Unstructured, Structured and Standardized Data

Have you ever accidentally put an object in the wrong place and get frustrated when you can’t find the object later on? The function of data repositories and information systems, are to store large quantities of data in the right place, so it can be easily accessed at a later date. The aim is to make the formatting consistent across systems so that different types of data are compatible with each other. Usually, systems will have data models in place that lay out the structure, manipulation and other aspects of the stored data in order to define and format the data to make it more accessible. These types of data are usually separated into three categories: structured, unstructured and standardized.

Structured Data

Structured data is the easiest type to capture and organize in a data model. This is data that lives in a fixed field within a file, like information you would find in a spreadsheet. In order to store structured data, you have to define which fields of data you are planning to store and organize it into rows and columns. An example of this is keeping a database of clients along with names, dates, addresses, currency and so forth. It also may involve restricting the data input in certain ways, such as number of characters or drop-down menus.

The advantage of structured data is that it can be easily entered, stored, searched and analyzed. Data like this is usually managed using a Structured Query Language (SQL). When data isn’t properly structured, it can mean that you’re missing out on some big marketing and advertising opportunities – which we’ll get to in a minute.

Unstructured Data

Unstructured data refers to the information that doesn’t live in straightforward rows and columns. This information is a little more complex and can’t usually be stored in tidy fields. It includes things like email messages, text documents, social posts, photos, videos, web pages and other kinds of documents. The information within these files might be organized, but the data still doesn’t fit into a database in a logical way. Studies have shown that between 80 to 90 percent of data in any company is unstructured – and this number is continuing to grow!

Because so much of every company’s data tends to be unstructured, it makes sense that businesses want to find ways to use this information to make better decisions. The issue is that it’s hard to analyze unstructured data. That’s why a number of different software solutions have been developed in order to try to sort through unstructured data in order to find important information that businesses can use in order to stay competitive.

Standardized Data

Standardized data is data that has been received in various formats and then transformed to a common format that makes it easier to compare the two. This helps make many types of data crisp, clear and easily accessible. For instance, if you have a field for street names, and some people write North Main Street while others write N. Main St., you could standardize the fields to be N. or S. and Street or St. It’s a way of making the data fit more neatly into fields so that search queries can be run more effectively.

Why Are They Important?

Businesses in every industry are scrambling to compile and measure more and more data every day. Structured data can offer vital information about customer interactions and consumer behavior that can help translate into increased sales if dealt with in the right way. Processing unstructured data is, arguably, even more important, but it does present some challenges. Text-based information sources like email, social media interactions and mobile data can often go completely ignored because it’s difficult to compile and analyze. However, this unstructured data can tell us a lot about the customer journey and give us powerful insights into buying habits.

How to Analyze Unstructured Data

There are a number of different, non-traditional ways to analyze unstructured data. Companies can review conversations on social media and analyze customer interactions in call centers or in-house, and then find ways to record them in a structured format. You can also set up automated processes to capture data, such as tools that monitor social media sites or RSS feeds. Figure out which areas are of interest to your customers, and organize this information into relevant categories. In this way, you can work toward standardizing the data to make it easy to compare it with structured data to identify trends and patterns.

What Does This Mean for Marketing?

Social data is powerful for driving your content marketing strategy, as it helps you understand what content to create and how much weight to give each type of content within your plan. You can use metrics from Facebook Insights, for example, to see how well different types of posts are performing. Other tools, like Optimal Social, can offer structured data on what types of content your readers are engaging with.

Another important use of structured data is to boost your search engine rankings. Web crawlers are now trolling sites to look for specific items and keywords, and also to verify that you have high-quality content on your site and are a company that deserves to pop up in user searches. This kind of rich data markup helps Google understand what your content means, not just what it is saying. Google analytics and other site analytics can help you optimize your site and your online content so that Google picks it up first.

About Diggen, Inc.

Diggen is a data marketplace to help marketers become and manage data driven enterprises. Data driven marketing initiatives accelerate growth, since it improves key performance indicator (KPI) metrics and increases revenue over 19%. However, marketers have monumental technical challenges accessing data assets, sourcing data providers, and integrating into their marketing technology stack.

 

Our platform uniquely combines a data marketplace to source all marketing data with middleware to integrate into all marketing technology stacks. For example use our intuitive web interface to append gender, age, location to email addresses and integrate into an email service provider. Therefore, marketers segment their audience for newsletter emails, which creates relevancy, more engagement, and increases conversions.

