AIP is a done-for-you data warehouse and transformation platform that brings Data Science capabilities to FP&A and Rev Ops.
Analyst Intelligence的外部链接
US，California，San Diego
If only there was a better way...
FP&A is so behind... Data Science & #businessintelligence have data pipelines and a data warehouse. Sales has a CRM and an entire #revops team. Meanwhile FP&A teams are manually moving data, VLOOKUP for transformations, and CSV as a database ??♂?
Use a no-code Automation Notebook to do this automatically. Analytical leaders love this, and not only does it save time, it provides you with peace of mind.
#fpa Teams - when setting up your data pipelines, one thing you should do is VERSION (snapshot) your data so you can... 1) Spot anomalies in historical data 2) Dig into variances long before month end 3) Instantly guide the CFO on changes 4) Analyze changes to committed pipeline 5) Revert to prior versions 6) Analyze and improve forecast accuracy It's easy, here's how you do it: When you are pasting data from a data source (hopefully with an automated data pipeline like in an Analyst Intelligence notebook, but even if manually) paste the data into a separate table with a column that has the "Archive Date" and set it to today's date. Now you have a version history - it's that easy. I recommend normalizing the data so that all of the data sources are in a single giant "Source Data Archive History" table. #fpa #accounting #revops #businessintelligence
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#fpa always deals with dirty data, so data wrangling is a core skill you need to learn. If your data is structured and perfect, then that means you’re not doing anything new. And if you’re not doing anything new then you don’t work in FP&A.
The migration of CFOs to Data-Driven Decision Makers starts with understanding the basics, then how and when to deploy them. These posts are a great first step to grounding yourself in the basics!
?? What's the Difference Between Citizen Data Science (CDS) and Data-Driven Decision Making (DDDM)? CDS and DDDM may seem like two sides of the same coin, but they have distinct focuses. CDS is about enabling individuals, even without formal data analysis training, to leverage data tools and methodologies to draw valuable insights. On the other hand, DDDM is a management approach where decisions are grounded in data analysis rather than intuition or simple observation. While CDS can contribute to DDDM by increasing the pool of individuals capable of analyzing data, these couldn't be more different. CDS is about who carries out the data science, while DDDM is about how decisions get made. Executives (especially Gartner fans) get DDDM, CDS, and self-service analytics mixed up a lot. Stay tuned for more insights on the practical application of data in decision making. ------------------------------------------- Follow me and hit the ?? to stay updated on my practical advice for modern #fpa + #revops and #businessintelligence teams.
?? It's so hard to demo the Analyst Intelligence Platform to FP&A teams. When something looks SUPER SIMPLE, there's often no fanfare unless you've been on the other side. It's nice when people "get it". ? What you see: - Connect data sets with 1 click - Clean and transform data with AI - Reuse your data anywhere - Get amazing support ? What you don't see: - Data engineering teams - Integration fees - Implementation (and fees) - Gotchas after you've committed - Long training and ramp up - Cloud storage configurations - Data warehouse complexity - IT resources - A dedicated team member to operate the platform The best user interface, for the best user experience, is no user interface at all. We love making our customers happy! #fpa #revops
Integrate Salesforce with Google Cloud in seconds. Once your data is in BigQuery, you can instantly bring it into Google Sheets, virtually every #businessintelligence system, and thousands of other systems. #ELT
?? Product Launch - Generate a forecast using Machine Learning without knowing how to write a single line of code! Once you've used AIP to automate your data wrangling, don't stop there. Get as advanced as you can handle with practical uses of artificial intelligence, machine learning, and business intelligence. #machinelearning #gemini #artificialintelligence #revops #fpa
?? Last week we silently released machine learning as part of our Analyst Intelligence Workflow Automation Notebook to a few beta customers! Our customers can already write #sql with #gemini, and we have a lot more innovation coming in this space. Send me a private message if you're interested in testing it out! #fpa #forecasting
Urvi Kapadia hits the nail on the head 30min through: Data Wrangling is key. 3 Huge problems make collaboration a challenge: 1. Data consistency 2. Data granularity 3. Data quality Data wrangling challenges, bumped up against tight deadlines, reduces FP&A's influence and reputation. FP&A is often only known as the budget team, and that's a big challenge with an equal opportunity.
Helping FP&A Professionals provide value to their businesses | Founder of The FP&A Guy | Host of 3 popular Finance podcasts | Microsoft MVP
??? The first episode of my brand-new podcast, FP&A Tomorrow, has dropped, and you will not want to miss it. ?? In this episode, we dive deep into the world of how FP&A and Product Management can collaborate and work better together. Three fantastic guests join me: ?? Dara Goroff, PMP- Vice President at I-CAR ?? Navya Rehani Gupta - Chief Product Officer at Peek ?? Urvi Kapadia- FP&A Manager at NetDocuments Key highlights from the discussion: ?? Effective FP&A partnership with Product involves understanding the customer, collaborating on pricing and bundling, and seeing the potential in new ideas. ?? Aligning KPIs and data strategy from the start is crucial for smooth collaboration. ?? FP&A can provide valuable strategic insights beyond just numbers, helping to identify new growth opportunities. ?? Building strong relationships, showing mutual empathy, and celebrating wins together are essential for successful cross-functional partnerships. ?? Two of the most important skills for FP&A professionals, according to Urvi, are data analysis to bridge organizational needs and storytelling to influence the business. Let me know in the comments what you liked most about this episode. Listen to the full episode to learn how you, as FP&A professionals, can better support your business's product people. https://lnkd.in/gTyTNRzk Don't forget to subscribe to FP&A Tomorrow for more insightful discussions on the future of Financial Planning & Analysis. ?? The title sponsor for this episode of FP&A Tomorrow is Abacum Remember FP&A Tomorrow makes it possible for you to earn CPE and FPAC credit for your AFP FP&A Certification through Earmark.
#fpa needs to catch up to #revops, and Rev Ops needs to catch up to #datascience. ?? Executives think data-driven decision making requires a big digital transformation initiative (spoiler, it does with legacy tools). ? Analysts think that you have to learn new coding languages (DAX for Power BI, SQL for most others) to build incredible dashboards. New tech, with companies like Metabase mean you can analyze data and build dashboards faster than you would in a spreadsheet, and the findings are immediately presentable, shareable, scaleable, and you don't need to maintain old files when they need to be refreshed. Best of all? Free.
You can find out how here: https://lnkd.in/gQXRvBMX
Neglecting a solid data foundation is a guaranteed route to analytics failure, hindering both insights and return on investment. A robust transformation pipeline is a prerequisite for success. #fpa #revops