At the end of the day, every company is really just a collection of decisions. Everything from how you engage customers to which products to build and what price to sell them shapes an organization and sets it up for failure or success.
Making decisions is an integral part of managing any organization, whether by executives making planning decisions or employees on the frontline, deciding how you engage with customers, partners, and vendors. The most successful organizations today are increasingly turning to data-driven decision making to inform decisions at every level as the most effective means of leveraging resources and identifying new opportunities. Data driven decision making relies on analyzing collected data using different data collection methods to answer questions and find insights that can inform judgements, help determine the right course of action, and guide overall strategy. This approach remains one of the top trends in analytics because it helps to improve organizational efficiency and effectiveness by enabling companies to understand what happened in their organization, why it happened, and how specific decisions might shape future business outcomes.
In this post, we will take a closer look at what data-driven decision making is, why it’s important, 5 steps to follow in your decision making process, benefits your business will realize, and challenges associated with it.
In its simplest form, data-driven decision making is the process of using insights gleaned from data to inform key decisions. Often, companies will rely on business intelligence platforms to create, disseminate, and drive action from data insights, often in the form of data visualizations. By utilizing this approach, businesses can make smarter and more informed decisions, not based on gut instinct or experience, but on the reality of what has happened before and the likelihood of it happening again. Data-driven decision making has revolutionized how companies do business – allowing them to make decisions faster, cheaper, and with greater accuracy. Companies leading their industries and seeing the most benefit from data-driven decision making are doing so by bringing this capability to every kind of decision maker, whether an employee on the frontlines, in the back office, or in the boardroom, through self-service analytics so every decision can be backed by data-driven insights.
For example, sales professionals can use a business intelligence platform like ThoughtSpot to evaluate sales performance and make data-driven decisions.
Data-driven decision making can help organizations in every industry make more informed decisions, improve operational efficiency, and increase profitability. By leveraging data to inform decisions, businesses can better understand their customers’ needs and preferences, anticipate changes in the market and develop strategies that will lead to long-term success. This in turn helps companies make better decisions faster, as all of the necessary information is readily available and ready for analysis. Furthermore, with data-driven decision-making companies can identify trends and patterns in their customer’s behaviors which can help them develop better marketing strategies, reduce customer churn, and improve customer service.
As data volumes continue to skyrocket and customers demand personalization at every turn, data-driven decision making has gone from optional to essential. In today’s digital world, companies have access to more information than ever before, which they can use to make better decisions and gain competitive advantages. But this comes with an expectation from consumers, users, and customers that you’ll use the data they share with you to better serve them - and that’s only possible with data-driven decision making. And you can rest assured if you’re not using data to make smarter decisions, your competitors are.
Data-driven decision making can provide businesses with numerous benefits, including increased accuracy and reliability, more objective decision making, improved efficiency, and competitive advantage. Companies seeing the most benefit from data-driven decision making have experienced this level of impact by empowering every kind of employee with the ability to ask and answer their own questions to inform their specific decisions. Companies like Disney, Verizon, Wellthy, CVS, and more are using AI Analytics from ThoughtSpot to arm anyone in their organizations with the ability to find data-insights and act on them.
Check how Wealthy capitalizes on the self-service analytics with ThoughtSpot.
Companies embracing data-driven decision making at all levels are already seeing massive improvements, with companies identified as leaders having grown revenue 10-30% in the same period that their competitors’ growth has remained flat. In a survey, more than 80% of leaders say that empowering every employee to make data-driven decisions will increase productivity, customer satisfaction, and product and service quality.
Every company can do the same, and see improvements across their organization.
By having reliable data, businesses can ensure that decisions are based on facts rather than assumptions. This leads to more accurate and reliable decisions that will have a positive impact on the organization in the long run. Having a single source of truth for your data, such as a cloud data warehouse, makes this even more seamless.
The loudest voice in the room shouldn’t be the one driving decisions. Data-driven decision making can also help remove any bias or subjectivity from the decision making process. By relying on data and analysis, businesses can ensure that decisions are based solely on facts rather than personal preferences, previous experience, or opinions.
With data-driven decision making, businesses can make faster, more efficient decisions as the process does not require any guesswork or trial and error. This is especially true when self service BI tools enable business decision makers to ask and answer their own data questions. In doing so, companies waste less time and resources and can focus on delighting customers.
Finally, data-driven decision making can give businesses a competitive advantage over their rivals. By using data more effectively, businesses can gain better insight into the market and make informed decisions that will help them succeed in the long run, identify opportunities their competitors may miss, and address any potential issues that can create a competitive opening proactively.
Every organization wants to be data-driven, yet a staggering number of leaders report abysmal progress in making this a reality. Although data-driven decision-making provides businesses with a wealth of insights and benefits, there are certain challenges associated with it.
Since many decisions require more than one source of information, relying solely on existing datasets may not provide an accurate picture of the situation. Furthermore, data can be inaccurate or incomplete, meaning that decisions may not always be based on complete or up-to-date information. With data marketplaces, common in today’s cloud platforms, finding ways to enrich this data has never been easier.
