Data is driving decision-making in more businesses and sectors, and in ways that would have been surprising just a couple of years ago.

As in: Oh my god, when you really get down and think about it (and read this article), Netflix’s development of the new buzzed-about show House of Cards was totally driven by data and insights they obtain from mining the habits of their subscribers. In fact, their entire business is only possible through the hyper-intelligent use of data.

And, that’s nothing, it’s just entertainment. Take a look at how that most basic of human drives — the desire to find a mate — is being shaped and recast, made efficient and effective through data wizardry. If, as the online dating companies claim, “20% of us meet our future spouse online,” then eHarmony, Match.com, et al are mining a lot of data with massive impact on the lives of many.

Data is critical to winning in the future of marketing.

As digital ad spending outstrips print and is poised to be the big dog in marketing, data grows right along with it. Down the road, nearly all forms of marketing will be digitized in some way— addressable and targetable. What this means is an even deeper trough of data for marketers, brands, agencies and media companies to mine for insights.

In short, all marketing relies on data but in the big game, the winners will be the ones who can sift through the biggest pile of sand and extract the gold nuggets. We’re seeing more marketers look for solutions to get a handle on the vast trove of data available to them and leverage it for smarter business decisions.

Facebook has a billion consumers on its platform, China has hundreds of millions of folks on mobile phones. All of these people, preferences and actions create data points that are of potential interest to marketers. How do we all benefit from our data being used? Why do we care? In the great value exchange, people will get better targeted messages from brands, some offers and savings, and maybe some interesting (ad-supported) content.

More facets of our lives are being impacted by data — mostly for good — and the onus is on each of us to understand what that really means.

Like the saying goes, “knowing is half the battle.” It’s an established fact that our data is being known, used and monetized by enterprises and entities far and wide. We all have a horse in this data-stakes, a few examples of which are:

• Politicians and government are using data in the democratic process, as in the data-driven Obama election victory in 2012.
• Insurance companies are using tons of data to model and predict who will get sick and cost them more money; or who should get lower car insurance rates.
• The growing use of wearable devices such as the Fitbit. They read our physical status and imagine what we (or others) will do with the data they throw off.

While there are many critical reasons to reform education and job training in this country, here’s one thing we definitely need to do: start preparing a generation of data scientists, analysts and engineers who know how to work with and leverage data to build our tomorrow. I know for sure that our future depends on it. We’re all data now.

Even today, the notion that a consumer can go to a website, be identified, trigger a live auction involving as many as a dozen or more advertisers, and be served an ad in real-time, seems like a marvel of technology. It takes a tremendous amount of hardware and, even more than ever, a tremendous amount of lightning-fast software to accomplish. What has been driving the trend towards ever faster computing within ad technology are new no-SQL database technologies, specifically designed to read and write data in millisecond frameworks. We talked with one of the creators of this evolving type of database software, who has been quietly powering companies including BlueKai, AppNexus, and [x+1], and got his perspective on data science, what “real time” really means, and “the cloud.”

Data is growing exponentially, and becoming easier and cheaper to store and access. Does more data always equal more results for marketers?

Srini Srinivasan: Big Data is data that cannot be managed by traditional relational databases because it is unstructured or semi-structured and the most important big data is hot data, data you can act on it in real-time. It’s not so much the size of the data but rather the rate at which data is changing. It is about the ability to adapt applications to react to the fast changes in large amounts of data that are happening constantly on the Web.

Let’s consider a consumer who is visiting a Web page, or buying something online, or viewing an ad. The data associated with each of these interactions is small. However, when these interactions are multiplied by the millions of people online at any moment, they generate a huge amount of data. AppNexus, which uses our Aerospike NoSQL database to power its real-time bidding platform, handles more than 30 billion transactions per day.

The other aspect is that real-time online consumer data has a very short half life. It is extremely valuable the moment it arrives, but as the consumer continues to move around the Web it quickly loses relevance. In short, if you can’t act on it in real-time, it’s not that useful. That is why our customers demand a database that handles reads and writes in milliseconds with sub-millisecond latency.

Let me give you a couple examples. [x+1] uses our database to analyze thousands of attributes and return a response within 4 milliseconds. LiveRail uses our database to reliably handle 200,000 transactions per second (TPS) while making data accessible within 5 milliseconds at least 99% of the time.

