What is Data Analytics
Propel Ops to
Better, Faster, Cheaper
Your Partner in Advanced Operations and Intelligent Automation
We’ve really done a lot of heavy operations, and we consider it one of our niches. We’re not just an embedded service provider but a full partner to get you to better, faster, cheaper.
Our Approch
We start with a full revamp of metrics, calculations, investigations, interpretations, and reporting. Then we help you efficiently trial intelligent automation (AI) and advanced experimentation for those tricky tests. We’ll design and build any advanced infrastructure/tooling required.
What is Data Analytics
Data analytics is the process of examining raw data to uncover patterns, trends, and insights required to make informed decisions. Typically our clients are “software enabled” and are generating more information than could ever be organized or crunched with spreadsheets - software, cerebral design, and advanced methods are required. Data analytics work begins with raw information collection and reporting, and in its fully-fledged state is a key shaper of strategic roadmaps across all departments
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Data Analytics - What is it?What is Data Infrastructure
Data infrastructure is the tools and systems that allow organizations to collect, store, manage, process, access, and serve data. Plug-and-play data infrastructure vendors have multiplied in recent years, but specialist engineers are still required for more advanced/custom requirements, complex vendor implementation, and bridging gaps with SWE.
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Data InfrastructureWhat is AI
What is AI
What is AI
AI (Artificial Intelligence) is a technology that enables machines to mimic human intelligence, allowing them to perform tasks like understanding language, recognizing images, or making decisions. Machine Learning is an older but more mature subset of AI - when its capabilities are sufficient for a problem, ML is more straightforward, definite, quick to develop, and supported by tools/vendors/automations.
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Practical AISolidify the Basics
Typically when we embed with Ops our first order of business is stabilization, meaning that we take what’s already happening and then make sure that it’s happening reliably. Areas that we usually work on include:
Metrics
Ensure that metrics used actually align with department/ subdepartment goals
Calculations
Ensure that metric calculations are correct
Raw Data
Ensuring that raw data collection is complete and clean, with enough historical tracking
Reporting
Building out full reporting for each subgroup’s needs
Unit Accounting
Top-to-bottom work to calculate the true profit of each individual unit
Frameworks
Quickly dig into causes for metric changes
Leading Metrics
Find the earliest possible sign of a problem, before it gets bigger
Accuracy of Interpretation
Don’t chase phantom problems or implement incorrect solutions
SOPs
Operationalize correct usage of data; ensure robust input data
BI Org Adjustments
Sometimes we make recommendations here
Incrementally Automate for Consistency, Speed, Cost, Safety
“AI” can consist of machine learning, language models, other language processing, computer vision, and other advanced methods. We are brutally practical about AIand are very careful about delivering quick incremental progress to our clients on only strong problem setups.
Typical examples of AI and advanced methods for operations include:
Typical examples of AI and advanced methods for operations include:
Automating a repetitive decision (more consistent and cheaper)
Scan huge quantities of messages to find the urgent or important ones
Automate a 3D or 2D visual task (more consistent and cheaper)
Automate the collation of reports
Optimization problems such as scheduling, route planning, and forecasting
Assign risk levels
Guess costs using comparables
After that we typically have strong proposals for either/both of AI and advanced experimentation. With AI (including Machine Learning, Computer Vision, Language Models, etc) there are often processes that are costly, error prone, or both that can be at least partially automated. With advanced experimentation, Ops groups are typically looking to try something out that can’t be done with a simple AB test.
AI and advanced experimentation often need to be coupled with advanced tools or infrastructure, which we specialize in designing and providing.
Practical AIAI and advanced experimentation often need to be coupled with advanced tools or infrastructure, which we specialize in designing and providing.
Run Experiments to
Test Out Hypotheses
It can be tough, in practical terms, to test out potential changes and accurately measure whether they’re helpful or hurtful. This can apply to a bespoke manufacturing process, large gig markets, and everything in between. We know how to run quick and dirty (but accurate) experiments to get things started, and then run longer ones after promising trials.
Advanced Infrastructure and Tools
We build advanced infrastructure and tools to enable:
01
Successive levels of AI deployment
02
Experimentation
03
Reducing reliance on SWE to safely enact changes within software + process
A Valuable Initial Convo, Regardless of Whether You Move Forward
Founders and startup leaders typically get a lot out of an initial convo with us, particularly
1. Shrewd assessments of LOE’s and true needs and
2. Elements of long-term vision.
Book an Assessment Convo1. Shrewd assessments of LOE’s and true needs and
2. Elements of long-term vision.
