And the difference between data and quality data, or data with integrity, is difficult to see. To use a buzzword of the day, the market for data is not transparent. To use another, it’s a swamp: an opaque, poorly understood mess. If you want to be a data-driven marketer, you need to make your way through the morass to interrogate your data. So here are seven questions to keep in mind in assessing data integrity:

Is your data fresh or stale? The average life of a cookie is 30 days. About 55 million people change their phone carrier every year, 60 million physically move, 43 percent of customer records are out of date or invalid, and 60 percent of data is incorrect within two years.

Data becomes obsolete quickly, yet many providers continue to use stale data because it provides the illusion of scale. The only data that matters is accurate data. Make sure you understand how data is collected, whether it’s corroborated against authoritative identity standards, and how often obsolete data is purged.

Is your data 3-D or flat? In the world of data, there are six key areas that matter to marketers: demo (age, gender, income); geographic (where they live/roam); attention (what they concentrate on); consumption (what they buy); behavioral (what matters to them); and intentional (what they’re about to do).

Data providers act as if people exist independently in each of these areas, as if any of the above is sufficient to define a person. I’d ask, are you just a demo? Just a measure of attention? Just a signal of intent? No. Real humans are a combination of all of the above. Collectively, consumers are diverse. Individually, they are multifaceted. Flat data (an individual attribute) is just a signal.

How modeled is your data? Here’s a truth: All data is modeled. Here’s another: At some point, models falter. Do you know at which point?

In order to be useful, data needs to have scale. Marketers seek a balance of specificity and reach. It’s important to understand the size of the initial seed audience versus the size of the total audience to develop a degree of confidence in the data you’re using. If it’s significantly modeled, how certain can you be that you’re still reaching your target?

How transparent is the modeling? Do you know your look-alikes? The data market tends to be opaque, and with data, the devil is in the details. If you don’t know how a look-alike audience is formed, you have no idea whether it can be trusted.

For example, most data sets use only digital identifiers and connections. Definitive email-to-cookie linkages generate only a 30 to 50 percent match rate. So the data you’re starting with may be less than half right. Statistical modeling creates hypothetical look-alikes off the total (which is less than half right), exacerbating the issue. If you don’t know the model, you can’t interrogate the veracity of the data set.

Is your data connected? Most data is digital, but I don’t know of a single person who lives life online only. The world is connected—online and offline. Connected data encompasses both. Most data is based on digital attributes only and is neither linked to offline identity nor normalized versus the population. In other words, it captures a small portion of reality. Data needs to be connected to reflect people’s 3-D lives.

Are you targeting individuals or households? Unless you’re targeting age or gender, you’re better off targeting households than individuals. Here’s an example: It’s amazing how many marketers still target women instead of adults, as if only women are shoppers. Today, 40 percent of primary grocery shoppers are men, and the majority of households share grocery shopping chores.

Targeting only individuals misses a big portion of the grocery-shopping population. Worse yet, most purchase data is generated from shopper card data, which exists at the household level. But generalizing the data from individual to households requires a connection to the offline world (see prior question). Does your data capture this?

Finally, how many profiles are there? There are 220 million adults in the United States. If your data provider has 3 billion profiles, it isn’t marketing to people, it’s marketing to data points. The data stream that we rely on as marketers grows exponentially each year. Today, there are more IP addresses for devices than people. New ways of parsing, organizing and leveraging data will be invented that will make the media landscape even more addressable and exciting.

But buyer beware, if you don’t kick the data tires and get a more complete understanding of the modern principles of data integrity, you may just be getting crap.

This article is a repost from AdWeek.com, and written by Julie Fleischer, VP of Product Marketing at Neustar. #bravo

Onboarding companies are multi-faceted and considered a fundamental part of the digital eco-system. An onboarder is the root of where all targeted display ads emanate. Below is a sample of what a traditional data onboarder facilitates:

• Pixel tags
• Publisher traffic aggregation
• Platform integrations
• Pixel syncs
• Cross device identification
• Persistent IDs
• Digital audience targeting across media, devices, and formats including: display, email, social media, addressable TV, and mobile (leveraging cross-device IDs)
• Digital audience segmentation
• Site personalization
• Customer or audience insight development for market research, modeling, and planning
• Measurement and attribution for both online and offline applications

These capabilities sound very technical and they certainly are. Yet, as technical as these processes may be, each process an onboarder performs connects back to the core of onboarding – data. If you do not have capabilities to manage, manipulate, analyze, and understand data, you cannot onboard correctly. The technology only enables the data to be utilized, distributed, and analyzed efficiently, but if you do not have an excellent data pedigree, none of the technology will repair poor data pairings.

According to Winterberry Group, consumer data onboarding refers to the process of linking offline data with online attributes. More specifically, it is the matching of two audience data sets: a first-party CRM data set belonging to a marketer and a digital data set belonging to a data provider. The match process uses a common identifier or match key to link the records. The match—with a certain degree of accuracy—provides the privacy-compliant identity resolution necessary to activate a distinct and expanding set of data-driven marketing use cases.

