How ForeOptics’ FOMI Methodology Enables Organizations to Succeed in their Analytics Journey
Today’s enterprises are driven by data and insightful analysis is essential within every private and public organization. As an asset, data has often been compared to oil: It needs to be acquired and refined before it is useful – both of which are expensive and time-consuming processes. Not every organization has the capability for this today. Furthermore, data needs to be analyzed and utilized in meaningful, repeatable ways to unlock its true value. Easy to say, hard to do. According to a Gartner, 85% of big data projects fail and VentureBeat reported 87% of data projects don’t make it into production.
Imagine if a factory line had an 85% scrap rate…goodness! This data point is dated (2019), but empirical evidence supports the general trend. Analytics projects are hard to develop, even harder to deploy.
The Adoption Curve
As enterprises begin to develop and implement their data and analytics roadmaps, they face a standard adoption curve (Fig 1).
Over time, users start to demand more sophisticated analyses as use-cases become more complex. Additionally, any organizational heterogeneity associated with analytics fluency—interpreting results and trusting the data and analytics methodologies—becomes apparent and progress slows.
At this point of the journey, not every project will be successful. Sometimes a use case can’t be supported with the data available. Some problems assigned to analytics are process or people issues that analytics and data can’t resolve. When these projects fail to generate results, leadership can start to doubt the efficacy of their investments. Organizations at this point in the curve have entered the trough in their implementation journey (3). Many organizations lose direction at this point, hit an evolutionary dead end, and struggle to find a way forward (4).
The Five Elements Framework
We have found that getting out of the trough is enabled by the ForeOptics FOMI with its 5 elements: Data, infrastructure, skillsets, organizational analytics fluency and appropriate valuation methodologies. These elements build on one another, and together they enable sustained value creation (Figure 2). Additionally, we have found that change management is (not surprisingly) a huge enabler for adoption and stickiness for analytics. And, like any other change management initiative, leadership involvement is key.
1. Data:
Data isn’t just “bits in a box”; it’s dynamic, moving from source to consumer down its own supply chain. Some data may require acquisition strategies to address issues associated with fair use, privacy, and access. Make-versus-buy decisions may apply and questions about data provenance and obsolescence will have to be addressed.
As use cases become more complex and multivariate, data epistemology -how the data is used to produce or develop an outcome – becomes increasingly important. Additionally, without a proper plan for data management, access, and governance, maturity will remain limited.
2. Infrastructure:
Enterprises must have dedicated infrastructure for capturing, cleansing, and manipulating data. This infrastructure must be secure, scalable, and able to handle large amounts of data with flexible refresh, back-up, and store requirements. Infrastructure strategies need to include connect and collect guidelines (how to connect to various enterprise systems or external data sets). Infrastructures will require the ability to handle various data types, including structured and unstructured data. An effective analytics infrastructure must also be flexible enough to accommodate changing data needs including any compliance considerations. If these criteria are not considered early in the implementation journey, maturity can be delayed.
3. Skillsets:
Enterprises must be capable of using and analyzing data and developing associated analytics, as well as identifying and addressing problems that can be solved with data or analytics. Ethical questions may arise (“We can build this but should we?”) that will require leadership to become familiar with technical, legal and governance considerations. A mature data-driven organization requires a combination of technical skills and domain expertise, including data analysis, statistics, programming, and data visualization. Enterprises may need to hire new talent or upskill existing staff to meet these requirements.
4. Organizational Analytics Fluency:
Stakeholders and leadership must understand analytics well enough to ask meaningful questions and properly interpret results. Leaders must foster a culture of data-driven decision making and ensure that all stakeholders understand the value of data and analytics. Enterprises must be prepared to “fail” – a lot – with their analytics development and deployment, and be prepared to support data in its lifecycle, from data generation to archive.
5. Valuation Methodologies:
The whole framework must be supported by solid value propositions. Enterprises must be able to measure the impact of data and analytics investments and demonstrate value to stakeholders. Valuation can include financial metrics such as ROI and should capture the entire value chain rather than simple point-of-use analysis.
Conclusion
Unlocking the value of data requires a sustained, focused approach combining technical and organizational acumen. ForeOptics’ FOMI offers enterprises the expertise, infrastructure, and methodologies required to produce relevant, actionable insights. Using the FOMI, we have helped numerous organizations across various sectors to better use their data and develop analytics to address operational efficiencies, find new market opportunities and optimize supply chains. With our expertise, manufacturing firms have improved key operational metrics in yield, cost of poor quality, and decision cycle time reductions. Organizations have transformed their processes to do more with less, as measured by improved productivity.
Becoming a data driven organization can be intimidating, but there’s no reason to ‘go it alone’. At ForeOptics, we have practical, working experience with the challenges that enterprises face in their transformation journeys. Our proven framework will help identify and mitigate organizational and technical challenges and streamline the processes that enable your organization to fully unlock the power of data.