Wk6 Case Study Learning Write a 1,000-1,500 word paper including the following headings and content:
Case Overview – Provide an overview of the case det
Write a 1,000-1,500 word paper including the following headings and content:
- Case Overview – Provide an overview of the case details in 400 words or less.
- Research Design – What are 2-4 features of this research design?
- Learning from Data – What are 2-3 insights you learned from this research and data?
- References: One from this study and one additional reference from your course textbooks.
Include at least two P/QCRs (Paraphrase/Quotations, Citation, and Reference) – one from this dissertation and one from one of your textbooks.
Include at least two QCRs from at least two peer-reviewed journals that have been published in the last five years.
- Paraphrase or Quotation
- Citation (In-text APA)
- Reference (APA at the end of the paper in the final section)
Big Companies Are
Embracing Analytics, But
Most Still Don’t Have a
by Thomas H. Davenport and Randy Bean
FEBRUARY 15, 2018
For six consecutive years NewVantage Partners has conducted an annual survey on how executives
in large corporations view data. Each year the response rate increases, and the reported urgency of
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making effective use of data increases as well. This year the results are both more encouraging and
more worrisome than in the past.
Six years ago, the primary focus of questions and answers in the survey was big data, which was
relatively new on the business scene. In the 2018 survey, the primary attention has moved to artificial
intelligence. AI is now a well-established focus at these large, sophisticated firms. There is both a
stronger feeling that big data and AI projects deliver value and a greater concern that established
firms will be disrupted by startups.
The survey includes senior executives from 57 large corporations. The industry group with the most
firms represented in the survey is one of the most data-intensive: financial services. Companies from
the life sciences, manufacturing, telecom, and online industries also participated. The actual
respondents are changing somewhat from the first surveys: It has always involved a high proportion
of C-level executives responsible for data, but this year chief data officers are 56% of the respondents,
up from 32% last year. Only 12% of firms in the 2012 survey had even appointed a chief data officer.
While AI gets the headlines here and elsewhere in the world, the survey addresses both big data and
AI. Terminology comes and goes, but the constant is a data explosion and the need to make sense of
it. Big data and AI projects have become virtually indistinguishable, particularly given that machine
learning is one of the most popular techniques for dealing with large volumes of fast-moving data.
It’s also the case that statistical approaches to AI — deep learning, for example — are increasingly
popular. Therefore, we view traditional data analytics, big data, and AI as being on a continuum.
Virtually all of the respondents (97%) say they are investing in these types of projects.
Perhaps the best news in this survey is that companies continue to believe they are getting value
from their big data and AI projects. 73% of respondents said they have already received measurable
value from these initiatives. That number is half again higher than in the 2017 survey, which suggests
that more value is being achieved as companies grow familiar with the technologies.
The types of value received are perhaps consistent with other previous types of technology.
Consistent with our view that big data and AI are extensions of analytical capabilities, the most
common objectives — and those most likely to achieve success — are “advanced analytics/better
decisions.” Thirty-six percent had that as their top priority, and 69% of those had already achieved
success with the objective. Better customer service and expense reduction are also common
objectives. Just over one-quarter of firms (27%) are pursuing some combination of innovation and
disruption, speed to market, or data monetization initiatives. Data monetization programs had the
lowest priority and the lowest percentage of success (27%).
One of the greatest issues for concern in the survey for large enterprises is the risk of disruption from
new entrants. Almost four in five respondents said they feared disruption or displacement from firms
like those in the fintech sector or firms specializing in big data. The technology judged most
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disruptive is AI — by far. Seventy-two percent chose it as the disruptive technology with the most
impact — far more than cloud computing (13%) or blockchain (7%).
Another important and continuing issue is the slow speed with which these established firms make
the shift to a data-driven culture. Virtually all respondents (99%) say their firms are trying to move in
that direction, but only about one-third have succeeded at this objective. This gap appears every year
in the surveys, and the level of success hasn’t improved much over time. Clearly firms need more-
concerted programs to achieve data-related cultural change. Many startups have created data-driven
cultures from their beginning, which is a key reason why large, established firms fear disruption from
One of the approaches that firms have established to deal with data-driven disruption and change is
to establish new management roles. However, there is still a lack of clarity about how different data-
oriented roles (chief information officer, chief data officer, chief digital officer, chief analytics officer,
etc.) relate to each other.
With respect to the chief data officer role, there is substantial disagreement about the major
responsibilities of the role and what types of backgrounds are appropriate for CDO jobs. Thirty-nine
percent say their CDO has primary responsibility for data strategy and results, but 37% assign that
responsibility to other C-level executives, and 24% say there is no single point of accountability for it.
In terms of backgrounds, 34% of respondents believe the CDO should be a change agent from outside
the company, while 32% believe the person should be a company veteran from inside the firm. Role
clarity in senior data-related roles is critical for both leading AI/big data projects and accomplishing
cultural change. And while all respondents believed it important, the majority of firms still lack an
enterprise data strategy.
This continuing rise in the importance and challenges of big data is one of the most important
features of contemporary economy and society. The survey results over time provide interesting and
useful documentation of this revolution. The rise of AI is only exacerbating this trend. The keys to
success are to determine how your firm should respond, assign clear responsibilities for data strategy
and results, and then move ahead to execute the needed changes in a systematic and effective
Thomas H. Davenport is the President’s Distinguished Professor in Management and Information Technology at Babson
College, a research fellow at the MIT Initiative on the Digital Economy, and a senior adviser at Deloitte Analytics. Author
of over a dozen management books, his latest is Only Humans Need Apply: Winners and Losers in the Age of Smart
Randy Bean is CEO and managing partner of consultancy NewVantage Partners. You can follow him at
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