 

Visit Diggen at http://www.diggen.com.

 

Sources:

  1. http://www.innovatingstuff.com/2012/12/03/structured-and-standardized-data-sources/

  2. http://acumenmd.com/blog/human-condition-structured-unstructured-data/

  3. http://www.webopedia.com/TERM/S/structured_data.html

  4. http://www.webopedia.com/TERM/U/unstructured_data.html

  5. http://digitalmarketingmagazine.co.uk/digital-marketing-data/how-understanding-unstructured-data-is-useful-for-customer-insight/2198

http://www.npws.net/blog/how-structured-data-can-enhance-your-online-marketing

What is a Marketing Technology Stack?

Let’s go on an adventure- a Digital Adventure! Let the data be our map and the marketing tools be our guide. As we journey through understanding the importance of a marketing technology stack, the tools picked in internal data architecture will be largely dependent on your final destination, and the journey you take to get there.

Choosing your Destination:

You never spontaneously jump on a plane without knocking out a few key details prior to flying. In an article written by Scott Brinker, the second largest obstacle for digital marketers, at 39%, is lack of an effective strategy for their marketing technology tools.

When planning a trip it all starts with the final destination. Once we know where we want to go, then we can start researching various modes of transportation, price, comfortability, ease of experience, etc. that will impact our journey upon arrival. If we look at planning your strategy for a marketing technology stack like planning a trip, it all starts with your goals and objectives. The decisions about the tools to use will all stem from having a clear vision of where you want to go.

In the first ever Stackie Awards- we now have the top 4 marketing technology stacks recommended for digital marketers: (Travel Channel has nothing on us!)

  • DataPipe
  • Intelligence Bank
  • UberFlip
  • John Wiley & Sons

(See Stacks)

These 4 stacks are determined by function, buyer journey, system architecture, and technology integration. What’s most important to analyze about these stacks, is every tool chosen keeps the central goal in mind. Every tool added into the marketing technology stack can be integrated and/or stacked together to help the marketer reach their final destination.

Building your Itinerary:

The experiences/activities you pick for your itinerary are largely defined by the objectives of your trip. Do you look to travel for common tourist destinations or do you travel for cultural immersion to gain unique memories that no one else will have? Like travel, your technology stack imparts similar goals. Do you want a simple set of tools that will scratch the surface with tracking data or do you want a full immersion into the depths of data that 25+ tools (when integrated properly) can provide?

Some of the experiences you can get from the tools are:

  • Lead Management
  • Sale Enablement & Automation
  • Analytics and Reporting
  • Data & Programs
  • Content & Social

When pairing your objectives and destination, the amount of tools chosen in your architecture could range anywhere from 5 to 35 and beyond.  

Culture Immersion:

Digital Culture is still relatively new. What’s the proper etiquette for gathering consumer data? Where should your data live? What are the most widely accepted platforms vs. the new platforms that one might consider trying?

When we immerse ourselves into the culture of the journey and the destination, the outcome is comprised of the 4 P’s of marketing- product, price, place and promotion. The technology stack that we use to collect consumer data, influences the external decisions that marketers make for product design, shelf placement, sales distribution models and more.

For example, an untrained shopper that’s never been immersed into the digital marketing realm doesn’t realize that the reason they’re being shown product A vs. product B has been predetermined by a series of algorithms that have studied their search history, buying habits, and other potentially influential data.

When you look at tools in your marketing technology stacks, it’s’ important to pick tools that will grow with you as the increasingly savvy consumers get more accustomed to relinquishing more access to their data.

Another critical factor to consider when selecting tools is the total cost of ownership (TCO). It is evident to determine initial project costs when setting up, but ensure complete success by encapsulating business needs for product maintenance, required people skills, and necessary support processes. Decisions should always be justified by forecasting a return on investments, but also delivering on actual business value.

Alright digital explorers! You’re now ready to begin your digital journey. Remember, the tools you pick in your marketing technology stack will determine the experiences that you create on your journey to marketing excellence.

references:

https://www.ensighten.com/blog/what-marketing-technology-stack-and-why-should-you-care/

http://chiefmartec.com/2015/06/21-marketing-technology-stacks-shared-stackies-awards/

http://chiefmartec.com/2015/10/integrating-marketing-technologies-thats-easy-part/

 

UX Marketing Competitive Advantage

Developing a user experience without consumer data is like trying to diagnose a patient without an examination. Gone are the simple days where marketing could push messaging to consumers with little insight into their responses. In today’s digital world, marketing has morphed into brand interactions and creating brand experiences. UX plays a significant role in the flow of communication, and luring consumers to engage in a buyer journey.

