Eleanor Treharne-Jones’ Post

Getting Executive Buy-In For Data Initiatives 5 Questions To Ask ⬇️ In fast-growing organizations, data quality and reliability often take a backseat until a crisis hits, like an unexpected outage or a flawed machine learning model. Unless it’s a real threat to the business, only small investments in data reliability are likely to get buy-in. But once an outage does occur, you can be prepared with an action plan, and get buy-in while everyone still feels the heat. Ask: 1. How much time do data engineers spend on data reliability issues? 2. Do executives trust the data they use? How many decisions lack data backing? 3. What's the potential cost of an ML model outage? Could it be $1,000/hr or $1M/hr? 4. What are the compliance or PR risks of inaccurate customer data? 5. How might a lack of data reliability opens the company up to public embarrassment? You can try the Wall Street Journal test for this: Let's say the WSJ discovered that all your customers were sent invoices for 3x their actual usage. Would the business be impacted by this news coverage?

Getting Executive Buy-In For Data Initiatives 5 Questions To Ask ⬇️ In fast-growing organizations, data quality and reliability often take a backseat until a crisis hits, like an unexpected outage or a flawed machine learning model. Unless it’s a real threat to the business, only small investments in data reliability are likely to get buy-in. But once an outage does occur, you can be prepared with an action plan, and get buy-in while everyone still feels the heat. Ask: 1. How much time do data engineers spend on data reliability issues? 2. Do executives trust the data they use? How many decisions lack data backing? 3. What's the potential cost of an ML model outage? Could it be $1,000/hr or $1M/hr? 4. What are the compliance or PR risks of inaccurate customer data? 5. How might a lack of data reliability opens the company up to public embarrassment? You can try the Wall Street Journal test for this: Let's say the WSJ discovered that all your customers were sent invoices for 3x their actual usage. Would the business be impacted by this news coverage?

Unlocking the True Potential of Data Science: Addressing the Data Quality Conundrum In the age of data-driven decision-making, businesses worldwide embarked on a journey towards a promised land of heightened productivity and innovation. However, for many, this anticipated boom remains elusive. Why? The culprit often lies in the quality of data fueling our sophisticated analytics engines. Despite substantial investments in cutting-edge data tools, a significant portion of data ingested into these systems is plagued with issues – mislabeling, omissions, inaccuracies – creating a drag on efficiency rather than the envisioned productivity surge. To remedy this situation, it's imperative for companies to undertake a three-pronged approach: 1️⃣ Rally Employees to the Cause: Every member of the organization must understand the critical importance of data quality. It's not just an IT concern; it's a strategic imperative that impacts every facet of our operations. 2️⃣ Measure Data Quality Holistically: Gone are the days when data quality assessment was confined to select departments. We must adopt a comprehensive approach, measuring and monitoring data quality across all functions and tasks to identify and rectify deficiencies at their roots. 3️⃣ Relentlessly Attack Data Quality Tax: Let's confront the sources of our data quality woes head-on. Whether it's outdated processes, inadequate training, or technological limitations, we must leave no stone unturned in our quest to purge our data pipelines of inefficiencies. By embracing these principles, we can pave the way for data science to fulfill its promise as a catalyst for transformative productivity gains. It's time to elevate data quality from a mere afterthought to a cornerstone of our organizational strategy. #DataScience #DataQuality #Productivity #BusinessStrategy #DataAnalytics #DigitalTransformation

Data is the lifeblood of modern businesses, fueling insights, guiding decision-making, and ultimately shaping your company's success. However, in today's information age, data can quickly become overwhelming. Scattered spreadsheets, siloed databases, and inconsistent formatting create a data management nightmare, hindering your ability to leverage this valuable asset.

