Leveraging Operational Intelligence for Proactive Insights, Part 3

Lyndsay Wise's picture
 By | juni 15, 2018
juni 15, 2018

Part 1 and Part 2 of this blog series evaluated the importance of tying business goals to technology outcomes, specifically in relation to business intelligence and analytics use. These blogs also discussed technology use and industry and competitive factors to evaluate what is needed for operational intelligence use and why business outcomes are tied in to overall success.

Part 3 looks at practical ways to shift towards operational intelligence and take advantage of frameworks and technologies that support the organization’s BI maturity needs. These include:

1. Actionable Insights

Saying that BI and Analytics need to be tied to actionable outcomes is easier said than done. Many organizations focus on creating BI applications that replace legacy reporting solutions but do not take into account different ways of evaluating data needs. It is important to provide accurate reporting and analytics. At the same time, operationalizing analytics access requires a new way of thinking as information is delivered differently and operational insights needs real-time mindset to decision making.

Doing so effectively requires the ability to leverage operational intelligence – from an architectural standpoint to make sure that the database technology available supports real-time data access. Beyond capabilities, developing actionable insights involves a shift in mindset. Organizations should evaluate what their top business challenges are and the information they need to gain insight into the causes and effects of those challenges. This may be an iterative approach but organizations can start by identifying the insights they are getting from operational insights that were not available to them before hand and develop an understanding of the implications of having access to this new level of insight.

2. Automation and Digitization

Part of operational intelligence involves the ability to automate processes and create a holistic view of the organization through better visibility into data. From a business perspective, companies should evaluate the benefits of automation and digitization beyond time savings alone and create accost benefit analysis to understand how better and automated access to data supports operations and information insight. Doing so may require a lot of work, but ends up being worth the payoff as it supports better overall insights and also ties into data governance, reliability, value, and broader master data management. The reality is that leveraging operational analytics to support better proactive insights requires looking at data with fresh eyes and taking a holistic approach to data management and analytics access. By automating processes and digitizing access to data assets, organizations can support direct correlation between data access and business value.

3. Data Governance and the Rest

Data governance and broader data management should not be left out when looking at operational intelligence. Without strong data assets and the ability to trust data, it becomes a challenge to validate information outcomes on many different levels. Although data governance, security, and privacy are all areas that organizations struggle with, many still see these initiatives as separate from analytics. Unfortunately, these companies will only get so far with their BI initiatives due to a lack of understanding of the importance of data governance and how it supports business outcomes. If an organization does not have an understanding of what their data means and how it is managed, it becomes difficult to tie any analytics outputs to business outcomes.

In conclusion, operational intelligence is complex and requires a shift in technology adoption and the way people think about analytics in general. As we move forward, simply developing a set of metrics without tying them to proactive business insights, will lead to a lack of value coming from analytics deployments. To get the most out of BI and analytics, organizations need to tie the business outcomes they desire to operational intelligence and the development of broader business goals.