Kavya Gupta

What should the North Star of a data team be? It’s not data quality, literacy or insights. It’s clarity. For me, clarity is the single binding concept which ties together all that a data team does. Improving data quality? That’s because it provides better clarity to the business because the data they are looking at is accurate. Building a report? If it doesn’t clarify what the operating state of the company is then it’s failed. Building a visualisation? We should work to make sure it tells as clear a story as possible. Training the wider business how to analyse data? This helps people gain clarity by themselves. Greater business clarity is the value driver of the data team. We can’t always push the decision button but we can work damn hard to make sure people are 100% clear which one to push and why.

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Big Data Ldn is nearly upon us. Data Storytelling and Viz... Let's Get the Basics Right. Data is everywhere but how much of it is communicated effectively? Lots of work goes into the curation of data sets only for it to fall at the last hurdle as the key insights are lost through poor visualisation and storytelling. This session will cover some of the foundations of data storytelling and data visualisation. Think of it as a to do list that will help you ta-dah your stakeholders. #bigdataldn #dataviz #datastorytelling #datavizualisation #datavisualisation #dataanalytics

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Analytics in the UK... There has been a lot of talk here on LI recently about data professionals needing to be more business savvy and vice versa... Data leaders needing to influence business leaders, data being seen as a business asset, data needing to add value blah blah blah... Many of us have been having this same conversation for what seems like an eternity, and yet we are not really any further forward... Why? What are we doing wrong (or not doing)? Why isn't The Business listening? Well, maybe, just maybe, it's time to change the record... Data will never be "given" a seat at the top table, Data must demand one! The Business will not adopt a data culture, Data must create one! Data should not have to apologise for asking questions or driving change, explain why it's not just "tech" and how it is a value creator... Data leaders should be loud and proud... Data should be celebrated and embraced, otherwise there really will be "no future" for us! So, Never mind The Business, here's The Data... Now sing (shout) after me: We're so pretty, oh so pretty, we're Data! We're so pretty...

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💰 "Quantifying the value of data and analytics initiatives" Last week, just before Big Data LDN, I attended the 7th edition of the London Data Product Management Meetup. Loved the format with around 30+ people, lots of peer chat to warm up around drinks and small breakout discussions on specific topics. We discussed "Quantifying the £££ value of data and analytics initiatives", and here are 7 key highlights / takeaways: 🎯 Reverse the charge on business users: The data & AI teams should not be responsible for value assessment alone, and they should rely on the requesting business stakeholders to assess expected value and then report on realized one 🎯 Ask top-down questions to assess the negative impact of data:  On top of assessing the positive gain from products, you can also assess how the lack of data, or bad data quality would negatively impact the ability to drive business decisions. A great to identify the critical capabilities where monitoring & robustness are required 🎯 Ask bottom-up questions to look at potential usage:  Don't forget to look at your existing Data, Analytics & AI products to identify other potential use, as that might be a low hanging fruit to deliver more value at marginal cost 🎯 Beware of the potential disconnect between data quality improvement and outcomes:  Improving data quality does not always have a direct impact on better outcomes. So before investing, ensure you have a good understanding of the requirements for the different use cases, and only invest where it really matters. 🎯 Manage stakeholders with prioritized list:  Stakeholders often come with urgent demands, expecting to reshuffle the backlog and as such putting the Data, Analytics and AI teams at risk. By consolidating and maintaining a prioritized list of the different demands (along with expected value & estimated cost), and making it visible, it become easier to make those stakeholders prioritize their new demands versus existing ones (or request additional budget for the team to increase bandwidth). 🎯 Think about business vs technical readiness:  When evaluating the complexity of new use cases and products, evaluate both the technical readiness (a.k.a. the ability to build and deliver) and the business readiness, in other words the potential impacts in terms of training, process change, culture change on the business to actually adopt the product. 🎯 Deliver iteratively / adjust effort vs value / find ways to test and derisk:  It should probably be a given, but adopting agile practices to Data, Analytics & AI products is key, to deliver value iteratively, therefor maximizing chances of adoption, maximizing opportunities to gather feedback and continuously improve, and controlling the potential risks in terms of feasibility, costs, etc. Those are very practical tips that everyone can apply in its own context. What do you think about those advice? Anything to add or change here? #DataProductManagement #DPM #ValueDriven #DataProduct

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Is it my imagination (in a Liam Gallagher voice) or are spiders in the UK on protein shakes and working out because they are getting bigger every year? Anyone else notice this? Sometimes the actual data is not important to start a conversation or a discussion. It isn't about 2dp accuracy, it is about getting your point across in the most effective way. #datavisualisation #datastorytelling #dataviz #datacommunication #batweavel

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