08-05-2018 Door: Lyndsay Wise

Leveraging Operational Intelligence for Proactive Insights

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The lines of business intelligence are blurring as organizations move beyond reporting and dashboards towards leveraging analytics, automation, and alerts within their operations. From monthly and weekly reports that help organizations manage their visibility into performance, towards the need for real-time visibility – a visible shift in analytics adoption highlights the need for BI and analytics to be integrated tightly into an organization’s operations.

This is nothing new: operational intelligence and real-time streaming have been discussed for years. The interesting aspect of this shift is that, despite the technology that exists, many organizations still struggle with their analytics and live in a reactive world, where they are putting out fires instead of identifying opportunities and transforming potential challenges into opportunities for a better customer experience and more efficient operations management. Looking at analytics more broadly and aligning its adoption with desired business outcomes help support this shift.

At the same time, an organization needs to be ready to make some changes. Whether it is technology and developing the right infrastructure or implementing change management to align business processes and enhance collaboration, in many cases businesses have process and technology challenges that need to be addressed before they can leverage analytics proactively. These challenges need to be tied directly to technology adoption. Basically, technology decisions cannot and should not be made in a vacuum.

Organizations should also understand their current state and where they want to be so they can work towards achieving their mission. The following considerations provide a starting point for companies, and will be expanded upon in my upcoming blog posts.

High-Level Considerations for Proactive Analytics Adoption

1. Technology: Technology goes beyond building a data warehouse and a set of analytics deliverables. Organizations require a way to understand innovation and how current infrastructures will be able to support growth and changing needs over time. Additionally, not all types of dashboards or analytical apps are created equal. Technology needs to support quantifiable business value that is defined by business-oriented consumers.

2. Industry and competitive factors: Many trends, including Internet of Things (IoT) and blockchain, can change the way organizations adopt technology, the way they gain a competitive advantage, and what they can achieve. To take advantage of the best solutions, it becomes important to take the time to research options, technologies, trends, and learn what other organizations are doing.

3. Actionable insights: BI and analytics need to be tied to actionable outcomes to provide value. Doing this requires tight alignment between technology and business. This goes beyond developing a set of metrics: it requires the ability to tie metrics or alerts to proactive operations and the ability to gain insight into what is occurring. Developing this successfully starts with looking at a business challenge or opportunity and understanding what the end goal is.

4. Automation and digitization: Many organizations are still stuck trying to manage their data and connect the dots. Being able to automate processes and create a holistic view of the organization supports proactive insights.

5. Data governance and the rest: Data integrity, security, and privacy are all areas that organizations should consider part of their overall BI and analytics strategy. Data and analytics are not independent entities, as strong data, and trust in that data, is required to ensure that analytics not only make sense but provide business value.

Lyndsay Wise

Lyndsay Wise is the Solution Director, Professional Services Division for North America, responsible for helping customers align their business strategy successful data management. Before joining Information Builders, Lyndsay worked as an industry analyst for 12 years, founding WiseAnalytics in 2007 and covered research areas related to data visualization, analytics, BI in the cloud, and implementation strategies for mid-market organizations. She provided consulting services as well as industry research into leading technologies, market trends, BI products and vendors, mid-market needs, and data visualization. In 2012, Lyndsay wrote Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize ROI to help provide organizations with the tools needed to evaluate open source business intelligence and make the right software decisions.

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