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Thinking About Analytics Readiness

This article touches upon an important but under-discussed topic of analytics readiness, including whether and when organizations should engage in analytics.

By Dorit Nevo, Rensselaer Polytechnic Institute.

Business analytics is continuously on the rise. A recent CIO survey by Gartner placed BI and analytics as the top investment priority for managers in 20151. And as with other successful innovations, analytics too creates a bandwagon effect, with nearly every organization now asking “how can we get into analytics?” While the tools, the benefits, and the maturity progression have been extensively discussed2, organizations really should begin by asking “are we ready for analytics?” And analytics readiness has yet to be explored.

Broadly defined, organizational readiness for a new innovation spans the availability of organizational resources needed for successful adoption3. Common resources previously considered include financial and technological capabilities pertaining to the innovation at hand. Innovating mindfully, thus means taking into account available organizational capabilities and resources and matching these with the innovation 4.

Focusing on the readiness factors relevant for business analytics, this post questions the sufficiency of financial and technological capabilities and explores other readiness factors for business analytics. It presents the insights of thirteen analytics executives from six different perspectives (finance, retail, healthcare, IT, marketing, and services), who came together in a Delphi study to explore readiness factors for analytics. The key question these executives addressed was “what are the top readiness factors for business analytics?” In three mediated brainstorming rounds they first generated a broad list of items and then repeatedly ranked this list in order of importance until consensus was reached. Figure 1 presents the ten final factors with their average importance rank and respective standard deviations.

Analytics readiness factors

Figure 1. Top Ten Analytics Readiness Factors

Key readiness factors

The above factors are grouped into four key readiness factors that organizations should consider in the context of analytics: strategic, domain, cultural, and operational readiness. Ensuring readiness across all factors is important for organizations as they enter analytics, because several of these factors can later transform into important analytics success factors5.

Analytics readiness

Figure 2. Analytics Readiness Factors: from strategy to operations

Strategic Readiness: making the business case

“The business must first have an idea of what it is that they are trying to measure: does the business have a clearly defined use case for the use of analytics? What business problem are they attempting to solve? Unleashing analysts on data without goals will usually result in achieving nothing.”

At their core, business analytics investments are not significantly different than other IT driven initiatives. Consequently, tying analytics to organizational strategy is crucial for success. Key to this is getting everyone on board, ensuring an executive sponsor, and assigning a project champion. An important partner that was identified by panel member is the IT department, with a need to develop protocols unique to the implementation of the analytics technology. Part of the strategic readiness factor is also willingness to make investments in analytics but such that these investments follow a clearly identified business case. Strategic readiness entails clearly defining objectives for analytics, problems the organization hopes to solve with analytics, and the type of questions that can (and cannot) be answered.

Domain Readiness: skills, tools, and data

“Has the organization invested in the infrastructure to easily access and analyze data? Has the organization invested in analytical tools and software that will enable these functions? Has the organization invested in training, or recruiting, dedicated staff that has the technical training and aptitude to leverage the data?”

Domain readiness entails identifying key skill gaps and investing in required talent. Beyond new talent, training of existing employees is also needed, to allow users to learn new analytics tools and ensure proper utilization. Domain readiness also involves an understanding of the complex organizational data landscape. In this environment, multiple sources with multiple ownerships, and problems of data quality, data integration, and data governance are abundant. Organizations should have a good understanding of which data is needed to obtain desired insights. Once identification and scoping are done, the question of availability arises: “is the data available and has the organization considered how different sources can be integrated?” Data quality assessment follows, to ensure consistency and understand the extent of cleaning that is required. Finally, data governance requires an understanding of ownership and access rights, especially with sensitive and regulated data such as financial or health data. Beyond data, investment in the appropriate analytics infrastructure (tools and technologies) is also needed. While there are great platforms for managing data at scale – including streaming analytics - at some point a decision is needed on the technology landscape to ensure that training and skill building can commence.

Cultural Readiness: data-driven decision-making.

“Is the organization's leadership ready to make data-driven decisions as opposed to intuition or bias?” “Does the company have internal processes and appropriate culture to embrace insights generate by the analytics team and to leverage the work?”

Perhaps the most intriguing readiness factor is that of analytics culture. Although the panel did not fully agree on the importance rank of this factor, they unanimously indicated it among the top ten factors. Specifically, panel members brought up the need for an analytics mindset and the willingness to let the data take you places that you might not think about or agree with, challenging conventional wisdom. As one panel member noted, “the enterprise has to engage in the use of the analytical results even if the whole organization is not analytical. I am not advocating turning all the employees into “Citizen Analysts” but instead to use analytics as a primary driver to decision making.” Indeed, for many organizations use of analytics is a disruptive cultural shift that needs to be addressed. While this was the main facet of culture that panel members identified, secondary facets included an understanding of the importance of data sharing and making analytics a core part of personnel assessment. Creating an analytics culture early on can help organizations become better with analytics down the road, as they develop their analytics maturity6.

Operational readiness: planning for analytics

“Develop a hypothesis of how the results of the project will generate value to the organization and define the method for defining that potential value.”

Analytics is no small feat. Proper planning, timing, budgeting, and clearly defined milestones would make the difference between success and failure. Two interesting factors fall under the domain of operational readiness. First, the organization should be ready to measure success and identify key performance indicators and metrics to evaluate progress towards the organizational goals with analytics. Second, the organization should develop a clear dissemination plan, a way to bring analytics insights to those who need them. Coming into analytics with such dissemination plan can also help alleviate the gap from insights to applications later on 7 . Finally, panel members also identified the need to benchmark and collaborate with industry partners in order to leverage analytics. Learning about best practices and success stories from other institutions is important readiness prior to engaging in analytics.


The purpose of this article was to touch upon an important but under-discussed topic of analytics readiness. While the literature and practice are booming with maturity models and identification of successful cases with analytics, much less has been discussed about whether and when organizations should engage in analytics. Faced with this new and exciting innovation, organizations bear the risk of mindlessly following in others’ footsteps, leading to disappointing results. Analytics can deliver all that it promises if organizations are mindful about their own unique capabilities and ensure early fit with strategy, skills, culture, and operations.

1 http://www.gartner.com/imagesrv/cio/pdf/cio_agenda_insights2015.pdf
2 Beyond the extensive literature on the benefits of analytics, maturity models such as Gartner’s descriptive, predictive, and prescriptive analytics or IBM’s Analytics Quotient have proliferated in recent years.
3 C. L. Iacovou, I. Benbasat, A.S. Dexter, “Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology”, MIS Quarterly 19 no. 4 (1995):465-485.
4 N. Ramiller and B. Swanson, “Innovating Mindfully with Information Technology”, MIS Quarterly 28 no. 4 (2004):553
5 D. Kiron, R. Shockley, N. Kruschwitz, G. Finchand, and M. Haydock, “Analytics: The Widening Divide”, Sloan Management Review research report (Fall 2011)
6 Kiron et al. 2011
7 S. Ransbotham, D. Kiron, and P. Kirk Prentice, “Minding the Analytics Gap”, Sloan Management Review (Spring 2015)

Bio: Dorit Nevo Director of MS program in Business Analytics Associate Professor, Information Systems Lally School of Management, Rensselaer Polytechnic Institute.