UX and flow of communication

Every interaction between mobile devices, is done through the flow of communication with data. The healthcare sector is the best example for us to breakdown to explain how strong UX is needed to improve systems, continue to collect data, and personalize!

The current challenge

Healthcare is enriched with data and many looming industries that would benefit from it. The challenge is that most of the data collected stays stagnant, and therefore keeps innovation in technology for healthcare stagnant as well. UX can help solve this problem by creating an intuitive and easy flow of data between primary physicians, public health sectors, and other adjacent industries that might benefit from the merging of data.

The solution

“One of the simplest things people want is intimacy with applications,” says Debra Lilley, vice president of cloud services at Certus Solutions. While this is a great quote, it’s not a simple one. Like a medical diagnosis, creating an individualized intimate user experience, has a brevity of variables and considerations; culture, environment, traditions, personal tastes, personalities are all influences on a potential UX strategy.

UX and the competitive advantage

Since healthcare is extremely slow to innovate, it’s creating huge opportunity for startups to enter the market and disrupt the industry:  “Oscar is a health insurance startup that hopes to change the way that people buy and interact with their health care coverage by using technology paired with simple and intuitive design”, Gigaom. Oscar’s competitive advantage is leveraging simple user interface, to make the flow of information and establish connections between providers, physicians and the user. This experience is vastly different from the headache of calling providers, and relying on outdated technology that’s inefficient.

By now you’re probably wondering what’s the tie-in to consumer data? Well, we are consumers of healthcare. Consumer data when it comes to healthcare, can help companies know when you moved, and recommend providers based on where you are now instead of where you were. Consumer data can help marketers know when it might be allergy season and to notify you that you might want to stock up on allergy medication because you’re sensitive to pollen. Consumer data can tell us a lot.

References:

https://www.enterpriseirregulars.com/102419/business-software-trends-update/

http://www.forbes.com/sites/oracle/2015/10/05/3-tech-must-dos-for-a-smoother-user-experience/

http://www.business2community.com/marketing/5-marketing-trends-you-should-be-tapping-01346271

http://globenewswire.com/news-release/2015/10/06/773908/10151831/en/Closing-the-Data-Divide.html  

https://gigaom.com/2015/09/16/why-google-is-taking-a-closer-look-at-disrupting-health-care/

 

Differences in Data

Have you ever been pre-judged by someone before they had a chance to really get to know you? When I look at where data-sets are today, that’s kind of how I feel. We are taking our best guest at personifying individuals based on a series of various data sets that semi-fit together. In our last blog we talked about the importance of content personalization. If we are going to get there, data interpretation is just as important as the data collected about a person.

Different Types of Data

Qualitative vs. Quantitative

The biggest distinction in reading quantitative vs. qualitative data, is whether something can be easily categorized or not. Quantitative data is data that can be categorized numerically. Your shoe size, your height, your income, your zipcode etc. etc. In other words, it’s the demographic information that can be easily collected.. Qualitative data, however, cannot be categorized numerically. In the words of Isaac Newton, “every action causes a reaction”. This reaction, or emotional response can be classified as qualitative data.  

Target, for example, takes to the twitter-world to see the emotional reactions to new product launches. They collect this qualitative data about the individual responses to better understand their customers and move one step closer towards content personalization.

These two data sets go hand-in-hand because you can infer many correlations such as, location eludes towards cultural responses, affluence levels indicate certain buyer behavior, and so on.

First Party Data

Finders keepers! Anything you collect about your customer, is yours to keep. This means your brand gets the first glimpse into your customer’s interaction with your brand. First Party Data is one of the most valuable data sets because you can deploy any data aggregation strategy to understand the exact relationship between you and your customer during the buying journey.

For example, if you want insight into how your customers interact with your website, deploying a heat-mapping strategy to collect data on what images individuals click on might be the best route for gathering this first-hand intel.