This leads into the last dimension, which is predictable high performance. Because so much of consumer-driven big data loses value almost immediately, downtime is not an option. Moreover, a 5-millisecond response has to be consistent, whether a marketing platform is processing 50,000 TPS or 300,000 TPS.

What are some of the meta-trends you see that is making data management easier (standardization around a platform such as Hadoop? The emergence of No-SQL systems? The accessibility of cloud-hosting?

SS: Today, with consumers engaged more with Web applications, social media sites like Facebook, and mobile devices, marketers need to do a tremendous amount of analysis against data to make sure that they are drawing the right conclusions. They need data management platforms that can absorb terabytes of data—structured and unstructured—while enabling more flexible queries on flexible schema.

In my opinion, classical data systems have completely failed to meet these needs over the last 10 years. That is why we are seeing an explosion of new products, so called NoSQL databases that work on individual use cases. Going forward, I think we’ll see a consolidation as databases and other data management platforms extend their capabilities to handle multiple use cases. There will still be batch analysis platforms like Hadoop, real-time transactional systems, and some databases like Aerospike that combine the two. Additionally, there will be a role for a few special-purpose platforms, just like in the old days we had OLTP, OLAP and special purpose platforms like IBM IMS. However, you won’t see 10 different types of systems trying to solve different pieces of the puzzle.

The fact is we are beginning to see the creation of a whole new market to address the question, “How do you produce insights and do so at scale?”

One of the biggest challenges for marketers has been that useful data is often in silos and not shared. What are some of the new techniques and technologies making data collection and integration easier and more accessible for today’s marketer?

SS: Many of our customers are in the ad-tech space, which is generally at the front-end of technology trends adopted by the broader marketing sector. We are just beginning to see a new trend among some of these customers, who are using Aerospike as a streaming database. They are eliminating the ETL (extract, transformation, load) process. By removing the multi-stage processing pipeline, these companies are making big data usable, faster than ever.

The ability to achieve real-time speed at Web-scale, is making it possible to rethink how companies approach processing their data. Traditional relational databases haven’t provided this speed at scale. However, new technology developments in clustering and SSD optimization are enabling much greater amounts of data to be stored in a cluster—and for that data to be processed in milliseconds.

This is just one new way that real-time is changing how marketers capitalize on their big data. I think we’ll continue to see other innovative new approaches that we wouldn’t have imagined just a couple years ago.

Storing lots of data and making it accessible quickly requires lots of expensive hardware and database software. The trend has been rapidly shifting from legacy models (hosted Oracle or Neteeza solutions) to cloud-based hosting through Rackspace or Amazon, among others. Open source database software solutions such as Hadoop are also shifting the paradigm. Where does this end up? What are the advantages of cloud vs. hosted solutions? How should companies be thinking about storing their marketing-specific data for the next 5-10 years?

SS: A couple years ago nearly everyone was looking at the cloud. While some applications are well suited for the cloud, those built around real-time responses require bare metal performance. Fundamentally it depends on the SLA of the applications. If you need response times in the milliseconds, you can’t afford the cloud’s lack of predictable performance. The demand for efficient scalability is also driving more people back from the cloud. We’re even seeing this with implementations of Hadoop, which is used for batch processing. If a company can run a 100-server cluster locally versus having to depend on a 1,000-server cluster in the cloud, the local 100-server option will win out because efficiency and predictability matter in performance.

What are top companies doing right now to leverage disparate data sets? Are the hardware and software technology available today adequate to build global, integrated marketing “stacks?”

SS: Many of the companies we work with today have two, four, sometimes more data centers in order to get as close to their customers as possible. Ad-tech companies in particular tell us they have about 100 milliseconds—just one-tenth of a second—to receive data, analyze it, and deliver a response. Shortening the physical distance to the customer helps to minimize the time that information travels the network.

Many of these firms take advantage of cross data center replication to include partial or full copies of their data at each location. This gives marketers more information on which to make decisions. It also addresses the demand for their systems to deliver 100% uptime. Our live link approach to replication makes it possible to copy data from one data center to another with no impact on performance and ensures high availability.

