Significance of Developing Analytics Capabilities to Gain Competitive Edge

by January 29, 2020 0 comments


Leveraging analytics to gain a competitive advantage over others is not a fresh idea, however, the path one may carve out to reach there can be novel to attain an effective and efficient analytics strategy. Such a strategy will help the organization derive the best value out of their data. According to Ramanujam Rao, Vice President at Hitachi, “In the past few years, some patterns have begun to emerge from successful implementations. These patterns offer insights on how businesses can establish some foundational principles that best leverage their investment in data.”

Let’s understand what could be the ways to enhance one’s analytical capabilities according to some significant industry players and experts.



According to Oracle, the new analytical capabilities call for high-level requirements such as:

More types of data and storage platforms are key to embracing the unstructured and schema-free data types found in most big-data. Once these new data types are addressed, an organization obtains greater business value from big data and broader and more agile data sourcing for analytics. The new storage platform includes all data for analytics, including data warehouse appliances, columnar databases, and HDFS. The low cost of the HDFS platform for historical data retention and parallel processing of more data and unstructured data holds the promise of expanding analytical capabilities to the next level.

Also, information exploration and discovery capability are the types of analytical techniques currently on the rise (based on technologies for SQL, NoSQL, mining, statistics, and natural language processing) and are all related to discovering facts about the business that was previously unexplored.

Moreover, most enterprises are expecting real-time capabilities from analytical solutions to support fast business processes and decision making. Traditional relational databases and batch-oriented Hadoop systems were not built for real-time operations. Real-time functions include Apache Storm on Hadoop, Cloudera Impala, and Event Processing Engine.



According to an Accenture report, some business challenges can be addressed if companies take the time to develop an enterprise-wide analytics-centric strategy and underpin it with an operating model designed to harness the power of analytics.

The report suggests infusing analytics into the decision-making process. To embed an “analytics first” philosophy into the business, leaders are well served by starting with the business issue first, then defining the most relevant data and analysis and then re-engineering decisions to use the resulting analysis and insights.

According to the report, organizing and governing analytics capabilities across the organization is also helpful. Specifically designing the most appropriate analytics-based organization construct and allocation of resources based on the maturity and needs of the business, where analytics insight will deliver the most value and the closest positioning to decision-making. A critical element of this is the ability to effectively manage supply and demand for analytics services across the business.

Moreover, the report states that sourcing and deploying analytics talent is a must. analytics talent is hard to come by, and analysts who have industry-specific experience are even harder to find. The companies need to revise talent management processes to reflect this reality in the sourcing, development, and recognition of analytics talent. While there is no one “right” operating model that works for every company, there are seven components that should be addressed to shape the appropriate operating model including Sponsorship & Governance; Organization Structure & Talent Management; Data to Insights; Capability Development; Insight-driven Decisions; Outcome Measurement; and Information & Data Management.

Furthermore, a Deloitte report suggests that “When it comes to the people dimension, building analytics capabilities is not linear and incremental. Instead, it is iterative and requires ongoing attention. Through sustained commitment, organizations gain the ability to leverage their previous learnings—enabling them to reap the rewards of earlier thoughtful decisions, generate increased value, and accelerate their wins as they move through each level of analytics maturity. The result? The impacts expand over time, empowering organizations to drive deep and lasting change at ever-faster rates.”

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