Second Party Data

Second party data is like the ultimate tease. A customer may be in a data relationship with someone else, but you’re still benefitting from that relationship. For example, a customer releases the rights for Google AdWords to track their search history but you’re still benefitting from that same relationship with AdWords. It’s common for brands to strategically partner in a data sharing strategy to obtain information that otherwise might be too costly to collect on their own. This is why second party data becomes valuable, and knowing what data you’d need to further complete the personalization puzzle will help define the strategic partnerships you can create.

Third Party Data

This data is the most widely adopted data collection strategy. Marketers depend on data collectors to aggregate intel on customers that they can use to develop a variation of marketing strategies. Unfortunately for third-party data, it’s becoming less common in strategy development as marketers want more first-hand insight aka. first party data.

Knowing what type of data you are collection, can help you figure out what pieces of data are missing that will help you complete the puzzle towards content personalization.

References:

http://www.huffingtonpost.com/advertising-week/turning-intentions-into-c_b_8137128.html

http://www.getelastic.com/beyond-product-recommendations-big-datas-role-in-personalization/

http://www.b2bmarketinginsider.com/strategy/are-you-using-first-party-data-to-drive-personalized-customer-experience

http://www.emarketer.com/Article/Marketers-Put-First-Party-Data-First/1012663

http://marketingland.com/can-marketers-find-best-customer-data-noses-139308

http://marketingland.com/second-party-data-digital-marketers-128254

http://www.signal.co/blog/data-sharing-second-party-data/

Personalization is The New Nirvana

Digital marketing, such as advertisements, we see are like works of art. Creative directors are tasked with composing a variety of elements from realism to sound composition, to developing an advertisement that will grab your attention, keeping your interest, instilling desire, and demanding action. The difference between art and advertising, is art is meant to be enjoyable, whereas an advertisement is meant to fulfill a monetary purpose. In today’s digital world, creating advertisements can get even more complex, such as adding in a layer of augmented reality, that will engulf the targeted customer into an entire experience. The problem creative directors have, is to build multiple works of art that will appeal to a variety of different personalities to drive the same end goal.

Using Data to Develop Marketing Content

True personalization is creating a group segment audience of 1. This is also known as ‘nirvana’. The challenge that presents itself here is that no two individuals are alike. Even the most extreme identical twins have a small degree of variation in their personalities. I want to talk about a concept that I like to call ‘Deep Data’. I want to differentiate this from Big Data, because I think Deep Data as a descriptor to determine a person’s psyche.

There are plenty of Big Data sources available to help creative directors build ads based on high-level segmentation. Understanding demographics, can help creative directors determine basic cultural differences, and develop advertisements that will fit in various markets. Where it starts to become tricky, is gathering the deep data sources. It’s much easier to figure out where someone lives vs. the emotional intelligence of that individual. The other challenge, is most people are willing to give up demographical information, but any info that would be seen as compromising to the person is much harder to attain.

This is where digital trust is crucial if we want to progress towards true personalization.

Gathering Deep Data

Between beacons, IP addresses, learning algorithms, cookies and surveys there are plenty of strategic ways to gather information about a person. The problem, is all this data gets collected and stored in various systems and there isn’t a current solution that combines them all into one. The other issue presented here, is tracking the success of individual campaigns. There are ways to track open rates, impressions, click through’s and conversions, but what this data doesn’t tell you, is why an ad didn’t work with those that didn’t convert, and why it worked with those that did.

As we move towards Nirvana, creative directors and marketers will need to be able to understand each individual and know exactly which mixtures of messages and content will create the efficacy that every business wants without losing the consumer’s trust.

References:

http://www.forbes.com/sites/sap/2015/07/11/marketing-nirvana-engaging-with-an-audience-of-one/

http://searchcontentmanagement.techtarget.com/feature/Location-data-adds-context-for-Web-personalization

http://www.forbes.com/sites/johnrampton/2015/09/03/better-data-enables-better-customer-segmentation/

http://www.martechadvisor.com/marketing-analytics/clickagy-launches-data-driven-content-providing-intelligent-on-page-optimization-using-audience-profiles/

http://www.dtcperspectives.com/getting-to-the-how-unlocking-identification-personalization-and-the-regulatory-landscape/

Crossing the Data Driven Chasm

George Orwell in his book 1984, depicts a society that is overseen by Big Brother. Big Brother in this case, was the government. The Government, utilizing a series of various video monitoring services, were able to control societies every move. Now while 1984 is a tale of fiction, there is some truth to what he wrote. We are entering an era of information overload so companies can create individualized experiences and build one-on-one relationships.  