In an era where data drives decisions, how confident are you in the quality of your data? You can be confident only if you measure data quality and can defend it. Businesses everywhere know the struggle: having lots of data isn't enough. It needs to be correct, complete, and trustworthy. The problem is finding those errors and fixing them before they mess up your decisions. This is where Data Quality KPIs save the day! By using Key Performance Indicators (KPIs) to measure things like accuracy, completeness, consistency, and timeliness, businesses turn that messy data into something they can rely on. These KPIs aren't just numbers, they're a roadmap to better data and smarter business decisions. So as we delve deeper into the digital age, remember: measuring your data's quality isn't just about maintaining standards; it's about setting your business up for unparalleled success. But who is responsible for that? If you work in data engineering and handle data transformation, you should not ignore data quality as part of the process. You should measure it and prove that your data is of good quality. It should be a fundamental step in data pipelines. When asked whose responsibility is data quality, we can't just say "that's not mine". #dataquality #datagovernance #dataengineering

The Data Deluge: Why Quality, Not Quantity, Matters Most As data leaders, we're constantly bombarded with information. Petabytes ingested, terabytes analyzed, exabytes stored – it's enough to make your head spin. But here's the truth: the real value lies not in the sheer volume, but in the quality of our data. Think about it: bad data leads to bad decisions. Inaccurate customer insights, flawed market predictions, biased algorithms – the consequences can be costly. It's like trying to navigate a storm with a faulty compass. So, how do we ensure our data is a guiding light, not a flickering candle? Here are a few thoughts: Focus on data provenance: Know where your data comes from, how it's collected, and by whom. Transparency builds trust and helps identify potential biases. Invest in data cleansing: Dirty data is like a clogged pipe – it hinders everything downstream. Regular cleaning ensures accuracy and unlocks real insights. Prioritize data governance: Establish clear policies and procedures for data access, usage, and security. This protects your data and fosters a culture of responsible data management. What are your thoughts on data quality? Share your experiences in the comments! #data #datagovernance #dataquality P.S. Looking for tips on implementing a data quality strategy? Let me know in the comments and I'll share some resources!

I love playing with shiny new toys. I don't love using shiny new toys I don't undertand in production I really don't love being told to use a shiny new toy to solve a problem it isn't meant for. When in doubt, make sure you ask yourself, "what problem am I trying to solve, and will this help me solve that problem?" Otherwise, you'll end up using the wrong tool for the job. #data #fridaymeme #dataengineering

You can't get value from poor-quality data, whether you're building a business strategy or using it as a base for a whole new product. Data reliability plays a crucial role in the success of #datadriven processes. Its purpose is to guarantee that the data you're working with meets specific standards and is trustworthy. Let's delve into the key factors of #datareliability and discover how to avoid working with unreliable #data. ⬇️

Why does it cost so much for Data?  You get an invoice for a sophisticated data tool but do you really understand your total data cost? Yes? No, you don’t It’s more complicated than you think. There is a need to go beneath the surface to understand data cost. Buckle up — We’ll break it down from sticker price to running analysis. Data Tools are more than :  —Dashboard Wizardry —Data Sorting Robot —Number Crunchers The Data Cost is determined using many factors. Like ;  —Volume of Data Processing and Storage —Complexity of Analytical Processes —Data Quality and Preparation —Tool and Software Selection —Type of Analysis Conducted —Scalability Requirements —Data Security Measures —Integration Complexity And the list is Unending. But….. The challenge with the cost? More data and higher costs, but a big chunk of it isn't getting used. We're gathering a ton of data, and it's costing us big time. That means our data collection is growing like crazy, with a 42% yearly increase (IDC), but the fees are going up too – 30-60% (TPICAP) more over the past two decades, plus an extra 5-10% in 2023 alone. Now, the real kicker is that most of this data, about 60-73% (FORRESTER) is left untouched, contributing nothing. Imagine spending money on something and not using half of it – that's what's happening with your data. In simple terms, you're wasting about 50% of the money we put into collecting and managing data. Not a fair deal, right? And this can’t go on in the long run. So, what’s the fix? To fix this, you need to get a tool that lets you use your data better, get more out of it, and ensure that not all that money goes down the drain. This is where Datamaze comes into role. We're building Datamaze, the AI's first data efficiency platform to bring your data costs closer to your business success. We’re offering a 40% reduction in cost so you can :  —Make most of your data —Control Data Growth  —Grow your Business Wanna know more about Datamaze? Dm me P.S. -  What should I post next? Comment below