Best Practices for Analytics Organizational Structure

September 30, 2020


How can I structure my organization to effectively leverage my analytics team?

Building a successful and efficient marketing analytics team is no easy task, especially now that technology is constantly booming and evolving, making it hard to keep up. Using a variety of tools and approaches when it comes to garnering data, analyzing it, and then creating actionable strategies is a multi-layered process that can elevate a brand's measurement, analytics, creative, and research abilities to engage with consumers at the right time and place.

It's not just about finding the right tools, however, but finding the right team and encouraging failure. Fostering a test and learn culture, and a culture where team members can take risks, is essential to growth. Ashish Joshi, senior director of global data, analytics, and data science at The Clorox Co., explained at an ANA conference:

"We have a few Ph.D.s, a few masters' degrees, and a few bachelors' degrees. They generally come from quant disciplines. There are people with data science, economics, and math backgrounds. We look for a few key things: quant and technical skills, business acumen, communication and interpersonal skills, and creativity. A lot of the problems we face on a daily basis require us to find creative new solutions."

Joshi also noted, "In terms of business ideas, we've instituted a robust process of testing, and we've run close to 50 in-market experiments. Fifty percent have been successful; the other 50 percent have failed. Our managers would always say, 'Your success rate is too high. You're not stretching far enough.'"

Building a strong foundation first, and then advancing analytics, was a major observation in a recent Gartner study. This approach also means companies need to set objectives and KPIs first to understand what to look for and focus on. For many companies, this means investing in AI capabilities to streamline processes and save time.

Efficiency is a major theme in the latest findings and choosing technology that fits a brand's needs uniquely. Deloitte's 2020 report found that 59 percent of respondents to Deloitte's Global CIO Survey identified AI as their organization's top investment area, with 70 percent likely to gain "AI capabilities through cloud-based enterprise software," and 65 percent likely to create "AI applications using cloud-based development services."

Below are case studies, brand examples, and best practices for analytics organization structures. 

Trends and Best Practices

  • Evolving analytics operating model can help advance companies' digital priorities. Deloitte, 2020.
    Fifty-nine percent of respondents to Deloitte's Global CIO Survey identified artificial intelligence and machine learning as their organization's top investment area. Cloud-based data platforms and analytics-as-a-service have made it easier and more cost-efficient to access analytics technologies through flexible consumption models. Deloitte looked at the trends driving organizations' increasing need for advanced analytics and a new data analytics organizational structure, and the benefits to the organization as a whole:

  • The Secret to a Genius Marketing Analytics Organization. Gartner, April 29, 2019.
    Historically, most companies organized and operated by channels and platforms. But there's a difference between how you organize and how you operate. Today, however, modern organizations are moving toward individual strengths, empowered decision making and an agile framework. By studying 10,000 job postings, Gartner experts uncovered how the best brands create successful marketing analytics organizations. The data study also showed three sets of emerging data and analytics skills: Data engineering, data science and advanced engineering, visualization, and reporting.

  • Here's How to Get the Most Out of Your Marketing Analytics Investment. Marketing Land, April 19, 2019.
    Companies today are clearly not demonstrating consistent return on that investment, a problem which often stems from a lack of marketing analytics leaders and the organizational structure necessary to effectively translate data and insights into action. To discuss this in more detail, Marketing Land chatted with Gartner to explore what CMOs and marketing leaders can do to buck the prediction and drive stronger results for their marketing analytics investment. The conversation solidified five ways CMOs can improve return on their marketing analytics investment, while also reinforcing why it matters:
    • Build organizational structure to apply better data.
    • Develop analytics leaders who bridge both data science with marketing strategy.
    • Hire a Chief Analytics Officer, or up-level the importance of analytics.
    • Focus on better data, not big data
    • Separate the signal from the noise to predict and optimize business outcomes.
  • Building Your Marketing Measurement Dream Team. ANA, September 5, 2018.
    Using data to drive marketing strategy and connect with consumers is a top CMO priority. But it's not an easy rodeo. Even though companies aren't suffering from a lack of data, they are often missing the talent required to lasso it. A sophisticated measurement and analytics program can add considerable horsepower to your marketing, from delivering better customer experiences to creating a competitive advantage.

    But if the team doesn't have the skills or the time to harness insights to drive strategy and decision making, it won't be able to deliver on marketing priorities. More importantly, the company runs the risk of falling behind more astute and agile competitors. To perform to the highest standards, though, every marketing measurement team should look to fill the following positions with the best talent they can find:
    • Director of Data Science and Analytics.
    • Data Translator or Storyteller
    • Data Scientist
    • Market Researcher
    • Systems Integrator
  • Building an Effective Analytics Organization. McKinsey, October 18, 2018.
    McKinsey conducted an extensive, primary research survey of over 1,000 organizations across industries and geographies to understand how organizations convert advanced analytics insights into impact, and how companies have been able to scale analytics across their enterprise. This article discusses how to design, implement, and develop the right organization and talent for an advanced analytics transformation. An advanced Analytics transformation usually requires new skills, new roles, and new organizational structures.


Cases and Examples

  • John Hancock: Managing Consumer Insights Agencies at John Hancock. ANA, December 4, 2019.
    John Hancock explained its approach to managing a wide variety of measurement, analytics, and research vendors. The organization's marketing analytics team plays an important role in efforts like the RFP kick-off and the measurement of media, sponsorship, content partnerships, user experience, attitudinal, and dashboard management.

  • McCormick's Analytics, Marketing Mix and In-Flight Optimization Program Evaluation. ANA, September 11, 2019.
    McCormick revamped its analytics division and procedures to drive growth and optimize business decisions. It uses in-flight optimization, data management platforms, and marketing mix modeling to improve effectiveness, costs, and forecasting. They are working to optimize key drivers of consumption and demand to enable better forecasts. McCormick has shifted away from siloed media planning to an integrated approach that allows it to scale more effectively. Its marketing department is now centralized and media-first, which means it selects the highest return on media tactics and develops creative to fill those tactics, and the media agency is the connective tissue for all partners. The goal is to achieve unified marketing measurement and maximize the profit return on media spend.

  • Clorox: Building an In-House Analytics Team. ANA, December 5, 2018.
    This resource discusses the Analytics team at Clorox. It says: "The Clorox Co.'s 40-person in-house analytics team includes analysts, advanced analytics specialists, and data scientists. In addition to shouldering its responsibilities for core analytic competencies, such as data, technology, models, and insights, the group is also accountable for interfacing with business teams, vendors, and IT.

  • How Should I Structure My Data Team? A Look Inside HubSpot, Away, M.M. LaFleur, and More. DBT, October 29, 2019.
    The data team is a new thing: it's not IT, it's not finance and it's not any of the typical business functions within an operating business. So, who does it report to? How does it interact with the rest of the organization? How big is it? These are all questions that are getting answered in real-time throughout the industry. DBT collected the "reference architectures" from its member companies to look at the how, who and, what of their data teams. One example from Away Travel:


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"Best Practices for Analytics Organizational Structure." ANA, September 2020.