Redpanda Data’s Post

Data is just numbers without insight! Learn how to convert raw data into meaningful insights with our guide. 🔢💡 #DataInsights #SaaS #Yurbi https://lnkd.in/dU8XB9vU

In data analytics, speed is everything. Businesses can take advantage of opportunities, spot anomalies, and promote proactive decision-making when they can quickly process incoming data and deliver insights in a timely manner. Realtime data analysis is not only possible, but critical to success: https://bit.ly/3ZwX1SU #Data #DataAnalytics #DataPrivacy #SaaS

Composable CDPs (C-CDP) are not a new idea, but when VC powerhouse @a16z starts talking about it, it's time to take notice. The idea of C-CDPs splits up the three components of a traditional CDP - Data intake, modeling, and activation - into modules that can be served by different vendors. Here's my take on this emerging trend in MarTech. C-CDPs are becoming popular because of two reasons: -> Data warehouse first approach to avoid data duplication with a monolithic CDP -> Ability to use best-in-class solutions at various stages of the data journey inside a CDP architecture This approach attempts to make C-CDPs more flexible and secure, cheaper, and faster to implement. While this may be attractive for some brands, the following considerations should be made by every potential buyer: -> Multiple vendors - Working with different vendors for different best-in-class modules can be very challenging from a project management perspective. -> Cost - while C-CDPs can cut costs by non-duplication of data storage, this can be offset by implementation costs. -> Ease of Tech - With multiple vendors involved, how much of the implementation is really no/low-code? -> Positioning - C-CDPs are positioned to link the Data warehouses to the activation platforms/channels. Perhaps the endpoints can be linked without involving an intermediary? I have seen brands doing that. -> Data Completion - C-CDPs plugin directly to the raw data in a warehouse and this gives them access to both transactional and behavioral data. While this is not the case for some CDPs, most enterprise-level CDPs are capable of this. While this post may seem critical of C-CDPs, it's not meant to be. There will be PMF for C-CDPs for a wide range of brands. But my argument is that there is no CDP silver bullet solution. Some brands may also benefit from a hybrid approach. For example, Salesforce Data Cloud for Marketing (DCM) provides flexible options like Bring-Your-Own-Model, which allows the use of 3rd party data models inside DCM. In conclusion, the best approach is to select a CDP that will fit your business requirements and organizational structure. #martech #composablearchitecture #cdp

Every #SaaS company can uplevel with Embedded DATA Analytics. Read our blog post to discover:   👉 The vital role of Embedded Analytics in modern SaaS platforms. 👉 The emerging trend of “DATA as a Product” in the current tech landscape.  👉 How this innovative approach enables successful SaaS companies to differentiate and grow.   Read, learn, and be inspired! 👉https://lnkd.in/e7XCSsvq   #analytics #data #embeddedanalytics #businessintelligence #businessanalytics

💡 Did you know mapping your data’s lineage can be easier than following your GPS? Data lineage tracks data over time to understand its origin, transformation, uses, and final destination. 🔆 And it’s not just for SaaS companies: Industries around the world can benefit from data lineage tracking, from health services to retail! 🚀 Start your data lineage mapping journey today to gain full control over your organization’s data to transform a daunting task into a powerful asset. Read the full article: #datalineage #datacatalog

Wiiisdom, the pioneer in AnalyticsOps, has announced Wiiisdom Ops for Power BI, a new governance offering designed to deliver trusted data and analytics at scale. Read the full news on Wiiisdom's website to learn more about this exciting new development in analytics governance: https://lnkd.in/d55Bi28N #analyticsops #powerbi #trusteddata #saas #media7 #dataanalyticsreport

Enhance operational efficiency with #AnalyticsAsAService! Stay agile, adapt quickly, streamline processes, and capitalize on emerging opportunities through data optimization. 💡📊 https://lnkd.in/gxzZkaDg #AaaS #SaaS #EmbeddedAnalytics #DataOptimization

I always go back to the "but, what about the data". Data-driven insights and outcomes all too often suffer from being sloppy. We can have all the beautiful solutions and capabilities in the world, but if we don't have a distinctly defined data based "insight", then we can't act on it. An actionable metric is more than a number that doesn't look right on a dashboard where Someone can say "We must Do Something" and someone's team is told to "Fix It". In data, most of the time, this fixing it step starts with reverse engineering the "data-driven" metric to build a query to get the data to analyze what needs to get done to fix it. Too often, the metric is a composite metric, so you end up in RCA mode to circle the distinct data set that needs to get fixed - then and only then do you have something actionable. And this should have already been an atomic metric that could act as your trigger, imho. Then the next thing you need is a clear understanding of decision-action-timing: Decision - Can I act on this? Should I? Must I? If you have a mature metric behind the trigger this should be easy, but Action - What action do I need to take, How do I fix this Timing - Can I do it right away? Should I? Must I? Do I need to wait until the next low activity window say overnight? Do I need to hold until a person downstream resolves some issue first? Do I need to wait until after month end? Etc. (Let's not even talk about impact analysis) Tools like this will ideally help to expose all of this. Without it, there will be additional firehoses to some poor team's work queue and overload them with "there are X% duplicates. Fix it"

How to get from data-driven insight to action? #ActionFrameworks for #analytics and #BI platforms help users compose and automate #DataDriven decision workflows. 🔵 Triggers include buttons (no-code), conditional drag-and-drop thresholds (procedural or functional), #AutomatedInsights (#AI-powered), reusable blocks/recipes (low-code) from marketplaces, or bring-your-own-model (code). 🔵 These write back to operational systems, #cloud applications or datasets. 🔵 #EmbeddedAnalytics actions make #PrescriptiveAnalytics contextual in the business moment. 🔵 Gartner considers Action Frameworks as a sub-capability of #Composability in the Analytics and BI Platform market. 🔵 They have a moderate benefit rating as an innovation. 🔵 The maturity today of these are embryonic, with a market penetration 1% to 5% of target audience. 🔵 We expect time to plateau will be 2–5 yrs. Gartner clients can read more about this in our Hype Cycle for Analytics and Business Intelligence, 2023: https://lnkd.in/dbTdJFRf HAVE YOU SEEN THESE? Please comment below, including links to any vendors with this in general availability.

Insightful read! Keren Ben Zvi, Chief Data Officer at PayU, emphasizes the crucial role of leveraging SaaS data to stay innovative and customer-focused. Real-time, accessible, and secure data is essential for guiding business strategies and supporting decision-making. This is our focus at #PayUGPO!