Analytics maturity enables strategic business decisions

Sanjeevagc

Business analytics as an analysis technology has evolved to become a critical tool today, as it helps organizations make smarter decisions and ultimately enhance business profitability. Industries such as retail, banking, insurance, etc. are early adopters in incorporating the business analytics as it has provided competitive advantage vis-à-vis industry peers. Changing customer preferences, cut throat competition and investment constraints have compelled organizations to relook at their existing business strategies. In addition, organizations will continue to face increasing pressure to stay profitable, understand customer needs and preferences with the most relevant offerings.  In order to churn the utmost benefits, CIOs however will need to develop business strategies mapping them with the maturity model of business analytics.

Business Analytics maturity model provides a framework to assess an organization’s current maturity level and conceptualizes the path for enhancing the effectiveness of Business Intelligence (BI)/Analytics. This framework can further help organizations to hasten the pace of maturity of its analytics systems, and benefit from its BI initiatives.  There are five levels of maturity:

Level 1 Capturing Transaction Data and One Version of Truth: This is the base level of analytics operation. Enterprises are required to be equipped with transaction systems and data should be captured instantaneously. This data forms the basis of the organization’s complete analysis and decision mechanism. It becomes more important in case an enterprise has a multiple discrete set of software applications to support the functioning. It helps in maintaining a single version of truth across various systems. Additionally, it is also important to consider the maintenance of ‘Master Data’ to ensure best quality of analysis. Even though, organizations have gone live with all modules of an ERP in shortest possible time, there were multiple issues in reporting and analytics as Master Data Management (MDM) was not in place. As a result journey seems to be incomplete.

Level 2 Defining and Measuring KPIs Periodically: Key Performance Indicators (KPIs) are a set of ideal parameters which need to be tracked to measure the performance of an enterprise. Systems are deployed for storing and measuring actual performance against KPI targets. Measuring actual performance of processes with these KPIs periodically provides visibility into the operational effectiveness of an enterprise. Most enterprises suffered a classical disconnect between the business process understanding, target setting and packaged software reporting. Packaged software was very apt to record past performance mostly in financial space. All other aspects in the balanced scorecard were difficult to measure and thus analytics at this level depended heavily on very minor automated data capture and major manual data feeds. These manual data sources and manipulations dented credibility of analytics. While the operations were connected by an ERP, performance measurement and operations remained different islands.

Level 3 Actionable Analytics and Timely Alerts: From the third level onwards, analytics is used for comparative analysis and to correct the future course of actions through KPI-Target Meet analysis. As major data sources are automated, the extent of manipulation is limited. Any shortfall in KPIs calls for discussion and possible corrective actions need to be taken to improve actual performance at this level. Not only this enables faster decision making but also lead the enterprise to a reactive environment which makes it a good follower rather than an industry leader.

Level 4 Defining Leading and Predictive Indicators: The fourth level marks an entry into the space of proactive analytics. There is strong need to lay out predictive indicators in order to lead the flock and achieve strategic objectives. Enterprises which form predictive strategies based on their and competitive products will qualify to be in the fourth level of analytics. Most of the Fortune 1000 enterprises are currently planning initiatives to achieve this level of analytics. This will allow them to become proactive in the decision making process, providing them with a leading and competitive edge over other reactive players in the market. These enterprises use data mining as an important tool for enhancing predictive capabilities. They have a very strong understanding of causals for forecasting market trends. Their product development strategies are based on techniques like “Quality Function Deployment” (QFD) and customer feedbacks / clinics.

Level 5 Applying Strategic Analytics: The last level caters to the strategic perspective of an enterprise which largely deals with long term sustenance and strategic excellence. The core of this level lies in answering the questions - “Are we in the right business?” “Can we achieve our strategic vision?” as earlier level focuses on “how do we improve and sustain?” This helps organizations to draft strategic investment priorities based on the present profitability.

This maturity model can be used as a roadmap for organizations looking to mature their analytics process. A majority of organizations world over are currently functioning at level 2 or 3. At these stages, actionable analytics are most critical and immediately focused on. However, systems should be designed keeping in mind the highest level of maturity to avoid costly rework later.