Without proper context and analysis, businesses may make incorrect assumptions based solely on existing data that may be biased or not capture the complete picture. As such, it’s important to consider other factors when making decisions, such as customer feedback, market trends and competitive analysis, and thinking how these can augment or enrich the data you currently have at your disposal.
Data privacy is a major concern for many businesses and consumers. With the increasing availability of customer information, companies must be aware of the potential risks associated with collecting and storing personal data. As such, organizations must implement appropriate measures to ensure that customer data is protected and managed securely. This is especially important when exposing data through self-service solutions, so look for tools that give you the ability to control access by user and down to the finest grain of data.
Data quality is a major consideration when making decisions. Poorly managed data can lead to inaccurate results or misleading conclusions, which can hinder decision making processes and lead to erroneous outcomes. To avoid this, businesses can track data quality metrics to ensure that their data is accurate, up-to-date and complete.
By understanding the challenges associated with data-driven decision making, businesses can ensure that they are making informed decisions based on accurate and up-to-date information. This not only helps them develop better marketing strategies and improve customer service but also helps to protect their customers' privacy.
Building an organization that can reap the rewards of data-driven decisions requires a mix of people, process, technology, and often, change management. The most successful companies implement self-service analytics as part of this change. In a nutshell, the process of data-driven decision making consists of five steps:
Every data project is really a business project. That’s why before making any decisions, it is important to identify and truly comprehend the problem at hand. To do this, businesses must take time to define goals and objectives, understand the context of the situation they’re looking to address, and ideate potential solutions. This step will also involve researching available data sources and determining which ones are most relevant and reliable for the task at hand, whether those are proprietary or third party data sources. Ideally, this should be done in partnership between the data teams and business users. This ensures that the business problem is front and center from the outset, and that solutions proposed from data teams will actually improve outcomes business users are looking to influence.
Once the true business use case has been identified, the next step is to collect relevant data that can help improve the decision-making process related to this challenge. This involves gathering data from both internal and external sources, such as customer surveys, industry reports, market research, and historical trends. Luckily, for many companies who have been collecting massive volumes of data in recent years, in part due to the ease of bringing this data together in a cloud data platform, you likely already have some of this data captured. The goal is to collect enough data or augment it with additional data to identify patterns and trends that could help inform the decision. This is where the art of the possible is essential. Don’t just default to what data you already have or have used before. Think creatively about what other data you could bring to bear to deliver better results. The good news for companies who have adopted the modern data stack: bringing all this data together through processes like ETL and ELT, mashing with third party data, and exposing to business users has never been simpler.
After capturing, cleaning, and organizing all of the relevant data, you’re ready to get started with the actual work of analyzing it to unearth patterns, outliers, anomalies, and trends that can indicate areas of opportunity or potential risk in regards to your defined business problem. Look for correlations between different variables, cause-and-effect relationships, and leverage statistical models that can help predict outcomes.
Ideally, this phase includes empowering business users, not just data professionals, to engage with the data directly. Doing so early in the process will help you identify potential data gaps, new use cases, training opportunities, and more that will be essential in driving adoption and value. Consider leveraging tools like ThoughtSpot that make it easy for business users to engage with your data without requiring technical skills or additional training so they can focus on adding their domain expertise to your initiative instead of trying to learn a technology. By doing this, businesses can gain a better understanding of their customers and the market as a whole, while getting a headstart on driving adoption from business stakeholders.
Once the analysis is complete, it is time to develop a plan to address the identified problem. This involves creating strategies for how to solve the problem, setting achievable goals and objectives, defining the right data KPIs to measure progress, and establishing a timeline for when all activities should be completed. It is important to ensure that the plan will lead to the desired outcomes, which is why bringing business users into the process early is so important. This continues at the implementation phase, where driving real adoption is required for meaningful impact. As part of these adoption initiatives, leverage your initial champions and stakeholders from the business to help sell the value and impact of the solution to broader audiences
The final step in the process is to evaluate the results of your efforts to build more data-driven decision making. By tracking key performance indicators, businesses can gauge how their efforts have addressed the specific business challenge, as well as how this has impacted the organization. Through this evaluation, businesses can not only improve their existing use case, but gain valuable insight into what areas of their operations could be improved and make more informed decisions in the future.
By following the five steps outlined above, businesses can make sure they are utilizing data to its fullest potential and making well-informed decisions that will lead to long-term success.
Data-driven decision making can be incredibly powerful for organizations that are ready to embrace the possibilities. Executives need to understand the importance of gaining intuitive and actionable insights from their data, as well as following qualitative steps to ensure that new data initiatives are successful. Not only does this ensure greater efficiency, effectiveness, and risk management, but regularly taking the time for analytics assessment can also help foster a culture of innovation and data literacy in the workplace.
Moreover, businesses must face and conquer the common challenges associated with implementing an effective data strategy: the need for more resources, siloed data sets, and failed/slow adoption among employees or departments. The ultimate goal is to make decisions in real-time with self-service analytics, allowing teams to make rapid decisions and be able to respond to sudden changes in trends or markets. So what are you waiting for? Sign up for a ThoughtSpot free trial today to learn how to achieve better business outcomes using AI-Powered analytics!