How many times did you see the word data in the description above and how many times did you see the word technology? [Data won 6-0] To onboard accurately and properly assist marketers in delivering relevant targeted ads to consumers, data must be the focal point.

It was not long ago, at the start of this component of the tech stack, that a connection made anonymously between an offline email and an online hashed email was considered acceptable for targeting. Or that any one associated in that email address’s household was also sufficient for targeting and helped to provide “scale”. But things are different now. Today, we can and do understand individuals online by utilizing data to deterministically match a CRM file to a known hashed email address. This process enables the identification and connection to each individual customer. (Device Ids can also be used to create connections).

But what about the universal concern of anonymity and PII? Creating associations and deterministically matching only helps group individuals into more targeted and accurate segments. All individuals are still anonymized for targeting and no PII is ever utilized within platforms – that is the job of the onboarder.

At the end of the day, onboarding must be data-centric. Marketers and brands demand individual and deterministic matching. The only way to accomplish this is by doing a lot of data work up front. First, an onboarder needs to work on the data being provided. This includes data hygiene, deduplication, NCOA (National change of address) procedures, and reviewing for data gaps within emails, postal addresses, or intelligence. Ensuring the brand’s data is standardized and able to be accurately matched is the most crucial first step of the onboarding process.

The second step is ensuring that the brand’s data has the greatest chance of being found online. This takes more data work to append as many email addresses, device IDs, mobile numbers etc. as possible to each individual record. Third, that data must then be matched against an onboarder’s identity graph to conclude what the individual deterministic match rate is. Again, a pure data function.

The building of an accurate identity graph is also pure data work. An identity graph is not a licensed consumer file, it is a living breathing entity that needs to be cared for and cultivated in real time. Each day, people experience transitions in their lives. Whether that be a birth, death, new email, new phone, new TV, new home, new car, or college etc., these individual transitions need to be captured, validated, and updated consistently. A true identity graph can achieve this and is therefore considered the foundation for all accurate matching, targeting, performance, ROI, attribution, analytics, programmatic selection and most of all results.

If you are serious about onboarding, stop worrying about scale for scale’s sake and devices by the ton. Instead, focus on making sure your data is being matched accurately on an individual deterministic basis. You will then not only see an increase in the performance of your campaigns, but an increase in your customer’s sense of appreciation and value.

Since the popularization of data, messages and priorities have continued to evolve. First, it was all about collecting data. Using past information about individual customers to answer hypotheses and in some instances to predict future needs and purchases. Next, technologies and intelligence (enter Data Scientists) became readily accessible to the benefit of many.

Data service providers and user-friendly platforms would accept your data, process it, and return it to you cleaned and ready for action. However, as shopping and purchase behaviors have become more complex, increased destinations coupled with increased devices and methods of purchases, a new solution is needed. Enter, data onboarding.

WHAT IS DATA ONBOARDING?
Data onboarding or offline matching is the process of transmitting offline data to an online environment where it is made available for marketing needs. Offline data can be matched at the individual level enabling marketers to produce and delivery highly targeted messaging, or at the household level for more general yet relevant messaging.

WHY ONBOARD YOUR DATA?
The ability to combine offline intelligence with online connectivity has unveiled an abundance of opportunities for marketers.

1. You have more to learn: No matter how much you know about your customers, there’s always more to learn. Data onboarding is the fastest and most effective way to gather more information about your customers. In addition to basic information such as gender, date of birth, demographics, and geographics, you can learn more about their personalities, online browsing habits, search habits, and their favorite places to shop both on and offline.

2. Your data is dynamic: CRM databases are only up to date for a brief period of time. Once a customer gets married, moves, or even changes their preference of ice cream brands, what was once current information becomes dated and effectively incorrect. Onboarding takes your offline data online allowing you to access the most up to date information for accurate targeting, profiling, and segmentation every time.

3. Increase your data value: Onboarding your data to where it can routinely undergo hygiene processing, be continuously enriched with key characteristics, and made available for online customer targeting increases its value both online and off. Lists and segments existing within a data management platform are readily available for monetization and lists pulled and extracted are guaranteed to contain the most up to date information available.

4. Expand your reach: Today’s customer is an omni-channel shopper. From tweens to Baby Boomers, consumers are shopping in store, in apps, on phones, and online. A rich customer profile complete with the most up to date information is required to reach your existing and potential customers wherever and however they choose to shop. Onboarding your data ensures that you have the most effective means of targeting your ideal audience.

5. Enhance the customer experience: Onboarding will make searching, segmenting, cleaning, enhancing, and monetizing your data simpler than ever before, but it will also enhance customer experience . Not only are messages more fine-tuned and appropriate to their intended recipients, they are delivered via the most appropriate channel for maximum convenience.

6. Directly attribute success: Successful campaigns make clients happy, but being able to directly attribute success to the use of your data makes clients loyal. Data onboarding provides a closed-loop solution that can track each individual customer through the sales funnel across all channels and devices over time.