Over the last year, we’ve have had customers experience a power failure at one data center due to severe weather, but with one or more data centers available to immediately pick up the workload, they were able to continue business as usual. It comes back to the earlier discussion. Data has the highest value when marketers can act on it in real-time, 100% of the time.

This interview, among many others, appears in EConsultancy's recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger.

A Conversation with Live Ramp’s CEO Auren Hoffman

When all marketers have universal access to an entire world of third party online segmentation data, advertisers are increasingly turning offline for an edge. Leveraging established and deep CRM data, marketers are matching their customer databases to online cookies for targeting and retargeting, and going beyond basic demographic data by bringing multiple data sets into the digital marketing mix. I recently interviewed Live Ramp’s Auren Hoffman to learn more about how traditional databases are getting matched to online cookies, and made available for targeting.

Offline data versus online data. You hear first-party data talked about like it’s the gold standard. Just how much more valuable is a company’s first party data?

Auren Hoffman (AH): First, some clarification: Offline does not equal first-party data; nor is online equivalent to third party data.

The gold standard is not first-party data. It’s the rich knowledge (and capacity for segmentation) that lies in a company’s CRM database, typically tied to a name/address or an email address (including purchase history, direct mail, email campaigns, and loyalty). That knowledge, which is largely (but not exclusively) first-party data, exists almost exclusively offline.

Oftentimes, this specific customer knowledge – first-party data belonging to a brand or business – is augmented by complementary third-party data (for example, zip code-based psychographic typing). Also added into the mix is certain online data (largely transactional, where the customer is known) that has been taken offline (into the CRM database).

This deep customer knowledge has – before now – really only been usable offline (to manage direct marketing, for example). Customer segmentation derived from CRM data is commonly used to target certain audiences with specific messages. That same knowledge has not been – could not be – used to achieve better targeting online through display advertising… until recently.

Companies such as LiveRamp take the knowledge about individual customers from offline CRM databases to form useful and rich customer segmentation that can be “onboarded” – taken online and used for highly-focused display advertising, in a safe and privacy centric way. For example, catalog recipients (from a CRM-driven direct marketing campaign) whom it is known both purchase online and focus on a particular product line in their purchases can be transformed into an online audience with a very focused marketing message. This is what LiveRamp does: translate rich offline data (first- or third-party, or both) into anonymized online segments that can be used to create highly targeted and therefore more effective display advertising. LiveRamp is the only company focused solely on providing data onboarding that can be used to achieve “CRM Retargeting” (using CRM data to enable highly-targeted display advertising).

It should be emphasized that onboarded data is anonymized – that is, unlike CRM data which is frequently used in its individualized form (specific customers tied to an email or postal), onboarded data is aggregated based on customer segments (e.g. a possible segment could be customers who have not purchased from the brand in more than six months) who receive a specific message (e.g. special incentive to return to the brand). So the customer’s privacy is protected, while the customer is still able to receive an offer or message likely to be of specific appeal. With CRM retargeting, brands can target last year’s shoppers with relevant ads about the upcoming holiday season to remind them about your brand’s offer, regardless of if, or when they’ve been to your site.

What kind of offline data should marketers consider bringing online? What offline data do you consider to be the most valuable in terms of audience targeting?

AH: Marketers should consider any data that allows them to create more targeted – and therefore more valuable – segmentation for use in online display advertising; which will vary depending on a brand’s business and messaging strategy. The most valuable such data is that which, when linked with focused messaging, is most likely to achieve resonance with the audience segment. Onboarded data, as noted above, is anonymized; consequently the objective is not to track down and message individual consumers (which would be intrusive), but rather to develop creative messaging to groups of (anonymized) customers (e.g. lapsed customers, or those with particular product or service requirements – for instance, customers with car leases about to expire might well be interested in incentives for a new lease).

Though the most valuable data is likely to be based on transactional history or product/service preferences, it is by no means limited to this. The most valuable data is that determined by the brand to create segmentation – and the accompanying messaging – needed to elicit a positive customer response and in turn ROI.

How should marketers manage their data? Now that data is so cheap to collect, transfer, load, and store the tendency is to make almost every piece of data available for analysis. Where should marketers draw the line? What about recency? Does the cost of keeping certain datasets (transaction events, for example) recent outweigh their potential value?

