Top Methods and Tools for Data-Driven Decision Making?

The shift from the emphasis on business processes to value the business data in the growth of the business is complete. According to statistics, 90% of global businesses understand the importance of data and insights when making business decisions. 

The trend has shifted significantly over the years to utilize business data and, implement a data-driven decision-making model for businesses to ensure timely growth and increased productivity. Global organizations like Google and Amazon have adopted a data-driven decision-making approach and are now successful in their niche.

Defining the Data-Driven Decision-Making approach

Data-Driven Decision-Making or DDDM is at the heart of many business organizations nowadays. These businesses rely on business data to steer them in the right direction in identifying problem areas in their work environment. The management then changes their business processes or applies different strategies to fix the identified problems.

This insightful way of representing data helps them to understand their business and the worth of their products and services in the market. The data may be quantitative coming from their sales and inventory balance sheets or maybe qualitative coming from different surveys or customer feedback forms. 

The data-driven approach to decision-making helps the organization understand its target audience and factors contributing to the popularity of its products among its customer base. It also helps identify the areas where improvements are required to make their product number one in the market. As said wisely by Jerry Yang,  the co-founder and former CEO of Yahoo, “There’s definitely a huge opportunity for businesses to transform their operations and decision making by using data. “ 

Data-driven decisions
Data-Driven Decisions

Top Methods to adopt for Data-Driven Decision Making

  1. Defining business Goals and Objectives

Whenever any business decides to switch its business model from process-driven to data-driven, it must start somewhere for this transition. The best place to start is to define business goals and achievable targets for their business. 

These targets can be broad and span the whole organization. Once, the long-term business goals are identified and in place, they can be broken down into small short terms goals and projects.

  1. Gather targetted Data

Data is the lifeblood of every organization and sometimes business users might get overwhelmed with the amount of data that accumulates in a business environment. The key point is to separate targetted data from this bulk and convert it into useful insights that may help management make important long-term and short-term decisions.

  1. Invest in a Great business Intelligence Tool

The next step is to find a business intelligence (BI) tool that converts the organizations’ raw data into meaningful data visualizations and sets up data dashboards according to the end-user requirements. This helps to bring the data into the desktops and in your managements’ hands in a way that is understandable for them. 

Many off-the-shelf business intelligence software tools are available in the market that can help businesses pinpoint their focus areas and observe their business data to come up with different strategies to improve product popularity and increase conversions in their niche.

  1. Personalization 

Once, the data team and IT team work together to purchase and set up the business intelligence software, they need to personalize the software according to their specific company goals and business data needs. Each business differs in its objectives and business processes.

This step would help them to bring out their own unique identity in their work environment and specify what they need from the business intelligence software to promote their business. 

Personalizing the data analytics and reporting software would help them to create data dashboards and data visualizations that could help them in their decision-making process and bring out the information that is useful to their business.

  1. Bring it all together

Now that the whole process is set up and all-important processes are in place. Still, there is always a chance to reevaluate and reassess the data-driven business model for any improvements and, regular updates for incorporating the evolving technology and newer practices are required.

Over time, there might be changes in the company objective or target goals, or the company might want to expand its business. These changes should require improving the data-driven model of the business. 

Best Tools/Reports for Data-Driven Decision Making

Next, we will explore a set of reports that are useful in giving meaningful insights for business organizations that are looking to use their business data for a data-driven business approach.

There are many varied charts, graphs, and reports that help channelize the data into visuals that would aid in decision-making for the organization. This section will explain some of the most valued reports that the business intelligence software offers its end-users.

Data visualization

Data visualization is the most appealing feature when the data analysts team researches to invest in a business intelligence software for managing their data analytics and reporting. The capability to generate multiple reports and visuals from the same data set, that highlights different areas of focus for the management is essential for detailed data analysis. It would help management make an informed decision about aspects of its work environment.

Data visualization
Data visualization

What if Analysis

Many times business management has their own sets of questions about their products or services. Observe a scenario where the company is looking to expand its product line and wants to bring some strategic changes to their company. They are worried about the long-term impact on the business after this change.

Data analysts can perform what-if analysis on the historical data and the market trends to answer their questions specific to this change impact. It would help management take decisions confidently and be more aware of the change it would bring.

Ranking reports

Ranking reports are very useful when a business has a stream of products and services. These reports can help them identify products that rank low in the report and change the marketing strategy for the product to improve its rank. Also, it will inform them about the products that are making the top ranks in the list.

Using this data, they can observe what strategies are working well and apply same to other products. Performing ranking analysis periodically can help businesses identify and work on their strengths and weaknesses.

Predictive analysis

Predictive analysis is a key feature of a business intelligence tool and also comes under the umbrella of data science techniques for big data analytics. Predictive analysis can help data analysts observe historical data to make predictions about customer interests and choices. 

Predictive analysis is very useful in providing businesses with actionable insights to make decisions about their products and services. Data analysts identify data trends that the business follows by monitoring the data from previous years and this helps them make a forecast about the future.

Conclusion

Business intelligence started with data reports and analytics and has evolved to something more sophisticated and desirable for businesses. The idea that data can help businesses inform about their future performance and help increase the revenue for the business is very appealing for businesses. New and creative features like predictive analysis and ranking reports increase the popularity of business intelligence software and create ways for businesses to improve performance and increase conversions.“We should teach the students, as well as executives, how to conduct experiments, how to examine data, and how to use these tools to make better decisions.”- Dan Ariely, professor of psychology and behavioral economics at Duke University and a founding member of the Center for Advanced Hindsight.

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