Of Cavemen, BI and Change Management
“It’s so easy, even a caveman can do it.” GEICO’s advertising campaign from 10 years ago promised customers an easier way to manage their insurance needs online. Today, so much of our personal and professional lives are managed by technology that it’s often difficult to remember how things used to be.
The benefits of Business Intelligence (BI) are well known, and with today’s tools, BI can seem incredibly easy as well. Done correctly, BI can succeed fast – driving new insights into corporate data over a very short timeline. Additionally, it’s “agile,” meaning that it’s not only fast, but can be narrow in scope in order to deliver business value in an iterative fashion.
Ultimately, BI promises to transform data into information from which business insights can be derived. By leveraging BI, companies often hope to transform their approach and attitude towards the environment in which they operate. This shift can be marked as a transition from a reactionary mindset to a strategy that is more proactive in nature.
Although most organizations aspire to be data-driven enterprises, there is the possibility of resistance due to how the new information is utilized. This transformational process can expose companies to a variety of risks that range from delayed project timelines to poor operational adoption of key project deliverables, and depending on where an organization exists on the scale of Analytical Maturity, the risk of cultural rejection can be significant.
First, let’s try to identify Analytical Maturity of BI. Here are the different stages of BI evolution:
- Walking Upright in the Data World: The use-case demand for information
- What this may look like: “I want to see sales growth by warehouse for the past 4 years.”
- The Cave Man Creates Fire: Descriptive reports and scorecards that relate to Key Performance Indicators are developed
- What this may look like: “I want to provide our sales representatives with a quarterly sales scorecard that aligns with corporate incentives.”
- The Renaissance: Discovery phase asking the “what if” and “why” lines of inquiry
- What this may look like: “What are the core commonalities in the bottom quartile of performers?”
- Industrial Revolution: Build predictive, testable models that can answer specific questions
- What this may look like: “If it’s the summer season, our history predicts that we will increase sales of grills, pool chemicals, and lawn equipment 200% from the previous quarter, which we believe will net us $1.2M this summer season from those specific product categories.”
- Technology Revolution: Apply predictive model insights into guided recommendations or operationalized logic-engines that target specific problems
- What this may look like: “If a customer buys pool chemicals in the summer season, offer promotions on grills and / or lawn equipment.”
As demonstrated above, KPIs and scorecards exist at a relatively early stage of our BI evolution. Furthermore, these can typically be done in a VERY short amount of time. Although it requires relatively low effort from a development perspective, implementing a sales scorecard for the first time, for example, exhibits a foundational change as it attempts to objectively comment on performance rather than utilize traditional anecdotal or tribal knowledge.
Now, think of a sales organization that operationalizes this scorecard by changing the formula for bonus payouts. In this scenario, the sales representatives’ bonuses were calculated by gross profit last quarter (aggregate value), but this quarter’s calculation adds a scorecard component of gross profit growth over prior year (incremental value) as this is in-line with the company’s growth targets.
No doubt, situations exist where this new performance indicator can cause substantial stress. Take a sales representative who, for example, has one of the company’s largest accounts that takes up a majority of the representative’s capacity. This account represents a client who has been stable and consistent for years, providing a steady sales volume for the sales representative. Being paid by calculating gross profit only, he or she made a lion’s share of compensation from this big account. With this scorecard deployment; however, the company now decides that it values growth as equal to total volume. The implications of this change can be large; ultimately, how a member of an organization operates on a day to day basis could foundationally change with the introduction of new metrics unearthed during a Business Intelligence transformation.
This is a prime example of a change management issue and how these types of issues can be compounded in the BI space. Although “it’s so easy, even a caveman can do it” is an oversimplification, the tools and technology in place are making it easier to address data needs quickly. Because it is so fast, sometimes the existing culture around information can be outpaced – even if it is spiritually and directionally accurate – and it’s important to be cognizant of these issues when engaging in foundational BI projects. Identifying the change management component early and often, especially by considering an organization’s Analytical Maturity, will ultimately foster the evolution of a data-oriented culture that has the ability to fully embrace BI projects and optimize the derived benefits. To learn more about our approach to BI or Change Management, contact us at firstname.lastname@example.org.