The document discusses advanced analytics and its growing importance for businesses. It notes that advanced analytics uses sophisticated techniques like machine learning and data mining to generate deeper insights from data. While big data and analytics are becoming increasingly important, many companies are unsure how to implement them effectively. The document recommends targeted efforts to work with data and build models, rather than massive overhauls, to maintain flexibility as technologies evolve. It also acknowledges challenges like risk of failure and need for expertise in interpreting probabilistic results.

1. MAKING ADVANCED
ANALYTICS WORK FOR
YOU
SHAUN KOLLANNUR

2. INTRODUCTION
• Big data and analytics have rocketed to the top of the corporate agenda.
Executives look with admiration at how Google, Amazon, and others have
eclipsed competitors with powerful new business models that derive from an
ability to exploit data.
• They also see that big data is attracting serious investment from technology
leaders such as IBM and Hewlett-Packard. Meanwhile, the tide of private-equity
and venture-capital investments in big data continues to swell.

3. INTRODUCTION(CONT. )
• The trend is generating plenty of hype, but we believe that senior leaders are
right to pay attention.
• Big data could transform the way companies do business, delivering the kind of
performance gains last seen in the 1990s, when organizations redesigned their
core processes.
• As data-driven strategies take hold, they will become an increasingly important
point of competitive differentiation.

4. ADVANCED ANALYTICS
• Advanced Analytics is the autonomous or semi-autonomous examination of data
or content using sophisticated techniques and tools, typically beyond those of
traditional business intelligence (BI), to discover deeper insights, make
predictions, or generate recommendations. Advanced analytic techniques include
those such as data/text mining, machine learning, pattern matching, forecasting,
visualization, semantic analysis, sentiment analysis, network and cluster analysis,
multivariate statistics, graph analysis, simulation, complex event processing,
neural networks.

5. WHY ADVANCED AND PREDICTIVE ANALYTICS IS
BECOMING SO IMPORTANT
• The current age of information and digitalization has brought with it new
technologies and methods for improving business operations and maintaining
competitive advantage:
• New big data technologies enable cost-effective storage, processing and analysis of large
amounts of data;
• Modern and intuitive user interfaces allow more user groups to draw insights and make
informed decisions; and
• Advanced analytics software enables better analysis, and analysis of relationships and future
events.
• Since modern techniques and technologies to accelerate or otherwise improve
decisions or processes along the value chain are now widely available, it is important
to carefully evaluate how advanced analytics can be used within your company in
order to keep pace with the competition.

6. HOW IMPORTANT IS ADVANCED ANALYTICS FOR
PROFESSIONALS
• Generally, most companies see advanced and predictive analytics as one of the
more important BI trends in 2017. However, there are a few differences in
viewpoint across various user and company types.
• Best-in-class companies and organizations in North and South America lead the
way when it comes to predictive and advanced analytics.
• On the other hand, the trend is much less important in telecommunications
companies and the German-speaking region of Central Europe.

7. BIG DATA: THE MANAGEMENT REVOLUTION
• According to research by Andrew McAfee and Erik Brynjolfsson, of MIT,
companies that inject big data and analytics into their operations show
productivity rates and profitability that are 5% to 6% higher than those of their
peers.
• Even so, our experience reveals that most companies are unsure how to proceed.

8. SKEPTICISM
• Leaders are understandably leery of making substantial investments in big data
and advanced analytics.
• They’re convinced that their organizations simply aren’t ready. After all,
companies may not fully understand the data they already have, or perhaps
they’ve lost piles of money on data-warehousing programs that never meshed
with business processes, or maybe their current analytics programs are too
complicated or don’t yield insights that can be put to use.
• Or all of the above. No wonder skepticism abounds.

9. SOLUTION
• Rather than undertaking massive overhauls of their companies, executives should
concentrate on targeted efforts to source data, build models, and transform the
organizational culture.
• Such efforts will play a part in maintaining flexibility.
• That nimbleness is essential, given that the information itself—along with the
technology for managing and analyzing it—will continue to grow and change,
yielding a constant stream of opportunities.
• As more companies learn the core skills of using big data, building superior
capabilities may soon become a decisive competitive asset.

10. CHALLENGES
• An important characteristic of advanced analytics projects is the comparatively high risk of
failure.
• Traditional reports only show data. If the data is correct, then reports are highly likely to be
reliable as well, as most modern environments are now quite mature and their reporting
methods and concepts have reached a high level of sophistication.
• However, there is less guarantee that advanced analysis will deliver the results expected.
• Today, a large number of standard algorithms and methods are available for specific use cases
(e.g., customer classification), and new ones are constantly being developed. Finding the most
appropriate one for a dataset depends largely on the abilities of the user and the software used.
• Furthermore, algorithms can also fail due to lack of data (e.g., the customer classification model).
If an advanced analysis project shows that no results can be found, it should be aborted and the
next project started.

11. CHALLENGES (CONT. )
• Furthermore, users of advanced analytics methods should have expertise in
working with probabilities. While classic professional reports almost always
produce the correct numbers, the probabilities generated by advanced analyses
have to be interpreted.
• The quality of a sales forecast or customer classification must therefore not only
be noted and communicated for every single analysis, but also continuously
monitored and optimized.

12. OVERVIEW
• Overall, the data shows that the trend for advanced and predictive analytics is on
the rise. This is especially true in best-in-class, Eastern European companies and
organizations from the United Kingdom and Ireland, as well as the
telecommunications industry.
• Surprisingly, advanced and predictive analytics is seen as less important by
companies from the telecommunications industry this year.

13. BIBLIOGRAPHY
• Making Advanced Analytics Work for You - Harvard Business Review
https://hbr.org/2012/10/making-advanced-analytics-work-for-you
• Advanced and Predictive Analytics: An Introduction
https://bi-survey.com/predictive-analytics

14. THANK YOU FOR READING