@ThingsExpo Authors: Zakia Bouachraoui, Yeshim Deniz, Liz McMillan, Elizabeth White, Pat Romanski

Related Topics: @DXWorldExpo, @CloudExpo, @ThingsExpo

@DXWorldExpo: Blog Feed Post

Big Data Success: Prioritize 'Important' Over 'Urgent' | @BigDataExpo #BigData #Analytics

To be successful, Big Data requires two key traits: focus and prioritization

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 two 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).

Table #1: Covey’s Habit #3 Time Management Matrix

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
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”[1])
  • 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

Figure 1: Proof of Value Engagement

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.

[1] 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.

The post Big Data Success: Prioritize “Important” Over “Urgent” appeared first on InFocus.

Read the original blog entry...

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

IoT & Smart Cities Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...