How to Leverage Data Analysis for Tech Innovation

I used to struggle with getting my tech projects approved until I learned to present their benefits as an irresistible offer. 𝗪𝗵𝘆 𝗺𝘂𝘀𝘁 𝘆𝗼𝘂 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝘆 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀? - 𝗚𝗲𝘁 𝗔𝗵𝗲𝗮𝗱: Using data means you're 23 times more likely to get customers, 6 times as likely to retain them, 19 times as likely to be deliver a profitable result. (McKinsey) - 𝗠𝗼𝗿𝗲 𝗪𝗶𝗻𝘀: Top teams - who finish >80% of their projects on time, on budget, and meeting original goals - are 2.5 times more likely to use quantitative management techniques. (PMI) - 𝗕𝗼𝗼𝘀𝘁 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲: Clear numbers and ROI make 60% of stakeholders more confident, leading to faster approvals and more robust support throughout the project lifecycle. (Gartner) What steps are you taking to demonstrate the value of your tech project? I've got a 5-step plan that'll make your project impossible to refuse. 𝟭. 𝗣𝗶𝗻𝗽𝗼𝗶𝗻𝘁 𝗬𝗼𝘂𝗿 𝗩𝗮𝗹𝘂𝗲 𝗗𝗿𝗶𝘃𝗲𝗿𝘀 📌 What makes your project shine? List every benefit. Increased revenue? Cost savings? Improved efficiency? Group these gems into clear categories. 𝟮. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗖𝗼𝗺𝗽𝗲𝗹𝗹𝗶𝗻𝗴 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 🔍 Collect data that will make your pitch rock-solid. Internal reports, market trends, industry benchmarks - get it all. Relevant, fresh data is your best friend. 𝟯. 𝗖𝗿𝘂𝗻𝗰𝗵 𝘁𝗵𝗲 𝗡𝘂𝗺𝗯𝗲𝗿𝘀 🧮 Time to flex those analytical muscles. ROI, NPV, payback period - calculate it all. Solid financials turn skeptics into believers. 𝟰. 𝗔𝗻𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗲 𝗮𝗻𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗥𝗶𝘀𝗸𝘀 🛡️ Every great plan needs a reality check. What could derail your project? List potential risks. Then, craft strategies to neutralize each one. 𝟱. 𝗣𝗿𝗲𝘀𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗼𝘄𝗲𝗿 💼 Package your project in a compelling presentation. Use clear visuals and concise explanations. Make it so convincing, they'll wonder how they ever lived without it. 𝙒𝙝𝙮 𝙩𝙝𝙞𝙨 𝙢𝙚𝙩𝙝𝙤𝙙 𝙬𝙤𝙧𝙠𝙨: - It transforms your tech vision into a business essential. - It shows you've considered every angle and potential hurdle. - It gives decision-makers the hard data they need. In the world of project approvals, vague ideas are like trying to pay with Monopoly money. But a well-prepared, data-driven proposal is gold. What's your top tip for creating an irresistible project proposal? Share your wisdom below!

Last week, I posted about data strategies’ tendency to focus on the data itself, overlooking the (data-driven) decisioning process itself. All it not lost. First, it is appropriate that the majority of the focus remains on the supply of high-quality #data relative to the perceived demand for it through the lenses of specific use cases. But there is an opportunity to complement this by addressing the decisioning process itself. 7 initiatives you can consider: 1) Create a structured decision-making framework that integrates data into the strategic decision-making process. This is a reusable framework that can be used to explain in a variety of scenarios how decisions can be made. Intuition is not immediately a bad thing, but the framework raises awareness about its limitations, and the role of data to overcome them. 2) Equip leaders with the skills to interpret and use data effectively in strategic contexts. This can include offering training programs focusing on data literacy, decision-making biases, hypothesis development, and data #analytics techniques tailored for strategic planning. A light version could be an on-demand training. 3) Improve your #MI systems and dashboards to provide real-time, relevant, and easily interpretable data for strategic decision-makers. If data is to play a supporting role to intuition in a number of important scenarios, then at least that data should be available and reliable. 4) Encourage a #dataculture, including in the top executive tier. This is the most important and all-encompassing recommendation, but at the same time the least tactical and tangible. Promote the use of data in strategic discussions, celebrate data-driven successes, and create forums for sharing best practices. 5) Integrate #datascientists within strategic planning teams. Explore options to assign them to work directly with executives on strategic initiatives, providing data analysis, modeling, and interpretation services as part of the decision-making process. 6) Make decisioning a formal pillar of your #datastrategy alongside common existing ones like data architecture, data quality, and metadata management. Develop initiatives and goals focused on improving decision-making processes, including training, tools, and metrics. 7) Conduct strategic data reviews to evaluate how effectively data was used. Avoid being overly critical of the decision-makers; the goal is to refine the process, not question the decisions themselves. Consider what data could have been sought at the time to validate or challenge the decision. Both data and intuition have roles to play in strategic decision-making. No leap in data or #AI will change that. The goal is to balance the two, which requires investment in the decision-making process to complement the existing focus on the data itself. Full POV ➡️ https://lnkd.in/e3F-R6V7

Not sure where to start with #DecisionIntelligence? Don’t worry - you don’t need to toss out all the hard work your team has already put into building models and dashboards. That work takes real skill and effort, and it’s important. The question now is: how well does it connect to the decision-making process of your stakeholders or customers? Here’s something I’ve learned through trial & error: I’ve built dozens of models and productionalized them in dashboards and CRMs over the year, and while each felt like a win at the time, whether or not they actually influenced a business decision was often anecdotal at best... and that's not good enough. Here’s two steps you can take right now to uncover real DI opportunities that can be prioritized in the new year: 1️⃣ Look at your existing production models. Where are the results going? Are they ending up in dashboards, emails, spreadsheets... or just sitting idle? Have you integrated the output into your stakeholder’s decision-making process, or are you expecting them to figure it out? Embedding insights directly into the decision-making process is the key to unlocking real value. 2️⃣ Now, check your dashboards. What decisions are they meant to guide? Do they go beyond providing a prediction or forecast to actually suggest what to do next... or at least highlight the decisions that need to be made? Or... are they more like a beautifully presented buffet of insights, where you’re hoping someone in line feels inspired to grab a plate? Closing the loop from data to outcomes isn’t easy, but that’s where DI can make all the difference. It ensures the right insights reach the right people, at the right time, in the right way (whether it’s to guide or automate decisions) while capturing the outcomes that enable you to continuously improve the ecosystem. You and your team have already put in the hard work. Now let’s make sure it has the impact it deserves. What decisions should your models and dashboards be guiding? Let’s chat! #DataScience #Analytics #DecisionMaking #DI #Leadership #Innovation #DecisionProcessEngineering #AI #ML #Data #MLOps #ROI #GenAI #AgenticAI