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Developing an Analytics Strategy: The Role of Culture

Robert Pitney

May 4, 2018

BLOG_Promoting Data-Driven Decisions_The Role of the Culture

Analytics enables organizations to more effectively accomplish their mission by revealing new insights that promote better decision-making.  When first getting an analytics capability going, organizations often focus on the technical building blocks, such as constructing an IT infrastructure, obtaining analytics software, or hiring data scientists and analysts.  However, when developing an analytics strategy they often overlook whether the organization is ready, and willing to develop the “cultural infrastructure” necessary to benefit from those investments.

My previous article, Analytics Assessment: Blueprint for Effective Analytics Programs, explained how an organization’s cultural infrastructure describes the environment where analytics will be developed and deployed.   Even the best models will fail to unlock value for business if they are not actually used to make decisions.  Therefore, when building an analytics strategy it is important to consider the organization’s willingness to adopt the three core tenets of Data-Driven Decision Making (DDDM): 

  • Measurement-oriented: DDDM relies on objective metrics to evaluate progress toward defined goals.
  • Results-oriented: DDDM strives to achieve sustainable results, with an honest accounting of the cost to achieve them.
  • Evidence-based: All decisions have a shelf life dictated by their continued effectiveness and must be regularly re-assessed using the latest evidence.  If new options demonstrate better results, then the organization should pivot to capture the improvement.

These tenets may sound self-evident and easy to implement; yet, five cultural facets, covering C-level executives to frontline workers, must align for DDDM to fully work.

The Five Facets of Cultural Infrastructure

Though analytics is largely a technical discipline, technology alone, even when it produces remarkable insight, is insufficient to unlock the value in an organization’s data assets.  To realize the full potential of data analytics, an analytics strategy must address five cultural facets in this order:

  • Understand and embrace the value of analytics: create a shared vision across the organization.
  • Establish the tone from the top: articulate the vision; set priorities, establish metrics of success, assign responsibilities, expectations and accountability.
  • Identify changes that will unlock business value: encourage a culture of continuous experimentation, evaluation, and improvement.
  • Work together to achieve those changes: establish a collaborative environment.
  • Promote awareness: schedule continued education and learning designed to empower decision-makers to harness data-based insights.

Elder Research evaluates an organization’s cultural infrastructure as a part of our Analytics Assessment service by exploring five areas:

  • Executive leadership
  • Shared vision for analytics
  • Culture of evaluation and improvement
  • Collaborative environment
  • Continuing education

Recognizing that each organization is different, the assessment delivers strategic recommendations to maximize the effectiveness of an analytics program within your organization.  But the five facets above are traits that score well across all organizations who benefit from applying analytics.  Let’s explore each one now in more detail.

Shared Vision for Analytics

The first facet of a data driven culture is a shared vision, which promotes a common understanding of what analytics offers your organization.  This starts with reviewing (or creating) an organization’s mission statement to understand why the organization exists.  This understanding focuses the analytics strategy to support the overall business strategy and can garner the necessary stakeholder support to sustain the analytics program. 

Once a high-level vision is established, two additional activities must be performed:

  • Define the scope and types of analytics: Investment in analytics has costs (resources needed to develop capabilities) and benefits (a more robust Data-Driven Decision-Making process). Just as Rome was not built in a day, this new capability can take months or years to fully develop.  The analytics strategy should include a roadmap to set realistic expectations on the types of analytics and corresponding candidate projects at each phase.  Metrics to evaluate effectiveness at certain milestones, not arbitrary timelines, should determine when an organization moves to the next phase.
  • Commit to and communicate the plan: While it is good to communicate the vision behind an analytics program, frontline workers may ask “What is in it for me?” or “What does it require of me?” To address this, clearly communicate the roadmap, along with its specific definitions of success that translate into the business processes these workers care about.  Establishing a consistent message of “top-down support” for analytics builds trust and reduces barriers to adoption by end users.

Executive Leadership

Armed with an analytics strategy and a roadmap, the next step is to prioritize and allocate resources to implement the technical and cultural infrastructure.  Success with analytics starts with endorsement from senior leadership, but be patient; analytics is an iterative process, and obtaining bottom-line results can take time.  With persistence and an honest examination of where performance fall short, significant benefits can be realized. 

Executive leadership must establish the “tone from the top” that promotes the use of analytics to drive business decisions.  This tone should:

  • Be Persistent: The analytics strategy should establish explicit support throughout all levels of the organization, making it clear that, while specific analytic solutions will always be evaluated for effectiveness, Data-Driven Decision-Making is the future of the organization.
  • Be Realistic: Set measurable and reachable short-term goals that will build momentum for long-term opportunities with greater potential to deliver business value.
  • Communicate Results and Accountability: Reward the persistent, ongoing effort needed to integrate analytics results by recognizing team members who tackle barriers to the data-driven decision making process. Communicate the benefits the company, or individual stakeholders, receive to demonstrate the value of analytics to increase adoption by others.

One way to effectively manage these changes is to designate and support an Analytics Champion.   

Culture of Evaluation and Improvement

With the rapid pace of disruption from new technologies and industries, the importance of continuous improvement cannot be stressed enough. The analytics strategy must call for regular evaluation of the effectiveness of analytics initiatives to keep pace with a continuously evolving business environment.  These evaluations need to be honest, unbiased assessments drawing on any available evidence to guide ongoing improvements.