AH: We’re agnostic on this. (That is, we’re not in the business of managing the data, just bridging the offline/online divide with onboarding expertise.) Each marketer must judge for him or herself the value of data in relation to its potential use for targeted segmentation.

How does it work? Please describe, in layman’s terms where possible, the various methodologies for matching offline data with an online consumer. (cookie matching, key value pair match, etc)

• A brand (or a brand's agency) provides LiveRamp with an encoded CRM safely through our secure upload portal.
• LiveRamp matches your offline data keyed off an email address to an anonymous online audience via cookies with extensive coverage and high accuracy.
• LiveRamp places the online audience on a brand's existing DSP or DMP (or we can suggest one of our partner platform's) & the display campaign runs as normal with a larger, more valuable, and more targeted audience.
• Your customers see a relevant and timely message from your brand
• LiveRamp does not buy or sell data. We do not collect any data from a site, our cookies do not contain PII, and we do not pass any site audience information to any third party.

This interview, among many others, appears in EConsultancy's recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger. He can be reached through his personal blog at www.chrisohara.com

A Conversation with David Spitz

When it comes to the role of the agency in data-driven digital media, few holding companies have put their money where their mouth is to the extent that WPP Digital has. After setting the tone with a bold acquisition of 24/7 Real Media, the holding company has gone on to place strategic bets on a variety of sectors within the Kawaja map. The question for marketers is whether or not they should be relying upon their agencies when it comes to technology and data. Many argue that the agency model cannot support the type of deep domain expertise needed for the complicated integrations, data science, and modeling that has become an everyday issue in modern marketing. So, should data management be the sole province of the Adobes and IBMs of the world, or is there room for agencies to play? I recently reached out to EVP of Strategy and Corporate Development David Spitz to ask about how he sees agencies working with large brands to define their data strategy.

WPP is working with some of the world’s largest brands. I suspect that many have siloed pockets of valuable data across their enterprises. What are the data challenges and, more importantly, opportunities for global brands?

David Spitz (DS): You are right; there are many data challenges across large enterprises. They range from organizational issues (what group or department should even be running these programs?), to legal and commercial issues (do we have the right to the data we want to use?), to skill set gaps, to challenges posed by legacy technologies, to lack of data standards across channels, brands, regions, or even campaigns. In my experience, though, one thing is clear: it is rarely lack of data that is the problem.

The most common single question we hear from the world’s largest brands is “Where do I start?” It helps to have a clear understanding of the opportunities and choose one or two to focus on to build confidence and momentum while keeping in mind what could come next. “Think big, start small” is one of my favorite phrases when it comes to data programs.

In terms of what those opportunities are, it really boils down to what I’ll call the 4 R’s – Reach, Relevance, Resonance and ROI.

Most companies that label themselves as “DMPs” are focused on Reach (e.g., targeting) or sometimes ROI (e.g., campaign evaluation, attribution), and mostly only in a digital sense. That might be a good place to start. However, I have also seen relevance (personalization) and resonance (social amplification) as the jumping off point for some brands. Either way, because these tools exist and can be deployed at relatively low cost, it is often best to start with digital-only applications before expanding the data program into multichannel territory.

Whether you are thinking digital or not, these four areas–Reach, Relevance, Resonance and ROI–probably represent 80% of the data opportunity for big brands, and between them you can usually identify at least one solid quick win.

When it comes to marketing, are these brands looking to their agencies for answers, or are they looking to the IBMs of the world? It seems like the agency’s ability to make an impact ends with the marketing team. Can you extend the agency’s value through to IT teams, and get everyone working together?

DS: When it comes to marketing, brands are absolutely looking to their agencies for answers. It is one thing to come up with an “enterprise architecture” and quite another to have it implemented. In many marketing functions, agencies are on the front lines of where the dollars get expressed, customer engagement happens, and [you are able to] understand what it takes to get data into a place where value can be realized.

Still, do agencies need to do a better job of partnering with CIO’s? Without a doubt. Various WPP companies have in place major partnerships with IBM, Adobe and Infosys to do just that, and at WPP Digital we recently invested in a company called Fabric and acquired a company called Acceleration, both of which specialize in marketing technology systems and, essentially, gap bridging between the CIO and the CMO.