The Chasm

Chasm, by traditional definition, is a deep fissure in the earth, ie. a canyon, gorge or abyss. In Geoffrey Moore’s book “Crossing The Chasm” he refers to the pivotal moment in which high-tech companies cross the marketing chasm from early adopters to widespread adoption.  

Crossing The Data Chasm

Creating the ToolKit

You wouldn’t cross the grand canyon without the appropriate tools, so why try to develop one-on-one marketing without the proper data sets? Currently on the market, there are various resources that when coupled together get you further to crossing the chasm. For example, pairing implicit data sets from Acxiom with explicit data sets from Keen.io will help you profile your audience while analyzing what they visit most on your website to better understand their interests. However, these sets together, will only get you so far and you’ll probably still fall into the chasm at some point. What this data doesn’t currently do, is offer the ability to import into a SaaS marketing technology like Salesforce, Marketo or Mailchimp, to create individualized profiles. When this can be done, we will be much closer to content personalization and crossing the data driven chasm.

Skills Needed to Cross

Data is useless if marketers don’t understand what it means. If we pack the right tool kit to help us cross the chasm, it won’t mean anything if we can’t figure out where the chasm is that we need to cross. Early adopters that are closer to crossing the chasm are the digital media and technology agencies that are experts in finding the channels, and creating interactive experiences with their audiences. There is a great data divide, however, for smaller businesses to effectively deploy the right toolkits needed to optimize their advertising efforts.

The Data Chasm continues to grow deeper as more data than ever before is being collected through sensors, wearables, tracking and more.The race to cross the chasm has begun, and what we will see happen is the puzzle pieces come together to create the perfect toolkit.  

References:

http://businessvalueexchange.com/blog/2015/07/09/open-data-drives-us-towards-the-information-chasm/

http://exelate.com/resources/news/new-iab-study-reveals-data-divide-early-adopters-leverage-cutting-edge-opportunities-in-marketing-data-but-barriers-remain-to-broader-use-of-new-practices/

https://gigaom.com/2014/05/06/5-technologies-that-will-help-big-data-cross-the-chasm/

Data Personalization

Hi- I’m Fabrice, the Founder and CEO of Diggen. I live in Southern California, but grew up on the East Coast. I’m largely passionate about startups, technology, and a big foodie. So why does this information matter? What’s the point of me giving you an inside look into who I am? Simple. Data Personalization.

The Purpose of Personalizing

Do you prefer these black shoes or those blue shoes? Do you want to be bothered in the morning, or in the evening? In my brief description above, you only learned a few things. I didn’t tell you what types of food I like. I didn’t share where in Southern California I live. Since consumers are complex, the software needs to be as equally complex. As a marketer- it’s your goal to create real-time personalized experiences for consumers, without coming off as creepy. Ultimately creating more relevancy for each consumer, so they become more engaged.

The Problem with Personalization

Consumers are complex. Because there are so many facets to a person, it makes it challenging to know exactly what they are thinking. Our industry has done a horrible job protecting the consumer and establishing digital trust. As a result, consumers are reluctant to release anything about themselves and we, the marketers, end up spending a ton of money on AB testing just to figure out what messages and imagery appeal to different people to create that real-time personalized experience.

But the problem only begins there! The other current challenge we are faced with, is the expectation by consumers to create a personalized experiences. In the ever evolving digital evolution, consumers are more demanding than ever for personalization but they equally demanding that their information is protected.

While 73% of consumers prefer buying from companies that personalize the shopping experience for convenience, 94% are concerned about data privacy and how companies use their data. While these statistics seem conflicting, there are methods to deliver a balance of relevancy and privacy to consumers. “Marketers shouldn’t have to select between relevancy OR privacy, but rather relevancy AND privacy.”

So what’s a marketer to do?

Tweet Quote: “Marketers shouldn’t have to select between relevancy OR privacy, but rather, relevancy AND privacy”

 

Diggen is the middleware. Think of it as the relationship coach in a really bad tug-of-war between partners. Marketers are using more tools and technology to help tailor the experience for customers, but in order to tailor those experiences they need data. There are many data providers out there focused on collecting the data but aren’t necessarily concerned with how the data is being used (which then causes mistrust from the customer). Diggen is the middleman by securely brokering the deal between data providers and the applications using the data. Diggen provides the security customers want, while vetting the data that CRM tools actually need which will then allow businesses to create the personalized marketing experiences that matter.

