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Data infrastructure improvements to support advanced analytics require significant investment. To get buy-in, show how data analytics contribute tangibly to business value.
Buy-in starts with visualizing the value data analytics bring to organizations. At a high level, it's all about saving money in some places and generating revenue in others. There are many ways to do both, from enhancing productivity to capturing new opportunities and monetizing data.
Here are eight practical aspects of how data analytics can contribute to business value, identified by industry experts, for organizations to target:
- Productivity enhancement.
- Business goal alignment.
- Better data management.
- Problem solving.
- Faster communication.
- Data monetization.
- Growth opportunities.
Check out the real-world examples of these benefits and tips on how to achieve them.
1. Enhance productivity
Systems that have been around for a long time are opportunities to improve productivity, said Sriram Narasimhan, senior vice president and global head of the data, analytics and insights practice at Wipro, a technology services and consulting company.
For example, computerized fraud analytics have been around for decades. It is often relatively easy to find ways to enhance these legacy processes.
Narasimhan worked with a major U.S. health insurance company that had an existing process to engage their provider network. It monitored payment integrity and provided guidelines for manual review of fraud, waste and abuse. They developed an AI-driven system to learn from examples and created an analytically driven chatbot to be the first point of contact for providers. The result was a nearly 40% drop in calls, a 60% reduction in wrongful denials and an overall business impact of $100 million.
Significant productivity gains can come from automating advanced analytics for reporting and forecasting. Without advanced analytics, expect to type into spreadsheets, said Joshua Swartz, a partner in the Digital Transformation practice of Kearney, a global strategy and management consulting firm.
"[Spreadsheets] often require extensive manual work to produce, contain data or calculation errors, and have substantial time lag, providing visibility days or weeks after the fact," he said.
Automation speeds up the process and provides more timely insight.
"If you receive a backward-looking report four to five weeks after the fact, then it's too late to make course-correcting decisions," Swartz said.
2. Provide specific goals related to KPIs to measure success.
Teams should not waste time pursuing metrics detrimental or inconsequential to overall success. Another way that advanced analytics can contribute value is by aligning business goals across the company.
Data analytics helps you identify and modify goals as you develop evidence to support how or why certain goals were not hit, said Caroline Carruthers, chief executive at data analytics consultancy Carruthers and Jackson as well as co-author of The Chief Data Officer's Playbook.
For example, if a company grew by 10%, but the market grew by 20%, then something was not right. While the organization hit its target, the data collected on the way may reveal more about decisions that could have improved the business in other areas.
"Goals shouldn't … keep you in the same place. They're there to help you move in the right direction and grow. This is something that can be enabled by data analytics," Caruthers said.
Consider which processes or decisions can improve with better analysis, said Josh Miramant, founder and CEO of Blue Orange Digital, an analytics consultancy. Once identified, these initiatives should be categorized by business impact. This is a great starting point to understand the prioritization of an initiative or KPI. By continuously monitoring goals and performance against KPIs for those goals, organizations can identify areas where they may fall short and take corrective action promptly.
Miramant finds that a proactive approach to goal setting and measurement contributes to increased efficiency, better decision-making and higher business value.
3. Eliminate duplicate data
Although data can drive many types of value, it also incurs liabilities in terms of management overhead and security risks. Use advanced analytics to identify duplicates in data sets and eliminate them accordingly, like Jerry Levine, chief evangelist and general counsel at ContractPodAi, a contract lifecycle management provider.
Data deduplication used to be a manual process that required a data analyst to sift through massive amounts of information. ContractPodAi's new process can remove duplicate data much faster. It enables clean data models for teams to work with.
"This sets up the team for success on future projects by maintaining a clean, streamlined data bank that can be easily built into their work," Levine said. Outcomes include more accurate reporting and fewer customer service problems.
4. Mitigate problems
Data analytics prevents issues as well.
"Data analytics is always talked about in terms of enhancing or boosting productivity or innovation but never in the context of what it helps mitigate," Carruthers said.
Data analytics can help you identify and celebrate workers who stop something negative from happening. This can provide information to encourage a change in overall behavior that is better for the organization.
5. Identify experts to streamline processes
Advanced analytics can also help identify the best person to address a particular question or problem. CData Software, a data connectivity platform, uses advanced analytics to identify subject matter experts in the support team to help sales close deals and better care for customers, said Jerod Johnson, senior technology evangelist at the vendor.
CData's support team uses Atlassian Jira project management software to track the number of tickets each representative handles. It also isolates and categorizes the products and services requested for each ticket. The sales team then feeds that data into reports and dashboards to identify the best support representative to contact when a prospective customer has a technical question regarding a specific product, feature or function. This setup improved internal collaboration and customer satisfaction.
6. Monetize data
Caroline CarruthersChief executive, Carruthers and Jackson
Many data-driven organizations have assets they can monetize with advanced analytics. Data sets that are unique to the organization and predict some future outcome of value to other organizations make good candidates.
"Advanced analytic models are a critical selling point for demonstrating a dataset's use case, quality and value," said Ed Murphy, senior vice president of data science at 1010data, a data analytics consultancy.
Organizations that prioritize exploratory analysis of their own data can often identify which datasets are monetizable. The value of monetization increases when data point to changes that can improve outcomes. Seek out a platform that enables data sharing and supports advanced analytics for this approach. A focus on metadata cataloging can also help you identify uses for a dataset internally or externally.
7. Identify new opportunities
Digital consultancy Avionos often uses advanced analytics to discover new opportunities for clients. They analyze customer behavior, market trends and other data, said Mary Schneeberger, managing director and studio lead at the firm.
For example, a retailer can identify which products are most popular among its customers. The information can be the basis to adjust their inventory and marketing strategy. Businesses can also gain insights into customer preferences and behavior by analyzing customer data. This helps the business tailor their products and services.
However, she cautioned that while advanced analytics can generate a lot of insights, not all of them are relevant to the business.
"Tracking or monitoring something just for the sake of capturing the data isn't going to translate into business value," she said.
Organizations need to align the measurement approach with core business goals and objectives. If the data is incomplete, inconsistent or inaccurate, it can lead to poor insights and decisions.
8. Personalize experiences
Advanced analytics can be vital in personalizing customer experience to increase customer satisfaction and engagement. Sport Buff worked on a project to create a scalable data platform that delivers a personalized sports viewing experience for their fan base, said Benn Achilleas, founder and CEO.
They worked with Ensono, a managed services provider and IT advisor, to scale the platform to support the FIFA World Cup Qatar 2022. The service overlayed graphics on the broadcast to give viewers extra information, engagement and interaction. Viewer data was centralized from multiple sources and analyzed by the Sports Buff team to personalize viewer experiences and optimize content in real time.
This proactive approach to personalization resulted in a 266% increase in watch-time on the platform compared to traditional viewing, Achilleas said.