The Benefits of Data-Driven Decision Making: How to Achieve It in 2026

If you find yourself suddenly wondering what the benefits of data-driven decision making – often abbreviated to the less tongue-twisting DDDM – are, you're hardly alone. You may be surprised to hear that it's a really old concept, given the sudden rise of the acronym. Suddenly, it's absolutely everywhere. In fact, 65% of organizations will make fully data-driven decisions by 2026, according to Gartner. In truth, it describes something we've always known about.  Many finance advisors have always striven to accomplish making decisions that are backed up by cold, hard data as opposed to the usual observations and (highly) educated guesses.

As data and the ways we have of analyzing it become increasingly more readily available and user friendly, the more finance leaders see the opportunity to leverage it for growth opportunities.

The advantages of a data-driven approach are clear, but achieving this cultural shift requires a structured approach. Below is the seven-step process for creating organizational alignment and successfully adopting a data-driven culture, each of which is explored in detail later in the blog:

• Create a goals-first culture

• Ensure data leadership is top-down

• Facilitate broad data literacy

• Hold your people accountable

• Foster an open and trusting culture

• Question everything during data-driven decision making

• Develop a learning culture

Better Business Decisions Start With Information, Not Instinct

You've probably heard the saying "trust your gut" at some point in your life, possibly even just this week. As a whole, we're fond of encouraging folks to trust their instincts, and in a lot of situations, that's a great idea! When you're choosing a putter, for instance, or when you can sense something's off with your health and a little voice is nagging you to go to the doctor. Those are excellent times to go with your gut instincts and trust your intuition.

As a steward and strategist tasked with protecting and growing your organization's assets, however? That's a different story. Our VP and General Manager of FAO Services – Megan Weis does a great job of breaking down the science behind why instinct-based decision making isn't a sustainable strategy in her webinar, Data-Driven Decision Making: A Blueprint & Case Study. We can't recommend it enough – especially if you didn't have a chance to catch her presentation at the AFP virtual event.

Until then, the short version is this: none of us is capable of being truly objective.

Our individual experiences, tendencies to misremember data points and base human instincts like sleep and hunger all contribute to imperfect decision-making. Intuition is changeable, but data are not. Anything that stands between objective analysis and actionable strategy comes with the risk of time- and money-wasting mistakes firmly attached to it. For finance leaders, those mistakes can come with million-dollar price tags attached, too.

Understanding the Benefits of Data-Driven Decision Making

Data-driven decision making has tangible benefits when implemented properly. Let’s explore a few of the main advantages:

Accurate Decisions: A Key Benefit of Data-Driven Decision Making

One of the biggest benefits of DDDM is accurate, quick decision making. This can save your organization a significant amount of money in the long run because you are no longer guessing when it comes to your financial strategy. Instead, you have concrete data to base your decisions on.

Making decisions based on your “gut” can sometimes work out. However, this type of decision making often leads to bias. Confirmation bias happens when you favor information that supports your existing beliefs, while availability bias occurs when you easily recall certain information. For example, if someone told you that the market for a specific product is going to skyrocket, that might influence your decision when deciding which new products to offer, even if there is no data behind that assumption.

Greater Decision Quality

In the early stages of DDDM adoption, there's a lot of experimentation. A/B tests and surgical tweaks to client-facing aspects of an organization's product or service offering can be run instantaneously, simultaneously, and continuously.

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And each time these experiments wrap, stakeholders have new and better data to work with. This data starts informing decision-making from the very outset, allowing finance leaders to cut cash flow to initiatives that are not generating the necessary results and redirect those resources into the initiatives that provide the most return.

As organizations become more and more comfortable with analyzing data and using it to craft strategy, outcome quality improves because the decisions that lead up to them have improved as well.

Quicker Decisions

Organizations that have differentiated themselves with effective data analytics and insights already understand what the companies that are lagging behind are just beginning to realize. One of the advantages of making data-driven decisions is that they can be made quickly and accurately. Quick decisions are more important than ever. A 2024 Oracle survey found that 74% of respondents agree that the number of decisions they make every day has increased ten times over the past three years.

