Insights

5 Steps to Get Big Analytics for a Small Price Tag

Getting started with analytics can be a large undertaking for any organization. Adding to the marketing hype and confusing the rest of the business are an increasing number of buzzwords like big data, business intelligence, predictive analytics and the internet of things.

Additionally, the emerging technology is advancing at an unprecedented pace. It is no wonder organizations find it tough to comprehend what all of this means for their business. When organizations analyze the cost of the software, hardware and resources, an analytics project may appear too daunting.

According to a Gartner study, over 50% of analytics projects fail. When we truly break down all the components in an analytics project, it comes down to a risk-and-reward ratio.

The following five steps help lower the risk and raise the reward.

Step 1: Do Not Pick a New Tool

A new tool will immediately add cost before any value is achieved. Outgrow your current toolset as the program expands. Don’t ‘grow into’ a new toolset. Most analytics projects do not fail because of the technology, they fail due to a lack of vision, execution and talent. Many organizations will look immediately to a new technology to solve their analytical problems. If the data isn’t available or is inaccurate, you can’t analyze it. A new tool will not solve this problem.

Step 2: Start Small

Analytics projects fail because they are too big. Big projects lack a unified direction, suffer scope creep and have too many stakeholders. Think low cost and fast time to value.

Step 3: Find a Quick Win

Find an area in the organization that is suffering from a lack of insight. Focus on solving this one problem. Ideally, the data associated with the problem is accurate and complete. Solve the problem and show a positive return on investment.

Step 4: Turn Analytics into a Profit Center

When touting the quick win that the analytics resources accomplished, include a return on investment (ROI) calculation. If the ROI is positive, add this as a reporting metric for the group. Shift the organizational thought process from analytics being a cost to a revenue or profit center.

Step 5: Ask for More

This can be a tough discussion to have. Ask the organization to trust you again by taking the benefit that was added in the organization through the analytics program and reinvest it in another small project or additional tools.

Risk

The approach outlined above has a low risk and cash outlay associated with it. The organization does not have to commit to a companywide expensive analytics program. However, they do have to commit to a small outlay of cash or resources to begin the analytics journey.

If successful, this approach will get the skeptics on board with the program, or, at least quiet their vocal disapproval. Most organizations can get through this step with tools that they have already purchased, taking the large investment out of the equation. Often the analytics will lead an organization to make a management decision. If the organization does not have the appetite to make that big decision, the culture is not ready for analytics, and no further investment ianas required.

Reward

The upside in a small analytics investment can truly change an organization. An immediate increase in revenue or a decrease in costs is usually the result of an initial analytics investment. For those organizations that have the appetite for change as well as a clear strategic plan for the organization and the analytics program, there is an opportunity to gain a true competitive advantage in the marketplace.

Regardless of the new buzzword for analytics or the ability to analyze more data faster, the fundamental concepts remain. Organizations want to make more intelligent decisions to better experiences, efficiencies, revenue and eventual profit.

Given that there are more data and tools than ever before, don’t get lost in the confusion. Stick to the fundamentals. Tools aid in the discovery and analysis of data. However, they alone will not solve organizational challenges.

Be cautious of putting a tool before the problem. And beware of a commissioned sales person disguised as an analytics professional.

Article written by Gabriel Gaultier
Image credit by Getty Images, DigitalVision Vectors, sorbetto
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