At various Gartner events, particularly our Business Analytics and Information Management Summits and Gartner Symposium, we hear some common questions about big data among IT and business leaders. Even as organizations embark on big data initiatives, many still have several vision and strategy questions regarding how to drive the most value from these vital projects. As they incorporate big data assets, organizations will need a set of best practices for information management and governance.
In Gartner’s recent publication, Answering Big Data’s 10 Biggest Vision and Strategy Questions, we expound on the the keys to navigating the nuances of big data, including:
- Knowing how to communicate the value and economics of big data projects.
- Understanding the many business uses and sources of big data — especially those beyond internal data — that are used only for decision making.
- Reconsidering information leadership, organization and skills by taking into account the different analytical skill sets that are required.
- Identifying key success factors that will improve strategy, planning and governance of big data initiatives.
- Developing your big data solutions in the context of the Nexus of Forces (i.e., information, mobile, social and cloud) initiatives in your organization — convergence of these technologies with big data will require rather difficult compromises.
This piece addresses the following most common client questions, and offers insightful answers from my colleagues Alexander Linden, Frank Buytendijk, Andrew White, Mark A. Beyer, Neil Chandler, Jenny Sussin, Nick Heudecker, Merv Adrian and me:
|Question Summary||Answer Highlights|
|Big data hype or substance?||Beyond all the discussions, adoption of big data is simply inevitable.|
|What are others in my industry doing?||Evaluate what leaders are doing in other industries to identify best practices.|
|Range of sources of big data projects?||Operational data, social media and enterprise dark data are all sources for big data.|
|Do we have a big data problem?||Your IT infrastructure should support the growth of big data; and your business should be able to achieve its objectives with the range of data being analyzed.|
|What is the value of big data projects?||The ability to analyze data in new ways, leveraging new sources, all in economically quicker ways.|
|Do we still need a data warehouse?||Gartner predicts that 90% of data warehouses will not be replaced.|
|What is big data analytics?||The application of analytic capabilities on enormous, varied or rapidly changing datasets.|
|What data can we use?||Privacy is both a legal issue and an executive-level ethics issue.|
|What skills do we need for big data?||Beyond data scientists, the systems supporting data scientists will require configuration, administration and management.|
|How does a data scientist differ than a statistician or BI analyst?||Data scientists tend to embody a more inclusive range of skills.|
Many organizations are in the midst of rapidly maturing big data efforts, but still questions and challenges remain. Whether your organization is just embarking on a big data initiative, or if you are further along the maturity curve and looking to take the next step in deriving value, Gartner can provide support and guidance.
Beyond these top-10, what are your biggest questions about big data vision and strategy? And if you’re ready to move on to planning and implementation, see our just-published research on Answering Big Data’s 10 Biggest Planning and Implementation Questions.
Follow me on Twitter @Doug_Laney
Read Complimentary Relevant Research
Top Strategic Predictions for 2019 and Beyond: Practicality Exists Within Instability
Technology-based change is happening continuously, and most organizations struggle to see the change in advance. Continuous change can...
View Relevant Webinars
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.