January 21, 2019
  |   Analytics, Blog, Digital Marketing, Leadership, Technical Development

Key Strategies For A High Performing Data & Analytics Program

Today’s digital businesses use, on average, 91 cloud services according to Netskope. The utilization of so many technologies creates fragmented data that sits in silos, and as a result, most organizations aren’t able to see a single view of their customer. This data and analytics focused Industry Expert Series will unearth the biggest challenges and root causes facing data-centric organizations today while providing ideas, recommendations and results-oriented solutions for savvy data, analyst, and martech professionals.

This Q&A, with Industry Expert Reid Bryant, will help brands understand the important role humans play in the evolving technological landscape and provide key strategies, tactics, and recommendations on how to optimize the customer journey with human capital and technology.

The Connection Between Humans and Technology

What roles will human creativity play in the evolving technological landscape?

Human creativity and strategic thinking provides a connection between technology the brand deploys and the insights that are created, shared, and monetized. Analytics and Machine Learning (ML) can look at a universe of data, surface rank-ordered insights, and even provide activation. It is, however, a false notion that data analysts and scientists could soon be irrelevant – they (and their teammates) are the solution-oriented personnel necessary for providing direction and keeping technology working seamlessly across multiple solutions.

For every machine that is built, every model that is developed, and every tool and technology that is invested in, there will be a human driving it and providing oversight. It’s important that companies invest in hiring the right people to ensure ML models are properly validated, deployed, and maintained.

Download Blue Acorn iCi’s whitepaper, “How to Amplify the Customer Experience With Analytics” to learn how to attract, convert, and retain shoppers with data.

Some ML applications will auto-deploy experiences based on its observations, while others will simply surface insights for the business to inspect and act at a later time. Either way, an analyst needs to take an insight surfaced by ML applications and survey it to see:

  • Is it real or is it a statistical manifestation of randomness?
  • If it’s real, can/should a brand act on it? And if yes, how so?
  • How will the analytics team guide the creative/UX to design a tailored experience?
  • How will performance be measured with a testing/ personalization tool?

Automation has and will continue to provide value in reducing data janitor tasks and automating portions of the workflow, but successful analytics programs will retain human oversight to ensure insights are real and need to be activated. Not every insight is or should be actionable, and at times success is knowing when to show restraint. Finally, human capital plays a huge role in unifying critical business units to facilitate a brands ability to deliver optimal customer experiences at scale. Analytics and experimentation programs are the activation layers that create insights, craft experiences, and measure performance. Engineering teams build the infrastructure that facilitates such actions by aiding in the process of data acquisition, storage, enrichment, and deployment.

Business intelligence (BI) teams aggregate information and use a specific technology to surface key performance data to the rest of the organization. It’s a combination of these other business units that allow the brand to develop unique solutions that will address the opportunities at hand.

Questions a brand should ask themselves around this process:

  • What infrastructure is needed to get the data from data source to storage and ultimately activation? When are real-time solutions critical?
  • How can we script repeatable tasks so they are constantly running without any human intervention?
  • How do we utilize BI teams and technology to surface this information to the rest of the organization?

Ensuring these questions are answered and a solid workflow is established will allow data to get into the hands of the right business user so they can digest it and take appropriate actions.

Read Part II of the Key Strategies for a High Performing Data & Analytics Program here.

NOTE: This content was originally posted by Tealium in an Industry Expert Series, and can be found here.

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