Big Data Success: Prioritize “Important” Over “Urgent”
Ugh. I see so many organizations get so close to the goal line with Big Data, and then get sidelined by something that is not nearly as important to the business. It is easy to see how these organizations get distracted as they get near the Big Data goal line, because the average CIO and Line of Business executive are continually fighting battles. They are so busy fighting battles that they forget to focus on winning the war.
To be successful, Big Data requires 2 key traits: focus and prioritization. Big Data success requires both the IT and Line of Business leaders to:
- Focus on what’s important to the business, and
- Prioritize “important” over “urgent.”
This is an organizational problem that has plagued organizations for years. This is not a unique problem for Big Data, but maybe is accentuated by the financial and business impact of delayed or lost Big Data-enabled business opportunities.
To get some guidance as to what organizations can do to address the focus and prioritization problems, let’s turn to our old friend Stephen Covey.
Put First Things First
Stephen Covey provided management guidance to this problem many years ago. I am a huge fan of Stephen Covey and his cultural shifting book “The Seven Habits of Highly Effective People.” Covey’s Habit #1: “Begin with an end in mind,” is the underlying foundation for our Big Data Vision Workshop; that before an organization launches their Big Data journey, that the organization first needs alignment and agreement as to what it is they are trying to achieve from a business perspective (a.k.a. business initiative).
Covey also provides guidance on the focus and prioritization challenge with his Habit #3: Put First Things First. Covey’s Habit #3 states (from an organizational perspective):
“Habit 3 is about management—focusing on your organization’s purpose, values, roles, and priorities. What are “first things?” First things are focusing on those things your organization finds of most worth or value. If you put first things first, you are organizing and managing time and events according to the [organization’s] priorities.”
Covey’s Habit #3 comes with a Time Management Matrix that an organization can use to help them to prioritize important over urgent. The Time Management Matrix leads to critical thinking about how organizations need to prioritize important versus urgent when they start focusing on Quadrant II thinking (see Table #1).
The time management decisions for Quadrant I (Urgent and Important) and Quadrant IV (Not Important and Not Urgent) are obvious, though it is amazing how many folks waste time in Quadrant IV (can you say “Candy Crush”?). But the real challenge is prioritizing Quadrant II over Quadrant III.
So when an organization has such a game-changing capability like Big Data (data plus advanced analytics) at their disposal, Covey recommends investing in Quadrant II activities (important but not urgent) that lead organizations to focus and prioritize the important tasks over the allure of the urgent tasks.
Choose Important Over Urgent
To achieve the biggest financial success with Big Data, have a plan and the organizational discipline to do the important things first. Don’t get distracted by the urgent. If you can’t manage the urgent, then you will never get to the important.
But the urgent is easy and tempting. It’s right in front of us. It’s something that we can jump on immediately and get that “productivity high” of checking that activity off of your To Do list. But that’s a false high. And in reality, focusing on checking those urgent tasks off of your To Do list distracts from the more important tasks on which your job and business performance are more likely measured.
This is happening at many organizations today. Even when they have done the envisioning work to drive IT and LOB alignment to identify those high-value/high-feasibility business initiatives, they stall. Many “urgent” tasks all of a sudden get in the way of the “important” such as:
- We’ve got to get our operational systems in alignment first
- We need to fix our data warehouse performance problems first
- We need to speed up our ETL (extract, load and transform) processes first
- We need to wait until we hire our CXO
- We need to gain more experience with [insert tool of the moment] first
- We need to wait until we see what competitor X does first
- We need to wait to make sure that this Big Data thing is for real first
- We’ve got to focus on closing out this quarter first
Lots of reason why one should wait, but organizations miss the most important reason for moving today including optimizing key business processes, uncovering new monetization opportunities and creating a more compelling, more profitable customer engagement.
Importance of the Proof of Value Engagement
So what should organizations do at this point? If the organization truly has done the envisioning work to drive IT and LOB alignment to identify those high-value/high-feasibility business initiatives, then we recommend the Proof of Value step next. This is not a proof of concept or technology – we already know from the multitude of use cases that the technology works. Heck, the NSA has been spying on us with this technology for years now (that clicking when you are talking on the phone isn’t just some random noise).
The Proof of Value engagement serves two purposes (see Figure 1):
- Can the new sources of internal and external data coupled with advanced analytics and data science actually improve the key business decisions that we need to make (“analytic lift”)
- And if we can achieve the “analytic lift”, what is the associated financial return on investment (ROI) from improving the performance of our key business decision
Prioritize Important Over Urgent
Don’t delay. Your organization needs to start realizing the business benefit of Big Data. But to realize those business benefits, the IT and LOB leadership must learn to focus on important versus urgent.
Stephen Covey can help organizations with his Habit #3 and the supporting Time Management Matrix. Understanding the difference and focusing the organizational resources on important over urgent will yield direct and measurable business benefits to your organization including optimizing key business processes, uncovering new monetization opportunities and creating a more compelling, more profitable customer engagement.
 In predictive analytics and data science, lift is a measure of the performance of an analytic model at predicting cases as measured against an existing model or process. Lift is simply the ratio of the improved analytic model response divided by current analytic model response.