Extracting value out of marketing data has always been difficult. Take these three foundational challenges, as examples:

  • Massive amounts of marketing data traditionally require experienced engineers, data scientists, and data visualization analysts to unpack, in order to extract business value.
  • Marketing technologies are starting to solve for the marketing data value solution but typically attack only one or two of the core challenges among engineering, analysis, and visualization.
  • Without solving for all three – engineering, analysis, and visualization -- marketing data will continue to underperform against the investment made in collecting it.

In order to unfurl the latent potential of massive amounts of marketing data, typically, there are three core competencies that are required:

  1. Engineers to collect, extract, normalize, join, and process large data sets across a spectrum of channels, technologies, and locations.
  2. Data scientists and analysts who intimately know your business and can create meaningful queries, algorithms, and models that will automate the value generation process.
  3. A team of data visualization analysts who can interpret and present the data in universally understandable ways.

This array of competencies isn’t native in most organizations, either due to resource limitations, skill deficiencies, organizational alignment or any combination of these. Yet, most businesses understand and yearn for tools that can put them at the heart of the Reuleaux triangle of engineering, data science, and data visualization ... without handcuffing their operations and budgets.

Each day seems to bring forth a new set of tools that looks to resolve these challenges. Many data visualization tools are quite compelling:

  • Ready-built dashboards: Some tools have replaced the need to custom build complex business dashboards that were often too difficult to interpret or maintain, but these tools still require skill positions to develop meaningful interpretations.
  • Data management systems: An increasing number of data management tools aggregate, normalize, and join large data sets, but still have difficulty crossing borders like known and unknown.
  • Automation tools: Finally, there are a growing number of tools that try to automate the interpretation of data through canned models or algorithms, yet still fall short as a way to answer simple questions specific to a business.

What this all means for marketers is an ever-growing set of complex realities that they will need to navigate if they hope to achieve any form of value from their marketing data.

Let’s take a look at this three legged stool one leg at a time.

Reality 1, Engineering: Marketing data is increasingly siloed, fragmented, and immovable.

We used to look for data transportability, but now we must now seek application transportability.

More and more data sets will live in a state of rest within its respective environment where the core data can never leave; the analysis must occur within its walls and the extract must be obfuscated.

What is therefore required is a technology solution that can move to where the data is located, query within its walls, and pull cohort level analysis that can be utilized across environments.

Reality 2, Data Science: Unlocking the truth behind the data is difficult under the best conditions.

What's more, the more robust and complex the data sets become, the more resources this will require.

For the last few years, it has become apparent that data science is the place to be. Given the number of schools building programs around data science, it’s clear that this is a long term career opportunity.

However, this trend doesn’t solve the short-term need marketers have to solve real challenges in building the models and algorithms necessary to power value generation from their data.

Without enough meaningful resources holding up this leg of the stool, marketers will continue to achieve surface level value from their data and never truly unlock the uncommon commonalities hidden in their data sets.

Reality 3, Visualization: Democratizing access to meaningful renderings specific to job function and role is critical.

Countless new and extremely powerful tools are available to furnish data and build compelling dashboards.

For the past few years, the problem has not been in the technology, but in how that technology is wielded. Templates and canned dashboards will typically only add to the confusion, because they are trying to do too much for too many.

Ultimately, marketers will require self customizing dashboards that answer specific business questions in ways that are meaningful to the role or function of the end user.

There is an old adage that says: good, fast, cheap … you can only pick two. In this case, without effectively covering all three core components of the data value exchange – engineering, data science, and data visualization analysis – marketing data will continue to suffer from the cycle of over investment in limited solutions.

Habu Data Clean Rooms cover all three core components. If you'd like to see it in action, we welcome you to request a demo.