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2015-06-16 06:03:21
Narendra Mulani
June 15, 2015
While the interests in analytics and resulting benefits are increasing by the
day, some businesses are challenged by the complexity and confusion that
analytics can generate. Companies can get stuck trying to analyze all that s
possible and all that they could do through analytics, when they should be
taking that next step of recognizing what s important and what they should be
doing for their customers, stakeholders, and employees. Discovering real
business opportunities and achieving desired outcomes can be elusive.
To overcome this, companies should pursue a simpler path to uncovering the
insight in their data and making insight-driven decisions that add value.
Following are steps that we have seen work in a number of companies to simplify
their analytics strategy and generate insight that leads to real outcomes:
Accelerate the data: Fast data = fast insight = fast outcomes. Liberate and
accelerate data by creating a data supply chain built on a hybrid technology
environment a data service platform combined with emerging big data
technologies. Such an environment enables businesses to move, manage, and
mobilize the ever-increasing amount of data across the organization for
consumption faster than previously possible. Real-time delivery of analytics
speeds up the execution velocity and improves the service quality of an
organization. For example, a U.S. bank adopted such a technology environment to
more efficiently manage increasing data volumes for its customer analytics
projects. As a result, the firm experienced improved processing time by several
hours, generating quicker insights and a faster reaction time.
Delegate the work to your analytics technologies. Uncovering data insights
doesn t have to be difficult. Here are ways to delegate the work to your
analytics technologies:
Next-Gen Business Intelligence (BI) and data visualization. At its core,
next-gen business intelligence is bringing data and analytics to life to help
companies improve and optimize their decision-making and organizational
performance. BI does this by turning an organization s data into an asset by
having the right data, at the right time and place (mobile, laptop, etc), and
displayed in the right visual form (heat map, charts, etc) for each individual
decision-maker, so they can use it to reach their desired outcome. When the
data is presented to decision-makers in such a visually appealing and useful
way, they are enabled to chase and explore data-driven opportunities more
confidently.
For example, a financial services company applied BI and data visualization to
see the different buckets of risk across its entire loan portfolio. After
analyzing its key data and displaying the results via visualizations, the firm
identified the areas in the U.S. where there were high delinquency rates,
explored tranches based on lenders, loan purposes, and loan channels, and
viewed bank loan portfolios. Users were also able to interact with the results
and query the data based on their needs select different date ranges, FICO
scores, compare lenders and loan types, etc. Due to the flexibility and data
exploration capabilities of the interactive BI and visualization solution,
insight-driven decisions could be made and actions could be pursued that would
benefit the business.
Data discovery. Data discovery can take place alongside outcome-specific data
projects. Through the use of data discovery techniques, companies can test and
play with their data to uncover data patterns that aren t clearly evident. When
more insights and patterns are discovered, more opportunities to drive value
for the business can be found. For instance, a resources company was able to
predict which pipelines are most risky from both physical and atypical threats
through data discovery techniques. Due to the insights gained, the firm was
able to prioritize where they should invest funds for counter-failure measures
and maintenance repairs.
Analytics applications. Applications can simplify advanced analytics as they
put the power of analytics easily and elegantly into the hands of the business
user to make data-driven business decisions. They can also be
industry-specific, flexible, and tailored to meet the needs of the individual
users across organizations from marketing to finance, and levels from C-suite
to middle management. For example, an advanced analytics app can help a store
manager optimize his inventory and a CMO could use an app to optimize the
company s global marketing spend.
Machine learning and cognitive computing. Machine learning is an evolution of
analytics that removes much of the human element from the data modeling process
to produce predictions of customer behavior and enterprise performance. As
described in the Intelligent Enterprise trend in the Accenture Technology
Vision 2015 report: With an influx of big data, and advances in processing
power, data science and cognitive technology, software intelligence is helping
machines make even better-informed decisions. As an example, a retailer
combined data from multiple sales channels (mobile, store, online, and more) in
near real-time and used machine learning to improve its ability to make more
personalized recommendations to customers. With this data-driven approach, the
company was able to target customers more effectively and boost its revenues.
Recognize that each path to data insight is unique. The path to insight doesn t
come in one single form. There are many different elements in play, and they
are always changing business goals, technologies, data types, data sources,
and then some are in a state of flux. Another main component of a company s
analytics journey depends on the company s culture itself: is it more
conservative or willing to take chances? Does it have a plethora of existing
data and analytics technologies to work with, or is it just starting out with
its first analytics project? No matter what combination of culture and
technology exists for a business, each path to analytics insight should be
individually paved with an outcome-driven mindset.
To do this, companies can take two approaches depending on the nature of the
business problem. First, for a known problem with a known solution such as
customer segmentation and propensity modeling for targeted marketing campaigns
the company could take a hypothesis-based approach by starting with the
outcome (e.g. cross-sell/up-sell to existing customers), pilot and test the
solution with a control group and then scale broadly across the customer base.
Second, for a known problem area, fraud for example, but with an unknown
solution, the company could take a discovery-based approach to look for
patterns in the data to find interesting correlations that may be predictive
for instance, a bank found that the speed at which fields were filled out on
its online forms was highly correlated with fraudulent behavior. Of note, when
determining which problem to address, companies should first focus on the one
that can offer the highest value, then it can choose a hypothesis-based or
discovery-based approach based on the degree of institutional knowledge it has
to solve that kind of problem.
Once insights are uncovered, the next step is for the business, of course, to
make the data-driven decisions that place action behind the data. It is
possible to uncover the business opportunities in your data and increase data
equity, simply.
Narendra Mulani is the senior managing director of Accenture Analytics.