Although companies are increasingly investing in analytic capabilities, many fail to see a positive impact on the bottom-line. The good news is proven tactics exist to help businesses get it right. Here’s how to overcome the five main challenges to achieving ROI from an analytics implementation.
Although companies are increasingly investing in analytic capabilities, many fail
to see a positive impact on the bottom-line. The good news is proven tactics exist to help businesses get it right. Here’s how to overcome the five main challenges to achieving ROI from an analytics implementation.
Challenge #1: Lack of business sponsorship and focusing on tactical analytics.
Solution: Pursue business sponsorship for an enterprise-wide adoption.
To date, companies primarily function in silos where analytics resources are used to solve an issue in a specific part of the business. Yes, this strategy could work for that single team, but it inhibits the business from receiving optimal benefit. For companies looking to maximize the full potential of their analytics solutions, they should obtain business sponsorship for an enterprise-wide analytics solution as it can remove the silos and enable a business to share a single view, an objective, and information.
However, gaining this sponsorship can be a business challenge. Accenture research shows many decision-makers still base decisions on intuition and experience rather than on facts. While intuition will always have a place in decision-making, organizations need to make sure facts and data insights aren’t easily overruled and are viewed as an asset for growth. When making a decision, businesses should consider both their instinct and data insights, and not just one or the other.
Once sponsorship is received, and whether it’s the CEO, CIO or a Chief Data Officer leading a company’s data initiatives, it’s important to bake the enterprise-wide approach into the business. If this step doesn’t occur, a business could continue to function with a silo approach that would generate duplicated labor efforts, budget expenses, and an overall sub-optimized scenario wasting company resources. To avoid this, businesses should drive a sponsorship waterfall of this new analytics approach through its leadership and apply a strategy that will standardize processes, tools and enable information sharing among the larger team.
Challenge #2: Wrong metrics
Solution: Measure what matters
When it comes to performance measurement, most organizations create confusion and frustration by measuring too many or inconsistent elements of an analytics project. For example, this can easily occur if a consumer packaged goods company is tracking revenues garnered from each of its brands and there is an overlap in the brand descriptions – chances are different parties will track different interpretations of a brand, therefore creating inconsistent key performance indicators (KPI) revenue numbers.
To simplify measurement, businesses should keep their main business objectives in mind when shaping a strategy and look for a small set of KPIs that will be reliable, consistently measured, accurate and timely. For instance, marketing KPIs could be sales revenue, lead-to-customer ratio, and landing page conversion rates.
When narrowing down specific KPIs to measure, these questions should be top of mind for all industries:
• What matters most to the business?
• What would be achieved by tracking this KPI?
• What will its impact be?
Establishing the right set of KPIs is important as they set the stage for faster and better decision-making. Once determined, it’s also important to communicate the fine-tuned KPIs with colleagues who are also involved in tracking performance within the organization to ensure a constant is known and followed.
Challenge #3: Limiting analytics to traditional data
Solution: Capitalize on new sources of data
Companies tend to stick to the mantra “tried and true” as a basis for gathering reliable sources of data to analyze. Businesses should move beyond traditional structured data types and chase the new opportunities that could be created by tapping new sources of unstructured and semi-structured data such as: social media, voice, web, geospatial data, geo-location data, visual data, and behavioral data. Additional insights can also be created from looking at data outside of a company’s walls and into third-party external data and public data.
Various industries have already benefited from this approach. For instance, seed suppliers are now using new satellite data to understand the impact of soil temperature on the growing of seeds.The ever-growing volume and variety of data can be overwhelming for businesses. In fact, a recent showed only 39% of organizations say the data they generate is relevant to their business strategies, and only 50% say their data is consistent, accurate, formatted and complete.
To manage the expanded data sets, businesses should only analyze the data that matters for your decision-making task. This can be done by reverse engineering – determining the desired outcome first, and then backtracking to identify the data sets needed to find the insights that will support the outcome. Adopting data discovery technologies is also an option as they allow for less-stress data experimentation. Both approaches can help businesses massage the data mountains down to molehills – so the impactful data can be found and applied for the business.
Challenge #4: Analytics in the rearview-mirror
Solution: Look through the windshield via advanced analytics
Historically businesses have looked in the rearview mirror, analyzing historical data, believing it is critical for operations. Over the past few years, a shift has occurred and businesses are now starting to change focus and look through the windshield to figure out their destination and where they’re headed. This can be accomplished through advanced analytics.
Businesses now have the opportunity to hone their functional analytic approach and incorporate advanced components for greater insights. Why? Today’s analytics technology, talent and culture have the capabilities to get together on this effort. It’s important to keep in mind, though, that exploring advanced analytics and its insights is only a first step for businesses. Placing action behind the insights is the necessary next step that will enable a business to truly take advantage of analytics, where a competitive advantage can be generated and analytics ROI be achieved.
Challenge #5: Lack of analytics talent
Solution: Acquiring the right analytics talent
Today, businesses are looking to add analytical talent – individuals with the ability to use statistics, quantitative analysis and information modeling techniques that will shape business decisions. The ideal candidate should also should specialize in the company’s industry and have the right work ethic that will gel with an existing culture and organization.
Here are different approaches to acquiring new analytics talent:To effectively pursue analytics ROI, taking a “design for analytics” approach from the start will reduce the potential for implementation challenges and set businesses up for success. Designing for analytics is a top down change with a revolution from the bottom up. An organization’s leadership needs to be onboard with an enterprise-wide approach and guide its mission to breakdown the silos to realize the true potential of Big Data and achieve ROI.
* Build skills internally through internal analytics training.
* Seek Data Scientists and more available, lesser skilled candidates.
* Partner with universities to access and attract skills
* Explore provider solutions such as Analytics as a Service.