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More than half of work is accomplished by knowledge workers–usually defined as those who must “think for a living” [Davenport, 2005]. I contend that all knowledge workers work with data. Since most learn about data individually (if at all), the opportunity to gain from communal or best practices learning has not been present. Most refer to this as a lack of data literacy. Whether applied at the individual or organizational level, literacy is a binary concept and our data needs are more varied. Data proficiency and data acumen are more descriptive/useful terms and these should also be used to describe today’s organizational data knowledge requirements. This program will describe five specific data knowledge requirement levels and objective behaviors that must be demonstrated by those operating at each level. Lack of this data knowledge has so far hindered society from fully realizing our collective potential benefits. More importantly, organizations adopting these data knowledge requirements can directly and immediately improve organizational knowledge worker productivity. Delegates will:
- Learn why the term data literacy is insufficient to describe the challenge and how the progression from literacy ➜ proficiency ➜ acumen is more operationally viable
- Understand five data knowledge requirements levels in terms of their data leverage type, data skills type, ethical perspective and behavioral focus
- Be able to match data knowledge requirements levels with types of organizational requirements
- Begin to estimate the dollar ranges of potential knowledge worker productivity improvements in their organizations
View the presentation, Achieving Data Acumen: Improving Workforce Data Literacy (and therefore performance!)
Management concerns fall into broad categories:
– Implementing change that effectively improves performance
– Processing lots of information without gaining appreciable insight
– Securing real returns from technology investments Data, of course, is at the heart of all of these and other organizational complaints.
A triple play of investments in data as an organizational asset is
- a necessary prerequisite and
- a primary enabler – permitting management to address these troubling issues.
Transformation may require some organizational discomfort. The first step is to recruit qualified organizational talent. The second step is to significantly overhaul the manner by which most organizations seek to obtain informational value. The third step is to implement a new type of strategy – one that integrates organizational functions and IT. By approaching data management in this different way, organizations can begin to gain the leverage that they seek. This talk will address each of these topics – illustrating how this triple play of new leadership skills, revised value propositions, and a re-positioning of strategic investments can alleviate the concerns.
View the presentation, Triple Play: Leadership, Value & Strategy in Data
Now that we are in the post-big data era (according to Gartner), it is important to take a step back from the hype that has characterized the Big Data Scientist movement of the past few years because two trends have become evident. First, Big Data projects have been about as successful as other IT projects – about 29% successful according to the 2015 Standish Group Chaos Report. Second, Data Scientists are generally assessed to be about 20% productive. The reason for both of these dismal statistics is simple—organizations are terrible about understanding how to use data as a strategic organizational resource. In fact, considering the data is our sole, non-depletable, non-degrading, durable strategic asset, it is really mind boggling how poorly it is managed. Having an actionable data strategy is the first, most critical step in exerting positive control over data and leveraging it in support of your organization’s business strategy. This talk will simply describe how you can: 1) Reduce the amount of data in your organization that is redundant, obsolete, or trivial (ROT); 2) Develop an inventory of the data that you have; 3) Determine how to prioritize among investments in data quality and improvement. Once understood, your organization will be better positioned to support its mission and take advantage of new and existing data sources while complying with relevant laws, regulations, and policies.
View the presentation, Data Strategy: It should be concise, actionable, and understandable by both business and IT!
Competing in today’s information-driven marketplace requires data-focused strategies that enable organizations to be competent, agile and innovative. This requires data to be at the heart of your organizational business model and strategy. So you have figured out your data strategy but how do you go about putting together the roadmap and implementation plan? Utilizing various case studies and client experiences, this workshop will help turn your strategy into action.
Takeaways:
- Getting started – how to identify what’s important and taking the first steps.
- How to create the vision – what artifacts need to be created to layout the long-term value, to show it physically look like and communicate what it’s going to take the organization to get there.
- Understanding how big data and analytics fits into a broader data strategy. In addition, what it takes to build big data and analytics into a broader data strategy road map.
- Identifying and addressing the cultural challenges that get in the way of a data-centric transformation – what are the steps and deliverables to change management?
- How to deliver value while building your data management capabilities – how to develop plans that meet both objectives.
- Case study example artifacts used to implement a data strategy.
View the presentation, Data Strategy: Focus on the Roadmap and Implementation
Reflections on Chief Information Officers (CIOs), combined with decisive performance measurements, indicate that IT management has been asked to do a job that it cannot do well. Data are assets that deserve to be managed as professionally and aggressively as comparable organizational assets. Studies show that approximately 10% of organizations achieve a positive return on their investments in data. In the face of the accelerating "data explosion," this leaves most organizations unprepared to leverage a non-degrading, strategic asset. The redress assigns this vital, lacking function to its rightful owner and driver, the business. While transformation may require some organizational discomfort, we are confident organizations that successfully create CDO will achieve improved organizational performance results that directly and obviously come from better organizational data management stemming from the leadership of the individual occupying the CDO position.
View the presentation, The Case for the CDO
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