Data driven decision-making (DDDM) is a key differentiator of successful organizations that can improve end-to-end processes, reduce cost and maximize return on assets. At AFP 2020, Megan Weis, Vice President and General Manager FAO Services for Personiv, will present the session, Data-Driven Decision-Making (DDDM): A Blueprint & Case Study. We recently spoke with her about what attendees about DDDM and why it matters for FP&A professionals.
AFP: Can you give us a working definition of DDDM why we should care?
Megan Weis: Data-driven decision making (DDDM) is defined as using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives and initiatives. It is the process of making organizational decisions based on actual data, rather than intuition or observation alone.
According to a survey of more than 1,000 senior executives conducted by PwC in 2016, highly data-driven organizations are three times more likely to report significant improvements in decision-making, compared to those who rely less on data. Those improvements include more confident decisions, the ability to be more proactive and cost savings from improved operational efficiency.
When organizations realize the full value of their data, that means everyone, from the top to the bottom, is empowered to make better decisions with data, every day. However, this is not achieved by simply choosing the appropriate technology.
AFP: Why aren’t people doing this already?
With the number of advanced business intelligence software packages available on the market today, it might be easy to think that all a business needs to do is deploy the tools and wait for the return on investment. However, there’s a big difference between making technology available to employees and having them embrace it.
In other words, a leading challenge in the quest to become data-driven is boosting user adoption rates of Business Intelligence tools. Harvard Business Review cites a recent study performed by NewVantage Partners in which 77% of executives report business adoption of data analytics initiatives is a major challenge.
Furthermore, most of these executives cited people and processes as the issue, as opposed to technology. The biggest obstacles to creating data-based businesses aren’t technical; they’re cultural. It’s easy to describe how data can and should be used in a decision-making process. It is far harder to make this normal, even automatic, for employees—a shift in mindset that presents an overwhelming and unachievable challenge for many.
AFP: How do you do this right?
Weis: Here are the top three ways companies get this right, I’ll go into more in my presentation, but these three are the crucial components. First, and most critical, to be successful, data-driven decision-making needs to start at the very top. Companies with strong data-driven cultures have managers who set an expectation that decisions must be anchored in data and that this type of decision-making is normal, not novel or exceptional.
Second, companies need to eliminate the silos within an organization. Analytics can’t survive or provide value if it operates separately from the rest of a business, so to become a data-driven organization, it must find a way to fuse the technical knowhow with domain knowledge. Those who have successfully addressed this challenge have generally done so in two ways. First, they’ve eliminated boundaries between the business and the data scientists. One way to do this might be to pull data scientists into line roles or project teams to help with a specific assignment, another might be to create new roles within functional areas to augment data analytics, these roles will have dotted-line relationships that create open, two-way lines of communication with data scientists. The second way this can been addressed is to pull the business toward data science, chiefly by providing training and insisting that employees are conceptually fluent in quantitative topics.
Third, leaders can exert a powerful effect on behavior by carefully choosing what to measure and what metrics they expect employees to use. Establishing the right key performance indicators (KPIs) is crucial. In many businesses there is a disconnect between the metrics they track and the results they want to be able to influence. It is important to have a variety of KPIs that allow you to see both the micro and macro and to understand how they work together. Once decisions are being made based on data, the team should continuously be making predictions about the magnitude and direction of such moves and should also be tracking the quality of those predictions.
An organization needs to make data-driven decision-making the norm, creating a culture that encourages critical thinking and curiosity. People at every level have conversations that start with data and they develop their data skills through practice and application. Foundationally, this requires that people can access the data they need. It also requires proficiency, creating training and development opportunities for employees to learn critical data skills. Finally, having leadership buy-in and a community that supports and makes data-driven decisions will encourage others to do the same.
AFP: How would a company do this wrong or in a way that could hurt them?
Weis: I think the biggest takeaway is that becoming data-driven is an evolutionary process that goes far beyond choosing technology to collect and cleanse the data. The technology is really just the tip of the iceberg. The wrong way to do this is to fail to realize that the underlying cultural shift is critical to success.
Being a data-driven organization takes more than expensive technology and quality data. Like all digital transformation, it requires the right internal processes and culture. The right incentives have to be put in place, as well as steps to ensure that data is driving decisions appropriately. Failing to recognize the cultural and process changes necessary can lead to not only wasting massive amounts of money on technology, but data misuse, which can be very costly.
Many organizations have the data, technologies, and even the expertise, but their culture and processes are not aligned with those elements to produce the best outcomes. For example, data might be a part of every decision made, but employees may be making decisions first, then cherry-picking data to back them up, which obviously is the wrong way to go about decision-making with data.
Businesses have more data than ever, but a culture rooted in top-down decision-making and old-fashioned tools like weekly reports and preconfigured dashboards means they cannot take full advantage of this data. Factors like these explain the disconnect between the huge investments being made in “Big Data” (close to $40 billion annually) and the disappointing results some companies report seeing (72% of C-level execs said they had yet to forge a data culture, and about half admitted they were not competing effectively on data and analytics).
AFP: Since people will be inspired by your discussion of this topic, how can they get started on this right away?
Weis: The biggest characteristic that I can call out for data-driven businesses is their openness to change. Be open to doing things in bigger and better ways and be open to taking feedback and course correcting as needed.
Encourage interest among team members to be more data-driven by providing them the opportunity to be creative and innovate. Companies need to encourage and empower people at every level to ask questions, experiment with data and act based on what they find. Organizations must ultimately consider whether they’re encouraging employees to keep their heads down and proceed with business as usual, or whether they’re empowering them to stay curious and share their insights. How managers and leaders receive employee suggestions plays a big part in whether cultural communication encourages people to incorporate data.
Lastly, apply changes gradually, don’t rush it, but be steady. It will take time, but the results will be worth it.
For more insights, don’t miss Data-Driven Decision-Making (DDDM): A Blueprint & Case Study at AFP 2020.