You are working on putting many of WPP’s global data resources together (the “Data Alliance”). Tell us about the project. Is this a global data exchange? Are there unique types of data within the Alliance that are unavailable elsewhere?

DS: Data is at the heart of a lot of what WPP does. You have to realize, WPP is not only the world’s largest communications services group, but if you looked at some of its operating companies as standalone you’d find inside WPP the world’s largest media buying company (GroupM), the second largest market research company (Kantar), and, with $4.7b in revenues coming from digital, including the likes of 24/7 Media, OgilvyOne, Wunderman, AKQA, VML, and Possible, WPP is the seventh largest digital company in the world – behind Google and Apple, but ahead of Facebook right now. So you can imagine, WPP as a whole is dealing with a lot of data.

What we are trying to do with The Data Alliance is analogous to the airline industry, where independently operated carriers have come together to create these inter-company frequent flyer programs (as in the Star Alliance) and coordinated route maps. The whole idea is to provide a more seamless customer experience while at the same time providing efficiencies for the member organizations. Without going into too many details, The Data Alliance is focused on three things: Creating greater interoperability across its members’ platforms and data sets, streamlining how we as a group engage with third-party partners (to make it easier on an Acxiom or Exelate, for example, to work with us broadly), and creating a more seamless experience for clients who are working with us more than one discipline (e.g., media, market research, CRM, and digital).

How we do this will involve many different tactics over time, for example, pooling of certain technology development efforts and greater standardization around certain things like policies, data structures, commercial terms, and API’s. You can speculate about some of the new products and business models that would result out of a program like this, but right now the primary focus is simply on creating the best solutions we possibly can for the top 30 clients who are our “frequent fliers” if you will.

Unlike some other holding companies, WPP has taken an active role in investing in, and acquiring, digital media technology. The “stack” that you are assembling at 24/7 Media, and some of the social media technology investments you have made suggest a commitment to being more than just a typical agency. The Data Alliance initiative is also instructive. Tell us what you look for in differentiated technologies.

DS: WPP comes at it very much from a client-side perspective and has partnered with technologies like Omniture and Buddy Media that share that view. In the cases of those two businesses in particular, both of which WPP invested in, there was beyond the obvious criteria also a strong cultural fit with the management team and a good amount of overlap between WPP’s customer base and theirs, so it just made a lot of sense.

In the case of 24/7, while they were known as a publisher-side technology before WPP acquired them in 2007, the intent was always to leverage their audience reach and technical know-how to build what people would now call a DMP/DSP – the tools that now power Xaxis. There were not any established players doing this at the time, so the 24/7 acquisition enabled GroupM to build these capabilities much faster than they could have otherwise. The acquisition of iBehavior, which operates a DMP of a different sort (mostly offline transactions), is also consistent with this strategy and is similar in that it’s accelerating Wunderman’s route to market with several new initiatives.

To your broader point about being not just a typical agency, I don’t believe agencies need to control all of the underlying technologies, but I do think that the techniques involved in connecting and analyzing diverse data streams – and doing so in a scalable, efficient and privacy-safe way – are too important a skill set for a company like WPP to outsource entirely. When digital is the direction most marketing channels are headed, and the ability to measure everything and act on data is a large part of what makes digital so exciting, not having a data integration and data sciences function (granted, it may be called something else) inside an agency holding company in ten years will seem as unusual as not having a media group would today.

This interview, among many others, appears in EConsultancy's recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger. He can be reached through his personal blog at www.chrisohara.com

A Conversation with Bridget Bidlack of [x+1]

Today, data is like water: Free-flowing, highly available, and pervasive. As the cost of storing and collecting data decreases, more of it becomes available to marketers looking to optimize the way they acquire new customers and activate existing ones. In the right hands, data can be the key to understanding audiences, developing the right marketing messages, optimizing campaigns, and creating long-term customers. In the wrong hands, data can contribute to distraction, poor decision-making, and customer alienation. In order to combat that problem, there are now over a dozen data management platforms (DMPs) configured to help marketers and publishers leverage their first party data, and take advantage of the growing universe of 3^rd party data. I recently sat down with a DMP veteran, Bridget Bidlack, to ask how one should approach choosing a platform.

