Are you a “data elite” or a “data laggard”?
Eckerson Group founder Wayne Eckerson predicts the gap between those who embrace data-driven decision-making in the workplace and those who don’t might be unbridgeable by 2025.
More than ever, organizations rely on advanced analytics to predict future outcomes and act in real-time. Analysts expect the predictive analytics market to reach $22 billion by 2026.
As industries open up post-Covid-19, trailblazing organizations are combining historical data with statistical modeling, data mining techniques, and machine learning to make decisions to increase bottom lines. In essence, it all comes down to data-driven decision-making (DDDM).
This article will discuss the benefits of DDDM while investigating its application in the IT, healthcare, finance, manufacturing, transportation, and eCommerce sectors. Let’s dive in.
The Importance of Data-Driven Decision Making in Business
Harvard Business Review defines DDDM as utilizing various data points to inform organizational strategies, decisions, and actions for optimal results. In other words, DDDM relies on metrics — not gut feelings.
For example, a product manager might have an idea for a new deliverable based on a few conversations with customers. But further quantitative research on purchase patterns reveals that the demand likely won’t be high enough to offset the cost of development.
In addition, historical information may reveal new ways to cut costs, increase efficiencies, and optimize production for maximum profitability. But this is just one example of many applications for DDDM in business. According to a Finances Online report, 57% of companies say effective decision-making is the most prominent benefit of data analytics.
Of course, being data-driven means more than investing in the technologies that facilitate the collection and analysis of crucial data. It means translating those metrics into useful insights and concrete action plans.
Benefits of Data-Driven Decision-Making (DDDM)
Adopting a DDDM culture has many benefits, but three, in particular, stand out:
1. Reduced Risk
Undoubtedly, intuition has its place in leadership. But executives who back their instincts with data are better equipped to proactively face roadblocks.
For instance, say an organization plans to venture into a new market or launch a new product. The only way leadership can move forward with minimal risk is to analyze consumer data trends.
Alternatively, should the launch completely flop for unforeseen reasons (hey, it happens), leadership will more easily be able to defend its position to board members.
2. Increased Agility
Collecting data without taking action is useless. Managers who analyze data without using it to inform objectives are probably better off learning how to bake bread.
The biggest obstacle to bridging the gap between data collection and data action is often inaccessible reporting. But companies that manage to create accessible data analysis systems pivot, innovate, and troubleshoot better than their competitors.
3. Decreased Waste
To achieve quality standards, data must be timely, complete, accurate, reliable, and relevant. Organizations that apply DDDM in an expense-reduction strategy will most likely achieve their goals.
It’s almost impossible to know where and how money is being lost without accurate reporting.
6 Leading Industries Shifting to Data-Driven Decision Making
Regardless of the industry, one of the most reckless decisions an executive can make is to ignore the vitality of data. We’ve all heard about global powerhouses like Google, Tesla, and Meta at the forefront of leveraging big data for making even the most minuscule decisions.
But how are less frequently discussed teams using data-driven decision making?
1. Information Technology (IT)
According to the latest forecast by Gartner, global spending on information technology (IT) may reach $4.5 trillion in 2022. This amount reflects a 5.5% increase from 2021. Experts also predict data center systems investments to grow by 5.8% as companies commit to adopting new software and technologies.
Leading IT departments continue to leverage big data operations to manage assets. In addition, many companies are switching from Active Directory to cloud directory platforms that support 360-degree visibility over devices, networks, and applications.
The Jumpcloud Cloud Directory combines identity and access management (IAM), multi-factor authentication (MFA), single sign-on (SSO), and Directory Insights reporting into one centralized tool. Admins can instantly pull up reports to investigate events across user activities, LDAP inquiries, MDM commands, and more. In addition, such solutions provide digital audit trails that help meet compliance requirements without the stress of manual data collection and analysis.
