What is Data Analytics
Value Per Dollar
and Foresight for Every Department
What early stage startups love most about us is that they get a unicorn-creating data team
while balancing financial resources towards hands-on resources (ICs).
Highly experienced fractional CDOs join every few months to predict the best future both for the data team
and for each department. The team operates with the full best practices of a kickin' function.
Strong near-shore resources are available in many scenarios.
Introduction
Many of our early-stage customers start out with a single full-time analytics engineer (sometimes offshore) coupled with a part time analytics manager at 6 hours per week. Typically these clients also require some upfront data infrastructure work and strategic planning, but generally limited to the first few weeks of partnership.
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: Maslow’s HierarchyWhat 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 AIVisionary But Brutally Practical Assessment of Needs
Usually within an hour of assessment conversation we can tell you:
1. Continuous Resources: “Standard”
Where do you have needs that are real but relatively conventional?
2. Continuous Resources: Complex, Creative, or Otherwise Challenging
We help clients identify what data areas require something more; common examples include category-defining product analytics + FP&A analytics, advanced operations, and “messy” projects.
With a bit more conversation we can also tell you:
3. Upfront Needs
Where do you have needs that are real but relatively conventional?
a.
Infrastructure
b.
Cleanup
c.
Alterations to SWE + BI practices
d.
Strategic Planning
Visionary But Brutally Practical Assessment of Needs
Usually within an hour of assessment conversation we can tell you:
1. Continuous Resources: “Standard”
Where do you have needs that are real but relatively conventional?
2. Continuous Resources: Complex, Creative, or Otherwise Challenging
We help clients identify what data areas require something more; common examples include category-defining product analytics + FP&A analytics, advanced operations, and “messy” projects.
With a bit more conversation we can also tell you:
3. Upfront Needs
a. Infrastructure
b. Cleanup
c. Alterations to SWE + BI practices
d. Strategic Planning
For both continuous and upfront resources, we provide you with a menu of options and clearly explain the benefits/drawbacks of each.
How it Works:
Assessments and Proposals
Long-Term DNA
Flatiron is fundamentally structured as a long-term partnership company. We’d much rather that you start small with us and feel really comfortable - financially and trust-wise - before increasing resourcing.
We like to say that early startups like us because we’re the best value, and then keep us as they grow because it remains difficult to replicate what we do.
We like to say that early startups like us because we’re the best value, and then keep us as they grow because it remains difficult to replicate what we do.
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.
See also:
AI
Generally we advise getting Data Analytics into a strong place before considering AI and Machine Learning - usually the payoff per dollar expenditure is much higher in a company’s early stages. However, on some occasions there is in fact a high benefit to implementing a straightforward AI model. Likely examples include automating an expensive or error prone process, profiling customers for targeted actions, and forecasting/optimizing.
We are brutally realistic about AI, especially for early stage companies; our primary goal is to ensure that you’re not wasting time and money.
We are brutally realistic about AI, especially for early stage companies; our primary goal is to ensure that you’re not wasting time and money.