Consider the research and development leading up to a product launch, for instance. For the sake of this example, let's say an SaaS startup is rolling out a new mobile feature: in-app messaging. By using a combination of data gleaned from user behavior, market research, and survey responses, product development can confidently roll out the features that their clients want without having to clear any hurdles thrown up by trial-and-error.

Because they don't need to guess at what users want, they don't need to correct course to the tune of hundreds of thousands of dollars to correct course when they're wrong. They've already got a foot over the starting line at the start of the race while competitors are still tying their shoes during iterative phases.

Continuous Improvement via Data-Driven Decision Making

It may seem odd to think of data and the processes we use to analyze and act upon it as something that's living and changing – but that's core to its role in organizations as a driving force behind continuous improvement.

It helps to put it in the broader context of machine learning. An algorithm that can be used for predictive modeling, for instance, is only as good as the data that's being fed into it. It can only use the numbers it's already working with. As you add more quality data to the equation, the results you get expand to include them.

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If you're starting from square one with a solid predictive modeling algorithm, the results you see from there improve upon that already well-functioning process. It's improving upon what's already been improved. This releases decision-makers from approaching challenges and end-goals scattershot. You can home in, and then home in again on what's already working and reap the benefits of this continuous improvement.

The Disadvantages of Data-Driven Decision Making

Despite the overwhelming list of benefits, data-driven decision making does have a few drawbacks that need to be considered. While many of these disadvantages can be overcome with the proper planning and protocols, it’s still important to understand the impact this style of decision making can have on your organization.

Reliance on Technology

One of the largest drawbacks associated with data-driven decision making is that the technology that's often paired with it is heavily marketed as a do-everything solution. It's not. Finance leaders and skilled accountants are more crucial than ever in an organization that's working toward a DDDM culture.

Skilled finance professionals play a crucial role in data-driven organizations because they are the ones who will ultimately be tasked with interpreting the data and, well, making the decisions. Data doesn't exist in a vacuum; it comes with its own context. Forgetting that can be costly on its own.

Data Misinterpretation

Leaders should be careful not to use data to support or justify a decision that's already been made. It's surprisingly easy to do! Let’s say that you’re looking at a major supply line improvement. If you don’t pay attention to the multiple pathways to that improvement, you might assume that a single initiative is responsible for the good news. If that initiative isn't truly at the core of the improvement, doubling down on it and rolling it out to scale could be costly and counterproductive.

If anyone could parse data and make decisions with it, there really wouldn't be a need for skilled CFOs and Controllers. It's up to leadership to approach the information gleaned from data with curiosity and prudence.

Reliance on Inaccurate Data

Forgetting that bugs exist and placing all of your trust in data by assuming it must be accurate because "data doesn't lie" is a real risk when it comes to adopting a DDDM culture within your organization.

Entry errors, syncing snafus, and outdated information are all major dirty data culprits, and that's important to remember because data cleanliness is key to reaping the benefits of the data-driven decision model. It's also another reason humans will never become obsolete in a data-driven model. You simply can't make good decisions based on bad data – you need human eyes on the numbers and human hands to scrub them up when they start to cause problems.

Incuriosity is an enemy to growth. Are you suddenly staring at predictive models with numbers that are just too good to be true? They might be! Or they might not – you won't know until you pop the hood and take a look at what's going on in there.

Shutting down the skepticism center of your brain is never a great idea, but it's important to keep it wide open when you're working within the data-driven framework, especially in those early stages.

How to Become Data-Driven in 2026

Now that you understand the pros and cons of the data-driven decision making model, the natural question for finance leaders is "how do I create a data-driven culture within my own organization?" You may have noticed that we're emphasizing the creation of a culture around DDDM and not the model itself. That's because research on the topic consistently finds that leadership cites people and process (93 percent) above technology (7.5 percent) on the list of obstacles to implementation.