Financial institutions were not prepared to close under short notice during the onset of the pandemic. A 2020 Exasol study, conducted among decision-makers in the UK financial services industry, found that nearly 88% of executives faced pressure to make decisions faster than ever before.
Grappling with unforeseen losses, financial institutions are now relying on every bit of available data to improve customer relations, sales, and investment functions.
Finance companies are adapting their data infrastructure into models that allow them to assess risks, detect fraud, make rapid decisions in a crisis, personalize services, and leverage algorithms to trade.
Both lending institutions and insurance companies are leveraging machine learning to establish customer patterns that establish reliability. Analyzing historical data may also provide insight into why losses were accumulated, and may highlight risk areas, allowing a bank to galvanize its resources towards making more profit.
DDDM is the backbone of smart mobility, providing for connecting several elements of technology and transport. It also facilitates a rethinking of the transportation infrastructure used in daily life to improve safety, save time typically wasted in traffic, and create room for innovative technologies.
Modern transportation calls for consistent and high-quality data streams. Take the U.S. Department of Transportation (DOT)’s Safety Data Initiative. It’s responsible for identifying factors that contribute to severe crashes. With the help of advancements in machine learning, data visualization, and modeling, safety experts can estimate crash risk, provide situational awareness, and improve planning capability.
DDDM has also facilitated the safe integration of drones into the national airspace. It has made it possible to increase small model hobbyist Unmanned Aircraft Systems (sUAS) from 1.1 million in 2017 to 2.2 million in 2022.
Manufacturing companies leverage big data for better product, trend, and cost analysis and a clearer understanding of their markets, competitors, and customers. To collect and analyze this data, companies rely on advanced technology like sensor infrastructures that are synonymous with Industry 4.0. These include fiber-optics sensors, computer vision, and wireless sensor networks.
Industry 4.0 or smart manufacturing leverages emerging advancements like cloud computing and the Internet of Things (IoT) to enable algorithms that analyze data, predict emerging asset situations, and recommend predictive maintenance actions.
Healthcare providers accumulate an overwhelming amount of data from several sources. The sheer vastness of data collected — electronic health records (EHRs), patient portals, research studies, payer records, and more — has presented a huge challenge for providers wanting to board the DDDM train.
The technology that synthesizes, categorizes, and translates said data into user-friendly reports has been available for years. But the majority of healthcare providers have yet to invest in healthcare data analytics until recently. Experts suggest the use of big data presents many benefits including superior diagnostics, medical research, preventive care, and a reduction of adverse medication reactions.
One example, on the consumer-side, comes from Google-owned Fitbit. The device sends the physical activity data of wearers to cloud servers, before sharing it with physicians wanting to improve health and wellness programs.
Besides informing patient recommendations, new technologies are inspiring U.S. medical experts to evaluate value-based healthcare models. Unlike traditional healthcare models, value-based delivery models reward doctors for helping patients improve their health. Expect to hear more groundbreaking applications in precision medicine, iOT wearables, and machine learning for healthcare in the near future.
6. Retail and eCommerce
Established brands like Amazon have, at their disposal, a treasure trove of data to analyze and use to their advantage. They leverage this availability to open new routes to the market and drive more sales.
However, the most applicable way that e-commerce stores use big data is to map customer journeys and align operations with their business goals. In the end, it becomes easy to deliver greater value to their customers.
Streamline Data-Driven Decision Making with JumpCloud
Data does more than just help organizations verify, understand, and quantify. It informs impactful decisions and charts a course for successful organizations. Although being data-driven takes time to implement, the payback is worth it.
Are you ready to level up your organization’s IT department? IT managers can now leverage tools such as JumpCloud Cloud Directory’s System Insights to gain greater visibility of user, device, and application events in real-time.
With advanced reporting, admins can specify reports by device, LDAP, RADIUS, SSO, and more. Eliminate the time spent collecting and formatting logs across multiple sources and translate data into actionable insights that enhance security efforts with JumpCloud today!