Your high-performance future is hidden in your data.
In so many ways on the cusp of 2021, we are in the fight of our lives. Imagine conducting military operations without dynamic intelligence, surveillance and reconnaissance (ISR). Enterprise leaders often don’t have the instrumentation required but still must navigate their organizations through turbulence and flak to successfully stay on-mission, along a growth trajectory. The only conceivable way for a military-precise response to threats and overcoming uncertainty is to incorporate insights around the key drivers for your organization into your operational planning.
Much like a situation room briefing, the invariable questions asked by executives of their presenting teams are “Where did we get the data?” and “How do we get better visibility?” In times of economic volatility, these questions point to the digital lifeblood of the organization, and no matter your industry you need to know where to get the right data to the people who need it the most.
In this blog, we help you perform your own ISR and quickly assess your standing against the following three stages to rapidly identify needed improvements in your business intelligence accuracy and completeness.
Stage1: Data Engineering (aka plumbing)
- Data discovery – What is the back office topology? Are you pouring information from your core systems into a data lake, or has it devolved into a data swamp?
- Data profiling – How do we create a metadata layer from disparate streams, formats and outputs to ready for data scientists?
- Data modeling – Can we build schema to achieve the elusive but aspirational single version of the truth?
Stage 2: Data Operations start at the foundation. Yes, GIGO.
- Data hygiene – Newsflash: Your raw data is polluted. Cleaning this up can be daunting, but leveraging deep expertise and experience with a Center of Excellence model can purify your data and trace issues back to source systems.
- Intelligent ingest - Machine Learning (ML) techniques like fuzzy deduping and automated field mapping can significantly accelerate the onboarding of data into analytics engines.
- Data integrity - Audits reveal problems in the underlying collection, management, exchange and output architecture.
Stage 3: Analytics and Visualization – Continuous Intelligence (static dashboards are so 2019)
- Turning the analytics engine - Sources evolve and emerge and require dynamic data models to accommodate competitive, financial, and regulatory demands change. Refine without reinvention.
- Apply AI/ML libraries and algorithms - Leverage your data science team, or rent one to employ constantly evolving libraries, powers of inference, correlation, and projection.
- Visualization without interpretation - Depict your findings so they speak for themselves without ever needing clarification. Just because you can depict data in an overly ornate aesthetic doesn’t mean you should.
Macroeconomic uncertainty? Commercial ambiguity?
Operational Intelligence is the enterprise imperative.
Continuous intelligence can optimize customer engagement, refine targeted marketing, significantly enhance field services efficiencies, and inform self-healing networks and supply chains. Unfortunately, many organizations fail to fully survey the terrain they must negotiate to fully synthesize the intelligence siloed in departmental systems. Engage with us today to collaborate on a highly relevant approach to leveraging the potential of our digital transformation practices to ensure business as usual—even amid the most unusual.