What might happen? The power of predictive analytics


Written by Carrie Majewski, Vice President of Growth Strategy, SQA Group

For companies that want to advance from data curious to data committed, one of the most powerful places to start is to establish data ecosystem foundations (e.g. data collection and storage best practices, quality parameters, KPI setting, baseline literacy training, tools adoption and usage, etc.). Foundations that are essential to have in place in order to do remarkable things with regard to advanced analytics and AI/ML acceleration.

When you’re just starting off, data wins might be things like…

Full access into organizational KPIs at departmental levels

Weekly and monthly team ceremonies where leaders report on their metrics

Use of dashboards and visualizations to convey data more powerfully

Increased data literacy org-wide

Data tools available and being put to use

Quality issues continually being addressed and remediated

Huge and important wins!

But as companies advance along their maturity journey, how they want to win with data often starts to change. They begin to eye things like AI/ML adoption and acceleration, progressive KPI-ing (more on that here), eliminated data siloes, data science, automated analysis delivery and… for many…

Predictive analytics. Or using data to shift from asking “what happened?” to “what might happen?”

Predictive analytics, at the simplest of levels, is about leveraging advanced analytics and modeling techniques to make predictions about the future… future outcomes, trends, events, performance, etc.

Companies can leverage predictive analytics across so many different domains and focus areas from answering questions about buying behavior to supply chain management to product development to risk management and so on. In fact, it’s become such a pervasive focus and investment of leaders across all domains that the predictive analytics market alone is expected to grow from $12 million in 2022 to $38 million by 2028.

So, what steps can you take today regardless of the functional area you support? Here are a few quick tips:

  • Data as Treasure: What role do you want data to play in your day-to-day? Do you want it to be a core team member whose main focus is to “report the news,” or share what happened? Or do you want it to be a treasure? Trustworthy, full of insight, and rife with untapped opportunity worthy of steering your future. The role you want data to play is in fact a choice. Because no mater the functional area you lead, once you decide the significance of data in shaping both your day-to-day and future objectives, you can turn attention to making sure data foundations are in place — storage, quality, governance, literacy, etc. — so that you can then leverage advanced analytics and AI/ML techniques to turn your trusted data into a treasure of possibility. Lean into your partnership with your data/tech peers (or a third-party provider) to ensure you are fortifying the core pillars of your data ecosystem so that you can pave the wayfor data to be a business treasure.
  • Think Like a Futurist: According to research from the Institute for the Future, as a human population, most of us naturally struggle to think about the “far future.” In fact, 27% of Americans rarely or never think about their lives five years from now, as compared to 60% who think about the near future — one month from the present — every day. As you reflect on your relationship with the future, consider how this might help or hinder how often you spend time pulling your team, department, or organization to the future. For example, do you spend more time reviewing historical or current data? Predicting no more than a few weeks out? Forecasting months and years out? As you shift towards prioritizing predictive analytics, see if you can also strengthen your personal focus on the future, channeling the mindset and behaviors of Futurists.
  • Identify and Aim: Instead of starting big — going from descriptive to predictive team or organization-wide — consider picking a specific use case against which having predictive analytics would greatly help. For example, do you most need insight into customer buying patterns? Pricing models? Risk mitigation? Cash flow? Product stickiness? Once you have your top use case identified, work with your data/tech teams on building specific models and analyses with the data you already have (or pinpoint the data you need to begin tracking) and start deriving insights for that specific opportunity at hand. Leverage this mini use case to establish the proof of concept and generate the momentum needed to then drive greater predictive analytics impact.

Your journey from descriptive to predictive can begin at any time. And it often just starts by asking one simple but powerful question… what might happen in the future with our business?

Originally posted here

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