Digital Transformation: 8 Best Practices for Building an Analytics Roadmap

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Digital Transformation 8 Best Practices for
Building an Analytics Roadmap
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The idea of creating an analytics roadmap for the
enterprise can be daunting, what with the
expansion of new data sources, the proliferation
of new analytics systems and tools, and the
tremendous demand from the business for faster
insights. But without a roadmap, advanced
analytics efforts may underdeliver, increase
complexity, or worse. When organizations dont
have a common vision and roadmap for analytics,
each function in the organization tends to
establish a partial digital platform to solve for
their immediate needs but in a siloed way
making the overall analytics portfolio
ineffective, says Ashish Verma, managing
director and leader of data and analytics
modernization at Deloitte Consulting.
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In addition, as data continues to rise in both
volume and variety, storage across multiple
business functions without capturing value can
lead to increasing insight costs without revenue
growth, Verma says. IT organizations that
develop an analytics roadmap create visibility
into the transformation required and can better
socialize those changes with stakeholders
throughout the enterprise, says Verma. Theres
no denying that, as ISG partner Prashant Kelker
says, it is inherently difficult to create a
digital transformation and analytics roadmap.
But smart practices are emerging around the
process. IT leaders mapping out analytics plans
should consider these steps
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1. Involve the business at the outset
By all means, include key stakeholders as early
as possible. This will reduce project delays and
ensure that all are aligned on the roadmap, says
Verma. Understand that you will have to address
varying needs and levels of understanding.
Knowledge of AI and analytics will be different
for the entire organization, Kelker says. The
marketing team may be very smart in these areas
as they have been doing it for years. But finance
or HR, hearing it for the first time, will need
significant support to be able to understand and
use analytics.
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2. Start with business objectives, not technology
The biggest challenge we see with organizations
going down the analytics journey is that they
start with a technology-led approach, says Jimit
Arora, a partner with Everest Group. For
example, companies feel invested to buy the
latest big data tools and visualization
technologies, and then determine how to create
optimal usage. Those seeking to create an
effective analytics strategy should start with
key business objectives, such as top-line growth,
cost reduction, or risk management.
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3. Assess the current state
Conduct an audit of all existing analytics and
data technology in place and how they map to the
organizations analytics needs and goals, Verma
advises. This should include developing a
maturity model to measure proficiency across key
dimensions and capabilities for the analytics
platform. Take some time to simplify the sprawl
that may have occurred to date. Legacy
applications often incorporate redundant data and
processing routines. Involve enterprise and
solution architects early to streamline data and
processing efficiencies through analytics-enabled
architecture, Verma says.
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4. Envision the desired future state
Conduct sessions with key leaders and business
partners to prioritize and align on scope to be
delivered in the future state to improve
acceptance, Verma says. When creating the vision
for the analysis, weigh costs and benefits to the
business, alignment to corporate objectives,
total cost of ownership, and return on investment
to ensure the model delivers value from analytics
offerings.
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5. Perform a gap analysis and prioritize
Identify and prioritize the key capabilities that
will enable the organization to accelerate its
analytics capabilities in a way that will deliver
benefits quickly. Once the business objectives
are in place, enterprises need to make a set of
decisions about four distinct building blocks,
Arora says. Those building blocks are data (both
structured and unstructured), tools (technology
for data ingestion, analysis, and visualization),
analytics and data science talent, and
infrastructure (a foundation capable of handling
the volume, variety, and velocity of the data and
the complexity of the analytics).
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6. Build for today, design for tomorrow
Integrating advanced analytics will be an ongoing
process, and available technologies and
capabilities are evolving quickly. Design a
flexible scalable architecture to support future
state analytics workloads which will include
machine learning and artificial intelligence,
advises Verma.
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7. Create a framework for analytics
implementations
Develop an end-to-end process for conceiving,
developing, and implementing advanced analytics
capabilities. As part of this framework,
businesses should consider including focused and
measurable activities that can help improve
decision-making and drive innovation in products,
services, and internal operations through
analytics platform modernization, says Verma.
Make sure to socialize and market these best
practices.
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8. Create a phased approach to analytics
transformation
This will ensure meaningful outputs and allow
for agile changes to the business needs, says
Andrew Alpert, managing director with Pace
Harmon. Companies that are new to analytics will
learn exponentially from the initial deployments
to better inform future needs. Getting these
elements right during the first phase is more
important than implementing functionality,
Alpert says. From there, it will be a journey
to establish an optimal data and information
model for which information to capture and why,
as well as to mature from reports to descriptive
analytics and eventually into predictive
analytics, Alpert says. The architecture can
evolve over time as well. In addition, within
each analytics deployment, IT leaders can
mitigate delivery risk by breaking up
capabilities into phased releases. This will
provide adequate time and effort for platform
stabilization upfront to optimize acceptance
risk, Alpert says.
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roject.com/article/2019/2/digital-transformation-8
-best-practices-building-analytics-roadmap
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