Standing up a data analytics organization:

Where to start?

Standing up a data analytics organization: Where to start?

Posted by Jordan Wiggins, Don Miller and Jennifer Baldwin Koger on May 14, 2015.

Data analytics, the science of examining raw data (coming from anywhere internally or externally) with the purpose of drawing conclusions about that information, has been a hot topic for several years. Many companies are racing to develop analytics organizations and resources within their company. Others are tentative, uncertain whether the effort will yield measurable impact, actionable results, and actual benefits.

For most companies, the first challenge of data analytics is determining where to focus to generate specific insights, given a wealth of available data. Many case studies show how a particular function delivered well-applied analytics science to the business, but what does it take to build this capability and organization? Here are four ideas to get started.

  • Vision: Set targeted and specific outcomes
    Building analytics capabilities starts with determining your specific desired outcomes. What are the two or three challenges or goals your organization, customers, or stakeholders want to address or achieve? What do they imagine data analytics can do for them? At Deloitte, we have a series of 3-minute online guides across a broad stretch of topics and industry applications for data analytics to help kick-start your visioning conversations (see them here).
  • Structure: Build centrally, deliver specifically
    Data analytics needs are often identified across nearly every function in an organization. So where do you start? Decentralized analytics resources embedded within functions provide targeted data analysis, but can be challenged to identify cross-functional insights. Building an Analytics Center of Excellence creates an organization model that delivers targeted insights in one or two functions to start, standardizes development of your analytics capabilities, and creates a scalable model for its growth. It’s also a strategic way to make a single analytics initiative broadly impactful.

We also suggest starting with a proof of concept. Pilot projects allow for an organization to develop an analytics baseline, find their footing, and learn what is needed from a technology, data, and talent standpoint to drive business impact from analytics insights. It also enables the organization to better track costs, monitor hiccups, and understand the ROI of its analytics approach before deciding to scale up its analytics resource model. Lessons learned can be applied elsewhere in the organization, whether across business units, service lines, front and back office, or various departments.

  • Capability: Focus on “what” more than “how”
    The heart of a data analytics organization is its industry-focused analytics capabilities and the talent who drive and apply those capabilities. Understanding where to find data, the interrelationships between data elements, and how to structure, analyze, and draw informed conclusions is more important than which tool you use. Tools will certainly accelerate and scale your analytics capabilities and impact, but your talent will inspire and create opportunities for innovation and insight. Strive to be capability-focused and tool-agnostic.
  • Environment: Grow a data analytics ecosystem
    Just as plants require the appropriate environment to thrive and flourish, so does a new organizational function and the new resources brought in to support it. Data scientists and specialists favor collaborative work spaces, such as open office or lab environments, that allow for quick discussions and scrums to iterate solutions. Your organization should also have the appropriate analytics technology or tool licenses to support your analytics talent. As you build your analytics capabilities, that talent will also need structure and a plan for growth, including standardized roles and responsibilities, career paths, and incentives. In consideration of the time and cost to create an appropriate environment, some companies have opted to employ hybrid organization models — mixing external consulting providers with their own resources to accelerate their development.

Remember: Start small, then scale
Companies wanting to stand up an analytics organization are overcoming skepticism and gaining executive advocates by first tackling small projects that yield impactful and measurable results, and then applying that analytics capability to improve strategic decision making across the enterprise. Starting small, showing analytics value, and then scaling your analytics capabilities is an appropriate approach to building an analytics organization that’s both valued and valuable.

Jordan Wiggins Jordan Wiggins, is a principal in the Media and Entertainment Practice of Deloitte Consulting LLP, where he focuses on driving analytics based solutions that enable organizations to utilize raw data more effectively in their future business decision-making process.
Don Miller Don Miller, is a senior manager in the Organization Transformation & Talent Human Capital Practice of Deloitte Consulting LLP, where he focuses on helping clients improve performance through building organization structures to execute new capabilities through their workforce.
Jennifer Baldwin Koger Jennifer Baldwin Koger, is a consultant in the Organization Transformation & Talent Human Capital Practice of Deloitte Consulting LLP, where she focuses on helping clients improve performance and efficiency through technology adoptions, change management, targeted communications, and training.
Contributor: Michael J. Walsh

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