Despite analytics software being widely available for decades, adoption rates across organizations (even high-tech ones) are still abysmally low. According to a NewVantage 2020 survey on data and analytics, less than 27% of organizations have successfully adopted analytics, while 73% want to use data to understand their business better and 72% want to use analytics to improve their products and services.
An IDC survey commissioned by Sisense in 2020 revealed that respondents were most interested in improving operational efficiency (56%) or customer experience (50%). Identifying new revenue streams or business opportunities (39%) and increasing innovation (30%) were also important.
So what does all that mean for product builders? The bottom line is that your company is collecting data, and you need to be smart about analyzing it and taking advantage of the insights you’re deriving. Customers and in-house users are also becoming more savvy and demanding actionable intelligence infused seamlessly into their products, services, and experiences. In this article, we’ll look at how DNV took intelligence from its massive datasets and put the right bits of information into its platforms in the right way (not simply embedding reports and dashboards) to drive impact for its users. Hopefully you can see ways to improve your own offerings with data.
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Understanding the difference: Reports vs. analytic application
A traditional report is typically static data presented in tabular or text format with basic graphs and charts. The idea is to highlight certain raw numbers or datasets.
An analytic application, on the other hand, displays key performance indicators (KPIs) that enable active monitoring of performance, efficiency, or other business metrics and actions. Metrics and visualizations should reveal actionable insights and advise people on what to do next instead of simply being a dump of data. When a user logs in to the system, whether they are an expert in the domain or not, they should immediately know what to do next.
This is what we call “actionable intelligence.” Insights drawn from analyzed data are important, but tying those insights to a specific action is what makes that piece of information truly valuable. Decision fatigue is very real and impacts us all more and more today, so making the next action clearly understandable to the user can truly help evolve your product or internal workflows.
DNV’s Cascade Insight turns data into actionable intelligence
DNV is an independent expert in risk management and assurance, operating in more than 100 countries. Through its broad experience and deep expertise, DNV advances safety and sustainable performance, sets industry benchmarks, and inspires and invents solutions.
DNV, through its Electric Grid Reliability and Performance product center, embeds Sisense to provide a platform for creating and deploying high-value analytics to utility companies. This platform, Cascade Insight, is a purpose-built analytics system that delivers actionable intelligence about utility asset performance.
Cascade Insight leverages operational data ingested from grid-edge technologies, integrations with other business systems, and mobile software to construct a foundation for the effective creation and deployment of high-value analytics. The platform enables clients to get ahead of maintenance issues and avoid service outages in their grids, saving money and delivering better value and service to customers.
To provide value to customers, DNV follows a few key principles to go beyond delivering a report to delivering actionable insights. Oftentimes, it is a matter of design as opposed to the technology.
Start with high-level rollups and actionable metrics
When delivering insights, it is important to start with high-level metrics that deliver immediate value. All of the data that is coming to the system — wherever it comes from — boils down into an instant snapshot that is the most important insight the user will see that day. DNV includes 14 standardized dashboards out-of-the-box that deal with everything from managing equipment to workers.
A common thread among the main dashboards when it comes to design is the inverted triangle principle. For example, the summary dashboards show the number of high-risk units, overdue mail exchanger orders, or unacknowledged alerts. These vital pieces of information are the first thing a user sees when logging into Cascade Insight. This ensures that the end user immediately knows that there are issues that need to be addressed; nothing gets lost in information overload.
Once users have been provided with the high-level metrics, they are also able to drill down into the details to find what needs to be addressed. Drill paths leverage chained dashboards and are great ways to enable this experience for end users.
DNV’s display shows users clear visual cues based on the domain and industry needs. For example, utility companies exist in the real world and deal with physical infrastructure; in this context, geographic data is important. DNV leverages maps to show problem areas. Red dots on a map are far easier to consume than a pivot table and immediately show that a situation is in need of attention. Users can quickly discern the next action they need to take.
Be explicit in showing value
When building and designing dashboards, it is important to always ask yourself, “How does this information help users make better decisions?”
In one example, “Active equipment without Parents” is a huge problem area for the users of Cascade Insight often caused by bad data. DNV leverages analytics to do a gut check on the quality of data and surface the data gaps so the users can alleviate the issue for their end users.
Imagine hundreds of workers in the field performing inspections at substations, gathering reads and engineering-based diagnostic data on equipment, which is then followed by integrations with other systems, resulting in massive, complex data. It is nearly impossible and highly inefficient to parse through that manually or with Excel. DNV delivers concise and actionable analytics like the “medium to high-risk equipment” to present the most relevant information to end users. As soon as you look at the chart, it is immediately obvious that “red” is bad. The user can then click on the chart to drill down into that dimension and get more information.
Building better apps with data
Every company is becoming a data company. Users, internal and external, are demanding data and actionable insights in their apps and workflows, and companies that don’t deliver these will lose in the evolving business world.
Even though every company is becoming a data company, every company isn’t an analytics company. If you’re struggling with how to add actionable intelligence to your product, service, or experience, embedding could be the answer. Building analytics from scratch may seem easier than it really is, while relying on the expertise of an established analytics company can supercharge your offering with less time and energy on your part.
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Former Sisenser Shruthi Panicker holds a BS in Computer Science as well as an MBA and has over a decade of experience in the technology world.