 

Sources:

http://www.fluiddrivemedia.com/advertising/marketing-messages/

http://www.emarketer.com/Article/Marketers-Stuck-on-Basic-Data-Personalization/1012763

http://www.dmnews.com/marketing-strategy/drizly-chugs-down-data-to-drive-personalization/article/428643/

http://venturebeat.com/2015/07/22/80-of-consumers-have-updated-their-privacy-settings-and-other-barriers-to-personalization/

http://insight.venturebeat.com/state-of-marketing-technology-hyper-personalization?utm_source=vb&utm_medium=refer&utm_content=editorial-post&utm_campaign=somt-personalization-report

http://insight.venturebeat.com/state-of-marketing-technology-hyper-personalization?utm_source=vb&utm_medium=refer&utm_content=editorial-post&utm_campaign=somt-personalization-report

http://0ca36445185fb449d582-f6ffa6baf5dd4144ff990b4132ba0c4d.r41.cf1.rackcdn.com/Make%20It%20Personalized.jpg

https://uploads.www.gigya.com/2015/07/16143453/Gigya_Infographic_2015PrivacyPersonalization.jpg

 

What Marketers Need To Know About Diggen

Now, more than ever, a premium has been placed on the ability to effectively gather and utilize information about people for data driven marketing to successfully create more relevancy for customers. This consumer data is comprised of their background, interests, online activity and behavior, jobs, income levels, and lifestyle. So much so, that big corporations routinely spend millions of dollars to collect, manage and monetize their consumer data.

The situation can leave small and mid-size companies with limited resources operating at a distinct disadvantage. But that will no longer be the case now that we have launched Diggen as the one-stop shop for consumer data.

What it means

Our software platform offers marketers of big and small organizations easier and more economical access to consumer data. We limit the disruption to a marketer workflow by integrating into all the popular marketing tools like CRM software, email service providers, content management services, ecommerce platforms, as well as marketing automation, optimization and a/b testing tools. Diggen will in short order, level the playing field for all companies to access the same consumer data capabilities as much larger corporations.

If you are wondering about the name Diggen, it comes in part from the word digital and in part from the word genome, which refers to the complete set of  genes or genetic material information present in a cell or organism.

At the core, that’s what we intend to accomplish with our middleware technology to enable companies seamless access to valuable and comprehensive consumer data, so they can better understand their target audience, improve key performance metrics, and increase overall revenues.

Starting with basic consumer data attributes, such as age, gender, lifestyle, education, and income level, marketers will begin to understand their audience, create more relevant marketing campaigns, and personalize their website and mobile apps to position their brand and appeal to specific groups that make up their target audience.

No developer requirements

As a data agnostic platform, Diggen will make marketing personalization possible for all businesses that would like to customize their messaging and content to target consumers on an individual level. Diggen’s interface really simplifies the management and flow of consumer data between various marketing tools and other data supply sources without any obstacles.

Our platform and business model will reduce the cost of owning data and eliminate a multitude of development, integration and maintenance costs. Diggen will provide the integration work, ongoing support and all necessary maintenance.

Whatever the information source, as the industry’s premier clearinghouse for consumer data, Diggen can eliminate for good, the technical and cost challenges that prevent marketers from leveraging personalized marketing messaging to engage their target demographic.

The service will make it much easier for C-level marketing, technology and privacy executives to leverage new tools, and technology and maintain mandatory compliance initiatives more effectively with impending regulation demands on consumer data. The benefits will be ongoing as we add new features, bring on more data sources and integrate more marketing tools.

Privacy and trust

With such an important role to play, safeguarding consumer data is paramount and we will make privacy and trust the hallmark of our services. With the sensitive nature of consumer data, it needs to be treated differently because everyone benefits from a more transparent, controlled, and authoritative solution. Diggen will take every measure to provide confidence to all stakeholders including consumers, marketers, developers, platforms and data providers.

It will not only be necessary but practical as we move toward our ultimate vision to provide a platform for consumers to control their personal data, while continuing to deliver tremendous value to marketers.

As a company we are not unfamiliar to data challenges and solutions. Our roots were firmly established in local search data developing and refining business listings. Once responsible for setting the standard on the internet for search fields and data listings, we are now hoping to take consumer data to another level with ease of access, more personalization and an alignment of stakeholders.

If you would like to learn more about Diggen and how it can help simplify your access to consumer data, please fill out our contact form.