The third facet of culture is to identify the changes needed to unlock value.  This is where the rubber meets the road for all three tenets of Data-Driven Decision-Making.  Fostering a culture of evaluation and improvement for analytics involves recognizing and rewarding those who:

  1. Are willing to try new approaches: Analytics often challenges commonly held assumptions across the organization. By definition, “gaining insights” achieves a deeper understanding than was known previously.  The willingness to actually follow these insights is the key to realizing the benefits of analytics.
  2. Demonstrate data is a strategic asset: Good data science requires good data, generated internally or obtained through outside sources. The insights from analytics, in part, are only as sound as the data used to generate them.  Once data is considered an asset, activities to generate, protect, and curate it become second nature and are naturally integrated into business processes.    
  3. Generate unbiased numerical metrics: A culture of evaluation and improvement requires resources and creativity to know what to measure and how to effectively measure it. Metrics that objectively and accurately quantify results in a manner free of bias or pre-conceived notions are critical to generating the evidence needed to guide future decisions. 
  4. Pivot decisions based on the evidence: Be willing to admit when a decision is sub-optimal when evidence suggests this is the case. Analysts and decision makers should continually find themselves asking, “What does the data say?”

Collaborative Environment

To achieve the greatest impact, the analytics strategy must ensure that the scope of the analytics initiative is enterprise-wide.  Organizations traditionally maintain disconnected data or process silos that make it difficult for departments to share data and lessons learned. Collaboration, including a willingness to openly share data and insights, enables analytical results to grow organizational knowledge.    

The fourth facet is the need to work together to achieve cultural change.  Realize that the baseline culture may have developed over years, or even decades, of living with silos for data and business processes.  This culture can be deeply entrenched, evidenced by business units that rarely interact for the common good of the organization. 

As technical infrastructure reduces hurdles for data sharing, an organization must overcome cultural inertia to disrupt the status quo. Instilling a collaborative environment across the organization facilitates practices that embrace the tenets of Data-Driven Decision Making.  Each business unit should assess the value of the data available from other business units that could provide new insights.  This requires empowering employees to access analytic results that can streamline processes or improve the quality of their work.  At a higher level, this collaboration should use data to identify synergies and downstream impacts, and to create a holistic view of the customer/constituent experience.  

Continued Education and Learning

Data science is a rapidly evolving discipline.  As described by my colleagues Andrew Fast and John Elder, there are many levels of Analytics, beginning with reporting and descriptive statistics, and growing to predictive and adaptive methods including statistical analysis, data mining, simulation, optimization, and machine learning/artificial intelligence.  As more advanced approaches and new techniques evolve, analytics will answer more complex questions.

The last facet of culture addresses the need to promote ongoing awareness that empowers decision-makers to harness data-based insights to make better decisions.  This requires continued education on the latest approaches and techniques and the benefits they can provide.  This education need not be overly technical, but can instead focus on the types of questions that decision-makers may be able to answer using analytics. 

How to Know When Culture Has Changed

Although the Data-Driven Decision-Making process is our destination, it may not have a well-defined finish line.  As culture evolves, metrics to evaluate the effectiveness of analytics and their impact on the organization will improve.  However, keen observation can provide a good indication that the culture has changed. For example: 

  • Stakeholders note an area that needs improvement when reviewing metrics for analytics effectiveness.
  • Working in a collaborative group, stakeholders from across the organization generate new questions to ask of the data.
  • Realizing there is insufficient data to answer that question, the group mutually agrees to collect the data necessary for future analysis.

Assessing Your Analytics Program

No matter how far along your organization is toward developing an analytics program, an Analytics Assessment or an Analytics Executive Strategy consultation can assess your progress and identify the cultural and technical components that will maximize the effectiveness of your analytics programs.  These assessments provide:

  • Strategies for using analytics to improve organizational decision-making.
  • Recommendations on how to grow analytics capabilities and foster a culture of data-driven decision making.
  • Plans for short-term and long-term analytics opportunities, prioritized based on feasibility and return on investment.

During the analytics strategy session, Elder Research develops an analytics roadmap that ranks candidate project along three dimensions — Cost, Return on Investment, and Actionability — to identify low-cost, high-return actions that foster a Data-Driven Decision culture and build analytic momentum.

Summary

For an analytics strategy to be successful, organizations must invest in technical and cultural infrastructure. Five specific cultural components must be addressed in order to foster an environment where data is used as a strategic asset vital to accomplishing organizational goals. This process can be facilitated by conducting an analytics assessment to inform an analytics strategy that is aligned with overall business goals.

Download our latest Ebook to learn about key considerations and best practices for leading a data analytics initiative. 


Related

Read the blog Analytics 101: Assessing Project Value

Download our brochure on Analytics Assessment: Planning for Success with Analytics

Learn more about developing an effective Analytics Strategy and Assessment


About the Author

Robert Pitney Analytics Engagement Manager Robert Pitney enjoys listening to the needs of clients and finding ways that data can be used to solve problems, increase efficiency, or prevent fraud. Previously, Robert worked six years in the private sector as an IT Systems Integrator followed by eight years in the government sector as an Information Security Manager and an IT Auditor. In these roles, he learned the importance of analyzing data to ensure the continued security and integrity of key information technology systems, and became a Certified Information Systems Security Professional (CISSP) and Certified Information Systems Auditor (CISA). Robert joined Elder Research after earning a Master’s of Science in Analytics from North Carolina State University, from which he also had earned undergraduate degrees in Economics and Computer Science. He actively seeks to cross-pollinate ideas from these fields to maximize positive impacts for clients.