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The 7 Personal Agility Traits That Differentiate Data Transformation Winners and Losers

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The 7 Agile Traits That Differentiate Data Transformation Winners and Losers

Written by: Patrick N. Connally, PMP 

Agility drives transformation. In our modern economy, the greatest industry lifts, and shifts, have been driven by agile business plans, operations, and thinking. Think of Amazon.com’s retail dominance. Think about the popularity of Uber, Lyft and AirBnb – each has transformed the travel and transformation industries. 

In December, I attended a local AgilePhilly seminar featuring Raji Sivaraman, co-founder of AgilityDiscoveries. During the session, she introduced her PA Lighthouse™ Model, built with her co-founder, Michal Raczka. I found that their 7 agilities can be used for organizational agility, especially in the huge ocean of the ‘Data’ world (where I currently focus). 

Data analytics is exploding. Our digital economy generates massive amounts of data. In 2010, the Sloan Digital Sky Survey was collecting 140 terabytes of data, Walmart was collecting 1,000,000 customer transactions every hour (Data, Data Everywhere, 2010). In 2012, IBM estimated that 2.5 exabytes – 2.5 billion gigabytes – of data was generated everyday in 2012 (Wall, 2014). Astounding numbers. But the growth continues. According to MarketingProfs.com, every minute: 

Amazon.com generates $258,751.90 in sales; 

  • 103,447,520 spam emails are sent; 
  • 15,220,700 text messages are sent; 
  • 3.6 million Google searches are executed; and 
  • Americans use 2.6 million gigabytes of internet data (Nanji, 2017) 

The true value of data isn’t in the storage, or amount, of data. Rather, the true values lie in an organization’s ability to generate insights based on the data in their enterprise. Yet, numerous organizations struggle to maximize their data. 

So what drives successful data transformation? Seven Agile traits differentiate data winners and losers: Cerebral, Change, Education, Emotional, Learning, Political and Outcome Agility. 

Cerebral Agility: data (analytics) is highly cerebral. Data is factual, and binary. It tells a concrete story. Putting data to good use requires a critical analysis of current data needs, and data gaps. Absent this insight, organizations will struggle to build and communicate meaningful stories about the impact of their data analysis; 

Emotional Agility: while there is a strong mental connection to data, the emotional connection to data cannot be underestimated. Data tells stories, and sometimes, ugly stories about the (sad) reality of their current or future state. Data is also highly personal. Within organizations, fiefdoms and data kingdoms can arise – HR doesn’t want to share certain data elements; marketing may need and want to understand or leverage key operational / sales data, and the IT group may feel an overall ownership / stewardship over all enterprise data. These challenges and organizational silos require leaders to have high emotional IQs, specifically the ability to adapt, improves and manage numerous stakeholder groups;

Related: Personal Agility for Organizational Agility

Change Agility: almost nothing changes as quickly as data. Data is fluid, changing with each new customer, transaction or social media account. While your data is changing, your stakeholders business needs are evolving. Strong data leaders are able to create and govern in a shifting environment; 

Learning Agility: the data world is exploding with a multitude of tools, technologies and data trends requiring a culture of (constant) learning. Encouraging, and in some organizations, creating a culture where stakeholders and business users constantly seek to gain insight into what data is available, why strong data governance and analysis means, and more importantly, the ability to think forward about the key business outcomes they need to generate; 

Education Agility: new skills are required to take advantage of the data being generated. Increaseily, organizations are becoming less concerned with the volume of data and are (wisely) focusing on the context and relevance of data. Mastering education agility requires leaders to unlock those skills, analytical capabilities, and create multi-disciplinary and cross-functional skill-sets and insights to truly unlock the power of data; 

Political Agility: earlier, I highlighted the organizational silos and leadership challenges facing some organizations. While the data may be simple, generating, and sustaining, political consensus on the value of data may be any data analysts biggest challenge; and 

Outcome Agility: what is the roadmap for your data, and what is your end state. Nothing could be more critical than focusing on the outcome of the story you want to tell. Identifying and generating genuine and sustainable data insights requires leaders to clearly articulate goals, and an attitude of continuous improvement. These attitudes, and the engagement strategies required to sustain them, are pivotal for effective data transformation. Times like this, the old adage ‘if you don’t know where you’re going, any road will get you there’ rings true. Clearly defining your organizational vision, roadmap, and targeted goals requires visionary leaders and inspired constituents. 

Driving successful data transformation requires intentionality. The journey leans on leaders to master more than just a mastery of data and analytics. Driving change and transformation through data analytics require an agile mindset. Leveraging the seven agility dimensions offers the opportunity to effectuate and propagate change and maximize the impact of data and analytics.

References 
Data, data everywhere. (2010, February 27). Retrieved December 26, 2017, from  http://www.economist.com/node/15557443 
Nanji, A. (2017, August 03). The Incredible Amount of Data Generated Online Every Minute  [Infographic]. Retrieved December 26, 2017, from  https://www.marketingprofs.com/charts/2017/32531/the-incredible-amount-of-data-generated-online-every-minute-infographic 
Wall, M. (2014, March 04). Big Data: Are you ready for blast-off? Retrieved December 26, 2017, from http://www.bbc.com/news/business-26383058 
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