IBV study on analytics, part 14: Driving change with analytics as a core competency

Global Banking Industry Marketing, Big Data, IBM

This is part 14 of our series on the findings and text from the IBM Institute for Business Value’s latest study and paper “Analytics: A blueprint for value - Converting big data and analytics insights into results” from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca Shockley. 

In our last post, part 13, we looked at the three levers of Technology that help achieve business goals through effective application of technology. In this post we will discuss the three levers of Organization, with recommendations in the areas of Culture, Measurement and Trust.


Drive change with analytics as a core competency. Seniors leaders must drive change with analytics as a core competency within the organization. In most organizations, cultural norms are set from the top down. As such, executives and business unit leaders must transparently use analytics to make their own decisions, and espouse the merits of a fact-based culture to set the needed expectations and engrain the behaviors within the organization.

In organizations that excel at analytics, leaders ensure that the mechanics of data-driven decision making don’t impede the ability to act on data. They proactively work to establish the relationships needed to engender trust in the data, and they measure the amount of influence data has on business outcomes to demonstrate its value to the organization.

As summarized in figure 17, we offer the following recommendations to assist organizations in honing the capabilities associated with the Organization levers of Culture, Measurement and Trust:


With data moving from intake to insight at an ever faster pace, organizations need to provide the right data at the right time to the right people to make better decisions. Organizations must streamline the data cycle to deliver more timely and relevant insights to reach end users and decision makers. Leading organizations automate key portions of the analytics cycle to more effectively arm decision makers with the information they need. Embedded analytics and machine learning offer new opportunities to shrink the data cycle.

Automating data feeds and routine tasks also increases the productivity of analysts, whom we found routinely spend as much as 75 percent of their time tracking down data and cleaning it up. Allowing analysts more time to focus on developing insights instead of tables and charts is not only a more productive use of valuable resources, but also increases the likelihood that data for the decision process is relevant, timely and consistent.

Moreover, executives need to actively endorse the power and benefits of data and analytics. One of the most effective ways to demonstrate this endorsement is using data and analytics to support decisions transparent to the organization. Decisions based on facts should be presented as such, reinforcing the behavior while also exposing the thought process to scrutiny, which in turn builds trust.


A critical part of that transparency is measuring the outcomes of analytics investments. If an organization cannot pinpoint the value of analytics strategies, it won’t be motivated to invest in them or to develop and act on insights. Moreover, it could be investing in strategies that only deliver low-value returns and missing opportunities to improve future outcomes.

fig17.jpgThe only way to solve this is to measure value. This starts with the governed discipline of the strategy, follows through to implementation, and carries on with an ongoing evaluation of results as long as relevant. Organizations must measure to understand what works, what doesn’t and how to increase the value of analytics. 

The first step is to extend the rigorous metrics process put in place to fund analytics efforts to create a feedback loop based on the outcomes. The value of an analytics investment can best be understood when the cost-benefit analysis of the funding request is examined based on the actual costs and the actual benefits delivered. Without this level of evaluation, the ability to distinguish between an effective marketing campaign and an interesting idea becomes impossible. 

As such, organizations must identify and define the specific key performance indicators (KPIs) expected to be impacted by each analytic investment at the time of funding. These KPIs should be aligned to the target business objective and justified with the forecasted outcomes—tangible and intangible—expected to be delivered.

Once analytics investments have been transformed into implemented capabilities, the organization needs to create an audit process and feedback mechanism to evaluate the investments’ impact to value creation, as measured by the pre-defined KPIs.


Trust and personal relationships established through face-to-face interactions may seem archaic in a world of social media and digital networking, but Leaders recognize trust is a key ingredient to value creation through analytics.

Trust has the power to break down the resistance to change that comes with every cultural transformation because it empowers people to act on data they did not create. Decision making is all about putting your own and the organization’s reputation at risk, with every action regardless of how large or small. If people do not understand where data comes from and how conclusions were reached, even at a high level, they will be skeptical.

The solution is human interaction. Invest the time needed to create trustworthy relationships. This requires executives and analysts alike to talk to people: understand what concerns they have about the data and analysis provided, learn what they know about the data they manage or analyze and discuss how to work better together. It is important to strive to remove as much of the anonymity as possible between the data creators and users, between dependent executives and, most importantly, between the business unit and IT. 

Organizations already accustomed to a fact-based culture are beginning to transform roles to share responsibilities and outcomes between business and data analysts, as well as business and IT executives. The distinction between “business” and “IT” is blurred by expectations that business analysts understand the data, where it comes from and how to use it, while data analysts grasp how the business works, what the key measures are, and how analytics can impact the business. In this model for the future, business executives are fluent in the technologies available, while IT executives drive to harness those capabilities to deliver business outcomes.


Our research makes it clear that there are specific activities that can help organizations derive more value from their data. The nine levers—combinations of activities focused on analytic development and delivery—help organizations accelerate value creation, simplify analytics implementation and streamline analytic investments. 

By examining their own activities through the lens of the levers, organizations still struggling to harness the insights buried in their data can begin building a value-based analytics strategy. Organizations that learn from the Leaders in our survey and follow our recommendations stand to answer the question posed in the beginning of how to extract value out of analytics investments. By embracing analytics to drive smarter decisions and positively influence business outcomes, these organizations are well positioned to join the Leaders in outperforming their industry and market peers

Catch up on the entire series so with parts one through thirteen: