In the world of marketing, data governance is never the focus from a failing analytics perspective. As Wpromote’s Director of Digital Analysis, I know how important clean and consistent data is to the advanc models and forecasts marketing execs adore, but I’ve also witness firsthand the resistance to spending time and effort on the brock data that makes those models work Best Practices Are Essential to Advanc.
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Execs are understandably more excit about the shiny new tools and their potential ROI. But if you bypass the foundational step of consistently labeling, managing, and updating your data, your business can waste hundrs of hours and lose millions of dollars on models that will never work.
Garbage In, Garbage Out
Why Bad Data Governance Leaves Your Advanc Data Modeling Outcomes A Mess .So let’s got the bad news out of the way: if your brand’s sparkling new, fresh out of the box, advanc marketing model is built on bad data… it’s trash.
It’s unlocking the power of phone number data and running targeted marketing campaigns that speak to an audience. For businesses, this phone number list means crafting a message tailored to meet the needs of each single customer with the most accurate and verified phone lists-instantly producing better engagement and higher conversion rates. Personalized campaigns cultivate greater loyalty with customers, increased overall marketing effects. Get quality phone data to create campaigns reaching out to customers with a real engagement, rather than just any customer.
If you’re lucky, it might work for a little while, but you’ll never be able to get it to work long-term. Because it’s been built and train on bad inputs. And too often the way people try to “fix” a broken model is by building something even more complex. If you haven’t fix the initial flaws in the data, it’s still broken.
Advanc model bas on bad data governance
Think about it like a skyscraper: the cool glamorous tower part that everyone is excit about is your advanc model. But if your tower is built on a bad or flaw foundation, you’re going to have some major problems (just ask the residents at 432 Park Ave). Advanc data models propping up a bad model bas on flaw data.
Building a bunch of intricate scaffolding to prop up your tower without addressing what’s broken in the foundation might make things better for a time, but it isn’t addressing the fundamental problem. If the data it’s all built on is still bad, your beautiful model could eventually go the way of the London Bridge.
All Fall Down: The Bias-Variance Tradeoff and Other Tales of Bad Data Governance.
The magnetic pull of a model-centric (as oppos to a data-first) approach to advanc data analysis is hard to ignore. But pioneering experts in the field like Andrew Ng are pushing data scientists to resist the attraction of building fancy models to fit messy data. One major consideration for any analyst using statistical modeling is the Bias-Variance Tradeoff.
There are two major errors associat
Overfitting: High variance, low bias Your model is extra sensitive and ends up focusing on random noise. It’s not always immiately obvious that something is wrong because the model is capable of producing reliable insights corresponding to specific sets of data, but they can’t be accurately appli to future learnings or additional data sets.
When you don’t have enough signal from your data, your model misses relevant patterns in the data, failing to accurately prict outcomes.
If you’re relying on advanc data modeling, you ne to hire people with a rare combination of talents. But with a data-centric approach, you might not actually ne a unicorn hire that combines digital marketing expertise with statistical modeling and computer science skills.
Using better data means how to manage an seo and sem positioning strategy in traditional, less complex machine learning models are likely to solve your problems, which means you don’t necessarily ne experienc data scientists to do the work. Instead, data analysts can pull valuable insights from these simpler models while learning the nuts and bolts of data science in a (relatively) clean environment.
When a system isn’t performing well, many teams instinctually try to improve the code. But for many practical applications, it’s more effective instead to focus on improving the data.
Andrew Ng Founder & CEO Landing AI
But that’s not the only part of your strategy you should reconsider. Organizations that throw advanc models like neural networks at problems with high bias should stop and evaluate their approach. They’re in danger of using some very expensive bandaids on a wound that won’t ever heal without going back to the very beginning: the data. And all of this is preventable.
That’s why every client seo mails using Growth Planner, our high velocity mix mia model in Polaris, is closely pair with our data governance offering. It’s not because we’re mean, it’s because we know that Growth Planner (or any model for that matter) won’t work if it’s bas on bad data. It’s how we know that the insights from Growth Planner are accurate, actionable, and drive actual value. We practice what we preach.
The thing with those old sayings
By establishing mature data governance best practices, your data scientists can build advanc models that work and provide valuable insights that drive business growth.
Enterprises can save millions of dollars by crossing their t’s and dotting their i’s with data governance that ensures the foundation of your advanc analysis is sound because it’s built on the right taxonomies, it’s clean, and it’s complete.
But data governance is not just about saving money you’d otherwise be throwing away. It’s about profitable growth. It might not be exhilarating to talk about the minutiae of how your business treats state designations (do you use the full state name or the abbreviation?), but it’s the only way you’ll be able to build and deploy advanc models that give your business a competitive ge through accurate analysis, insights, and prictions.
Data Governance Best Practices
When it comes down to brass tacks, data governance is just good business. Firms that adopt data governance best practices will win in the coming age of AI. Companies that neglect to establish these processes will be outmaneuver.
Optimize Your Time: With strong data governance in place, data analysts can spend more time building models and less time cleaning up what’s not working after the fact. It also sets you up to avoid wasting time running sophisticat models only to find that your results are worthless.
Spend Less Get More Value
The better your data is, the less sophisticat your algorithms ne to be. By doing the essential legwork to get your data house in order before building your model, you’ll be able to use simpler models that require less investment but produce exceptional results.
Democratize Your Data Analysis: When you’re running fewer baroque models, you won’t have to hire an entire team of data scientists arm with PHDs to understand the outputs. You can let less experienc analysts handle the job and reliably provide quality insights.
Make Better Marketing Decisions
When you optimize your time, spend less on tech, and make your data analysis more accessible, you’re already at a tremendous competitive advantage from a cost savings perspective. But you also have the opportunity to build better models, beautiful models, models that accurately prict and forecast what you ne to do next or where you ne to spend, or which channels will see the best ROI. Models that work.
