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 truth, it describes something we've always known about and 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. Continue reading to learn all about the benefits of data-driven decision making.
The reason for its sudden prolific rise should be obvious: it's parallel to the leaps and bounds technology has taken forward in recent years. 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.
Seven-step process for creating organizational alignment:
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
At the end of the day, the biggest benefit of DDDM is that it allows you to make accurate decisions rather quickly, which can save your organization a lot of money in the long run. Why? Because you're no longer working from the gut when it comes to financial strategy.
A Data-Driven Culture Benefits the Quality of a Decision's Outcome
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.
Adopting DDDM Reduces the Time Organizations Spend Making Judgement Calls
Organizations who 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.
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.
Data-Driven Decision Making Leads to Continuous Improvement
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.
Differentiating What Works – And What Doesn't – With Data
Data is a tool. Like all tools, it's most effective in competent, skilled hands. There's a fair bit of speculation about what role the technology that facilitates DDDM will ultimately take: will predicitive models that can be generated at the touch of a button, what use will corporations have for the person that used to crunch the numbers and create forecasts that way?
Well, a lot.
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 that 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.
The Disadvantages of DDDM Include Misinterpretation of Data
By now, we've covered some benefits of data-driven decision-making, but what about the disadvantages?
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! If you're looking at a major supply line improvement, for instance, if care isn't taken to tease out the multiple pathways to that improvement, one 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.
DDDM Cons: Unquestioning Trust in Technology
Bugs exist.
Forgetting that and placing all of your trust into 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.
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.
Don't Let Low-Quality Data Tank Your Results
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 off of bad data – you need human eyes on the numbers and human hands to scrub them up when they start to cause problems.
According to data from Leadspace (formerly ReachForce), in the space of a single hour, 59 businesses change their mailing addresses, 11 change their name and 41 open for their first day of operations. Think about what that means on a broad scale, and what it means to your business. Is your data up to date? Are you inundated with duplicates or outdated entries? To truly be effective with DDDM, those issues need to be corrected.
How to Become Data-Driven in 2024
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:
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Create a Goals-First Culture
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.
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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.
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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.
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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.
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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.
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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.
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Develop a Learning Culture
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 leaders find that time with custom-built teams of offshore support while substantially reducing costs for over 35 years. When you're ready reinvest time and money into initiatives like DDDM, get in touch. We'll help design a unique solution that centers your organization's goals and gets you there faster.
