Overall Satisfaction with Alteryx
I gave consulting to enterprises using Alteryx to streamline their data to insight pipeline, it included finance firms, retail & cpg, manufacturing, airlines... major problems that are common were reaching out the multiple data sources and tumbling them which creates big tables, joining data and enriching... Alteryx was fast and easy on that... It has great geospatial capabilities as well. And good AutoML capabilities. Forecasting is a bit simpler than expected unfortunatley and optimization capabilities were small scale...
- Good at sifting huge data on premises... It fits big data in your desktop
- The productization of an analytics pipeline is extremely easy, lowcode, nocode...
- New AutoML capabilities and it's implementation using Alteryx promote turns a desktop capability to enterprise scale...
- It requires embedded BI with Natural language querying capabilities...
- They introduced GenAI capabilities with lowcode nocode but the scope is a bit limited, it includes LLMS but not Stable diffusion like Image generators, and the timing was a bit late
- Alteryx totally lack Event stream processing, both listening and small (device embedded) model deployment capabilities which are required in Iot or AIoT businesses
- Extremely fast ETL with minimal investment on Big Data platforms
- Very fast deployment and scheduled use of models for sales ops, risk ops and similar...
- There is an optimization capability as well but packaged solutions are limited... It bears great potential for ROI in routing, scheduling, pricing, inventory optimizations...
- There is a forecasting capability as well it's limited, if Autoforecasting with hierarchies are introduced It bears great potential for high ROI in demand sensing and MPP demand forecasting...
We built with one of my customers, an NBFI, a cheque risk scoring algorithm (using Random forest and XGboosting algos) with high ROI, which earned IDC award in corporate banking. A good econometric forecasting model saved a Retail clients huge benefits like low inventories, better pricing, less out of stock... Scheduling capabilities coupled with predictive model resulted in self-learning pipelines from fresh dataset which is important for many companies today...
IBM is old school, deployment capabilites and automation capabilities lack compared to many market players... Also opensource integrations were limited. Alteryx is good on both deployment and opensource integrations. New versions, IBM WatsonX, is expected to be better the but it was expensive still would be compared to AYX... SAS is old school as well... It takes a SAS team to even install SAS viya properly a few weeks if not months... By this time you would have modeled and deployed the Predictive models and it would already proved their investment...
Do you think Alteryx delivers good value for the price?
Yes
Are you happy with Alteryx's feature set?
Yes
Did Alteryx live up to sales and marketing promises?
Yes
Did implementation of Alteryx go as expected?
Yes
Would you buy Alteryx again?
Yes
Using Alteryx
4 - Finance and tax departments for automating reporting and regulatory reporting processes
Risk management for risk modelling, scoring and lending decision making calculations.
Actuary for again risk modelling for insurance businesses and premium calculations.
Sales teams for sales performance tracking, lead scoring, web scraping.
Risk management for risk modelling, scoring and lending decision making calculations.
Actuary for again risk modelling for insurance businesses and premium calculations.
Sales teams for sales performance tracking, lead scoring, web scraping.
4 - Computer science or
Industrial engineering experience who have received
self paced e-learnings and then gathered their 1st level of Alteryx credentials.
Industrial engineering experience who have received
self paced e-learnings and then gathered their 1st level of Alteryx credentials.
- ETL
- Data quality
- Predictive modelling
- Scheduled reporting
- Using forecasting models we created what if scenarios for marketing and pricing
- Predictive modelling with scheduling we created continuous learning pipelines...
- Marketing optimisation cases like attribution modelling
- Automated pricing and quota preparation
Evaluating Alteryx and Competitors
Yes - SAS 9.x SAS Viya IBM SPSS Modeller
- Integration with Other Systems
- Ease of Use
I would include Dataiku into the process it's a great competitior to Alteryx compared to Legacy and expensive IBM and SAS
Alteryx Implementation
- Implemented in-house
Yes - download Alteryx and setup Alteryx with extended capabiltiies
download industry specific packages and setup as well
get scheduling licence setup
test data source connections
download industry specific packages and setup as well
get scheduling licence setup
test data source connections
Change management was minimal
- Locale should be setup and tested for Turkish and other languages
Alteryx Training
Alteryx Support
Pros | Cons |
---|---|
Quick Resolution Good followup Knowledgeable team Problems get solved Kept well informed Immediate help available Support cares about my success Quick Initial Response | Need to explain problems multiple times |
No
I didin't know that if it's available not mentioned much.
I didin't know that if it's available not mentioned much.
Yes - Yes it was solved with the next patch
CTO himself wrote me an answer! Wow!
Using Alteryx
Pros | Cons |
---|---|
Like to use Relatively simple Easy to use Technical support not required Well integrated Consistent Quick to learn Convenient Feel confident using Familiar | None |
- Data source connections
- SQL and in-db SQL operations
- Predictive modelling
- Reporting function were a bit old school
- R and Python integrations