PMG Digital Made for Humans

2016 Data Strategy Checklist For Digital Marketers

6 MINUTE READ | December 1, 2015

2016 Data Strategy Checklist For Digital Marketers

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Dustin Engel

Dustin Engel has written this article. More details coming soon.

Congratulations!  You’re just wrapping up a (hopefully) successful 2015.  You’re also well into 2016 planning.  Based on what we’re hearing and seeing, 2016 seems to be the year that advertisers will put data strategies in the lead, more so than any year we’ve seen.  These data strategies can be new forms of CRM activation, new ways to visualize holistic business performance, attribution and advanced data activation.

Silicon Valley has also noticed this trend.  Most noticeable is the growth of cloud-based data services such as Amazon Web Services, Google and Microsoft Azure.  There is also a renaissance in data discovery tools from larger companies such as Tableau, to upstart technology dashboarding companies such as Beckon, OrgamiLogic and Domo.

You can boil this down to one simple reason, there is simply so much data in an advertiser’s hands.  It feels like a good thing to have all of this data, but actually it’s one of the digital industry’s biggest forms of stress on an organization and its leadership.  That stress is the realization that you have an underutilized asset with a high perceived cost and complexity to turn into a competitive advantage.

Insights and data activation (not just the data itself) is the currency that powers today’s digital media channels.  Without insights and the ability to activate data into media channels in a curated and automated manner, advertisers will have that persistent feeling of being left behind.

  1. How usable is my data?:

This is the eternal question of whether the data is clean and can be trusted, and this is indeed the biggest question to answer.

To answer this question, an advertiser needs to conduct a comprehensive audit. The way that PMG approaches a client data audit is a clear breakdown of dimensions (the columns in a data table) and measures (the numbers and attributes that populate the columns) across your key data sources and to showcase errors visually (to showcase the gravity of the error and the prioritization that should be assigned to resolving).

Simplistically, an audit will be comprised of three pieces:

– Dimension Map – A tree hierarchy of key dimensions to identify mapping errors or points of inconsistency.  This identifies areas of improvement in the data system being audited or in the trafficking process when advertisements go out the door.

– Measure Audit – This is where we reference measures being seen across multiple data systems (such as sales) and compare the totals.  The common misconception is that these numbers SHOULD total up to the same amount.  The reality is that this is rarely the case.  Simply put: different data sources see the world in different ways.  It is vital to have experience in your organization or in your data services partner to understand the nuance and know whether inconsistency is expected or is an actual problem.

– Resolution Plan – The plan essentially addresses how you turn unclean legacy data into usable data and how to make sure the data is clean going forward.  What may surprise you is that this is not always as complicated as it seems.  Understanding the error in the data typically leads to a method of repackaging legacy data to make it usable.  It’s never “what’s done is done, so let’s move on.” We’ve also typically found automated processes or a process playbook (with alerting of errors when they are generated) will typically address the data cleanliness going forward.

  1. How much labor is involved in utilizing my data?:

Efficiency is the key when it comes to utilizing your data.  The saying of “too many chefs in the kitchen” is extremely applicable to data.  If your data automation is lacking, you will simply need more bodies to do the work.  The more bodies you have, the more likely you will create divergent processes and more manual errors.  Therefore, investing in a solution to automate your data is key to data survival.

At PMG, we’ve actually made a multi million-dollar investment and dedicated significant time and resources in our homegrown, proprietary data platform to automate data from our clients’ disparate systems; more than 20 platform-specific data sources such as Google Analytics, Facebook Atlas, and Adobe Marketing Cloud with built-in data blending and standardization. Our combined data output is then easily accessible by platforms such as R Studio and Tableau.  We’ve also constructed this data platform on a scalable, cloud-based platform so that we can scale up or scale down to best suit our clients’ needs and provide them with enterprise-grade reliability.

If PMG is not your agency, don’t worry (well, maybe worry just a little). There are many emerging and established solutions in the market.  If you’re a small or medium-sized business, Datahero is a great, inexpensive solution for automating data from data sources such as Salesforce, Google Analytics and Marketo with automated data visualization built into the product.  For marketing organizations in large enterprises, solutions such as BIME and Beckon offer access to API’s and dashboarding services.  It’s getting easier every day to automate and utilize your data.  So don’t just have a plan to get to your data.  Have a plan to use it to the fullest.

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  1. How impactful is my data?:

This is where the art and science come together.   The opportunity for making data impactful is significantly aided by data cleanliness (trust) and automation (ease of data sourcing).  The rest is left to nuance and context.  The digital marketing technology landscape is extremely fragmented. A commoditized view of the technologies that create the data can be a huge pitfall for advertisers.

Understanding the ins and outs of data sources leads to unique ways to blend different data sources (joining or stacking complementary data sources together) to create unseen opportunities or identify areas of improvement.

This nuance and context also enables the statistical modeling.  As typically models need to be contextually fit to be useful, understanding of the source is imperative to make statistical modeling a competitive advantage.

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Lastly, data visualization is an iterative and contextual game.  While the large scale dashboarding companies provide efficiency value that can be easily quantified, we have found that your data visualization and digital strategy should be highly interconnected.

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This is why our data team at PMG has seen the success and generated the client satisfaction that we have over the past 18 months.  Nuance, context and a keen understanding of the media programs we operate for our clients (and the rare few that we don’t).

To account for this nuance and context, it is imperative that you have a data lead on your team that has a comprehensive understanding of your business and is highly experienced in the nuances of digital media.  That will ensure whatever solution you choose will not just generate efficiency, but it will also generate impact.

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In summary, get ahead of your data challenges in 2016.  Once you do, your stress level will drop and your optimism and will grow. And isn’t that what we’re all looking for when as we ring in the new year?

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