To the unpracticed eye, it seems like many DMPs do exactly the same things. What are some of the subtleties and differences between the major platforms?

Bridget Bidlack (BB): It’s true that, to someone unfamiliar with the technology, the differences may seem subtle, but that’s often the case no matter what you are discussing. I recently came across a catalog that featured a violin bow for $22,000. To me they all look alike but to a virtuoso the right bow can make all the difference in the world.

That’s the way it is for marketers and the technology they rely on every day. DMPs are very different in the capabilities they provide; the approach and level of integration they are capable of; their ability to adapt to future media channels and market demands; how well they can scale in terms of the amount of data they can ingest, manage and store; and their ability to deliver actionable analytics regardless of the audience touch point.

Smart marketers who evaluate their needs and assess the full range of solutions to find the one best able to suit their needs will benefit today and in the future.

Many DMPs sprang forth from a network background. Is there an advantage to having a heritage in the online media business? Is it better to leverage a “pure play” DMP that has been built from the ground up?

BB: It’s really important to bear in mind the differences between a DMP designed exclusively for display media and an enterprise DMP designed for the needs major brands that require multi touchpoints.

Too often people behave as though display advertising is the be-all and end-all of marketing, and that’s probably true inside an agency. But enterprise marketers have a much broader palette of customer and prospect touchpoints they need to manage. That’s where a purpose-built enterprise DMP really shows its value. So, what are the differences between a display-focused DMP and an enterprise DMP?

• First, an enterprise DMP ingests and normalizes data from a wide variety of sources
• Second, is to automate the way data is organized and segmented
• Third, is to be configurable enough to use an organization’s unique approach to audience identification and data match key models
• Fourth, is to make the enterprise’s unique data actionable across ALL touch points in real time
• Fifth, is to deliver consistent messages and enforce offer eligibility across all channels – not just display,  but important customer channels such as email, click to chat and SMS for example.

You have worked with some of the world’s largest and most aggressive marketers to help them leverage their data. What were some of the challenges you encountered at the enterprise level that surprised you?

BB: This probably doesn't come as a surprise, but in large organizations it is sometimes difficult for individual departments to put the greater good of the overall organization ahead of their own goals. Typically this is because of the way individual departments are measured. It's important to understand the needs of all departments and how an enterprise DMP can help meet those needs. The costs and benefits of DMPs are enterprise-wide and their benefits should be evaluated that way.

Some organizations have created systems that provide DMP-like capabilities. In these situations, a company can weigh the total cost of ownership and benefits of building out the full DMP functionality versus working with an available enterprise DMP. There are a number of factors to consider: speed to market, ROI, domain expertise and consumer privacy, to name a few.

Large organizations have many disparate data sets that are used in many different ways. Sometimes, just getting a list of all the different data sources and attributes is a challenge. Often, there isn’t a shared taxonomy that can be used across departments. Data management and permissions can also become an issue as different departments might have rights and permissions to different data sets that others do not. All of this points to the challenge of finding a unique ID to link all of an organization’s data for a given customer together in a way that makes it accessible and actionable where and when it is needed.

How big is the market for DMPs? How many companies actually have the data challenges that warrant leveraging a “big data” platform for marketing?

BB: The market is growing so fast that this is a difficult question to answer. Any marketer would love to have one platform to reach their customers across any current or future channel. Some marketers might claim they’re comfortable limiting their reach to channel-specific audiences available through specific ad networks or email providers, but that’s rare. Sophisticated marketers want to use the full force of tools, technology and insights at their disposal. They want to use their own data along with third-party data, they want to take into account interactions on their website, as well as those taking place on other marketing channels to inform every message put in front of a consumer. To do otherwise seems like marketing with one hand tied behind your back. Who would choose that?

What are some of the considerations to bear in mind? The number of disparate data systems they are working with, the number of touch points they use to reach their consumers, how frequently the data they depend on is updated, how quickly they need access to the data and the sheer amount of data that they have on their customers. They also need to ask themselves whether their goals can be met with internal systems or by using multiple point products. In most cases it will be more efficient, economical and effective to work with a complete platform able to meet all their needs.

Let’s pretend all current DMPs have exactly the same attributes right now. What should I look for on a DMPs product roadmap to tell me they are going to offer the next great differentiator? Is it Hadoop compatibility? Fast query speeds, based on different storage abilities?

