Living in a data-driven world is a double-edged sword. While we have staggering amounts of data at our fingertips, we also run the risk of turning it into bad data science. And a decision made based on poor analytics can have a negative impact on business. But we’re not here to talk about the pitfalls of common data mistakes – we’re here to talk about credible data science and how to use it.
Credible data-driven decisions are not just about numbers themselves – they’re also about the willingness of stakeholders to trust the guidance given to them by their analytics teams. That trust can come from a few places:
Once those three things are in place, it will be much easier to collaborate with stakeholders on data-based decisions. Some ways to do that include:
No matter how good an analytics solution is, there is no be-all, end-all format that will work for every single data need. While we can use learnings and formats from previous projects to guide us, we still need to view each analytics project that crosses our desk as unique. That being said, you may not always need advanced analytics to solve a data problem.
The first thing we do when we get a new assignment is to carefully analyze what’s being asked for and what we currently have. After carefully looking at data sources, KPIs, the client’s requests, and more, we see if we can simplify the approach to match the problem. Oftentimes, big questions can be answered with simple solutions – but sometimes they can’t. That’s where advanced analytics come in.
“Advanced analytics” is a broad group of services that includes predictive analytics, advanced segmentation modeling, text and sentiment analysis, and many other machine learning and statistical models used to get more out of your data. Sometimes these are needed. When they are, we always make sure that our solutions provide actionable insights that decision-makers can use to enhance their business.
If we don’t know what the client’s looking for, then we don’t know what to look for – it’s as simple as that. The first thing to do when a new analytics project shows up is to make sure your team is aligned with the appropriate stakeholders. Data is a rabbit hole, and it’s easy to get lost down any number of branching paths. Making sure that you understand what specific problems a client wants to have solved will go a long way towards keeping a project relevant, useful, and credible.
Fully flesh out the problem, with stakeholders, when defining to find out what the desired outcomes of the analyses are. It’s important to make sure this isn’t done only in technical terms, but in terms that accurately describe the effect on the business. Once you’ve done that, make sure that you have a plan for putting your learnings into action that supports those outcomes.
Solving a business question takes a huge investment of time and work, and many business questions will likely be recurring. That’s why it’s important to document and keep the work that you do. Over time, you’ll be able to optimize and build upon previous work to answer questions faster and faster every time. Through this process, you’ll be able to build a repository of processes, techniques, code, and more that can make you more efficient.
In today’s world, it’s not enough to just have data – that data must be clean, credible, and handled by skilled analysts who know how to make the most of it. Luckily, our analytics team is here to help – LaunchP.A.D.™, our suite of analytics and insights offerings, was built to help you analyze data that results in smarter, more informed decision making.
Learn more about LaunchP.A.D.™ and fill out the form to start making more informed data-based decisions with the help of our team.