Bringing Data Into Decision-Making At All Levels Of The Organization

Bringing Data Into Decision-Making At All Levels Of The Organization

One of the most famous sayings among programmers is “in data we trust.” The same notion is now slowly becoming prevalent within the business communities as well. Having an online presence doesn’t suffice anymore. The business concern has to go beyond the usual norms to satisfy the needs of the customers. Established businesses like Amazon, Google, and Uber are already using customer preference data to push personalized services for the customers. Therefore, it is clear that data driven decision making has a significant role to play at all organizational levels of a business.

Data-driven Decision-making (DDDM) - what is it?

Business owners used intuition, guesswork, and observation to play essential roles in the decision-making process of different businesses earlier. In retrospect, educated guess required the available information, which can be considered a precursor of DDDM. However, in current situations, the definition of Data-driven Decision-making stands as follows: “it is the process of taking strategic business decisions at all levels of the organization that aligns with targets, objectives, and initiatives at all levels of an organization, depending solely on quantifiable and actionable data, which includes facts, metrics, and information.”

The efficacy of the decisions will depend on the applied analytics for decision making. It is mainly used to achieve a competitive edge in the decision-making process. Still, it can be applied to reduce the cost of running the organizations more efficiently and reduce the operational cost. 

Sectors using DDDM

With the advent of using data to shape the business decisions of the organizations, there is no specific post responsible for the implementation of DDDM. In the early days, an IT specialist was given the job of data analytics and decision making. Nowadays, managers don’t need any necessary IT training to generate the required reports. The development of business intelligence tools has reduced the dependency and paved the way for faster operation.

DDDM is now used in almost every sector, including financial services, manufacturing, telecommunications, healthcare, travel and hospitality retail, and entertainment. You can find a career with good experience and training in Big Data in any of these sectors. The positions available are diverse and high-salaried, like chief data officer (CDO), chief technology officer (CTO), chief information officer (CIO), chief digital officer, and chief analytics officer. Candidates interested in management positions can work as social media managers, management consultants, project managers, or business development managers.

Implementing DDDM in the organization

Responsible individuals working in the business sector have long since understood the benefits of data driven decision making. Statistics state that organizations using DDDM in their decision-making process are 23 times more likely to acquire customers has a six-fold more chance of retaining them, and are 19 times more likely to be lucrative. However, the implementation of data points should be ethical and in line with the company’s goal and philosophy.

An admonitory example is the Apple Card issued by Goldman-Sachs, whose faulty algorithm gave women lower credit limits and higher interest rates, leading to an investigation from the authorities. The steps a business should follow to implement DDDM in their business can be given as follows:

  • Identifying the need

    The company must identify the most pressing concerns and the most critical areas of its business goals. Is it trying to take advantage of development or analyzing an issue? In one sentence, what is it trying to achieve? Getting the answer to this question is the first step.

  • Strategy to set targets

    After that, the company must set an analytical objective about what can be realistically achieved with these data. Some of the essential questions needed to be asked are the following:

    1. Who will be responsible for data collection and analysis?
    2. What is the personnel required for the project?
    3. Would the concern need professionals from outside or in-house analysts can do the job?

    Setting the answer to these questions will help in decision making with data at the following stages.

  • Data targeting

    The company should then determine the type of data they need and the method of collection. Collecting the data that answer the primary business questions will be optimally effective.

  • Collection and analysis

    Now the appropriate personnel will have to gather and analyze the data. Some of the data can be found in-house, while others may have to be purchased externally. Data sources could be anything from software, environmental sources, online, cameras and imaging platforms, or workforces. The data price is now becoming more economical, but one must keep the costs in check.

    After collection, the data has to be analyzed and strategic decisions made. One of the key benefits of data driven decision making is that the assessments can be judged from multiple angles like text, speech, video, or image through various analytics platforms. 

  • Implementation of the insights

    The decision-makers can then transform the analyzed result into actionable concepts and projects.

Instances of the efficacy of DDDM

Modern business intelligence gradually understands the importance of data driven decision making. The use of analytic tools and software platforms is also helping, and several companies have benefitted from the various aspects of DDDM. Here are some instances:

  • Lufthansa Group, the global aviation giant, used one analytics platform over the data streaming from its 550-plus subsidiaries, resulting in greater flexibility in decision-making, increased departmental autonomy, and a 30% increase in its efficiency. 

  • The Charles Schwab Corporation, one of the largest publicly traded financial services firms worldwide, went for a BI platform that supports analysts and rookie business users, which reduced their data load significantly and help them make business decisions aided by clear, actionable insights.

Other data driven decision making examples that gave the companies a profitable boost are available, which shows the efficacy and power of this new discipline to improving the customer experience, motivating operational force, and decreasing risk. Most companies from all sectors are depending on DDDM to identify challenges, utilize opportunities, and make decisions. Implementing this method into all the levels of a business organization can optimize its functioning and cause a scalable increase in revenue. 



Wersel Marketing Team