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How to Align Analytics with Business Strategy

Originally published August 20, 2013

Today, analytics has become a top priority for most enterprises as they strive to find avenues for revenue growth and operational efficiency. However, with the exponential growth of data, enterprises are struggling to make significant progress. There is an urgent need to identify opportunities, costs and then clearly quantify benefits that are measurable.

Some of the challenges that enterprises face as they strive to prioritize analytics include:

A general lack of understanding of analytics
Various analytic applications with different definitions include business, customer, marketing, multi-channel, web, social media, mobile and supply chain analytics.

Understanding actionable analytics
Enterprises understand the value of making data driven decisions, but struggle in measuring the ROI of their analytic programs.

The limited availability of analytic talent

There is a shortage of specialized skill sets to fit into analytics roles. Gartner predicts that there will be 4.4 million jobs globally through 2016, but only one-third of them will be filled.
With these challenges in mind, this article highlights and explores the fact-finding that needs to be accomplished in order to surmount the challenges surrounding analytics adoption. There are five steps that ensure that analytics is not an afterthought, but an integral part of any business or technology investment decision. The steps include:
  1. Map Metrics Behind Business Initiatives

    This step is crucial because it results in understanding the top organizational priorities and how analytics can help. The easiest way to start is by analyzing the key metrics that drive the funding for projects or program definition and delivery. These metrics are often defined by the business roadmap and are also reflected in the portfolio of program and initiatives in progress.

    As an example from the consumer banking industry, alignment of analytics may map to the following key drivers:

    • Digital e-enablement to reduce costs and improve responsiveness
    • Integration to improve cross sell and customer service
    • Data consolidation to provide a 360 degree view of the customer for marketing effectiveness thus driving new acquisitions

    Alignment to strategic objectives includes answering relevant questions such as: “How will we measure the performance?” and “How will we take corrective action and understand causal elements?” Defining these programs will be enhanced if we drill down into questions such as feature set prioritization based on analysis of historical data and leveraging predictive analytics.

  2. Keep the Evolution of the Business Initiatives in Mind

    Before starting any analytic initiative, it is important to not only consider the actions that can be enabled but also how the analysis will be implemented. The intent should be to leverage the analysis in the previous step to help refine the business initiatives over their lifetime. The key question to answer is “How will we enable the analytics that a program may need to evolve and provide the desired business value?”

    This knowledge helps in analyzing and developing the data landscape and the analytic model required. Integration of data sources, processing capability and, finally, reporting for implementation can be planned as the program is defined.

  3. Understand the Data Landscape

    The next step in building the analytics business case is to assess the current data landscape and create a target state.

    The business landscape is ever-changing and one may not have all of its needed data available. However, a data management roadmap can be created if the business objectives are available. An inventory of data categories and elements is critical to avoid reengineering processes every time new analysis is required.

    The key elements of the roadmap include: data sources (online / offline, third party / internal), quality of data, storage and consolidation, latency (real time, batch), processing (staging, BI), reporting, actionability and tracking.

  4. Consider Cross-Industry Best Practices

    Each enterprise and industry is at a different place in the analytics maturity curve. The best practices from one industry must be leveraged in another. What has been learned is often easily transferable, despite variations in data and key performance indicators. Retailers can lend customer analytics to insurers, who can in turn provide inputs on risk modeling.

  5. Analyze and Leverage Partner Capabilities

    The analytics maturity of an enterprise determines what type of partnerships works best for them. This enables them to leverage partners to help. It is important to develop an analytics road map and build a plan that aligns with corporate goals while elevating analytic maturity. Enterprises need to build new analytic capabilities and mine data, develop frameworks or develop predictive models as outlined in the analytics road map. It is also important to use cutting-edge tools when one lacks expertise (it is important to evaluate different tools to select the one that best meets requirements). And finally, enterprises need to consider integrating new data sources while leveraging the partner’s big data and data processing expertise to link data silos or new data sources.
A business case for analytics is crucial for every CIO. Embarking on the journey itself fulfills the CIO-CEO partnership. The first critical step is to define the enterprise’s business objectives and make analytics an important part of all initiatives, starting with the conceptualization stage. This will ensure happy shareholders.

  • Melanie MurphyMelanie Murphy
    Melanie is the Senior Director of Analytics within Mindtree’s Analytics & Information Management practice. Melanie has 18 years of IT experience with the last 14 years focused in analytical and customer marketing applications. She currently leads strategic consulting engagements, guiding clients through the customer marketing landscape, including customer engagement and database marketing. She assists clients in determining ways to monetize their information assets through enhancements to data strategy, analytic processes or product offerings.



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