How Strategy Can Fuel Or Frustrate Your Big Data Initiatives

Author

Brent Dykes

December 15, 2016

In a recent study by BARC, companies reported several different benefits from their big data initiatives: better strategic decisions (69%), improved control of operational processes (54%), a better understanding of customers (52%), and cost reductions (47%). Organizations that were able to quantify their gains from analytics reported on average their revenues increased by 8% and costs decreased by 10%. If you’re running a company in today’s increasingly data-intensive economy, you may be wondering what more you can do as a business leader to help your organization tap into similar benefits.

Not surprisingly, the same study found that senior management was a significant determinant (61%) in whether or not an organization was able to successfully embrace analytics. Being willing to invest in the best analytics tools or sharpest analytics talent may seem like the most obvious support you can provide as a business leader. However, there’s something else you can do that’s highly beneficial and it won’t cost you a dime—clarify your business strategy and help align analytics to it.

It seems like a straightforward process—take the key priorities and focus areas of your business strategy and tie them to what can be measured with analytics. However, when compared to the other aspects of analytics such as collecting, integrating, reporting, and analyzing data, determining what to measure is often far more difficult than it should be. Why? Because many organizations routinely fail to properly define and clearly articulate what their core business strategies are.

When Harris Interactive and Franklin Covey surveyed over 23,000 employees some years ago, they discovered 80% of workers reported having no clear line of sight between their tasks and their team and organizational goals. In a recent Australian study, 71% of employees couldn’t correctly identify their company’s strategy. Another MIT study showed that 45% of middle managers couldn’t name even one of their company’s top five priorities. This predicament is similar to a college marching band where the students don’t know what tune to play or which direction to move. While each student individually knows how to play their assigned instrument and can improvise, they will never achieve the band director’s intended vision if they are unsure what to play, where to move, and when to time their actions.

I’ve run into this strategic disconnect on several occasions in my years as an analytics consultant. In one memorable instance, I was in a meeting with 15-20 product marketing managers at a top technology company. We were struggling to define the measurement requirements for their various web properties. At the end of the meeting, one of the product marketing managers suggested I ask their senior management team about their online strategy and then “let us know what it is when you find out.” Ouch.

 

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When the majority of employees might not be clear on their company’s strategic direction, the responsibility for aligning analytics with the business strategy cannot be shirked by senior management. While many executives aspire to make their organizations more data-driven, many fail to recognize how dependent analytics is on having a clearly defined business strategy—and, as a consequence, their responsibility to properly define and clarify the key priorities.

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.

 

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Ideally, a clear strategy will define the success metrics and targets upfront. However, without a clear strategy, too often success is left open to interpretation–and manipulation (Image/Brent Dykes).

 

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.

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This article was written by Brent Dykes from Forbes and was legally licensed through the NewsCred publisher network.

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