Simplify your analytics strategy

SIMPLIFY YOUR
ANALYTICS STRATEGY
SHAUN KOLLANNUR

INTRODUCTION
• 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.

FIRST INSIGHT
• 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.
• Real-time delivery of analytics speeds up the execution velocity and improves the
service quality of an organization.

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.

SECOND INSIGHT
• 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.
• 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.

SECOND INSIGHT (CONT..)
• 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.

“Recognize that each path to data
insight is unique”.
The path to insight doesn’t come
in one single form.

• The real-time delivery of analytics could help a company speed up the execution
of analytics projects and improve the overall service quality of an organisation.
This can be done by accelerating and liberating data through a data supply chain
that’s built on a hybrid technology environment. By combining a data service
platform with emerging big data technologies, for instance, businesses can
manage, move and mobilise the ever-increasing amount of data across the
organisation for use at a much quicker pace than previously possible.

• Managers should insist their company executives to make a hypothesis/
discovery-based approach to solve business problems. • They should make
intellectual decisions by relying on Business Intelligence.
• Managers should be able to uncover the data patterns using data discovery
methods. • Advanced analytics can help managers handle their inventories.

EXAMPLES OF THESE TECHNOLOGIES INCLUDE:
• Analytics-as-a-Service. Cloud-based analytics solutions can increase an organisation’s speed to insight,
decision-making and business outcomes. For example, the Accenture Insights Platform is an analytics-
as-a-service solution that is flexible, contains leading technologies and follows a consumption-based
commercial model that eliminates the need for large, upfront capital expenditures from businesses.
Through this approach, companies can receive real-time actionable insights and enable their decision-
makers to drive change for a competitive advantage.
• Next-Gen Business Intelligence (BI) and data visualisation. Next-gen business intelligence brings data
and analytics to life to help companies improve and optimise their decision-making and organisational
performance. BI turns an organisation’s data into an asset – it places the right data at the right place
(mobile, laptop, etc.), at the right time, and displayed in the right visual form (charts, heat map, etc.) for
each unique decision-maker, so they can use the insights to reach their desired outcome. When the
insights are presented to decision-makers in a visually appealing and useful way, they are enabled to
more effectively pursue and explore data-driven opportunities.

• Machine learning and cognitive computing. Machine learning can take place alongside outcome specific data
projects. At its core, machine learning is an evolution of analytics that leverages large amounts of data – sensor,
social, internal, external, etc. – to identify and define new associations between the data to discover solutions. With
machine learning, data scientists are usually needed to determine the set of algorithms that can solve business
problems and learn from the data to find patterns, and apply its knowledge to future situations. As an example
machine learning benefit, a company would be able to learn from past consumer behaviours and predict behaviours
of new customers. Machine learning is also a key enabler to cognitive computing, where automated systems start
behaving with almost human intelligence to sense, understand and act.
• Analytics applications. Advanced analytics applications place insights into the hands of individual business users,
enabling them to make data-driven business decisions. The applications can also be industry-specific, flexible, and
tailored to meet the needs of the individual users across organisations — from finance to marketing, and from C-
suite to middle management to data scientists. For example, a supply chain manager could use an application to
support decision making to optimise the company’s inventory and a CMO could use an application to more
effectively manage a company’s global marketing spend.

• For example, for a known problem with a known solution — such as customer
segmentation for targeted marketing campaigns — the company could take a
hypothesis-based approach by starting with the outcome (e.g. up-sell to existing
customers), pilot and test the solution with a control group and then scale
broadly across the customer base. Alternatively, for a known problem area such
as fraud that could have an unknown solution, the company could take a
discovery-based approach like machine learning to look for patterns in the data
to find interesting correlations that may be predictive.

• After key insights are uncovered, the next step is for the business to make the
data-driven decisions that place action behind the data. By accelerating data,
delegating work to analytics technologies and understanding the outcome-
focused path to insight, it is possible for a company to pursue the business
opportunities in their data and attain data-driven benefits.

Simplify your analytics strategy

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