Our VP and General Manager of Personiv's outsourced accounting service line, Megan Weis, outlines a seven-step process in her on-demand webinar for creating organizational alignment and quashing resistance while pivoting to DDDM:

1. Create a Goals-First Culture to Support Data-Driven Decision Making

   If you're a science fiction fan, you know that "42" is the answer to "Ultimate Question Of Life, The Universe and Everything", according to Douglas Adams' Hitchhikers Guide To The Galaxy. The catch, of course, is that no one knows what the question is.

   That makes for a clever literary joke and an important change leadership lesson, especially when it comes to data. If you don't establish the questions you're using data to answer, the answers you get will be just as inscrutable as "42".

   Questions like "what are the biggest opportunities for additional supply chain profits?" or "where can we reduce expenses without sacrificing value" or "which customer segments are most likely to leave positive reviews of our product online?" will enable you to build KPIs into your model and cascade them down to business units to create their own sub-KPIs around.

2. Ensure Data Leadership Is Top-Down

   Leaders that actively promote and participate in any new initiative are the ones that have the most success implementing those initiatives, whether it's switching to an eco-friendly policy in the snack room or putting a data-driven agenda through an organization. It's also important to consider creating C-Suite roles – Chief Data Officer or Chief Analytics Officer – to leverage data strategically.

3. Facilitate Broad Data Literacy

   Anyone directly involved in that strategic process will obviously have extensive training and a robust skillset in the tools and concepts around data. In the same way that a CFO has a strong accountancy background, so too will a CDO have a sound analytics background.

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   But widespread adoption requires that everyone dips their toes into data, not just "the numbers people". Making opportunities for professional development and continued education in at least "Data 101" means that everyone can play their part in implementing the strategies that analysts work hard to develop.

4. Hold Your People Accountable

   Accountability begins at the top, but follow-through is what ensures organizational ownership. Be clear about your expectations, including the outcomes and timeline for adopting a data-driven model, and how you plan to measure success. That should necessarily include individual performance.

   Giving people the tools they need to succeed, checking in frequently and making it clear that participation is compulsory by setting standards around performance will identify the adopters and holdouts within your organization.

5. Foster an Open and Trusting Culture

   Data – and its benefits – should be a cross-department resource and asset. Shrouding or siloing the information it provides can breed resentment, sandbag strategy and create unnecessary hurdles to widespread adoption. Accessibility, on the other hand, creates a culture that demystifies DDDM and where everyone can leverage its potential.

6. Question Everything During Data-Driven Decision Making

   A good business rule in general, the propensity to never stop questioning is especially important in data driven organization and keep stakeholders from succumbing to that "blind trust" we talked about when it comes to the drawbacks of data. Depersonalizing work, encouraging objectivity and keeping the floor open for healthy debate will allow your organization to use data as a tool of innovation and keep complacency at bay.

7. Develop a Learning Culture for Sustained Data-Driven Decision Making

   Finally, don't fear the f-word: failure. If your team has good reason to believe that they'll be berated or worse if they don't perfectly stick the landing the first time and every time, they'll be understandably averse to trying anything new.

   And because so much about DDDM is new that it's not hard to see how that aversion can kill your agenda in its cradle. Tolerate reasonable risk-taking, allow people to learn from failure and shake things up are all part of a learning culture that clears growth obstacles.

Going forward, organizations with strong data-driven decision making models will be industry differentiators. They'll have the competitive edge. We're already seeing that the organizations that have a sound analytical strategic arm are the ones pulling ahead of the pack, and that's reflected by the consistent sentiment among finance leaders that implementing the model and the technology to buttress it is high on the list of their priorities.

Putting the benefits of data-driven decision making to work for your organization represents a substantial shift, and it's one that requires having the time to focus on its implementation. Personiv has been a leader in finding that time with custom-built teams of offshore support while substantially reducing costs for 40 years. When you're ready, reinvest time and money into initiatives like DDDM, start your data-driven transformation with Personiv. We'll help design a unique solution that centers your organization's goals and gets you there faster.