BB: If I were in the market for a DMP and all things were equal, the items I'd like to see in a roadmap would be:

• A robust and constantly expanding set of self-service tools to allow end users to manage and use their data independently and in a scalable way
• Continued investment in analytics and modeling to allow customers to understand data in the ways that will make it work best for them. There should also be a balance of pre-defined reports that provide deep insights out of the box, as well as the ability for users to customize them to meet their own specific needs
• The ability to adapt to emerging market trends and new technologies
• Attribution modeling that provides the ability to implement custom approaches into the media planning, buying and decisioning processes

Integrations seem to be the name of the game. How important are existing server-to-server integrations? Are DMPs becoming truly “plug and play” as they plug into more and more various technologies?

BB: Having open web service APIs is important for any DMP that claims to provide 'plug and play' capabilities. This approach makes it fast and flexible and easy to integrate with new partners, channels and data sources. Without this type of framework, integration can become a nightmare of custom code, delays and missed opportunities.

What about data? Does the company with the most data win? Should I select a DMP based on the ability not just to manage first party data, but for their ability to link my data to the wider universe?

BB: The idea that more data equals better performance is much too simplistic. When it comes to data, the things that matter are how it is filtered, analyzed and put to work to inform decisions. Quantity isn’t the key at all; it’s all about having the right data and being able to act on it to reach customers and prospects at the right time through the right channels.

The ability to centralize, normalize and make data actionable through any touch point needs to be at the core of any enterprise DMP. The DMP should also close the loop by ingesting campaign data from all channels and vendors, as well as offline activities like in-store sales and call center interaction. The data can be surfaced in a way that is meaningful to the marketer. This means marketers need the ability to define custom attribution models to reflect their unique sales funnels. Based on this information, marketers can measure ROI and inform future strategies.

Data is key but it has to be available, accurate and actionable for it to have the kind of impact that marketers demand.

Will be still be talking about “DMPs” in 2 years, or is there another acronym coming along that marketers need to be aware of?

BB: In the future, marketers will continue to invest in learning about and tapping into the latest channels, networks and screens through which consumers are living their increasingly digital lives. Whenever new channels, networks and screens emerge, there will be an evolution and expansion of the data available to marketers. This means that the systems and technologies for ingesting, testing and validating data will continue to be valued – probably even more than they are today.

Smart marketers increasingly understand the importance of being customer-centric and this implies the need to be data-centric. Knowing this they will continue to invest in data management technologies. They will also bring these capabilities in-house as they have in the past with their core CRM and operational data. Even as the hardware and software running their data management platform migrates to the cloud, it will still be viewed as an "owned" solution. This means that the technology companies that marketers partner with to develop and execute their marketing campaigns will need to continue to invest in becoming data savvy and fluent with the tools and systems in the marketplace.

This interview, among many others, appears in the recently published Best Practices in Data Management from EConsultancy, by Chris O’Hara. Chris O’Hara is an ad technology executive, and the author of Best Practices in Digital Display Media, and a contributor to a number of trade publications, including iMediaConnection. He can be reached through his blog at www.chrisohara.com

That’s kind of the way it is with online advertising. Marketers want to go big, reach the biggest audience possible. But it has to be the right audience receiving the message otherwise those ad dollars will go to waste. Understanding and mastering that tension – macro/micro, buckshot/laser, Yao Ming/Spud Webb – is key to targeting success in RTB.

In my current role I often hear companies ask, “How many categories is the right amount in a taxonomy?” This is an interesting question and there are a ton of opinions out there. The IAB has put forth a recommendation as to the structure and makeup of a taxonomy that aims for standardization. It is recommended that companies in the contextual or semantic space follow at least the first two levels in a hierarchical structure, and further granularity is at the partners’ discretion. Google (of course) has their own approach, a taxonomy that’s derived from their keyword search products. What they both have in common is that their taxonomies go deep – too deep.

The nodes have it, or do they?

Companies often tout the size of their taxonomy, seemingly for bragging rights. To me it makes little sense. Say you could find some category in a logical tree structure (if it’s presented that way) out of some 3000 categories. The tiny volume of impressions that can be purchased by targeting the last node in the tree does not make it worth anyone’s time or money. Back in the day I worked for a network that offered a self-service interface for buying contextual categories. We had over 5000 categories in our taxonomy, but no scale in any one category other than the top levels. As a network this made it challenging to engage with clients effectively, and there was not significant repeat business for the product.

