Roadmap to Becoming a Data-Driven Organization

Data Strategist Corner Series

Robert Pitney

Date Published:
April 24, 2020

Data analytics is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information to support the decision-making process. Every organization can benefit from an effective data analytics program that uses data insights to more efficiently and effectively accomplish their mission. Developing an enterprise-wide core analytics group — that consolidates analytics initiatives within the organization and facilitate communication between business units — requires significant organizational and cultural change. Cultivating data-driven decision-making starts with the executive leadership.

Change Starts with Executive Leadership

Executive leadership establishes priorities, allocates resources, and secures funding for analytics initiatives. To institutionalize a data-driven culture, it is important for executives to be data literate. Data literacy is the ability, confidence, and willingness to engage with data to acquire information, construct or review analyses, and communicate insights to inform decisions. It is distinct from proficiency; however, the data literate must be familiar with the methods used to create models, apply a critical eye to any insights derived from data, and be able to communicate those findings to non-specialist stakeholders. Being data literate is essential for executives and managers who wish to enact a data strategy that will succeed.

Leadership must encourage and incentivize key employees in the organization to become data literate, to embrace and adopt analytics as a part of their normal business processes, and to incorporate analytics into their decision-making. However, facing resistance is common when instituting an analytics program. Creativity and motivation play an important role in winning over the doubters. We recommend the following strategies:

  • Empathize with the stakeholders.
  • Explain how success will empower them to make even better decisions.
  • Clarify how the tools you are providing will help them to do their job more effectively.
  • Make them part of the process so they can contribute and have skin in the game.
  • Get them excited about how deployment will make their work more effective and interesting.
  • Explain how a failed deployment might deter future attempts at necessary innovations.
  • Use other successful deployed cases or examples to make real the success and benefits at stake.

To help the executive team overcome this resistance we recommend appointing an Analytics Champion. This advocate and change agent disseminates the analytics strategy and fosters an analytics culture where everyone is comfortable using data-based insights to improve the quality and effectiveness of their decisions.

Once the leadership team is on-board, we recommend they form two important committees to guide analytics activities:

1. Create an Analytics Steering Committee

The Analytics Steering Committee (ASC) should consist of key leaders from each business unit (BU) that is practicing or could benefit from analytics. This group will provide the cross-functional and cross-BU leadership and collaboration necessary for a successful enterprise analytics program. The ASC should meet every two weeks and on topics such as:

  • Establishing and guiding short and long-term vision
  • Discussing and prioritizing projects
  • Guiding translation of business questions into metrics
  • Reviewing return on investment and model performance
  • Connecting with business leaders on priorities
  • Cascading communication throughout the organization

The primary obligations of the ASC are to highlight strategic data analytics priorities (i.e., capabilities, data, talent acquisition, tools and platforms, engagement) as well as tactical/operational data analytics needs (i.e. organization and structure). Because selecting projects is one of this committee’s most important jobs, information on candidate projects should be gathered and submitted using a standard proposal format. A project proposal should always start with the business question or problem faced. If analytics projects are not grounded in solving a specific business “pain”, they can become a technology research experiment decoupled from business objectives and deliver little real value.

Proposals for projects should include:

  • Business case justification (including the end users of the results)
  • Metric of measurement and expected ROI
  • Overall approach
  • Assumptions
  • Milestones
  • Timeline and Time Requirements
  • Roles and Responsibilities (including Subject Matter Expert, Project Owner, Staff and their BU)
  • Communication Management
  • Cost

2. Establish A Data Governance Committee

Studies — by MIT, Gartner, and others — have revealed that companies with a data strategy outperform their peers, have greater return on assets, and enjoy greater market value. Data is an organizational asset, and analytics is a tool that can create business value by generating insights and guiding decisions to benefit the organization and its customers or other stakeholders. Unlike traditional assets, data is not consumed or degraded when used, but it does have a lifecycle and so requires an organizational commitment to manage it properly. The role of a Data Governance Committee (DGC) is to establish clear data definitions, develop comprehensive policies, and oversee documentation used by internal business units to collect, steward, disseminate, and integrate data on behalf of the organization. The DGC also manages the availability, usability, integrity and security of enterprise data assets. Key committee activities include:

  • Assessing data compliance and alignment with strategic goals
  • Conducting data inventories and ownership designation
  • Overseeing data collection and generation
  • Driving implementation of records retention and disposal policies
  • Sponsoring information risk assessments and advising the ASC on recommended responses to those risks
  • Reviewing the effectiveness of information security safeguards and incident response

To accomplish this wide set of responsibilities, the DGC must be comprised of members who represent the diverse roles involved in accomplishing the above tasks.

Additional Recommendations

Commit to trying new approaches and encourage others to do the same. From decision-makers down to each employee who supports the organizational mission, analytics will likely challenge long-held assumptions. The process of “gaining insights” implies reaching a deeper understanding.  Most importantly, a willingness to follow these insights is required to actually realize the benefits of data analytics.

Evangelize the importance of data as a valuable asset. Enterprise analytical endeavors provide insights derived from the examined data, so insight quality is bound by the quality of the source data. Success stems from cultures that demonstrate the “data is a core asset” mindset by prioritizing sound data collection and quality assurance. The (US) Foundations for Evidence-Based Policymaking Act of 2018 is federal legislation, passed last year, that enforces many best practices around data management into every federal agency.

Be willing to pivot when it makes sense. A culture of evaluation and improvement encourages being willing to recognize when a process is not meeting its objectives, or a model is not optimal when data suggests it. In such cases, an organization must be ready to pivot its efforts to a better direction. Skill in analytics leads to trust in the measurements of project effectiveness. Pivoting direction is not failure; it’s part of the essential process of continuous improvement.