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Mastering the Five Stages of Analytics Maturity

What Is Analytics Maturity?

Analytics Maturity
Analytics Maturity

Most organizations use analytics across their operations, but there are still many that have misconceptions about how well they utilize analytics to inform their business decisions,” explains Venkat Viswanathan, founder and chairman of LatentView Analytics. LatentView developed a free online tool, the Analytics Maturity Self-Assessment, allowing individuals to answer a survey about their use and understanding of analytics. At the end, they are rated on a scale of five stages, classifying how well they handle and execute their analytics approach. In this slideshow, LatentView identifies the five stages of analytics maturity, assessing an individual’s own understanding and use of data. Each stage will then outline the actions that can be taken to advance to the next level of this maturity model. “By understanding these stages, the right steps can be taken to develop an accurate and substantial data plan to further grow an organization,  Viswanathan explains.

Stage 1: Analytical Novice

“Companies in Stage 1 may be lagging behind in adopting an analytics strategy to drive business decisions, potentially eroding their competitive edge,” explains Viswanathan. “The development of a sound data management strategy has not begun yet or is in its infancy, and data quality and consistency may be poor. Analytics is driven mainly by the use of spreadsheets, and business leaders need to gain a better understanding about the value of analytics.”

Stage 2: Tips to Advance

“Immediately set up a discovery phase to identify potential use cases for data-driven decision-making, and engage external consultants or analysts to educate key business stakeholders regarding the significance of analytics,” Viswanathan says. “Prepare a high-level cost and benefit analysis of top use cases, while creating an inventory of systems and data assets that highlight the key datasets available in the enterprise and their flow across the system landscape. To accomplish this, be sure to begin forming a data team comprised of business and data analysts and data engineers.”

Exhibits Analytical Aspiration

In Stage 2, it is evident that an interest in adopting analytics to drive business strategy has been developed, but these interests still need to be augmented with action,” Viswanathan explains. “The organization has identified its need for data infrastructure, but the strategy team is not participating in discussions about analytics usage. Business leaders are curious about using analytics, but they have not established a clear vision of how to continue.

Stage 3: Tips to Advance

“Run analytics training programs across business groups to re-skill team members, and encourage them to participate in crowdsourcing competitions and open source data projects,” Viswanathan suggests. “Organize workshops among key business stakes holders to identify use cases for data-driven decision-making, and validate the use cases with analysts and/or external consultants. Select appropriate tools and platforms to build the data foundation, and strengthen the data team by adding data modelers and analysts, integration architects, and data quality specialists.”

Established Analytical Platform

“Receiving a Stage 3 rating means a platform for execution has been built and the business is ready to take its first steps toward business transformation,” Viswanathan says. “The organization has established an infrastructure for structured data storage and access, with analytics developed by BI tools and shared across the organization. Focus is shifted to predictive analytics, along with reporting, and business decisions are made based on insights generated by data.”

Stage 4: Tips to Advance

“While an analytical platform is established, progressing to stage four will mean strengthening the data and architectural foundation to verify it caters to multiple workloads such as reporting, self-service, data mining, predicating analytics, simulations, and optimization,” Viswanathan says. “Among other significant steps of advancement, it is important to select appropriate tools and platforms in order for the organization to perform advanced analytics and visualization for data discovery.”

Business Outcome-based Analytics

Stage 4 indicates the business is doing a great job in formulating and executing business-focused, department-led analytics projects for the organization,” Viswanathan says. “Data is managed on consolidated platforms to support advanced analytics along with BI reporting, and analytics centers of excellence are set up at the department level. It is at this stage that leadership recognizes analytics to be a competitive differentiator

Stage 5: Tips to Advance

Reaching the final stage of the analytics maturity model involves marketing the benefits of data-driven programs,” Viswanathan says. “Link specific analytical projects to broader organization-wide initiatives, and build an analytics-focused architecture that ingests structured, unstructured, and semi-structured data that can be exploited for complex use cases. Advancing to stage 5 will mean moving up the value curve from diagnostics to predictive and prescriptive analytics and augmenting the data team with specialists. Build an analytical center of excellence to collate best practices and disseminate knowledge across the organization – formulated as a profit center so that revenue generating initiatives can be funded.

Analytics-driven Business Strategy

Achieving Stage 5 reveals the organization has ensured analytics are aligned to business goals,” Viswanathan concludes. “The organization has a coherent data infrastructure, facilitates big data and real-time streaming capabilities, uses analytics to drive strategic business decision for all divisions, and customer experience officers (CXOs) are strongly inclined to reference data and analytics in all decision-making.

Source : Information Management

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