/ How strategy can fuel or frustrate your big data initiatives (part 2)

Imagine the following conversation between Alice—the CEO of a hot, new augmented-reality startup (Wonderland GO)—and her data scientist, the Cheshire Cat. CEO Alice asks the Cheshire Cat, who is sitting at his desk, “What should we measure in our new executive dashboard?”

The cat asks, “What do you want to achieve?”

“I don’t know,” CEO Alice answers.

“Then,” says the cat, “it really doesn’t matter, does it?”

Without a well-defined set of business goals, it’s hard to know what should or shouldn’t be measured. It’s difficult to anticipate what business questions will need to be answered by the data. Ultimately, without strategic alignment, it’s far more challenging for analytics tools and teams to deliver substantial value to their organizations.

What happens to analytics when the strategy isn’t clear?

When your strategic objectives aren’t well-defined, a number of bad practices can drag down your analytics efforts. If your company is allowing these practices to occur, they may give your organization the false impression it is data-driven. However, the misaligned data created by these practices may actually be doing more of a disservice to your organization than you think.

Mistake #1 – Cherry-picking metrics after the fact. Without the “true north” provided by clear strategic objectives, people can measure their performance by whatever metrics make them feel or look good. Berkshire Hathaway CEO Warren Buffett observed, “At too many companies, the boss shoots the arrow of managerial performance and then hastily paints the bullseye around the spot where it lands.” From managers to ad agencies, too many parties are allowed to use data to mask performance issues without the possibility of remediation.

Mistake #2 – Starting with metrics, not business goals. Organizations often focus on identifying metrics or key performance indicators (KPIs) rather than clarifying their strategic objectives. The danger of this shortcut is that it can lead to metrics that aren’t aligned with your unique strategic priorities. It can lead to tracking metrics that are just easily accessible, ‘what we’ve always measured’ or simply ‘what everyone else is measuring’ rather than what’s truly most appropriate. Instead of optimizing performance in areas that matter to your business, misaligned metrics can lead your employees unwittingly down the wrong paths and away from the business outcomes you really want.

For example, when Kevin Peters took over as the CEO of Office Depot in 2011, the office supply company was experiencing declining sales and profitability even though its in-store metrics for customer service were very positive. Peters wondered, “How could it be that we were delivering phenomenal service to our customers, yet they weren’t buying anything?” He later discovered that they were measuring bathroom cleanliness and fully stocked shelves—metrics that might be important to restaurants or supermarkets—but weren’t as relevant to an office products retailer targeting small business owners. Peters overhauled the company’s in-store metrics and aligned them to their strategic goals focused on driving sales and profitability.

Mistake #3 – Measure & report everything. When the strategic objectives are fuzzy, well-meaning analytics teams often end up capturing and sharing a wide variety of information. They hope some of the data will somehow meet the needs of the business. However, this see-what- sticks approach often generates more noise than signal. People attempting to consume all the shared information can quickly become paralyzed and overwhelmed by the sheer volume of data. In addition, it’s an inefficient use of the valuable analytics talent that most companies have in short supply. Without an ability to clearly distinguish between what is relevant and irrelevant to the business, the analytics team will waste time and effort on capturing, preparing, and analyzing the wrong data.

Mistake #4 – There’s no time for strategy, just “git r done.” In the absence of a clear strategy, most employees are comfortable with just getting stuff done. However, when the focus is purely on day-to- day tasks and responsibilities, you often see an emphasis on activity measures (vanity metrics), not performance measures. While solid tactical execution is important, strategy ensures the right activities are done well. As Sun Tzu stated, “Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat.”

For example, a non-profit organization was continually rolling out new marketing campaigns without a clear, coherent strategy and relied on a “just get the content up” approach. However, nobody defined what they wanted visitors to do based on the campaign content. Time and time again the analytics team would analyze campaign performance and highlight the same gaps (“It’s not clear what the intended goal for this content was, and that lack of clarity was reflected in the user behavior.”). The non-profit’s “git r done” approach led to lots of motion but very little meaningful progress—an unsustainable position for an organization with limited resources.

How to clarify your strategic objectives for analytics success

Most business leaders have a vision for where they want to take their companies and what steps need to be taken to reach their ideal state. The challenge is to convey the strategic vision with enough detail so that your analytics team has a clear understanding of how to align their measurement efforts. The following steps can help you to clarify your strategy and ensure analytics is aligned with it over time:

  1. Break down your vision into key strategic objectives. What are the top 3-5 outcomes your company needs to accomplish over the next 6-12 months?
  2. Simplify your strategic objectives. If you have more than five top priorities, can they be consolidated or simplified without losing something (less is more)?
  3. Verify the alignment of your senior management team. If you asked other senior executives about the top 3-5 outcomes, how consistent are their answers?
  4. Prioritize your strategic objectives. Which objective is most important to your company’s success? Not all objectives carry the same weight, and it may be important to rank them or assign weights to them.
  5. Assign an owner to each key strategic objective. Which senior executive owns each particular strategic objective?
  6. Have each owner provide more detail about how each objective will be achieved. For each strategic objective, what are the measurable business goals?
  7. Make the business goals as specific and clear as possible. If you already have a specific metric in mind, what is the intended target (e.g., $2.5M in revenue)? What is the deadline for reaching the target (e.g., end of Q4 2016)? Are there any scope limitations (e.g., only North American markets)?
  8. Establish a cadence for future alignment. How frequently should the strategic objectives and business goals be reviewed with the analytics team to maintain alignment?


Sir Winston Churchill has been falsely attributed to the following thought- provoking statement about strategy: “However beautiful the strategy, you should occasionally look at the results.” While it may not have been uttered by Churchill, it still speaks truth. The only way you really know if your “beautiful” strategy is working is to measure it. While your analytics team may not always grasp the beauty of your strategy, they will always appreciate its clarity. There’s no room for misaligned metrics in a world that is increasingly managed and driven by data. Business leaders must do their part to remove the strategic ambiguity that might be holding their organizations back from achieving more with their data.

**This article was originally published on Forbes.com on December 14, 2016.