Now, things are a little different with RTB. Scale is less of an issue than it has ever been, however if you think about it, having excessive granularity just makes no sense to an advertiser. True, it seems appealing to target only the narrowest topics, but at end of the day all you really succeeded in doing is wasting time and energy setting up campaigns that will fail from the get go. Granularity becomes the enemy of scale. There needs to be a balance between the number of categories and appropriate granularity. When you need a magnifying glass, you don’t use an electron microscope. Talk about overkill.

The semantic advantage

You see, the problem is that those massive taxonomies are actually a legacy of the keyword-targeting approach to online advertising. Once you realize that it becomes clear why these (oftentimes competing and inconsistent) taxonomies have gone haywire. There is a limitless set of keywords that advertisers can choose to target, so it’s axiomatic that the taxonomies that support that approach have to be pumped on steroids.

In contrast, semantic based methods used to derive categories are more accurate and actionable than keyword based methods. Because this approach looks at the entire page, it is able understand the difference between phrases that challenge contextual targeting, like ‘Paris Hilton’ vs. ‘the Hilton in Paris’. This is because semantics looks at the words and how they are structured in a sentence and paragraph to identify patterns of how the words relate to one another

Once you understand the value of a semantic based system vs. a keyword based system, you will understand that keyword based systems are actually less useful to advertisers.

The bigger the nodes, the harder they fail…

Take the 80/20 rule (please!) Let's say 80% of your scale is coming from 20% of your categories. That means that you have 60 categories of scale (or value), to an advertiser.

There are roughly 10B impressions available through RTB. Even if you could classify 90% of that (I only know of one that can and does), your looking at 9B impressions per day. Doing the math, you’ll see that 80% of those impressions will be contained in the 60 or so categories of scale, 25 of those will be top level. That leaves you with 35 meaningful categories and 2940 fragments without meaningful scale or value.

So why the obsession with nodes? It comes from a few things. One is that people like to think that ‘bigger is better’ or ‘the more the merrier’ and so on, and this may be the case with many things, like scale of impressions, or the number of QPS that can be achieved. Another contributor may be from a legacy of keyword targeting. Once buyers become more comfortable with newer, more efficient data to base their decisions on, we’ll see that expansive taxonomies, with their largely irrelevant deep-level categories, will be considered what they actually are: useless.

Balance is the name of this game. Balance between relevancy and scale of impressions.

Using only audience data would be like selecting members of an Olympic basketball team solely based on specific bits of data like age, height, and weight. Even if you’re a junior league coach, you probably know that a lot more goes into the decision of putting together your team; player compatibility, agility, hand-eye coordination, and an interest in the sport itself. Age, height, and weight data need a frame of reference (context), and the same goes for cookie data. All audience data needs context, and that’s where page level, semantic targeting comes in.

The advantages of semantic data are monumental and game changing. Relying solely on cookie data is a dead-end for advertisers. The key differentiator among buyers and publishers will be how companies interpret audience data and semantic data together. Semantics and other page level data can provide valuable information on a page’s content, quality, and safety, allowing buyers to assess the relevancy of the page’s meaning in the context of the ad. The high cost and lack of scalability of cookies further adds to the appeal of semantic targeting.

Another thing to consider if you are targeting a particular cookie list or set of users is there’s a good chance that the data you have is the same exact data your competitors have. This means that you are competing for the same impressions and paying more for those users to see your message.

Semantic targeting provides a solution for these challenges. It gives you more bang for your buck than user data alone by ensuring that your ads are placed on relevant, high quality and safe pages for your audience as well as your brand. In terms of scalability, utilizing semantic targeting data would allow you to scale your campaigns beyond the limitations of user based targeting.

With the increasing amount of inventory and content available today, it is vital to be able to view that inventory in a standard language, or definition. Semantic and page level data targeting is key in gaining visibility and normalization across a growing inventory from multiple sources.

Buyers must look beyond user data and cookies to page level data for impressions that are relevant, providing you with the results you want at tremendous value.