Benchmarking Your Analytics Initiative

IIA_Chart1

Analytics is both the lifeblood and livelihood of digital native companies such as Google and Facebook which thrive by analyzing the data generated by their Web position to drive their access to and engagement with millions of people. This analytics dominance by the digital natives has been expanded by a number of older companies such as Apple which have successfully adapted to the digital economy. In response, companies in established industries have beefed up considerably their data collection and analysis activities. But how good are they in mining the data for new and profitable insights?

The International Institute for Analytics (IIA) has recently conducted a study to measure the analytics maturity of 50 industry-leading companies across 12 industry segments. The IIA defines five elements that must be in alignment for organizations to succeed with their analytics initiatives:

  • high quality data.
  • enterprise orientation—integrated and coordinated perspective to managing systems, data and people.
  • leaders who fully embrace analytics and lead company culture towards data driven decision making.
  • analytics efforts must be aligned with specific, strategic targets that are also aligned with corporate objectives.
  • analytical talent that covers a range of skills from employees capable of using basic spreadsheets to accomplished data scientists.

The 5-stage maturity analytics maturity model helps companies measure their growth across these five elements:

Stage 1: Analytically Impaired (1.00 – 1.99). “Not data-driven.” These companies rely on gut feel to make decisions, and they plan to keep doing so. They aren’t asking analytics questions and/or they lack the data to answer them.

Stage 2: Localized Analytics (2.00 – 2.99). “Use reporting.” Analytics or reporting at these companies exists within silos.

Stage 3: Analytical Aspirations (3.00 – 3.99). “See the value of analytics.” These companies struggle to mobilize the organization around analytics, and as a result have difficulty becoming more analytical in their operations and decision-making.

Stage 4: Analytical Companies (4.00 – 4.99). “Good at analytics.” These companies are highly data oriented, have analytical tools, and make wide use of analytics. However, there remains a lack of commitment to fully compete on analytics or use it strategically.

Stage 5: Analytical Competitors (5.00 – 5.99). “Analytical nirvana.” These companies use analytics across the entire enterprise, both as a competitive differentiator and in strategy.

Comparing the industry segments of the companies it studied, the IIA came to the following conclusions:

  • The gap between digitally native companies and other companies is significant.
  • There are common market drivers and inhibitors of analytics capability—direct to consumer marketing plus investment in online and mobile operations vs operating under heavy regulation.
  • There is an uneven balance of power in healthcare—as government and market forces attempt to control healthcare costs, the pharmaceutical industry leaders appear to have a significant advantage in analytics capability over its primary customers.

Most industries score highest in the quality of data element and score lowest in the enterprise orientation element–indicative of the need to achieve data of a certain quality to leverage analytics, and of the difficulty in developing, coordinating and executing an enterprise approach.

IIA_chart3

Assessing analytics maturity scores for three defined functional areas (IT/Systems/Analytics, Finance, and Sales/Marketing) in five industries, the IIA makes the following observations:

  • The high performance of both IT/Systems/Analytics and Finance within Financial Services align with their heavy use of analytics in response to a variety of business, compliance and competitive drivers shaping the industry including fraud, cyber security, online payments, increased regulations, and mobile banking. Likewise, Retail group scores are reflective of competition in a low margin business and the historical emphasis on supply chain management.
  • Non-digitally native companies should be most concerned about the gap in the Sales/Marketing functional area. The Finance area appears to be closest to competing with their digitally native counterparts. The IT/Systems/Analytics area will likely benefit from continued technology advances and decreasing costs, largely driven by digital native companies.
  • The Sales/Marketing area is experiencing the most dramatic change from previous business models with the rise of online sales and mobile and social marketing and Digital Native companies are using customer data to drive a fundamentally different level of customer engagement.

Among the digital native companies, Amazon is the closest to achieving “Analytics Nirvana,” and the IIA warns that companies that don’t develop similar capabilities will fail to compete. A solid strategy coupled with strong leadership and commitment is what will enable traditional mainstream companies to ­­­­compete with digital natives or any startup threatening to “disrupt” their industries with the advanced use of analytics. In their analysis of individual company analytics maturity, the IIA has found that Ford and GM are competitive with Tesla Motors, and that Nike and Under Armour are essentially even in capability. Kroger, despite its position in the competitive, low-margin grocery business, has developed capabilities that exceed those of Wal-Mart and Target, possibly positioning it to effectively respond to Amazon’s expansion into its core market. Finally, the IIA recommends that companies assess their current state of analytics maturity and how they compare to both industry leaders and digitally native companies.

Originally published on Forbes.com

About GilPress

I launched the Big Data conversation; writing, research, marketing services; https://whatsthebigdata.com/ & http://infostory.com/
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One Response to Benchmarking Your Analytics Initiative

  1. Pingback: Benchmarking Your Analytics Initiative | A bunch of data

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