Business Analytics: Six Steps to Getting Results

 
 
Posted 2012-09-06
 
 
 

By John Lucker

Although reams have been written about analytics, and opinions abound about its potential to change how businesses think and behave, analytics in the end is an important and valuable driver in achieving decision-making success.

Discussions on analytic software and tools are certainly useful, but technology is just one piece of the analytics puzzle. To apply analytics effectively, businesses should think beyond technology to consider how an analytical approach affects many aspects of their operations.

This holistic approach to analytics involves six steps that progress from strategy and planning to ongoing performance management. In moving through these steps, businesses may be able to accomplish the following objectives.

·       Define the objectives for constructing an analytics road map.

·       Create the strategy for achieving the objectives and achieving a return on the investment.

·       Do the actual analytics work.

·       Embed analytics inside business operations and processes.

·       Integrate enabling technology.

·       Guide the organization through the inevitable change.

·        Monitor results against the anticipated ROI.

·          Create a continuous improvement process of tuning and enhancement.

Many analytics projects fail because too much time is spent doing what can be done with analytics versus focusing on what, specifically, the business should do with analytics. By focusing on the analytics process—from strategy to results—organizations can replace activity with achievement.

Step 1: Define an analytics strategy that builds in flexibility.

Embedding analytics into strategy and planning can help build a more flexible business with an ongoing ability to anticipate rather than react. Change begins when the organization recognizes the intrinsic and extrinsic value of data in sharpening conclusions and moving to a metrics-driven company culture. As the old saying goes, if it can be measured, it can be managed. 

Leadership should be tasked with evangelizing and operationalizing this vision. Leaders should speak in one voice with a consistent message that reinforces the value of an analytical approach. Business unit leaders should lead by example, demonstrating how analytics-derived facts are informing their decisions in new ways.  In this way, the organization can better articulate its values, the needs of its customers and what its offerings should be.

One specific technique is to design an end-to-end strategic road map composed of short-, medium- and long-term analytic achievement—each with a resulting ROI – where the value generated by prior projects feeds the cost of downstream efforts. Employed effectively, this method can create a self-funding mechanism that allows a company to control its analytic investments with the potential to achieve a significant reward. This road map process is at the core of defining analytic strategy.

Step 2: Perform focused analytics activities.

This step creates the DNA of the end-to-end analytics process. Here, strategy is transformed into tangible analytic results. Data mining, predictive modeling, optimization and demand analysis, customer segmentation, and supply chain and materials movement analytics are a small subset of potential components. The analytic activities involve algorithms and other technical components that should be integrated into technology, business and operational infrastructures in various ways.

During technical development, numerous rules of thumb should be considered. For example, technical perfection for analytic development is rarely necessary to achieve business objectives. Rather, the focus should remain on the end goal of strategic advantage, the affect of diminishing returns of technical development, and the inevitable pressures of time and speed to market. 

A common theme with analytic solutions is that the tangible ROI resulting from the full end-to-end analytic approach is typically quite visible and measurable to business management. Therefore, the focus should remain steadfast on the needs of the business and the requirements of the analytics strategy from Step 1.

Step 3: Tie analytics into business processes and operations.

Once a technical analytic solution has been constructed, it should come alive inside the organization’s operational ecosystem. This step is where broad thinking should be applied, rather than shoehorning new methods into old.   

Business is riddled with legacy thinking, operations, policies and procedures. Very often, it’s helpful to use this opportunity with analytics to take a step back and re-examine the way the business runs.

Many businesses have found analytics helpful as a catalyst for significant business process change. Such change may be far-reaching and cause significant disruption. It takes a major effort to assess and shepherd this change, but the result is often a great improvement in management criteria and performance metrics.

It is essential to think through how to integrate fact-based tools into business processes. Modernization of operational processes has been shown to be very valuable in the strategic analytics journey and deserves detailed treatment.

Step 4: Integrate the technology.

The required level of technology integration depends on the type of analytics being applied. Technology components can include scoring engines, signal processors, enterprise rules engines, supervised or unsupervised learning engines, and other foundational technology elements that make analytic outputs and tools available to operational processes.

In many instances, technology integration incorporates existing information management and business intelligence tools, systems and data, as well as standards-based messaging and platform-independent development methodologies. The holistic approach to analytics leverages these components and moves analytics from point applications to enterprise solutions.

Step 5: Embrace change management.

The application of any foundational change introduces a degree of turbulence into the business, and analytics is no exception. Many employees resist change and prefer traditional work routines to dealing with "yet another" change in the way work is done. This is why the execution of an analytics strategy should be planned, and change managed properly.

To be effective, businesses need training and communication, consistent executive sponsorship, nurturing of nontechnical influencers (line-of-business professionals) and a demonstrated link between the initiative and the business strategy.

Implementing a communications and training program can guide employees through the analytics transformation. Executive sponsorship is also important, since a strong, consistent message from leadership can build consensus and momentum.

As important as people are to the change management journey, implementing the right technology and processes is just as crucial. The goal is to align the systems, technology and data to the people and processes they are enabling. This approach embeds analytics into the front lines of the business, where everyone views it, uses it and realizes its value as a strategic, competitive asset.

Step 6: Establish a performance management discipline.

To win in the open market requires businesses to understand their failures as well as they understand their successes. Performance management gives executives an actionable form of understanding what's working and what's not.

Analytics offers a visual, more efficient approach to scenario planning. Offers can be tested on specific segments, and then adjusted based on results and feedback. This performance management approach can result in more precision and less guesswork.

Benchmarking is an important step in establishing a performance management discipline. Just as the human body demonstrates subtle nuances across individual parameters, so, too, do businesses of similar shapes, sizes and functions. Applying analytics can normalize data to define baseline performance and then spotlight changes to key performance indicators by action.

Then, by repeating steps 1 to 5, the inflection points become increasingly tailored to the organization’s own environment and comparable to others with similar operating footprints. This iterative “tuning of the engine” allows for feedback and loopback mechanisms to create continuous improvement.

Effective analytics projects avoid hype. They resist becoming distracted by the amazing capabilities of today’s analytics tools and technologies. Instead, wise businesses home in on specific challenges—current and anticipated—and how analytics can be applied to solve them.

The six-step analytics strategy presented here gives companies the ingredients to generate results from a fruitful analytics journey.

John Lucker is a principal and the global advanced analytics and modeling market offering leader at Deloitte Consulting LLP. He is also a U.S. leader in Deloitte Touche Tohmatsu Ltd.’s Deloitte Analytics Institute.