What Does Success Mean to You?

1. The first step in calculating the ROI of a data analytics project is to define success within the context of your organization and the use-cases you’re pursuing. Consider all of the ways that data—or, rather, insights—has either directly or indirectly contributed value to your organization.

2. Think back to your organization’s big picture objectives. Chances are, these efforts span multiple departments and use-cases—each requiring a different set of metrics for tracking its impact. What business problems were they meant to solve? What made you pursue this initiative in the first place?

3. On a surface level, consider whether internal processes have become easier, faster, or more accurate. For instance, are your data-driven decisions delivering better outcomes? Are social media metrics on the rise? You might start by measuring the impact of those noticeable gains, then move into other areas.

4. It’s worth noting that KPIs are central to helping organizations unlock the value hiding in Big Data sets. KPIs allow you to prioritize the information that best fits your goals—making it easier on teams to dig deep into relevant data while keeping the “noise” out of the picture.

How Is Data Being Used?

1. Once you identify your target use-cases and the metrics that represent success, look at how employees are currently using data. How are they leveraging Big Data to improve performance or deliver improved customer outcomes? Where are they falling short? Consider looking at individual performance data to get a sense of whether different analytics, BI tools, or training tactics are needed.

2. Another big part of measuring the ROI of data analytics is looking closely at all related expenses before digging into the KPIs. Make sure you’re not overspending on areas with little impact on the bottom line (i.e., spending too much to acquire new leads or wasting money on cloud storage meant for smaller data sets).

3. Becoming a data-driven organization typically requires significant investment in training and upskilling programs. While training comes with the benefit of decreasing employee churn, saving money in the short-term, educating the workforce is expensive. Keep in mind that it may take a long time to see the payoff.

4. How much have you spent on software, sensors, equipment, and devices to support this initiative? Here, you want to look at everything from monthly SaaS subscriptions to cloud storage and how much you’ve spent on devices and security solutions. While many of these components are essential investments, you might uncover opportunities to cut costs by upgrading or eliminating certain solutions.

Consider the Intangibles

1. Beyond the direct costs of launching a Big Data initiative, think about the factors that aren’t so easy to measure but are important nonetheless. For example, consider what your business will lose if you put off adoption or your initiative fails. What happens if you’re not the first in your industry to embrace next-gen data analytics? What can machine learning, AI, and natural language processing do to help you lock in the competitive advantage?

2. Innovation (or a lack thereof) is closely linked to employee retention. Talented employees will likely move on to companies that value innovation and invest in their success. Additionally, not investing in data analytics solutions can put your business at risk.

Operational Efficiency

• In an industrial setting, achieving operational efficiency might mean reducing overtime hours, delivering products to market faster, and streamlining production processes. You might analyze sensor data, overhead, indirect expenses, machine performance, downtime, and environmental conditions. However, operational KPIs might look different depending on whether you’re running the production line or the HR department.

• Sample operational KPIs:

• Downtime

• Cost-savings
• Time-to-market
• Overtime hours
• Cycle time
• Maintenance costs
• Production volume
• Capacity utilization
• Number of defects