Lori Schafer in CMS Wire: Predictive Analytics Reshapes Landscape for Data-Driven Leaders
- Tori Hamilton
- May 8
- 4 min read
Find the original article in CMS Wire here.
Forget rearview metrics—chief data officers are using predictive analytics to steer goal-setting with real-time, forward-looking precision.
The Gist
Data and AI integration. Predictive analytics allows businesses to go beyond historical data and use real-time insights to guide decision-making.
Data silos disrupted. Centralizing data into one unified location supports AI-driven decision-making and allows real-time visibility across teams.
Accuracy and speed. AI enhances business target-setting by improving accuracy in predictions. It lets companies adjust to unexpected market changes and hit goals more precisely.
All organizations and their business teams set annual goals and work tirelessly to meet them. This is nothing new. But the rise of AI and predictive analytics is having a tremendous impact on how quickly and accurately companies set targets.
Take consumer goods, for instance. A pasta brand will forecast how much profit its gluten-free spaghetti expects to turn in a year, and then it makes marketing and supply chain decisions to get as close to that goal as possible. In healthcare, a hospital sets a yearly mark to reduce operating costs and acts accordingly to meet that target.
Traditionally, these companies brilliantly study their historical sales and develop highly educated hypotheses on how their businesses will perform. However, with AI and data analytics, companies can now access real-time data and forward-looking insights to help them adjust and guide their business to make sure they hit their goals.
Think of it this way. The pasta brand never just lofted a dart at a dartboard, hoping it would stick. But, with data analytics, the company can fire a pinpoint dart at the board and adjust that dart’s flight in real time, guaranteeing a strike at the bullseye.
Many organizations are working toward this level of precision. What’s changing is that data and business teams can delete their sprawling spreadsheets and use predictive models and data to more accurately assess their business performance all year long.
However, to reach this level of effectiveness, companies need to improve how they use their data, harmonize it and implement AI and predictive analytics as a native foundation of their digital architecture. Then they can aim and fire darts with both speed and precision.
Overcoming Data Challenges
Previously, data analysts would identify annual business targets by reviewing historical data, accessing panel data for trends and studying macroeconomic reports to formulate educated forecasts. With AI and predictive analytics, companies can study forward-looking data, rather than leaning into historical insights.
Getting the data in order can be challenging, though. Common hurdles include integrating fragmented data from siloed areas of the business, harmonizing various internal and external sources of data into one location, and governing rules around how business teams work with data. Teams may also struggle with adjusting data and insights to account for unpredictable global events that can turn any business on its head (i.e., tariffs and supply chain challenges), as well as building a culture of trust in AI and data science, as teams accustomed to setting annual targets may resist change
Brands, retailers, restaurants, healthcare systems, financial services companies, hospitality and travel businesses have been setting annual business planning targets for decades and have relied on gut instinct over AI to do it. AI will never replace gut instinct, and setting targets and hitting performance goals is both art and science. But using data for forward-looking predictive analytics can provide teams with tools for stronger decision-making.
Centralizing Data and Predicting Annual Targets
Chief data officers are already using data to power their company’s decisions, and they’re using AI. A Gartner study estimates that by the end of 2025, 95% of data-driven decisions will be executed at least partially by AI. Companies are in the early stages of augmenting decisions with AI, understanding that clean, regimented data is the fuel for those decisions. Because of this, it’s important for CIOs and data teams to centralize and strengthen their data.
Here are some key steps toward refining data"
Eliminate data siloes by creating one centralized location where all data can be integrated together. Internal and external sources should flow to one moment of truth, where AI and analytics models respond to data requests in real time.
Allow unified, real-time visibility across departments so everyone, including CEOs and various managers, has constant access to reporting and data in the centralized location. This visibility is paramount, allowing teams to react to unexpected macroeconomic events or market changes as they happen and inspiring strategic decisions that keep companies on pace to hit their targets.
Implement an IT architecture that supports integrated enterprise data solutions. Also open doors for further innovation such as agentic AI capabilities, voice queries or generative AI tools that read and suggest immediate actions.
Transforming How Companies Set Targets
Enriched, centralized data can fundamentally improve how companies set future business targets, and it equips them to stay on pace to hit those targets. Unifying data and overlaying the information onto a native AI approach can generate clean, precise decisions in real time. This helps teams reduce guesswork, anticipate consumer behavior changes and mitigate the impact of market volatility.
AI and predictive analytics won’t eliminate the art of goal-setting, but they will strengthen the science behind it. They’ll help companies set reachable goals and support teams along the way. Throwing darts never looked so easy.
